Article

Dynamic pricing of multiple home delivery options

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Abstract

Online grocers accept delivery bookings and have to deliver groceries to consumers' residences. Grocery stores operate on very thin margins. Therefore, a critical question that an online grocery store needs to address is the cost of home delivery operations. In this paper, we develop a Markov decision process-based pricing model that recognizes the need to balance utilization of delivery capacity by the grocer and the need to have the goods delivered at the most convenient time for the customer. The model dynamically adjusts delivery prices as customers arrive and make choices. The optimal prices have the following properties. First, the optimal prices are such that the online grocer gains the same expected payoff in the remaining booking horizon, regardless of the delivery option independently chosen by a consumer. Second, with unit order sizes, delivery prices can increase due to dynamic substitution effects as there is less time left in the booking horizon.

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... Aspray et al. (2013) reiterate the high cost and complexity of fulfilment for groceries bought online. Asdemir et al. (2009) also emphasise the challenge of balancing utilisation of delivery capacity against time and profit margin in e-grocery retail. Recent studies by Larke et al. (2018) illustrate the complexity in achieving omni-channel retailing in even a mature market. ...
... An interview guide was developed based on the elements of logistics with consideration paid to the context of e-grocery operations outlined by Mkansi et al. (2018). An exploration of the eight e-retailers in terms of their logistics elements provided an opportunity to identify diverse and common practices existing between township and urban e-grocery players, in particular their effect on the cost of operation (Ishfaq et al., 2016;Aspray et al., 2013;Asdemir et al., 2009) and profit strategies recognised as a major barrier to entry. At the same time, the method provided an insight into the South African township context. ...
... Similar to the mobile application retailer configured model, the fulfilment centres of the mobile app wholesale configured e-grocery operators are mainly their competitors (traditional grocery retailers stores such as Pick n Pay, Woolworths, Shoprite Checkers etc., fresh produce markets and wholesalers/discounters like Makro) situated in the malls or geographical areas nearest to their market base segments. Of particular interest was how the mobile application wholesale configured model yield profit in an industry characterised by thin profit margins (Asdemir et al., 2009). The strategies outlined by the e-grocery retail include mark up from economies of scale strategy induced by aggregating demand at the downstream chain (see Appendix 3 for economies of scale quote). ...
... In [6], a model is presented that allows for a flexible horizon, but does not consider days of the week, nor seasonality. The customer arrival process is modelled using a non-homogeneous Poisson process, as inspired by scientific work in revenue management in the airline industry (see [15]). ...
... Optimal prices are calculated based on an "equal profit" policy, which means that the retailer makes the same profit in the remaining booking horizon, regardless the customer choice. Delivery prices can change based on order size, depending on the time left in the booking horizon [6]. In [9], the models are tested on fictitious cases for which customers are uniformly scattered on a 60 × 60 grid. ...
... In summary, we observe that the literature considers exclusively time slot allocation or time slot incentives. Those focusing on incentives often state that the closing of time slots for certain customers (i.e., time slot allocation) is a method that results in lost sales and customer dissatisfaction [6]. Hence, dynamic pricing is perceived as the best method, since it can balance the trade-off between lost sales and profits. ...
Chapter
Attended home delivery (AHD) is a popular type of home delivery for which companies typically offer delivery time slots. The costs for offering time slots are often double compared to standard home delivery services (Yrjölä, 2001). To influence customers to choose a time slot that results in fewer travel costs, companies often give incentives (discounts) or penalties (delivery charges) depending on the costs of a time slot. The main focus of this paper is on determining the costs of a time slot and adjusting time slot pricing accordingly, i.e., dynamic pricing. We compare two time slot cost approximation methods, a cheapest insertion formula and a method employing random forests with a limited set of features. Our results show that time slot incentives have added value for practice. In a hypothetical situation where customers are infinitely sensitive to incentives, we can plan \(6\%\) more customers and decrease the per-customer travel costs by \(11\%\). Furthermore, we show that our method works especially well when customer locations are heavily clustered or when the area of operation is sparsely populated. For a realistic case of a European e-grocery retailer, we show that we can save approximately \(6\%\) in per-customer travel costs, and plan approximately \(1\%\) more customers when using our time slot incentive policy.KeywordsTime slot managementDynamic pricingVehicle routingMachine learningCost approximation
... The transport mean travel cost thus depends on the resources consumption [A] (+) of the transport mean and on the share of (semi)fixed transport costs [B] (+) allocated (Dorling et al., 2017).  The travelled distance of the transport mean (Asdemir et al., 2009) is the average number of kilometres travelled to perform a delivery tour. It depends on the customer density [G] (+), i.e. the number of customers in the same area (Boyer et al., 2009). ...
... Customers are offered lower prices for time windows that optimise the delivery route of the truck (e.g. some close locations already have to be visited in the same tour), while higher prices are proposed for less efficient delivery options (Asdemir et al., 2009). Pricing policy is defined as dynamic since it varies each time a new order is issued (Klein et al., 2017). ...
... Nonetheless, implementing dynamic pricing policies increases the customer density compared to "simple" appointments. As a matter of fact, companies -implementing correct pricing strategies -may influence the choice of customers (who tend to select slots associated to lower prices) (Asdemir et al., 2009).  Mapping customer behaviour -based on a data mining process, mapping the customer presence consists in analysing a specific parameter that is correlated to the presence of the customer at home. ...
Article
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Purpose The purpose of this paper is twofold: first, to review and classify scientific publications dealing with those innovative solutions aimed at increasing the efficiency of last-mile delivery in business to consumer (B2C) e-commerce; and, second, to outline directions for future research in this field. Design/methodology/approach The review is based on 75 papers published between 2001 and 2019 in international peer-reviewed journals or proceedings of conferences, retrieved from bibliographic databases and science search engines. Findings Due to its importance in affecting the overall logistics costs and, as a consequence, the economic sustainability of a B2C e-commerce initiative, last-mile delivery process deserves particular attention in order to be optimised. The review highlights that, among the main factors affecting its cost, there are the probability to have failed deliveries, the customer density in the delivery areas and the degree of automation of the process. Innovative and viable last-mile delivery solutions – which may impact the mentioned drivers – include parcel lockers, crowdsourcing logistics, mapping the consumer presence at home and dynamic pricing policies. Eventually, some gaps and areas for further research activities have been identified (e.g. mapping customer behaviour, crowdsourcing logistics). Originality/value This review offers interesting insights to both academics and practitioners. On the academic side, it analyses and classifies relevant literature about innovative and efficiency-oriented last-mile delivery solutions, proposing directions for future research efforts. On the managerial side, it presents a holistic framework of the main factors affecting last-mile delivery cost and of viable innovative solutions that may be implemented to increase efficiency.
... In their computational study, they briefly evaluate the impact of different popular delivery time slots among customers by simply exogenously predefining different choice probabilities for different delivery time slots. Campbell and Savelsbergh (2006), Asdemir et al. (2009), andYa ng et al. (2016) propose dynamic pricing solution approaches. Campbell and Savelsbergh (2006) build on Campbell and Savelsbergh (2005) and examine the potential of giving incentives to certain customers to influence their delivery time slot choice. ...
... Thereby, the probability of choosing a delivery time slot increases proportionally to the given discount. Asdemir et al. (2009) propose a dynamic programming (DP) formulation for the dynamic time slot pricing problem decomposed by delivery area. The delivery capacity for each area and time slot is assumed to be given in advance. ...
... Hence, they do not deal with the solution of a VRPTW, but the delivery cost is fixed. Asdemir et al. (2009) build on discrete choice modelling for customer behavior and use the MNL model. Ya ng et al. (2016) also use the MNL and approximate the delivery cost for every customer request with an insertion heuristic approach loosely based on Campbell and Savelsbergh (2006). ...
... An interview guide was developed based on the elements of logistics with consideration paid to the context of e-grocery operations outlined by Mkansi et al. (2018). An exploration of the eight e-retailers in terms of their logistics elements provided an opportunity to identify diverse and common practices existing between township and urban e-grocery players, in particular their effect on the cost of operation (Ishfaq et al., 2016;Aspray et al., 2013;Asdemir et al., 2009) and profit strategies recognised as a major barrier to entry. At the same time, the method provided an insight into the South African township context. ...
... Similar to the mobile application retailer configured model, the fulfilment centres of the mobile app wholesale configured e-grocery operators are mainly their competitors (traditional grocery retailers stores such as Pick n Pay, Woolworths, Shoprite Checkers etc., fresh produce markets and wholesalers/discounters like Makro) situated in the malls or geographical areas nearest to their market base segments. Of particular interest was how the mobile application wholesale configured model yield profit in an industry characterised by thin profit margins (Asdemir et al., 2009). The strategies outlined by the e-grocery retail include mark up from economies of scale strategy induced by aggregating demand at the downstream chain (see Appendix 3 for economies of scale quote). ...
Purpose: This paper presents a mobile application supported township and urban e-grocery distribution models that uses a software application (app) to bridge the infrastructural barriers, costs, and complexities associated with e-grocery delivery operations in rural township areas. The use of apps reveals a slow transformation of society towards an inclusive model that integrate different types of workers in an informal context. Research Approach: Using a qualitative multi-case approach and semi-structured interviews, the study explored distribution practices of eight national emerging e-grocery retail businesses to demonstrate how mobile applications can facilitate South African urban and township e-grocery delivery models. Findings and Originality: The study reveals how the need to scale the use of new mobile application innovations fuels value-added services that power new e-grocery distribution models. Of interest is how the application aggregates demand rapidly, respond to demand within a short lead time, and how e-grocers use competitors’ stores as their fulfilment centres. Research Impact: The innovative mobile platform-based model offers emerging contextual insight of a pull e-grocery distribution model that demonstrates the supply chain innovations for addressing under-resourced and under-developed logistics infrastructure. Practical Impact: The mobile application value-added service business model offers a new wave of scaling e-grocery retail to rural and township areas constrained by technological, economic and road infrastructure. The apps transcend e-grocery barriers and enables small businesses with limited resources to leverage e-grocery market opportunities that are unimaginable in townships and rural areas.
... A heuristic decision criterion is applied for constructing an assortment of slots for each customer. Asdemir et al. (2009) study an online booking problem where each request is associated with a certain revenue and capacity consumption. The provider presents the customer with a set of time slots that do not violate the capacity constraint and determines a price for each presented slot. ...
... Yang et al. (2016) study a problem similar to that of Asdemir et al. (2009) but in a more general setting, where each request is associated with a location and the tentative routes are constructed using a GRASP heuristic inspired by Campbell and Savelsbergh (2006). A heuristic method is applied to estimate the opportunity cost of each arriving request. ...
Article
Full-text available
We present a model for policy optimization of the online booking of mobile personnel over a multiday horizon with a different cutoff for each day, where the goal is to maximize the expected ratio of accepted requests at steady-state. This model fits the practice of many service providers who allow booking of time slots over a horizon of multiple days and use availability control of the demand. Since the planning horizon is indefinite and the service horizon of each day overlaps the horizon of subsequent days, the objective is defined in terms of the steady-state performance. The interactions with the customers are performed in a single step: The system offers an assortment of time slots covering the next few days, and the user either chooses one of them or abandons the system. Upon the arrival of a service request, the provider estimates the opportunity cost of serving the request at each of the available time slots. We model this cost as a linear function and a Cobb–Douglas function of features that concisely represent the current system state. The assortment of time slots following each service request is constructed by maximizing the expected net gain from the assortment. The parameters of the opportunity cost functions are fitted using a simulation framework. The proposed method is benchmarked based on randomly generated datasets in various demand scenarios and geographies. The method is shown to outperform more straightforward baseline policies significantly.
... There are a number of ways to achieve this. Recent proposals include, for example, giving customers the choice between narrow delivery time windows for high prices and vice versa [3] or charging customers different prices based on the area and their preferred delivery time [4][5][6]. ...
Preprint
We study the approximate dynamic programming approach to revenue management in the context of attended home delivery. We draw on results from dynamic programming theory for Markov decision problems, convex optimisation and discrete convex analysis to show that the underlying dynamic programming operator has a unique fixed point. Moreover, we also show that -- under certain assumptions -- for all time steps in the dynamic program, the value function admits a continuous extension, which is a finite-valued, concave function of its state variables. This result opens the road for achieving scalable implementations of the proposed formulation, as it allows making informed choices of basis functions in an approximate dynamic programming context. We illustrate our findings using a simple numerical example and conclude with suggestions on how our results can be exploited in future work to obtain closer approximations of the value function.
... Competitive intensity in the UK grocery retail sector has recently increased with new market entrants, such as AmazonFresh and the German discounter Aldi that launched their new online initiatives. Equally, retailers have limited opportunity to counter this lack of profitability due to the cut-throat competition while consumers are unwilling to pay for the full costs of home delivery (Asdemir et al., 2009). To address these challenges, retailers have attempted to mitigate the costs by introducing higher minimum basket spend, increasing click & collect facilities and differential delivery charges, to even out costly peak periods in demand (Zissis et al., 2017). ...
Article
Full-text available
The grocery sector has transitioned into an omnichannel operating mode, allowing consumers to buy online and have their order delivered to their chosen address. The last mile delivery service leads to avoidable inefficiencies such as low asset utilisation and repeated trips to nearby neighbourhoods, increasing vehicle emissions, traffic, and operational costs. Combining historical order and delivery data of an online grocery retailer with secondary data publicly available on other retailers, we employ Monte Carlo simulation to estimate grocery home delivery demand per 1-hour time windows. We use the simulation output as an input to daily vehicle routing problem instances under independent and collaborative last mile delivery operation to estimate the impact of collaboration. Our analyses show distance savings of around 17% and route reduction of around 22%. These results can support policies incentivising vehicle and infrastructure sharing settings and decoupling the last mile delivery from the core grocery retail services.
... In particular, one can seek to exploit the flexibility of customers by offering delivery options at different prices to create delivery schedules that can be executed in a cost-efficient manner. To achieve this, recent proposals include giving customers the choice between narrow delivery time windows for high prices and vice versa (Campbell & Savelsbergh, 2006) or charging customers different prices based on the area and their preferred delivery time (Asdemir et al., 2009;Yang et al., 2016;Yang & Strauss, 2017). ...
Preprint
We study the dynamic programming approach to revenue management in the context of attended home delivery. We draw on results from dynamic programming theory for Markov decision problems to show that the underlying Bellman operator has a unique fixed point. We then provide a closed-form expression for the resulting fixed point and show that it admits a natural interpretation. Moreover, we also show that -- under certain technical assumptions -- the value function, which has a discrete domain and a continuous codomain, admits a continuous extension, which is a finite-valued, concave function of its state variables, at every time step. This result opens the road for achieving scalable implementations of the proposed formulation in future work, as it allows making informed choices of basis functions in an approximate dynamic programming context. We illustrate our findings on a simple numerical example and provide suggestions on how our results can be exploited to obtain closer approximations of the exact value function.
... We also observe an evolution in the approaches to model customer behavior: while earlier (and some more recent) works use simple probabilistic models Savelsbergh, 2005, 2006;Cleophas and Ehmke, 2014;Ehmke and Campbell, 2014;Hernandez et al., 2014); latest works leverage more advanced techniques, namely multinomial logit or non-parametric rank-based models (Asdemir et al., 2009;Klein et al., 2018Klein et al., , 2019Yang and Strauss, 2017;Yang et al., 2016). ...
Article
Full-text available
This paper explores future avenues of research for revenue management in last-mile delivery. First, we review earlier efforts in this field, which have focused primarily on the problem of attended home deliveries (AHD) of groceries. Second, based on a topological classification of last-mile delivery characteristics, we identify relevant extensions inspired in current industry trends. Finally, we outline how existing models should be extended for these new problems and discuss promising streams of future research.
... (Carlsson et al., 2016;Emeç et al., 2016;Hübner et al., 2016b). Other papers in this category also deal with pricing (Asdemir et al., 2009), in-store logistics (Belavina et al., 2017), and logistics performance (Stritto and Schiraldi, 2013). ...
Article
Full-text available
The food and grocery retail sector is undergoing a deep transformation fuelled by customers’ changing habits and new digital technologies. The logistics in this area is often challenging, especially considering the food characteristics and regulations. However, despite the relevance of the topic, the extant body of the scientific literature regarding the role of logistics in the food and grocery sector appears quite fragmented. This paper presents a Systematic Literature Review (SLR) aiming at consolidating the knowledge, analyse the development, clarify the trends and main topics, and highlight the gaps in the scientific literature concerning the role of supply chain and logistics in the food and grocery retail sector. Through the analysis of a corpus of 56 articles, the most critical research contributions on food and grocery retail logistics are discussed, highlighting the main trends over the years, as well as the applied research methods. Finally, starting from literature gaps, future research directions are identified.
... Some studies on the topic have been conducted, including the following. Asdemir et al. [31] used a multinomial logit model to incorporate customer choice behaviour; however, they mainly used this model to analyse the dynamic pricing of multiple home delivery options and did not consider the relevant connection between time slot and customer service choice. Ehmke and Campbell [32] investigated the interaction of commitment to a service time window and the reliability of actual deliveries in metropolitan areas, and their point is also not the customer behaviour. ...
Article
Full-text available
This study analyses consumer last-mile delivery service choice behaviour by developing a cross-nested logit model (CNL); we then compare the analytical results with three nested logit models (NL). The model parameters are estimated using the data from a questionnaire collected from consumers residing in Beijing, Shanghai, Tianjin, Guangdong, Zhejiang, Jiangsu, and Shandong. The direct elasticities and cross-elasticities are then calculated to assess the change in probability of each alternative caused by utility variables. Parameter estimation results demonstrate that the CNL model outperforms the three NL models. Consumers are usually reluctant to change the way they are served when utility variables are altered. Moreover, elasticity analysis results suggest that service factors have the strongest effect on choice probability, followed by socioeconomic factors and delivery activity factors. Thus, enterprises should first strive to promote the service experience of consumers in corresponding delivery services, then account for the effect of socioeconomic factors, and finally consider changing delivery service fees if they want to induce consumers to select a specified delivery service.
... This requirement implies that transitions in x are only possible in the positive direction and by at most a unit step along one dimension. Such models are typical for order-taking processes in revenue management (see Asdemir et al. (2009), Suh and Aydin (2011), Yang et al. (2016 and Yang and Strauss (2017)). Furthermore, we define a finite time horizon T := {1, 2, . . . ...
Preprint
We consider dynamic programming problems with finite, discrete-time horizons and prohibitively high-dimensional, discrete state-spaces for direct computation of the value function from the Bellman equation. For the case that the value function of the dynamic program is concave extensible and submodular in its state-space, we present a new algorithm that computes deterministic upper and stochastic lower bounds of the value function similar to dual dynamic programming. We then show that the proposed algorithm terminates after a finite number of iterations. Finally, we demonstrate the efficacy of our approach on a high-dimensional numerical example from delivery slot pricing in attended home delivery.
... This requirement implies that transitions from x t to x t+1 are only possible in the positive direction and by at most a unit step along one dimension. Such models are typical for order-taking processes (see [2,18,20,21]). For the purpose of the case study, we will assume that the customer choice model follows a multinomial logit model, like in [7,20,21], i.e. ...
Preprint
We consider the revenue management problem of finding profit-maximising prices for delivery time slots in the context of attended home delivery. This multi-stage optimal control problem admits a dynamic programming formulation that is intractable for realistic problem sizes due to the so-called "curse of dimensionality". Therefore, we study three approximate dynamic programming algorithms both from a control-theoretical perspective and in a parametric numerical case study. Our numerical analysis is based on real-world data, from which we generate multiple scenarios to stress-test the robustness of the pricing policies to errors in model parameter estimates. Our theoretical analysis and numerical benchmark tests show that one of these algorithms, namely gradient-bounded dynamic programming, dominates the others with respect to computation time and profit-generation capabilities of the delivery slot pricing policies that it generates. Finally, we show that uncertainty in the estimates of the model parameters further increases the profit-generation dominance of this approach.
... Dynamic pricing: Allows for finer levels of gradation of incentives than (dynamic) slotting. Offering price incentives can be used to increase the attractiveness of time slots during which the order can be delivered more efficiently, see [4,34,21,33]. ...
Preprint
Full-text available
Attended Home Delivery systems are used whenever a retailing company offers online shopping services that require customers to be present when their deliveries arrive. Therefore, the retail company and the customer must mutually agree on a time window during which the delivery can be assured. When placing a new order, the customer receives a selection of available delivery time slots depending on the delivery location and already accepted orders. Then, the customer selects his/her preferred delivery time slot and the order is scheduled. In general, the larger the selection, the more likely the customer finds a suitable delivery time slot. We denote the problem of determining the maximal number of feasible delivery time slots for a potential new order as the Slot Optimization Problem (SOP). It is common practice to hide certain delivery options from the customer or offer them at different rates in order to steer the incoming demand such that the expected profit is maximized. In any case, before offering any delivery time windows, their availability must be determined. Thus, the SOP must be solved quickly in order to allow for a smooth booking process. In this work, we propose an Adaptive Neighborhood Search heuristic that allows to efficiently determine which delivery time windows can be offered to potential customers. In a computational study, we evaluate the efficiency and effectiveness of our approach on a variety of benchmark instances considering different sets of delivery time windows.
... In recent years, there have been many efforts to solve the last-mile delivery problem. Previous researchers mainly focus on distribution modes (Goethals et al., 2012), cost analysis and pricing (Asdemir et al., 2009;Yang et al., 2014), crowdsourcing delivery (Wang et al., 2016;Devari et al., 2017) and vehicle routing problems (Stenger et al., 2013;Zhou et al., 2018), etc. For example, Goethals et al. (2012) introduce a concept of the unattended delivery model of e-retailing and study consumers' perceptions. ...
... Pricing decisions affect both the current revenue and long-term customer loyalty. Modifying delivery prices to balance supply and demand is commonly employed in Attended Home Delivery (AHD) (Asdemir, Jacob, & Krishnan, 2009;Yang & Strauss, 2017). ...
Preprint
Same-day delivery for e-commerce has become a popular service. Companies usually offer several time delivery options with the earliest one being next hour delivery. Due to tight delivery deadlines and thin margins, companies often find it challenging to provide efficient same-day delivery services. In this work, we propose a holistic scheme that combines the optimization of routing and pricing for same-day delivery. The proposed approach is able to take into account uncertainty in travel times, a crucial factor for delivery applications in urban environments. We model this problem as a Markov decision process. We apply a value function approximation technique to compute opportunity costs. Based on these opportunity costs, as well as the customer choice model and travel time distribution, we optimize the prices for various delivery deadlines. We perform extensive computational experiments to compare the proposed model with baseline policies. We also investigate how the (potentially wrong) choice of travel time distributions affect the performance of the proposed optimization scheme. Through numerical simulations of realistic scenarios, we observe that compared to the deterministic model, the proposed approach can reduce the number of missed deliveries up to 40%; at the same time, it can increase revenue by more than 5% compared to the baseline policies. We explore new issues that arise due to the stochastic nature of the problem such as the effect of penalties for missed deliveries on pricing structure and overall revenue.
... In particular, it corresponds to the so-called parallel flights problem with availability control as described, e.g., by [30], where time slots represent alternative, substitutable products. [3] solve the dynamic pricing problem as a parallel flights problem by assuming the number of acceptable orders per delivery area and time slot as known. Similarly, [7] approach the slotting problem sequentially by first applying a routing procedure on forecasted order requests to obtain the number of acceptable orders per delivery area and time slot. ...
Article
Full-text available
Attended home delivery requires offering narrow delivery time slots for online booking. Given a fixed fleet of delivery vehicles and uncertainty about the value of potential future customers, retailers have to decide about the offered delivery time slots for each individual order. To this end, dynamic slotting techniques compare the reward from accepting an order to the opportunity cost of not reserving the required delivery capacity for later orders. However, exactly computing this opportunity cost means solving a complex vehicle routing and scheduling problem. In this paper, we propose and evaluate several dynamic slotting approaches that rely on an anticipatory, simulation-based preparation phase ahead of the order horizon to approximate opportunity cost. Our approaches differ in their reliance on outcomes from the preparation phase (anticipation) versus decision making on request arrival (flexibility). For the preparation phase, we create anticipatory schedules by solving the Team Orienteering Problem with Multiple Time Windows. From stochastic demand streams and problem instance characteristics, we apply learning models to flexibly estimate the effort of accepting and delivering an order request. In an extensive computational study, we explore the behavior of the proposed solution approaches. Simulating scenarios of different sizes shows that all approaches require only negligible run times within the order horizon. Finally, an empirical scenario demonstrates the concept of estimating demand model parameters from sales observations and highlights the applicability of the proposed approaches in practice.
... A well-known third source of data for demand forecasting are the digital trails left by website visitors as to their tastes, their requirements (Salvador & Ikeda, 2014), and their willingness to pay for the available logistic services (Asdemir et al., 2009). The resulting massive information from site visitors can be built upon for planning, forecasting, and sharing information with upstream partners in the chain only after careful and extensive transformation using methods of descriptive and prescriptive analytics (Boone et al., 2019;Evtodieva et al., 2020;Schaer et al., 2019). ...
Chapter
This chapter discusses how data and information from operations across the supply chain can now be processed to support managers in making intelligent decisions. Specifically, we introduce the topics of Algorithms, Analytics, and Artificial Intelligence, which underpin such decision making. Beginning with some of the classical algorithms that were developed decades ago in Operations Research, we discuss how, today, decision making relies on increasingly massive amounts of data and highly sophisticated and powerful mathematical models. We show how an algorithm helps by either describing the world, forecasting it, or providing the decision-maker with prescriptive recommendations and suggestions to consider. To help readers wanting to dig deeper, we provide a bird's eye view of the fields of Analytics and Artificial Intelligence. We give some examples of the most impactful current approaches, together with a brief overview of the mathematics involved. We then present possible paths in which these tools may transform how supply chains may work and be managed in the future. Most of the presentation aims at providing a brief introduction to the central topics. The intention is to spike interest for further reading. To that effect, a wealth of sources are provided for those who wish to dig deeper.
... SFS or ROPS N/A N/A ( Boyer, Hult, & Frohlich, 2003 ). They compare attended and unattended home deliveries in terms of delivery cost ( Yrjölä, 2001 ), delivery time window ( Lin & Mahmassani, 2002 ), delivery lead time ( Hsu & Li, 2006 ) etc. Several papers theoretically or empirically study the operations of home delivery from the perspectives of cost and pricing ( Asdemir, Jacob, & Krishnan, 2009;Boyaci & Ray, 2006;Campbell & Savelsbergh, 2006;Yang & Strauss, 2017 ), vehicle-routing ( Ehmke & Campbell, 2014;Minis & Tatarakis, 2011;Pandelis, Kyriakidis, & Dimitrakos, 2012;Tatarakis & Minis, 2009;Tsirimpas, Tatarakis, Minis, & Kyriakidis, 2008;Wang & Cheng, 2009;Zachariadis, Tarantilis, & Kiranoudis, 2013 ), inventory management ( Hariga, 2010;Izui et al., 2010;Kang & Kim, 2010;Lee, 2017 ), fulfillment policy ( Acimovic & Graves, 2015;Jasin & Sinha, 2015;Onal, Zhang, & Das, 2017 ) etc. As for omni-channel retailing, with the advent of technologyenabled alternatives for product delivery, some researchers shift their attention towards emerging fulfillment modes, e.g., buyonline-pick-up-in-store (BOPS), ship-to-store (STS), ship-from-store (SFS), and reserve-online-pay-in-store (ROPS) modes. ...
Article
Retailers increasingly adopt the buy-online-pick-up-in-store (BOPS) mode of order fulfillment. We study BOPS in this paper by developing a theoretical model in which a physical retailer (store) adopting BOPS uses a recommended service area to fulfill orders from both determined (online) and casual (offline) customers in one order cycle. We obtain three major findings: (i) The ratio of unit inventory cost to BOPS customers’ arrival rate to the store is the key factor that determines the size of the BOPS service area. (ii) From the point of view of product type, we provide the retailer with practical guidelines for judging whether a certain type of product should be allowed for BOPS or not. (iii) When orders can be cancelled, a moderate cancellation policy (MCP) is more beneficial to the retailer than a liberal/strict cancellation policy (LCP/SCP). Furthermore, compared with the reserve-online-pick-up-and-pay-in-store (ROPS) mode, BOPS is less profitable under LCP/SCP, but they have the same profitability under MCP.
... We also observe an evolution in the approaches to model customer behavior: while earlier (and some more recent) works use simple probabilistic models Savelsbergh, 2005, 2006;Cleophas and Ehmke, 2014;Ehmke and Campbell, 2014;Hernandez et al., 2014); latest works leverage more advanced techniques, namely multinomial logit or non-parametric rank-based models (Asdemir et al., 2009;Klein et al., 2018Klein et al., , 2019Yang and Strauss, 2017;Yang et al., 2016). ...
Preprint
This paper explores future avenues of research for revenue management in last-mile delivery. First, we review earlier efforts in this field, which have focused primarily on the problem of attended home deliveries (AHD) of groceries. Second, based on a topological classification of last-mile delivery characteristics, we identify relevant extensions inspired in current industry trends. Finally, we outline how existing models should be extended for these new problems and discuss promising streams of future research. Abstract This paper explores future avenues of research for revenue management in last-mile delivery. First, we review earlier efforts in this field, which have focused primarily on the problem of attended home deliveries (AHD) of groceries. Second, based on a topological classification of last-mile delivery characteristics, we identify relevant extensions inspired in current industry trends. Finally, we outline how existing models should be extended for these new problems and discuss promising streams of future research. http://hdl.handle.net/1721.1/114146
... (iv) dynamic pricing: Allows for finer levels of gradation of incentives than (dynamic) slotting. Offering price incentives can be used to increase the attractiveness of time slots during which the order can be delivered more efficiently, see references [19,[31][32][33]. ...
Article
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The steadily growing popularity of grocery home-delivery services is most likely based on the convenience experienced by its customers. However, the perishable nature of the products imposes certain requirements during the delivery process. The customer must be present when the delivery arrives so that the delivery process can be completed without interrupting the cold chain. Therefore, the grocery retailer and the customer must mutually agree on a time window during which the delivery can be guaranteed. This concept is referred to as the attended home delivery (AHD) problem in the scientific literature. The phase during which customers place orders, usually through a web service, constitutes the computationally most challenging part of the logistical processes behind such services. The system must determine potential delivery time windows that can be offered to incoming customers and incrementally build the delivery schedule as new orders are placed. Typically, the underlying optimization problem is a vehicle routing problem with a time windows. This work is concerned with a case given by an international grocery retailer’s online shopping service. We present an analysis of several efficient solution methods that can be employed to AHD services. A framework for the operational planning tools required to tackle the order placement process is provided. However, the basic framework can easily be adapted to be used for many similar vehicle routing applications. We provide a comprehensive computational study comparing several algorithmic strategies, combining heuristics utilizing local search operations and mixed-integer linear programs, tackling the booking process. Finally, we analyze the scalability and suitability of the approaches.
... According to the application context, it has developed a Multinomial Logit Model (MNL), Nested Logit Model (NL), Mixed Logit model (ML), General Extreme Value model (GEV), Multinomial Probit model (MNP), etc. (Train, 2009). In the field of urban delivery, Multinomial Logit models were mainly used to describe customer's choice of delivery time slots, such as Asdemir, K. (Asdemir et al., 2009). It is relevant in the customer choice behavior between last-mile delivery modes and time slots. ...
Article
A multicircle order acceptance strategy was proposed to decide whether to accept customer requests for specific last-mile delivery modes (including attended home or reception box delivery) and time slots. The strategy was composed of initializing acceptable time slot allocations, reallocating acceptable time slots, matching reference sites, and assessing time slot deviations. A strategy-oriented insertion algorithm for dynamic vehicle routing problems with hard time windows was constructed, thereby showing the proposed strategy achieves a better balance between revenue and distance than the “first come and first served” strategy. Always accepting global optimization result is not significantly better than adopting the result conditionally or based on simulated annealing theory. The distance and revenue gradually increase with the number of reference sites, while revenue/distance ratio decreases. The vehicles are available to serve more attended home delivery orders with a gradual increase in time slot interval, thereby leading to an increase of AHD and total revenue.
... To balance the demand among the different time windows, incentives can be used to shift demand from popular to less popular time windows. Revenue management techniques as discussed in Asdemir et al. (2009), Yang et al. (2014 and Klein et al. (2017) analyze the customer's willingness to pay as well as the likelihood of choosing a specific time window and determine prices for delivery time windows. We do not consider pricing approaches in this paper. ...
Article
In the competitive world of online retail, customers can choose from a selection of delivery time windows on a retailer's website. Creating a set of suitable and cost-efficient delivery time windows is challenging, since customers want short time windows, but short time windows can increase delivery costs significantly. Furthermore, the acceptance of a request in a short time window can greatly restrict the ability to accommodate future requests. In this paper, we present customer acceptance mechanisms that enable flexible time window management in the booking of time-window based attended home deliveries. We build tentative delivery routes and check which time windows are feasible for each new customer request. We offer the feasible long delivery time windows and let our approaches decide when to offer short time windows. Our approaches differ in the information they consider with regard to customer characteristics as well as detailed characteristics of the evolving route plan. We perform a computational study to investigate the approaches’ ability to offer short time windows and still allow for a large number of customers to be served. We consider various demand scenarios, partially derived from real order data provided by a German online supermarket.
... Some households may lack safe and secure delivery or pick-up locations or the transport options necessary for curbside pickup.26 Many online retailers, including food retailers, offer dynamically priced delivery, whereby customers are offered differential delivery prices based on the delivery deadline, delivery routes, and other factors.37,40,41 Customers may be offered discounts for longer or less convenient delivery windows, or may choose to pay a premium for greater convenience, narrower windows, or speedier delivery.42 ...
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The USDA Online Purchasing Pilot, which allows SNAP participants to shop and pay for groceries online, rapidly expanded during the COVID-19 pandemic. From March 2020 to March 2021, the number of participating states increased from 5 to 47. This brief assesses whether the Pilot promotes healthy food access (using the criteria of availability and utilization) and nutrition security, including household food security and healthy dietary behaviors. The findings show that the Pilot is not currently meeting these aims equitably. Availability of stores participating in the Pilot is still lacking, particularly in rural food desert areas and in communities reliant on small, independent grocers. Despite increased retailer participation in the Pilot, and growing acceptability and utilization of online food purchasing, there are many personal, structural, and financial barriers that make further adoption challenging. The brief offers recommendations to promote a more equitable expansion of the program. Policies that: (1) provide technical and financial assistance so that local retailers and producers can participate in the Pilot; (2) support utilization among SNAP participants through structural changes; and (3) strengthen and enforce requirements for authorized retailers can overcome current limitations and help ensure healthy food access for all SNAP participants.
... In contrast to our work, Pfrommer et al. (2014) and Waserhole (2014) use platform's expected cost of repositioning vehicle as the objective. Another stream of literature studies the optimization of dynamic delivery fee for the attended home delivery firms, e.g., Campbell and Savelsbergh (2006), Asdemir et al. (2009), Klein et al. (2015. The key trade-off addressed in these works is how to use price to incentivize customers to allocate their demands to different delivery time slots such that the profit (delivery fee minus the cost associated with service type and time slots) is maximized. ...
Thesis
This dissertation studies the development of provably near-optimal real-time prescriptive analytics solutions that are easily implementable in a dynamic business environment. We consider several stochastic control problems that are motivated by different applications of the practice of pricing and revenue management. Due to high dimensionality and the need for real-time decision making, it is computationally prohibitive to characterize the optimal controls for these problems. Therefore, we develop heuristic controls with simple decision rules that can be deployed in real-time at large scale, and then show theirs good theoretical and empirical performances. In particular, the first chapter studies the joint dynamic pricing and order fulfillment problem in the context of online retail, where a retailer sells multiple products to customers from different locations and fulfills orders through multiple fulfillment centers. The objective is to maximize the total expected profits, defined as the revenue minus the shipping cost. We propose heuristics where the real-time computations of pricing and fulfillment decisions are partially decoupled, and show their good performances compared to reasonable benchmarks. The second chapter studies a dynamic pricing problem where a firm faces price-sensitive customers arriving stochastically over time. Each customer consumes one unit of resource for a deterministic amount of time, after which the resource can be immediately used to serve new customers. We develop two heuristic controls and show that both are asymptotically optimal in the regime with large demand and supply. We further generalize both of the heuristic controls to the settings with multiple service types requiring different service times and with advance reservation. Lastly, the third chapter considers a general class of single-product dynamic pricing problems with inventory constraints, where the price-dependent demand function is unknown to the firm. We develop nonparametric dynamic pricing algorithms that do not assume any functional form of the demand model and show that, for one of the algorithm, its revenue loss compared to a clairvoyant matches the theoretic lower bound in asymptotic regime. In particular, the proposed algorithms generalize the classic bisection search method to a constrained setting with noisy observations.
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E-grocers with an attended home delivery service model operate in a highly competitive market characterized by thin profit margins. To ensure a profit-maximizing delivery schedule, the requirements for the joint management of demand and the vehicle routes are substantial. Therefore, we study an e-grocer's operational problem of managing demand by means of dynamic time slot allocation. The purpose of dynamically allocating time slots is to influence customers’ choices by offering a selection of time slots to a customer request, such that the overall expected profit of the resulting delivery schedule is maximized. The time slot offer decisions mainly depend on a request's opportunity cost. Hence, we first propose a mixed-integer linear program to approximate this opportunity cost, which scales linearly with the number of decision variables. In this approximation, we consider the consequences of expected time slot offer decisions for future customers on the final delivery schedule. We explicitly incorporate customer choice behavior using a generalized attraction model. Second, we propose a non-linear binary program and its linearization based on the underlying choice model to make time slot offer decisions using the approximated opportunity cost. Due to the formulation's structural properties, it can be efficiently solved. In a computational study, we show the superiority of our approach in comparison to benchmarks applied in the academic literature and its applicability in an online environment.
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The purpose of the study is to identify logistic elements germane to e-grocery businesses, and to reveal the challenges collateral with each logistic element. Further, it strives to create a better understanding of specific remedies that have been employed by top e-grocery retailers to overcome existing challenges while aligning identified challenges with Turban’s framework. Extensive semi-structured interviews were conducted with management staff in three of the top ten global online grocery retailers and another that was a market leader in a European country. The qualitative data collected was transcribed and coded using a non-hierarchical axial coding to identify emerging themes in content analysis. The results expose a range of challenges that could be compartmentalised into three broad categories, in harmony with the different stages of the order fulfilment process. Interestingly, the study found that most challenges were operational rather than tactical or strategic in nature. While the study expands existing knowledge, its revelation that most challenges lie in the management of roles and responsibilities domain is instructive. This makes it imperative for practitioners to focus on this specific area if meaningful improvement in e-grocery retailing performance is to be realised. This research offers a systematic understanding of supply and distribution challenges, including remedies utilised to ameliorate the effect of the challenges from the perspectives of the top companies in the industry. These remedies can be invaluable for existing and emerging e-grocers. © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.
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When a local seller launches an app channel, it can either deliver online orders itself or use a third party service. Given this background, an interesting problem is whether and how a local seller's channel strategy will be affected by these delivery options. To investigate this problem, we focus on a local seller facing these two delivery options. For each option, besides setting the online and offline prices, the seller also determines its delivery service coverage. Assuming that consumers are evenly located along an infinite Hotelling line, we propose a joint pricing and delivery distance decision model and derive the local seller's optimal decisions in two subcases, i.e. the seller is a price taker or a price setter. By analyzing and comparing the seller's optimal decisions in these situations, we find that (i) the local seller's channel strategy will be changed dramatically by the delivery option – the seller will abandon the offline channel when it delivers itself; (ii) whether the seller is a price taker or price setter will also influence the seller's channel strategy; and (iii) unexpectedly, the highly developed just-in-time logistics is an important factor that helps the offline channel and app channel coexist in the Internet era.
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Originating from passenger air transport, revenue management has evolved into a general and indispensable methodological framework over the last decades, comprising techniques to manage demand actively and to further improve companies’ profits in many different industries. This article is the second and final part of a paper series surveying the scientific developments and achievements in revenue management over the past 15 years. The first part focused on the general methodological advances regarding choice-based theory and methods of availability control over time. In this second part, we discuss some of the most important generalizations of the standard revenue management setting: product innovations (opaque products and flexible products), upgrading, overbooking, personalization, and risk-aversion. Furthermore, to demonstrate the broad use of revenue management, we survey important industry applications beyond passenger air transportation that have received scientific attention over the years, covering air cargo, hotel, car rental, attended home delivery, and manufacturing. We work out the specific revenue management-related challenges of each industry and portray the key contributions from the literature. We conclude the paper with some directions for future research.
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Delivery service becomes one of the most important issues that significantly influence customer purchase behavior, especially for retailers in tourist attractions. This paper builds a model to investigate the strategy optimization of delivery service for such retailers. Meanwhile, the retailer optimizes the retail price to maximize the expected profit in different types of delivery service scenarios. Our results indicate that, given the perceived value of a delivery service, the optimal strategy is to provide free delivery service when its cost is relatively low, but to not provide delivery service when the cost is relatively high.
Article
Purpose This paper investigates the logistics management in the e-grocery sector. It contrasts the key issues faced by practitioners and the topics addressed in the academic literature, to identify potential misalignments between research and practice and propose avenues for future efforts. Design/methodology/approach This work adopts a twofold methodological approach. From an academic perspective, a systematic literature review (SLR) is performed to define the topics addressed so far by scholars when analysing e-grocery logistics. From a managerial perspective, a Delphi study is accomplished to identify the most significant issues faced by logistics practitioners in the e-grocery context and the associated significance. Findings The study develops a conceptual framework, identifying and mapping the 9 main logistics challenges for e-grocery along 4 clusters, in the light of a logistics-related revision of the SCOR model: distribution network design (area to be served, infrastructures), order fulfilment process (picking, order storage, consolidation, delivery), logistics-related choices from other domains (product range, stock-out management) and automation. These elements are discussed along three dimensions: criticalities, basic and advanced/automation-based solutions. Finally, the main gaps are identified – in terms of both under-investigated topics (order storage and stock-out management) and investigated topics needing further research (picking and automation) – and research questions and hypotheses are outlined. Originality/value This paper provides a threefold contribution, revolving around the developed framework. First, it investigates the state of the art about e-grocery logistics, classifying the addressed themes. Second, it explores the main issues e-grocery introduces for logistics practitioners. Third, it contrasts the two outcomes, identifying the misalignment between research and practice, and accordingly, proposing research directions.
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Attended home delivery describes the delivery of goods by e-grocers or e-tailers to customers within an agreed time window. Because customers expect narrow time windows, offering such services may lead to expensive fulfillment operations. This has led to research on how to influence customers’ bookings using time window pricing or slotting. In this paper, we reconsider the problem of demand management through dynamic pricing for attended home delivery services. This problem is usually modeled as a stochastic dynamic program, but even small instances cannot be solved to optimality due to the curses of dimensionality. The major challenges consist of finding feasible time windows for an incoming customer, estimating the opportunity cost, i.e., the future monetary loss due to accepting a booking, and optimizing the time window prices in real time. In this paper, we propose a route-based approximate dynamic programming approach to tackle these challenges. The approach carefully combines and partially extends state-of-the-art methods in attended home delivery, dynamic pricing, and dynamic vehicle routing. In an extensive simulation study, we compare its performance with state-of-the-art benchmark heuristics. The results indicate a superior performance of our approach in terms of both profit and number of customers served.
Article
In e-commerce, customers are usually offered a menu of home delivery time windows of which they need to select exactly one, even though at least some customers may be more flexible. To exploit the flexibility of such customers, we propose to introduce flexible delivery time slots, defined as any combination of such regular time windows (not necessarily adjacent). In selecting a flexible time slot (out of a set of windows that form the flexible product), the customer agrees to be informed only shortly prior to the dispatching of the delivery vehicle in which regular time window the goods will arrive. In return for providing this flexibility, the company may offer the customer a reduced delivery charge and/or highlight the environmental benefits. Our framework also can accommodate customized flexible slots where customers can self-select a set of regular slots in which a delivery may take place. The vehicle routing problem (VRP) in the presence of flexible time slots bookings corresponds to a VRP with multiple time windows. We build on literature on demand management and vehicle routing for attended home delivery, as well as on flexible products. These two concepts have not yet been combined, and indeed the results from the flexible products literature do not carry over directly because future expected vehicle routing implications need to be taken into account. The main methodological contribution is the development of a tractable linear programming formulation that links demand management decisions and routing cost implications, whilst accounting for customer choice behavior. The output of this linear program provides information on the (approximate) opportunity cost associated with specific orders and informs a tractable dynamic pricing policy for regular and flexible slots. Numerical experiments, based on realistically-sized scenarios, indicate that expected profit may increase significantly depending on demand intensity when adding flexible slots rather than using only regular slots.
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In attended home delivery, challenges arise in practice because of the short strict time windows, stochastic customer requests, and varying customers’ preferences for delivery slots. In this study, we focus on integrating dynamic time slot incentives and order delivery with the intention of reducing overall delivery cost and improving profitability. The proposed incentive mechanism is able to exploit the variability in the marginal fulfillment cost of an order and the customers’ preferences to influence the customers’ selection of delivery slots. We present an approximate dynamic programming approach to estimate the marginal fulfillment cost using the operational vehicle routing cost while accounting for future orders. We demonstrate that the proposed incentive mechanism can achieve a high level of savings (of up to 70%) with respect to the benchmark customer-free-choice scenario. It is also noted that the proposed mechanism effectively exploits higher order density and vehicle availability to achieve a higher level of savings.
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To plan attended home deliveries efficiently and profitably, dynamic slotting can tailor the set of time slots offered to the customer. While most existing approaches focus on short-term criteria like revenue, marketing research highlights additional business objectives. For instance, distributing deliveries evenly across the region can ensure the visibility of branded trucks. Alternatively, prioritising influential customers can entice them to recommend the service to others. To accommodate additional objectives, we propose a multi-criteria approach that allocates delivery capacity to time slots and areas in two steps: First, considering spatial information on potential future customers and their values, it computes a predictive routing; second, it solves a revenue management problem to identify the most relevant customers given the set of slotting criteria. A comprehensive computational study demonstrates the effects for three exemplary criteria: revenue, visibility, and social influence. The results show that, depending on the demand scenario, additional criteria can be accommodated at little cost to revenue. However, the interaction of routing effects and offer set optimisation can cause unexpected shifts when changing the weight of criteria in the objective function. Hence, we propose a simulation-based process to let decision makers evaluate the effects of multi-criteria constellations.
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We study the dynamic programming approach to revenue management in the context of attended home delivery. We draw on results from dynamic programming theory for Markov decision problems to show that the underlying Bellman operator has a unique fixed point. We then provide a closed-form expression for the resulting fixed point and show that it admits a natural interpretation. Moreover, we also show that – under certain technical assumptions – the value function, which has a discrete domain and a continuous codomain, admits a continuous extension, which is a finite-valued, concave function of its state variables, at every time step. Furthermore, we derive results on the monotonicity of prices with respect to the number of orders placed in our setting. These results open the road for achieving scalable implementations of the proposed formulation, as it allows making informed choices of basis functions in an approximate dynamic programming context. We illustrate our findings on a low-dimensional and an industry-sized numerical example using real-world data, for which we derive an approximately optimal pricing policy based on our theoretical results.
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Supply chains in general and last-mile logistics in particular, have been disrupted due to COVID-19. Though several innovative last-mile logistics solutions have been proposed in the past, they possess certain limitations, especially during COVID-19 motivating the need for an alternative last-mile logistics solution. We present a review of literature related to last-mile logistics and supply chain disruptions to identify the limitations of existing last-mile delivery practices during COVID-19. Using a stylized analytical model, we then propose that “mobile warehouse” can be an effective solution to last-mile logistics issues faced during COVID-19 and beyond under certain conditions. A mobile warehouse is a truck dedicated to a particular geographical location and carries the inventory of various products based on the estimated demand requirements for these products in that geographical location. We provide the condition under which the B2C e-commerce providers find it profitable to adopt a truck as a mobile warehouse to sell high demand items quickly.
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We consider dynamic programming problems with finite, discrete-time horizons and prohibitively high-dimensional, discrete state-spaces for direct computation of the value function from the Bellman equation. For the case that the value function of the dynamic program is concave extensible and submodular in its state-space, we present a new algorithm that computes deterministic upper and stochastic lower bounds of the value function similar to dual dynamic programming. We then show that the proposed algorithm terminates after a fnite number of iterations. Finally, we demonstrate the efficacy of our approach on a high-dimensional numerical example from delivery slot pricing in attended home delivery.
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We consider the revenue management problem of finding profit-maximizing prices for delivery time slots in the context of attended home delivery. This multistage optimal control problem admits a dynamic programming (DP) formulation that is intractable for realistic problem sizes due to the so-called "curse of dimensionality." We therefore study three approximate DP algorithms both from a numerical and control-theoretical perspective. Our analysis is based on real-world data, from which we generate multiple scenarios to stress-test the robustness of the pricing policies to errors in model parameter estimates. Our theoretical analysis and numerical benchmark tests indicate that one of these algorithms, namely gradient-bounded DP, dominates the others with respect to computation time and profit-generation capabilities of the pricing policies that it generates.
Preprint
We consider a multi-agent optimal resource sharing problem that is represented by a linear program. The amount of resource to be shared is fixed, and agents belong to a population that is characterized probabilistically so as to allow heterogeneity among the agents. In this paper, we provide a characterization of the probability that the arrival of a new agent affects the resource share of other agents, which means that accommodating the new agent request at the detriment of the other agents allocation provides some payoff. This probability represents a sensitivity index for the optimal solution of a linear programming resource sharing problem when a new agent shows up, and it is of fundamental importance for a correct and profitable operation of the multi-agent system. Our developments build on the equivalence between the resource sharing problem and certain dual reformulations which can be interpreted as scenario programs with the number of scenarios corresponding to the number of agents in the primal problem. The recent "wait-and-judge" scenario approach is then used to obtain the sought sensitivity index. Our theoretical findings are demonstrated through a numerical example on optimal cargo aircraft loading.
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In logistics and mobility services, new business models such as “attended home delivery”, “same-day delivery”, and “mobility-on-demand” have been successfully established over the last decade. They have in common that customers order online, while the services are provided offline. To make such online-to-offline services profitable, the efficient operation of a vehicle fleet is an essential prerequisite. Therefore, researchers began to explore approaches for integrating demand management and vehicle routing to support such operations, and a rapidly growing body of literature emerged. However, due to the diversity of existing business models, the analysis and comparison of decision problems and solution concepts are challenging, especially across applications, making the search for appropriate models and algorithms for new problem settings non-trivial. Therefore, in this survey, we structure this innovative research area and review the existing literature from a methodological perspective. We present a generalized problem definition of integrated demand management and vehicle routing, derive a high-level formulation for the underlying sequential decision process, and present a corresponding mathematical model. We then describe and characterize solution concepts and algorithms from the literature in a structured way. We also present a tabular overview of the literature that connects applications and problem characteristics with solution concepts and allows researchers to quickly step through already studied combinations. Finally, we comment on the state-of-the-art from a cross-application perspective and discuss future research opportunities.
Thesis
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The rapid development of e-commerce has created new consumer demand, but at the same time it has increased the delivery pressure of express. In the era of Internet plus customers require the express delivery to achieve personalization and diversification. And the traditional single home delivery mode can not solve the problems such as low delivery efficiency, high delivery cost and low customer satisfaction.Thus the formation of a diversified last mile delivery service system is an important problem to be solved urgently in the express delivery industry. It includss how to designs reasonable pickup service and improve the quality of home delivery service. As the only phase of direct contact with the final customers, last mile express delivery has gradually become a key factor affecting the online shopping experience of consumers. Facing different delivery modes, customers often show bounded rational choice behavior, or rely on the traditional home delivery mode, or choose a single pickup mode, or select multiple delivery modes. In order to improve the customer's experience of the existing delivery services and solve the bottleneck of last mile delivery, the status of last mile express delivey modes and the decision making behavior of the customers were analyzed. Then, from the perspective of bounded rational customers, the quantal response equilibrium problem of different customer choice behavior when customers chose different last mile delivery modes was studied and two kinds of customer choice equilibria were discussed. Finally, according to the different decision-making enterprises, considering the different choices equilibrium behavior, the location of pickup points and the pricing of home delivery service are studied. Firstly, the paper analyzes the status of last mile express delivey modes and the decision making behavior of the customers. Through the literature review and the actual investigation, the classification standards of home delivery mode and pickup mode are put forward, and the problems in the operation process of existing delivery modes are revealed. The application scope of different delivery modes is compared and analyzed. At the same time, the customer's decision making behaviors including the customer demand characteristics, the decision-making factors and the decision-making process are analyzed. It reveals that the customer is not completely rational in the process of decision making, has the bias of decision making and shows the bounded rationality behavior of stochastic choice. Secondly, the quantal response equilibrium problem of different customer choice behavior is studied. Aiming at the correlational problem of different delivery modes, we take the customer selection between home delivery, attended Collection and Delivery Point (CDP) mode and unattended CDP mode as an example. The home delivery is simulated as an M/D/1 queue and the pickup point as differernt M/M/K/K queues. Nested Logit (NL) model is used to calculate the utility function of the pickup mode, and the customer choice models of three delivery modes are constructed. Then, the existence and uniquess of Nested Logit-Quantal Response Equilibrium(NL-QRE) in last mile delivery service system are proved. Aiming at the customer dependency problem of the home delivery mode, we take the customer selection between attended CDP mode and unattended CDP mode as an example. The pickup points are modeded as different queues. The utility function of pickup mode is modified by prospect theory (PT), and the customer choice models of two delivery modes are constructed. Then, the existence and uniquess of Prospect Theory-Quantal Response Equilibrium(PT-QRE) in pickup service system are proved. And the difference between the PT-QRE and completely rational choice equilibrium is analyzed theoretically. Numerical experiments verify the correctness of the choice equilibrium model, and reveal the degree of customer rationality and the degree of dependence on home delivery affect the design of the last mile delivery mode. Thirdly, the location problem of pickup points based on customer choice equilibrium is studied. We analyze whether the probabilistic-choice and optimal-choice choice behavior has an impact on the pickup point location. To improve the pickup point’s operating efficiency and benefit, the mult-objective optimization model based on NL-QRE is formulated. Faced with the home delivery mode, the attended CDP mode and the unattended CDP mode, customers show bounded rational behavior which could not accurately assess the home delivery waiting utility or pickup loss utility. The non-dominated sorting genetic algorithm II(NSGA-II) is developed to solve the established optimization model, and compared with the weighted method and the ideal point. To meet the different interests of the two decision-makers in the distribution enterprise and the customer, the bi-level optimization model for pickup points location is proposed. Customers have reference dependence behavior in the home delivery mode and are lack of accurate calculaiton capability to asses loss value of the pickup mode. An iteration algorithm is designed to solve the upper and lower model based on NSGA-II and immune algorithm. The results verify the validity and feasibility of the models and algorithms, and show the customer bounded rational behavior and the home delivery price affect the pickup point network’ operating efficiency and benefit. Finally, the pricing problem of home delivey based on customer choice equilibrium is studied. In the context of the fierce competition from the pickup service enterprises which service is free, the customer packages are easily rejected by the pickup points. In order to improve the revenue of the home delivery enterprises, the home delivery pricing model based on multiple delivery modes affecting each other is formulated. Aiming at the pricing model based on NL-QRE, the projection gradient method, the genetic algorithm and the sensitivity analysis based local search algorithm are designed, respectively. The results show that there are significant differences in the pricing strategies of different types of customers. Aiming at the pricing model based on PT-QRE, the improved projection gradient method is designed and compared with the genetic algorithm and the multistart local search algorithm. The results show that both the QRE and the PT-QRE affect the pricing model, and it is necessary for the enterprise to understand thoroughly the dependency of the home delivery mode and the degree of customer rationality.
Chapter
In the context of port communities, one of the most commonly used technological developments are Port Community Systems (PCS). The typical service offer of a PCS includes information exchange, electronic exchange of customs declarations and responses, control, tracking and tracing of the goods, and statistics. However, it has been argued that it is necessary to evolve the existing PCS into a renewed version, more suitable to the novel requirements posed by both developed and emerging economies, also incorporating the surge of new technologies. Such renovation should care for integrating new value-added services to the aforementioned typical PCS service offer. Therefore, we propose a new hinterland intermodal routing service to be included to the regular PCS functionality. Such new service is based on the development of a built-in optimization model, delivering a sustainable and cost-effective intermodal transport network. The proposed hinterland intermodal routing service could help mitigating the environmental impact of the Colombian hinterland transport and the national transportation costs, increasing the nation’s competitiveness and sustainability through a value-added service of a PCS.
Article
In this paper, we study an e-grocer’s tactical problem of differentiated time slot pricing in attended home delivery. The purpose of differentiating delivery prices is to influence customers’ choice behavior concerning the offered time slots, such that cost-effective delivery schedules on an operational level can be expected and overall profit is maximized. We present a mixed-integer linear programming formulation of the problem, in which delivery costs are anticipated by explicitly incorporating routing constraints, and we model customer behavior by a general nonparametric rank-based choice model. Concerning cost anticipation, we also propose a model-based approximation that enables application to real-world problem sizes. In a setup inspired by an industry partner operating in urban areas, we then perform a comprehensive computational study that reveals the value of the model-based approximation as a supporting instrument for an e-grocer’s pricing decisions in practice. In particular, we demonstrate the superiority of the model-based approximation for real-world problem sizes to several benchmark approaches applied in the scientific literature and in practice (e.g., a unit price approach and other standard pricing heuristics). The online appendix is available at https://doi.org/10.1287/trsc.2017.0738 .
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This paper proposes the term perishable-asset revenue management to denote the field that combines the areas of yield management, overbooking, and pricing for perishable assets. After summarizing the characteristics common to problems in this field, the paper discusses the objectives and constraints faced by decision makers. Then it offers a comprehensive taxonomy with 14 different elements and reviews the research that has been done related to each element. Finally, it suggests some important areas of future research that can help bridge the gap between theory and application.
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Home delivery of groceries is not yet a very popular service among consumers. One reason for the slow progress has been the time-consuming and expensive ordering process. In recent years, Internet-based solutions have solved most of the problems related to the order transaction process – making ordering simpler, cheaper and faster. However, there are still a lot of unsolved problems in the e-grocery business. One of the biggest obstacles is inefficient home delivery. This paper examines how different solutions for goods receipt affect home-delivery efficiency. Different alternatives for receiving the goods are presented and the service levels of these concepts are described from the consumer’s point of view. In addition, the costs for the e-grocer are studied. The efficiency of using a reception box is demonstrated by simulating two alternative receiving concepts.
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Electronic grocery shopping (EGS) with direct home delivery is gradually becoming an option for busy families. The early EGS operators offer service built on top of the traditional grocery shop and the Internet is only used as a communication tool to transmit the customer order. To become a viable option for consumers the EGS has to be supported by a completely new logistics structure in which the Internet is used to connect all parties in the supply chain to the same real time information. As a result, the supply chain, all the way from the supplier to the household, needs to be redesigned. In the USA, dedicated EGS companies have entered the marketplace with investors backing them up. Huge losses are experienced in the battle against the traditional players. In smaller markets the funds needed for this kind of action are not easy to generate. Describes an evolutionary model for traditional grocery traders to start EGS gradually, mainly based on investments already made. Studies, in detail, the cost structure of some essential elements of the new supply chain and presents the initial results.
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In many industries, managers face the problem of selling a given stock of items by a deadline. We investigate the problem of dynamically pricing such inventories when demand is price sensitive and stochastic and the firm's objective is to maximize expected revenues. Examples that fit this framework include retailers selling fashion and seasonal goods and the travel and leisure industry, which markets space such as seats on airline flights, cabins on vacation cruises, and rooms in hotels that become worthless if not sold by a specific time. We formulate this problem using intensity control and obtain structural monotonicity results for the optimal intensity (resp., price) as a function of the stock level and the length of the horizon. For a particular exponential family of demand functions, we find the optimal pricing policy in closed form. For general demand functions, we find an upper bound on the expected revenue based on analyzing the deterministic version of the problem and use this bound to prove that simple, fixed price policies are asymptotically optimal as the volume of expected sales tends to infinity. Finally, we extend our results to the case where demand is compound Poisson; only a finite number of prices is allowed; the demand rate is time varying; holding costs are incurred and cash flows are discounted; the initial stock is a decision variable; and reordering, overbooking, and random cancellations are allowed.
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The main hypothesis of the dissertation is that "e-grocery with home delivery can be more efficient than supermarket retailing handling a similar volume of sales". The e-grocery business should be seen as an assembly industry producing shopping baskets. Only in this way can the new electronic channel work efficiently. The e-grocery business is usually seen as a supermarket copied into an electronic form; it is seen only as an opportunity to buy products. Instead, the starting point of operational design should be the real needs of a household and take into account the possibility of adding new services for the customers. One of the conclusions of this research is that the operational costs of a distribution centre can be lower than those of a supermarket. Store-based order picking is less expensive than using a specialised distribution centre when turnover is less than one million euros. A turnover of more than 3 million euros means that a dedicated distribution centre appears to be more efficient than store-based picking. However, the distribution centre has to be purpose-built for shopping basket assembly with a reasonably stable workload. A combination of store-based picking and a specialised distribution centre has been introduced as an opportunity to create gradual low-risk growth in the e-grocery business. It seems that efficient home delivery can be achieved even with a moderate market share. Unattended reception is very important for the overall cost structure of the supply chain and enable service models that give flexibility in route planning and optimisation. However, the investments that unattended reception requires should also be taken into account. The cost efficiency of a home delivery service model can be described by the average mileage driven per order, which directly correlates with the number of stops per hour. New efficiency indicators are needed to measure the efficiency of the e-grocery business. Sales per distribution centre and sales per square kilometre are useful indicators when choosing home delivery service models and potential market areas. The most useful factor is sales per square kilometre. The critical sales volume appears to be 200,000 euros per square kilometre per annum. This sales volume can be achieved with 25 four-person households per square kilometre with 90 percent purchase loyalty. E-grocery retailing is a very local business and store-based picking is a good alternative if fast roll-out with a low level of investment is required. A distribution centre-based operation is potentially much more efficient, but it is a slower approach and needs more investment. Whatever service model is chosen, it should first be made to work in a fairly compact geographical area and then copied to new areas. Dissertation series / Helsinki University of Technology, Industrial Management and Work and Organisational Psychology, ISSN 1459-1936; 2003 / 3
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This review addresses the specific supply chain management issues of Internet fulfillment in a multi-channel environment. It provides a systematic overview of managerial planning tasks and reviews corresponding quantitative models. In this way, we aim to enhance the understanding of multi-channel e-fulfillment and to identify gaps between relevant managerial issues and academic literature, thereby indicating directions for future research. One of the recurrent patterns in today’s e-commerce operations is the combination of ‘bricks-and-clicks’, the integration of e-fulfillment into a portfolio of multiple alternative distribution channels. From a supply chain management perspective, multi-channel distribution provides opportunities for serving different customer segments, creating synergies, and exploiting economies of scale. However, in order to successfully exploit these opportunities companies need to master novel challenges. In particular, the design of a multi-channel distribution system requires a constant trade-off between process integration and separation across multiple channels. In addition, sales and operations decisions are ever more tightly intertwined as delivery and after-sales services are becoming key components of the product offering.
E-fulfillment in a multi-channel environment
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