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Designing Self-Storage Warehouses with Customer Choice
International Journal of Production Research
Abstract
Self-storage warehousing is a rapidly growing industry where consumers or companies can rent storage space for personal or company use, over a certain horizon. This paper addresses the question of how to design the facilities so that revenue can be maximised over a finite horizon, considering multiple demand requirements with respect to size, pricing, location within the facility, climate control, security or outside access, among others. Using a customer choice model to specify the probability of purchase for each fare product as a function of the set of fare products offered, we propose methods to design self-storage warehouses while allowing different customer choice behaviours. We model the problem as a mixed-integer program and solve it using column generation and branch-and-price algorithms. In addition, we study the impact of re-layout and methods to modify facility layout, since self-storage facilities are relatively flexible in layout and individual storage compartments can be adapted to changes in demand. We validate our model using the data of four self-storage warehouses, and show our method can improve the expected revenue by nearly 11% on average for these cases.
... Also, product allocation planning regarding handling constraints for workforce security management seems to be on the rise [86]. Another concept in self-storage warehousing with customer choice appears as a contemporary issue [113]. ...
... Another possibility with warehouse models is self-storage warehouses. Thus, [113] described a design for self-storage warehouse with customer choice. They used a customer choice model to evaluate the probability of purchase. ...
... The solution showed that proximity for storage location is the main factor to minimize the total travel time in assembly warehouses. [113] evaluated a selfstorage in Rotterdam for the design of self-storage warehouses with customer choices. The paper included layout design and a real case study for a specific non-standard warehousing aspect in self-storage. ...
Companies’ productivity is critical in contemporary warehouse environment to maintain efficiency and competitiveness within their supply chain. Warehousing operations are well known for their impact on the overall supply chain and need to be wisely managed. Among those activities, allocation planning and layout design are some of the most important concepts in practice. Current research tends to emphasize homogeneous environment, which leaves heterogeneous and non-standard cases with less attention, even today. This paper aims to review the literature regarding warehouse allocation planning and layout design methods that could suit practical industry problems, with a focus on heterogeneous and non-standard spare parts. It also aims to describe the current trends in these fields. Following a literature review methodology, a total of fifty-seven articles were reviewed to identify the methods developed and used. The reviewed papers were also investigated in order to identify research gaps and future directions. The analysis concluded that more research is needed to better understand and optimize heterogeneous and non-standard spare parts environments in terms of allocation and layout design. More practical case applications also remain a gap to address.
Article highlights
This article evaluates 8 concepts/parameters related to warehouse allocation operation. This is to ensure a deep analysis of heterogeneous and non-standard parts presence in literature.
A research gap is identified regarding heterogeneous and non-standard parts in warehouse allocation and layout design methods in the scientific literature.
This article evaluates 3798 scientific papers over a 20-year timeframe. From this, we present 57 methods and 8 gaps in scientific literature.
... Faber and Koster (2013) submitted that warehousing plays a significant role in the logistics performance of shipping firms by storing surplus goods within the warehouses until they are needed by the customer when prices fall and supply is bountiful, and when demand is high during the slow season (Pandian, 2019;Mpuon, Etim, Etuk, Odigbo & Arikpo, 2023b). According to Zhou et al. (2016), risk-bearing as a critical role of warehousing in the logistics performance of shipping firms is centred on the fact that commodities held in warehouses are vulnerable to threats such as theft, degradation, and inspection. Zhou et al. (2016) opined further that warehouses are constructed in such a way that these risks are minimized. ...
... According to Zhou et al. (2016), risk-bearing as a critical role of warehousing in the logistics performance of shipping firms is centred on the fact that commodities held in warehouses are vulnerable to threats such as theft, degradation, and inspection. Zhou et al. (2016) opined further that warehouses are constructed in such a way that these risks are minimized. Furthermore, they held that when items are housed in warehouses, contracts of bailment apply and that a warehouse custodian must take reasonable care of the products and protect them from different threats. ...
The demand for businesses to develop new methods to generate value and distribute it to consumers is rising in the highly challenging global market in recent times. This has forced the development of more effective and efficient warehousing techniques in the shipping industry. This study empirically investigated the role of warehousing in the logistics performance of shipping companies in Nigeria. Data were obtained from registered shipping enterprises and warehouse operators in Nigeria to test the hypotheses. Descriptive and inferential statistics were tools for data analysis. The conclusions of the investigation indicated that warehousing impacts shipping Companies' logistics performance significantly. Based on the findings from the results, it was established that if warehousing activities are successfully implemented in shipping companies, their logistics performance will be improved rapidly. Therefore, it was recommended that logistics managers and warehousing operators should devise strategies for improving their warehousing activities to achieve effective and efficient logistics performance.
... Warehouse layout design has a significant impact on the walking distance in picking operations and exceeds 60% [29]. The current literature on warehouse layout design also mostly focuses on travel cost or distance minimization as the optimization objective [6,12,14,[30][31][32], with less consideration of operating costs [33], storage location assignment [34], storage space utilization [35,36], warehouse throughput [37,38], and operational strategies [39][40][41][42]. Warehouse layout problems can be divided into two categories according to their specific design: one is the layout of facilities and equipment, and the other is the design of the internal aisles of the warehouse [6]. ...
The routing strategy for order picking is an important factor in the efficiency of warehouse picking, and improvements to the warehouse layout provide more routing options for picking. The number of storage locations to be visited during the picking operation also has an impact on the selection of routing strategies. In order to achieve an effective improvement in the efficiency of picking operations in warehouse distribution centers, this paper focuses on the leaf warehouse layout based on the previous single-command operation strategy and extends it to study the multi-command operation strategy, in which three heuristic routing strategies, the S-shape, the return, and the composite, are introduced to solve the walking distance problem of picking operations, with the study of the selection of the routing strategy for different numbers of storage locations to be visited. Based on the distance equation between any two storage locations to be visited in the leaf layout warehouse, travel distance models corresponding to the three routing strategies in the picking operation are constructed, and the cuckoo search algorithm is introduced to solve and calculate the travel distance of the composite strategies for the experiments. In addition, the computational experiments of the three path strategies are carried out according to the different numbers of storage locations to be visited in the picking operation. By analyzing the numerical results, we find that the composite strategy has the best overall results among the three routing strategies, with the average values of optimization rates exceeding 30% (the S-shape) and 40% (the return), respectively. At the same time, the return strategy outperforms the S-shape strategy when the number of locations to be visited is less than seven. As the number of locations to be visited increases, the S-shape strategy gradually outperforms the return strategy. From a managerial and practical perspective, compared to the single-command operation strategy that is the focus of the current research, the multi-command operation strategy we studied is more relevant to the actual situation of order merging picking in warehouses and can effectively improve the efficiency of picking operations, the competitiveness of enterprises, and customer satisfaction of e-commerce enterprises.
... In addition, the customers' satisfaction is not taken into account, given that the only objective is to maximise the provider's revenue. Gong et al. (2013) and Zhou, Gong, and Koster (2016) study the tactical problem of determining the facility design that better fits the matching between storage design (in terms of types and units per type) and market demand such that the final revenue of the self-storage provider is maximised. Another paper related to ours is Shi, Yu, and Dong (2021), which develops a dynamic and stochastic warehouse revenue management model for a retail platform (such as Amazon) that has to allocate its warehouse space for self-use and rent. ...
available open access at https://www.tandfonline.com/doi/full/10.1080/00207543.2022.2078249
Warehouses are key elements of supply chain networks, and great attention is paid to increase their
efficiency. Highly volatile space requirements are enablers of innovative resource sharing concepts,
where warehouse capacities are traded on online platforms. In this context, our paper introduces the
on-demand warehousing problem from the perspective of platform providers. The objective priori�tises demand–supply matching with maximisation of the number of transactions. If there is a tie, the
secondary objective maximises the number of suppliers matched with at least one customer and the
number of customers that have matches within a specific threshold with respect to the minimum
achievable cost. Besides the mathematical integer programming formulation, a myopic list-based
heuristic and an efficient matheuristic approach are presented and benchmarked against the per�formance of a commercial optimisation solver. The impact of several parameters on the platform’s
objective is analysed. A particularly relevant finding is that the pricing flexibility on the demand side
does not necessarily imply higher payments to the supply side. All data instances are made available
publicly to encourage more researchers to work on this timely and challenging topic.
... This is typical in hotel and airline industries, manufacturing systems (Gong et al. 2013;Dolgui et al. 2019), transportation networks (Papier and Thonemann 2010), and healthcare applications (Helm, AhmadBeygi, and Van Oyen 2011). Customer segmentation used by car parking practitioners is based on the theories and practices from revenue management and marketing science (Zhou, Gong, and De Koster 2016). ...
We study a recently introduced smart compact automated parking system (CAPS), deploying straddle carriers, which require little footprint area. The straddle carriers can elevate and move a car over other cars in the horizontal direction in deep storage lanes, thereby considerably increasing the car storage capacity. Customers can reserve a parking space with a mobile application, or just drive in. Customers with booking by mobile application receive priority over the drive-in customers. We build priority queueing network models to evaluate the performance of a CAPS with priority booking under two storage policies. We then develop approximation methods to solve the analytical models and validate them through simulation. Numerical experiments show that the waiting time of a customer with booking can be reduced by 9.2% and 3.4%, respectively, for the dedicated and shared storage policies, compared with systems without priority booking. Then we compare the dedicated and shared storage policies and conduct a sensitivity analysis on the arrival rates of two types of cars. To minimise the expected retrieval time, the optimal ratio of width to height should be around 1.3. Finally, we calculate the investment cost of a CAPS and compare it with a competitive cylindrical automated parking tower.
... They mostly aim to design a layout to minimize the transportation cost (or distance) for order picking [19,20,30,38,7,4,14]. Interested readers are referred to [9] for further details on this research. The other objectives considered in warehouse layout design problems are operational costs [39,28,43], product allocation [27,32,25], storage space utilization [10,6], warehouse throughput [29,31,23], and operating policies [8,41,36,21,1]. ...
Storing pallets of products on top of one another on the floor of a warehouse is called block stacking. The arrangement of lanes, aisles, and cross-aisles in this storage system affects both utilization of the storage space and material handling costs; however, the existing literature focuses exclusively on lane depths and their impact on space utilization. This paper fills this gap and studies the optimal layout design for block stacking, which includes determining the numbers of aisles and cross-aisles, bay depths, and cross-aisle types. We show that lane depths affect material handling cost in addition to space utilization and develop a simulation-based optimization algorithm to find optimal layouts with respect to both of these objectives. We also propose a closed-form solution to the optimal number of aisles in a layout. The results of a case study in the beverage industry show that the resulting layout can save up to ten percent of the operational costs of a warehouse. We present the computational efficiency of the algorithm and some insights into the problem through an exhaustive experimental analysis based on various test problems that cover small- to industrial-sized warehouses.
... This is typical in hotel and airline industries, manufacturing systems (Daniel et al., 1997;Zhao et al., 2011;Gong et al., 2013;Guler et al., 2014;Vahdani et al., 2015), transportation networks (Savin et al., 2005;Gans et al., 2007;Papier et al., 2010), and healthcare applications (Taylor et al., 1980;Helm et al., 2011;Geng et al., 2017). Customer segmentation used by car parking practitioners is based on the theories and practices from revenue management and marketing science (Gong et al., 2013;Zhou et al.. 2016). Some researchers have investigated automated car parking systems (Teodorovic et al., 2006;Liu et al., 2014). ...
Many cities struggle with space shortage for parking and transportation activities. We model and analyse a recently introduced smart compact automated parking system (CAPS), deploying straddle carriers, which require little footprint area. The straddle carriers can elevate and move a car over other cars in the horizontal direction in deep storage lanes, thereby considerably increasing the car storage capacity. Lifts move cars between different tiers and the depot. Customers can reserve a parking space with a mobile application, or just drive in. Customers with booking by mobile application receive priority over the drive-in customers. We consider two storage slotting policies: a shared policy under which both customer types share one deep-storage lane and a dedicated policy under which a CAPS stores only one customer type in one storage lane. We build priority queuing network models to evaluate the performance of a CAPS with priority booking under these two storage policies. We then develop approximation methods to solve the analytical models and validate them through simulation. Numerical experiments show that the waiting time of a customer with booking can be reduced by 9.2% and 3.4%, respectively, for the dedicated and shared storage policies, compared with systems without priority booking. Then we compare the dedicated and shared storage policies. Furthermore, we optimise the system dimensions. To minimise the expected retrieval time, the optimal ratio of width to height should be around 1.3. Finally, we compare the footprint areas of a CAPS and a competitive cylindrical automated parking tower by deriving closed-form expressions.
... Recently, extensive research is carried out on dynamic storage assignment and fluctuating demand based storage assignment policies (Diaz 2016;Li, Moghaddam, and Nof 2016;Manzini et al. 2015;Shi, Guo, and Yu 2018;Zhou, Gong, and de Koster 2016). ...
This paper reexamines the order picking process in a warehouse facing the challenges that e-commerce brings about and which are characterised by a very large number of small sized orders and returns. Implementing effective storage assignment strategies combined with efficient batching, in this context, is very fundamental to keep the warehouse’s responsiveness and order completion times up to the standards. This paper investigates a new storage assignment strategy, initially implemented by a large shoes and footwear wholesaler, to enhance the performance of the order picking process in its warehouses. The impact of this new storage assignment strategy on the performance of the order picking process is analysed via simulation. The performance of the system, measured in terms of total picking time as well as order lead time while taking congestion into account, is compared to the cases where conventional storage assignment strategies are implemented. A full factorial design is set up and the simulation output is statistically analysed. The results of this analysis are reported and thoroughly discussed. Attending to the results, the proposed strategy presents a remarkable potential to shorten total picking travel distances and order completion time and consequently customer satisfaction.
... Further details can be found in de Koster et al. (2007). Other researchers investigated this problem from the perspectives of operational cost (Thomas & Meller, 2015;Mowrey & Parikh, 2014;Zhou et al., 2016), space utilization (Derhami et al., 2017), product allocation (Moshref-Javadi & Lehto, 2016;Ramtin & Pazour, 2015;Li et al., 2016), operating policies (Ramtin & Pazour, 2014;Roodbergen et al., 2015;Guo et al., 2016), and warehouse throughput (Pazour & Meller, 2011;Lamballais et al., 2017;Zaerpour et al., 2017). ...
In block stacking warehouses, pallets of stock keeping units (SKUs) are stacked on top of one another in lanes on the warehouse floor. A conventional layout consists of multiple bays of lanes separated by aisles. The depths of the bays and the number of aisles determine the storage space utilization. Using an analytical model, we show that the traditional lane depth model underestimates accessibility waste and therefore does not provide an optimal lane depth. We propose a new model of wasted storage space and embed it in a mixed integer program to find the optimal bay depths. The model improves space utilization by allowing multiple bay depths and allocating SKUs to appropriate bays. Our computational study shows the proposed model is capable of solving large-scale problems with a relatively small optimality gap. We use simulation to evaluate performance of the proposed model on small to industrial-sized warehouses. We also include a case study from the beverage industry.
... The research studied warehouse layout mostly considered it with respect to transportation costs [8,16,20,13,19,15,17,18]. Few studies investigated designing the layout with respect to the other objectives such as storage costs [9], storage capacity [12,21], and space utilization [7,4]. Comprehensive reviews on the research about the warehouse layout can be found in [3,6,1]. ...
Storing pallets of Stock Keeping Units (SKUs) on top of one another in lanes on a warehouse floor is known as block stacking. This storage system is widely used in manufacturing systems and distribution centers. The arrangement of lanes in the layout of this system significantly impacts utilization of the storage space and transportation costs. Existing research that studies the layout for this system focuses exclusively on determining the optimal lane depth with respect to space utilization and ignores transportation costs. In this study, we develop a simulation model that computes several performance metrics to evaluate both of these objectives for a warehouse layout. It aims to take the stochastic variations exist in the real world situation into account. Designing the layout based on the historical data distinguishes this model from the analytical models in the systems with high level of uncertainty, where determining the required parameters for analytical models are difficult due to the high variations. We verified the model using the existing analytical models and developed an experimental analysis to show the trade-off between the space utilization and transportation costs in the layout design problem.
... The results show that it is an advantageous approach to the classic ABC method, but requires the formulation of a complex problem and its resolution through a simulation of a metaheuristic method, which is not easy. In the paper already cited, Zhou et al. [8] present a new approach called self-managed warehouses is shown, whose use has rapidly grown in the world and are a universal trend. The problem in such a warehouse is the need for re-doing the layout frequently, so it is necessary to have direct ways to reconfigure the space continuously. ...
Energy consumption in modern warehouses is today an important issue which has not received much attention in the scientific community. In this paper it is addressed the problem of warehouse design considering the energy costs incurred by vehicles and equipment in a fully or partially automated facility. Closed-form solutions are obtained by a formulating the Lagrangian of an operational cost function with equality constraints. The contribution of the paper is to develop formulas for reduced energy consumption and pollution, both relevant aspects in sustainable engineering systems. An example applied to a distributor of MRO items is presented. In this version the energy cost is integrated into the formula, modifying the method presented in [1].a
For companies involved in the supply chain, proper warehousing management is crucial. Warehouse layout arrangement and operation play a critical role in a company’s ability to maintain and improve its competitiveness. Reducing costs and increasing efficiency are two of the most crucial warehousing goals. Deciding on the best warehouse layout is a remarkable optimization problem. This paper uses an optimization method to set bin allocations within an automated warehouse with particular characteristics. The warehouse’s initial layout and the automated platforms limit the search and define the time required to move goods within the warehouse. With the help of historical data and the definition of the time needed to move goods, a mathematical model of warehouse operation was created. An optimization procedure based on the well-known Variable Neighbourhood Search algorithm is defined and applied to the problem. Experimental results demonstrate increments in the efficiency of warehousing operations.
The sharing economy, i.e., the cooperative consumption of goods and services offered by private households or companies via online market places, gains more and more attention. Most sharing platforms coordinate transactions by generating each consumer an individual list of suited and available resources to choose from. If plenty online requests arrive rather simultaneously and compete for the scarce shared resources, however, an optimization-based coordination of supply and demand promises much better matches (e.g., more satisfied requests). This paper focuses deterministic matching problems and provides a classification scheme for the resulting optimization problems occurring in different areas of the sharing economy. These matching problems vary, for instance, if immobile (e.g., parking space) or mobile resources (e.g., vehicles of a car sharing provider) are shared. With the help of this classification, we give a detailed overview on known and novel complexity results. Furthermore, we apply the example of sharing parking space and explore the potential contribution of deterministic matchings when applied in a dynamic, uncertain, and opportunistic environment.
In this paper, we study the dynamic pricing practices in the self-storage industry by introducing the widely used price controller, a term borrowed from control theories, among all self-storage service providers. Most self-storage service providers adopt the price controller method because of its effectiveness, simplicity, and straightforwardness. The price controller method adjusts the price per store's rental performance over the period. With noises on both move-ins and move-outs, the price controller method is a stochastic control model to determine the pricing trajectory at a sequence of discrete time spots to maximize the expected revenue growth. We find that the price controller method may outperform the myopic pricing method by evolving the pricing decision toward its optimum. Also, we discuss several unique properties of the self-storage industry and gain significant knowledge by analyzing the necessity of issuing a rental lease incentive to new customers.
This paper addresses the routes planning problem in a scenario where an UAV (Unmanned Aerial Vehicle) and a land-based transportation vehicle are used to deliver parcels to customer locations. We developed and implemented a solution based on the well-known Bellman-Held-Karp dynamic programming algorithm for the Travelling Salesman Problem that finds the least-cost routes for both the aircraft and the land-based transportation vehicle. We applied this algorithm to different, randomly selected commercial drones, with different maximum velocity and flight range, in order to select the best performing drone for the lowest price.
The use of smartphones and handheld devices in our daily activities has sharply increased. The added features of wireless technology and related applications on these devices make it possible to write emails, notes, and long text. Even though the most commonly used electronic input device is a keyboard, very little work has been dedicated for finding an optimal layout for this device. In this research, the aim is to propose a better layout for the single-finger keyboard in terms of rapid typing. The keyboard layout problem can be formulated as a quadratic assignment problem, which is one of the hardest combinatorial optimization problems. Some well-known literary works in English are chosen for estimating the keying-in-time. A variant of genetic algorithm, namely, the estimation of distribution algorithms is used to find a better layout. The new layout is found to be efficient compared with some of the existing prominent keyboard layouts.
The design of public storage warehouses needs to fit market segments to increase the average revenue in an environment of high demand. This paper presents a revenue model integrated with queuing and price-demand theories to solve the design and pricing problem for public storage warehouses. We consider two demand cases in the model, which are exponential demand and piecewise linear demand. We also develop a solution based on dynamic programming techniques to solve the problem. Using data from a warehouse, we conduct numerical experiments. Results show that our approach can improve the expected revenue of public storage warehouses with high demand by 16.6% on average. We further conduct sensitivity analysis on price, and investigate the relation between revenue and price.
In a supply chain’s order fulfilment process, it is often the warehouse that plays a central role in making the right product available to the right customer at the right time. This paper aims to improve warehouse performance by deriving an effective design method for the simultaneous determination of warehouse layout and the warehouse’s control policies. The authors consider layout variables for the warehouse such as the number of primary aisles utilised, the number of cross-aisles and the aisle length under several different design philosophies. Concurrently, control policies such as storage policies and routing are considered. Simulation is utilised to determine the performance of the various resulting scenarios. A screening and selection procedure is employed to reduce the required number of replications while achieving a predetermined precision in identifying the best configuration. The approach is applied for an industrial partner in this research and the results of experimentation are compared to a baseline scenario which describes a proposed new facility in the Netherlands. The results reveal a large potential for performance improvement.
This article presents an integrated computer simulation–stochastic data envelopment analysis (SDEA) approach to deal with the job shop facility layout design problem (JSFLD) with stochastic outputs and safety and environmental factors. Stochastic outputs are defined as non-crisp operational and deterministic inputs. At first, feasible layout alternatives are generated under expert decision. Then, computer simulation network is used for performance modelling of each layout design. The outputs of simulation are average time-in-system, average queue length and average machine utilisation. Finally, SDEA is used with Lingo software for finding the optimum layout alternative amongst all feasible generated alternatives with respect to stochastic, safety and environmental indicators. The integrated approach of this study was more precise and efficient than previous studies with the stated outputs. The results have been verified and validated by principal component analysis. The unique features of this study are the ability of dealing with multiple inputs (including safety) and stochastic (including environmental) outputs. It also uses mathematical programming for optimum layout alternatives. Moreover, it is a practical tool and may be applied in real cases by considering safety and environmental aspects of the manufacturing process within JSFLD problems.
A flexible product is a menu of two or more alternatives products serving the same market. Purchasers of flexible products are assigned to one of the alternatives at a later date. Gallego and Phillips [9] show that capacitated suppliers, such as airlines and hotels, can potentially improve revenue by offering flexible products in addition to traditional specific products. In this paper, we extend the concept of flexible products to networks. We study the network revenue management problem with flexible products in two different settings: one where the demand for each product is independently and exogenously generated; and the other where the demand is driven by a consumer choice model. We show that in both these settings the optimal value of the stochastic optimization problem can be closely approximated by the optimal value of a deterministic linear program. In the independent demand case the corresponding linear program is of modest size. When the demand is driven by a customer choice model, the linear program has exponentially many variables; however, we show that for an important class of consumer choice models the linear program can be efficiently solved using column-generation. We report the findings of numerical experiments with a real airline subnetwork and show how the results vary as a function of the key inputs.
An influential factor affecting the efficiency of a manufacturing facility is its layout. In a production facility, measure for efficiency can be based on the total cost of transporting the items between different departments and throughout the facility. However, other factors may influence efficiency of the manufacturing facility too. As such are: supporting the organisation's vision through improved material handling, material flow and control; effectively assigning people, equipment, space and energy; minimising capital investment; adaptability and ease of maintenance; as well as providing for employee safety and job satisfaction. By incorporating health and safety measures in the initial design of a facility layout, the organisation may avoid money and manpower loss resulting from industrial accidents. This paper proposes a facility layout planning methodology which integrates the occupational health and safety (OHS) features in the early design of a facility layout. The model considers transportation cost in the facility as well as safety concerns. By this means, the OHS issues are reflected prior to the construction of a facility.
The most desirable characteristic of a facility layout is its ability to maintain its efficiency over time while coping with the uncertainty in product demand. In the traditional facility layout design method, the facility layout is governed by the flow intensity between departments, which is the product flow quantity between departments. Hence, an error in the product demand assessment can render the layout inefficient with respect to material handling costs. Most of the research integrates uncertainty in the form of probability of occurrence of different from-to charts. In an environment where the variability of each product demand is independent, the derivation of ‘probabilistic from-to chart’ based scenario cannot be used to address uncertainty of individual demands. This paper presents an FLP (facility layout problem) approach to deal with the uncertainty of each product demand in the design of a facility layout. Two procedures are presented: the first procedure is utilised to assess the risk associated with the layout, while the second procedure is used to develop the layout that minimises the risk. Results from case studies have shown that the procedure produces a reduction of risk as high as 68%.
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.
Facility layout problems are related to the location of all facilities in a plant. Numerous research works related to facility layout have been published. The applicability of these various existing models may be limited by the fact that they all ignore competitive reactions to one's actions. In addition to external competitors, some internal problems of system such as material handling system design affect layout designs. For considering these effects, some researchers have investigated multi-objective approaches that in most cases lead to the optimisation of a weighted sum of score functions. The poor practicability of such an approach is due to the difficulty of normalising these functions and quantifying the weights. To the extent that competitors do react to a firm's actions and also the facility layout problem considers several conflicting objectives by distinct decision makers in the firm, the existing models may be oversimplifications of reality. In this paper, we modelled such a facility layout problem with conflicting objectives under a duopoly Bertrand competition as a game and solved it with a proposed simulated annealing meta-heuristic. Results obtained from solving some numerical examples confirm the effectiveness of the proposed model for the layout design.
The two primary functions of a warehouse include (1) temporary storage and protection of goods and (2) providing value added services such as fulfilment of individual customer orders, packaging of goods, after sales services, repairs, testing, inspection and assembly. To perform the above functions, the warehouse is divided into several functional areas such as reserve storage area, forward (order collation) area and cross-docking. The paper presents a mathematical model and a heuristic algorithm that jointly determine product allocation to the functional areas in the warehouse as well as the size of each area using data readily available to a warehouse manager.
This monograph provides a review of choice models in marketing from the perspective of a utility maximizing consumer subject to budgetary restrictions. Marketing models of choice have undergone many transformations over the last 20 years, and the advent to hierarchical Bayes models indicate that simple, theoretically grounded models work well when applied to understanding individual choices. Thus, we use economic theory to provide the foundation from which future trends are discussed. We begin our discussion with descriptive models of choice that raises a number of debatable issues for model improvement. We then look to economic theory as a basis for guiding model development, and conclude with a discussion of promising areas for future work.
Customer choice behavior, such as buy-up and buy-down, is an important phenomenon in a wide range of revenue management contexts. Yet most revenue management methodologies ignore this phenomenon---or at best approximate it in a heuristic way. In this paper, we provide an exact and quite general analysis of this problem. Specifically, we analyze a single-leg reserve management problem in which the buyers' choice behavior is modeled explicitly. The choice model is very general, simply specifying the probability of purchase for each fare product as a function of the set of fare products offered. The control problem is to decide which subset of fare products to offer at each point in time. We show that the optimal policy for this problem has a quite simple form. Namely, it consists of identifying an ordered family of "efficient" subsets S1, ..., Sm, and at each point in time opening one of these sets Sk, where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time. That is, the more capacity (or less time) available, the further the optimal set is along this sequence. We also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested, that is S1 \subseteq S2 \subseteq ... \subseteq Sm. Moreover, we give a characterization of when nesting by fare order is optimal. We also develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure.
Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for the warehouse, and consequently for the whole supply chain. In order to operate efficiently, the order-picking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.
This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references. In the forty years since the first publication on overbooking control, passenger reservations systems have evolved from low level inventory control processes to major strategic information systems. Today, airlines and other transportation companies view revenue management systems and related information technologies as critical determinants of future success. Indeed, expectations of revenue gains that are possible with expanded revenue management capabilities are now driving the acquisition
During the past few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature.
In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. (2004) [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network. Technical Report CORC TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York], and the follow-up dynamic programming decomposition heuristic of van Ryzin and Liu (2008) [van Ryzin, G. J., Q. Liu. 2008. On the choice-based linear programming model for network revenue management. Manufacturing Service Oper. Management 10(2) 288–310]. We focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-hard and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective and that the overall approach leads to high-quality, practical solutions.
The paper provides a comprehensive review of the recent development of revenue management in different industries. We discuss research on different revenue management strategies including pricing, auctions, capacity control, overbooking and forecasting. Related issues such as economic concerns, customer perception, competition and consolidation, implementation, performance evaluation, and common techniques and approaches used for solving revenue management problems are also discussed. Finally, we give our suggestion on some important areas that warrant further research.
We consider a self-storage warehouse, facing storage orders for homogeneous or heterogeneous storage units over a certain time horizon. The warehouse operations manager needs to decide which storage orders to accept and schedule them across different storage units to maximize revenue. We model warehouse operations as scheduling n independent multiprocessor tasks with given start and end times, with an objective to maximize revenue. With operational constraints like the maximal upscaling level, precedence order constraints, and maximal idle time, the established mixed-integer program cannot be efficiently solved by commercial softwares. We therefore propose a column generation approach and a branch-and-price method to find an optimal schedule. Computational experiments show that, compared with current methods in self-storage warehouses, our method can significantly increase the revenue.
In many application areas such as airlines and hotels a large number of bookings are typically cancelled. Explicitly taking into account cancellations creates an opportunity for increasing revenue. Motivated by this we propose a revenue management model based on Talluri and van Ryzin (2004) that takes cancellations into account in addition to customer choice behaviour. Moreover, we consider overbooking limits as these are influenced by cancellations. We model the problem as a Markov decision process and propose three dynamic programming formulations to solve the problem, each appropriate in a different setting. We show that in certain settings the problem can be solved exactly using a tractable solution method. For other settings we propose tractable heuristics, since the problem faces the curse of dimensionality. Numerical results show that the heuristics perform almost as good as the exact solution. However, the model without cancellations can lead to a revenue loss of up to 20%. Lastly we provide a parameter estimation method based on Newman et al.(2014). This estimation method is fast and provides good parameter estimates. The combination of the model, the tractable and well-performing solution methods, and the parameter estimation method ensures that the model can efficiently be applied in practice.
This article proposes modifications to the travel-time models for non-traditional warehouse aisle layouts, Flying-V and Fishbone, by incorporating a vertical travel dimension. The resulting non-linear optimization models incorporate Chebychev travel within the picking aisles. The obtained shape of the aisle and the percent improvement for a traditional warehouse are compared with the results found in previous research that ignores vertical travel. It is shown that the percent improvement diminishes as the height of the rack increases, with Fishbone maintaining a higher percent improvement over Flying-V. It is also shown that while the shape of Flying-V can be considerably altered by considering vertical travel, the Fishbone layout often maintains its recommended shape regardless of the height of the rack. The article concludes with recommendations for effective implementation of these two designs.
Quantity-Based RM.- Single-Resource Capacity Control.- Network Capacity Control.- Overbooking.- Price-based RM.- Dynamic Pricing.- Auctions.- Common Elements.- Customer-Behavior and Market-Response Models.- The Economics of RM.- Estimation and Forecasting.- Industry Profiles.- Implementation.
Self-storage is a booming industry. Both private customers and companies can rent temporary space from such facilities. The design of self-storage warehouses differs from other facility designs in its focus on revenue maximization. A major question is how to design self-storage facilities to fit market segments and accommodate volatile demand to maximize revenue. Customers that cannot be accommodated with a space size of their choice can be either rejected or upscaled to a larger space. Based on data of 54 warehouses in America, Europe, and Asia, we propose a new facility design approach with models for three different cases: an overflow customer rejection model and two models with customer upscale possibilities, one with reservation and another without reservation. We solve the models for several real warehouse cases, and our results show that the existing self-storage warehouses can be redesigned to generate larger revenues for all cases. Finally, we show that the upscaling policy without reservation generally outperforms the upscaling policy with reservation.
This article extends the analysis of table mixes for restaurants, on the principle that table mix helps drive revenue. The article presents the results of a simulation experiment of more than forty-six thousand restaurant contexts to evaluate the accuracy of “naïve table mix” calculations. An earlier study presented a simple method of calculating a recommended mix of tables in a restaurant, and another found that those calculations provided table mixes yielding within about 1 percent of the revenue provided by the optimal table mix. While the earlier simulation studies assumed that the space required by a table was directly proportional to the number of seats it contained, that is not always the case. Thus, this article presents space-oriented versions of the existing naïve table mix methods. The article also presents the results of a test of two other forms of the naïve table mix calculations, which include party revenue (or contribution). The simplest of the naïve table mix models proved to be the best, on balance. It yielded table mixes that generated more than 98 percent of the revenue achievable by near-optimal table mixes. As a caveat, however, in more than 5 percent of the simulated scenarios, the best naïve table mix model yielded less than 95 percent of the revenue achievable by a near-optimal table mix. These findings suggest that the naïve table mix calculations be used with caution.
Increasing global competition, rapid changes in technology and the necessity to respond quickly to a cost and quality conscious customer have changed the dynamics of facilities planning. Today's manufacturing facility needs to be responsive to the frequent changes in product mix and demand while minimizing material handling and machine relocation costs. In this paper, we present a framework for the design of a dynamic facility which can respond effectively to the changes in product design, mix and volume in a continuously evolving work environment. A genetic algorithm-based heuristic is used for solving the design problem and two test cases are presented to illustrate the use of the methodology.
Based on an approach recently proposed for obtaining global optimal solution of general 0–1 fractional programming (G-FP) problem, a theorem is presented in this research. Through this theorem, the nonlinear terms appearing in the formulation transformed from G-FP can be directly replaced by a set of linear inequalities. The G-FP problem can hence be easily and more efficiently solved as a mixed integer program.
Automated storage and retrieval systems (AS/RSs) and autonomous vehicle storage and retrieval systems (AVS/RSs) are two competing technologies for the handling, storage and retrieval of unit-loads in the reserve section of an automated warehouse. In this paper, we model variants of the two systems as an open queuing network (OQN) and use an existing tool for the analysis of OQNs, called the manufacturing system performance analyser (MPA), for analysing the performance of the AS/RS and AVS/RS. Experimental results are provided to show that MPA is a better choice than simulation to quickly evaluate alternate configurations of the two systems. We use MPA to answer a series of design questions for AS/RS and AVS/RS design conceptualisation.
Customer behavior modeling has been gaining increasing attention in the operations management community. In this paper we review current models of customer behavior in the revenue management and auction literatures and suggest several future research directions.
Revenue management has been used in a variety of industries and generally takes the form of managing demand by manipulating length of customer usage and price. Supply mix is rarely considered, although it can have considerable impact on revenue. In this research, we focused on developing an optimal supply mix, specifically on determining the supply mix that would maximize revenue. We used data from a Chevys restaurant, part of a large chain of Mexican restaurants, in conjunction with a simulation model to evaluate and enumerate all possible supply (table) mixes. Compared to the restaurant's existing table mix, the optimal mix is capable of handling a 30% increase in customer volume without increasing waiting times beyond their original levels. While our study was in a restaurant context, the results of this research are applicable to other service businesses.
In this paper we present a reference framework and a classification of warehouse design and control problems. Based on this framework, we review the existing literature on warehousing systems and indicate important gaps. In particular, we emphasize the need for design oriented studies, as opposed to the strong analysis oriented research on isolated subproblems that seems to be dominant in the current literature.
In spite of the importance of warehousing to the customer service and cost levels of many businesses, there is currently not a comprehensive systematic method for designing warehouses. In this paper, the current literature on the overall methodology of warehouse design is explored, together with the literature on tools and techniques used for specific areas of analysis. The general results from the literature have then been validated and refined with reference to warehouse design companies. The output is a general framework of steps, with specific tools and techniques that can be used for each step. This is intended to be of value to practitioners and to assist further research into the development of a more comprehensive methodology for warehouse design.
Gallego et al. [Gallego, G., G. Iyengar, R. Phillips, A. Dubey. 2004. Managing flexible products on a network. CORC Technical Report TR-2004-01, Department of Industrial Engineering and Operations Research, Columbia University, New York.] recently proposed a choice-based deterministic linear programming model (CDLP) for network revenue management (RM) that parallels the widely used deterministic linear programming (DLP) model. While they focused on analyzing "flexible products"--a situation in which the provider has the flexibility of using a collection of products (e.g., different flight times and/or itineraries) to serve the same market demand (e.g., an origin-destination connection)--their approach has broader implications for understanding choice-based RM on a network. In this paper, we explore the implications in detail. Specifically, we characterize optimal offer sets (sets of available network products) by extending to the network case a notion of "efficiency" developed by Talluri and van Ryzin [Talluri, K. T., G. J. van Ryzin. 2004. Revenue management under a general discrete choice model of consumer behavior. Management Sci. 50 15-33.] for the single-leg, choice-based RM problem. We show that, asymptotically, as demand and capacity are scaled up, only these efficient sets are used in an optimal policy. This analysis suggests that efficiency is a potentially useful approach for identifying "good" offer sets on networks, as it is in the case of single-leg problems. Second, we propose a practical decomposition heuristic for converting the static CDLP solution into a dynamic control policy. The heuristic is quite similar to the familiar displacement-adjusted virtual nesting (DAVN) approximation used in traditional network RM, and it significantly improves on the performance of the static LP solution. We illustrate the heuristic on several numerical examples.
Discrete choice models are appealing for airline revenue management (RM) because they offer a means to profitably exploit preferences for attributes such as time of day, routing, brand, and price. They are also good at modeling demand for unrestricted fare class structures, which are widespread throughout the industry. However, there is little empirical research on the practicality and effectiveness of choice-based RM models. Toward this end, we report the results of a study of choice-based RM conducted with a major U.S. airline. Our study had two main objectives: (1) to assess the extent to which choice models can be estimated well using readily available airline data, and (2) to gauge the potential impact that choice-based RM could have on a sample of test markets.
We developed a maximum likelihood estimation algorithm that uses a variation of the expectation-maximization method to account for unobservable data. The procedure was applied to data for a test market from New York City to a destination in Florida. The outputs are promising in terms of the quality of the computed estimates, although a large number of departure instances may be necessary to achieve highly accurate results. These choice model estimates were then used in a simulation study to assess the revenue performance of the EMSR-b (expected marginal seat revenue, version b) capacity control policies and the current controls used by the airline relative to controls optimized to account for choice behavior. Our simulation results show 1%–5% average revenue improvements using choice-based RM. Although such simulated results must be taken with caution, overall our study suggests that choice-based revenue management is both feasible to execute and economically significant in real-world airline environments.
This paper presents a detailed survey of the research on warehouse design, performance evaluation, practical case studies, and computational support tools. This and an earlier survey on warehouse operation provide a comprehensive review of existing academic research results in the framework of a systematic classification. Each research area within this framework is discussed, including the identification of the limits of previous research and of potential future research directions.
The paper provides a comprehensive review of the recent development of revenue management in different industries. We discuss research on different revenue management strategies including pricing, auctions, capacity control, overbooking and forecasting. Related issues such as economic concerns, customer perception, competition and consolidation, implementation, performance evaluation, and common techniques and approaches used for solving revenue management problems are also discussed. Finally, we give our suggestion on some important areas that warrant further research.
In the past, researchers presented a linear programming formulation for the economic sizing of warehouses when demand is highly seasonal and public warehouse space is available on a monthly basis. The static model was extended for the dynamic sizing problem in which the warehouse size is allowed to change over time. By applying simplex routine, the optimal size of the warehouse to be constructed could be determined. In this paper, an alternative and simple method of arriving at an optimal solution for the static problem is given. Three extensions of the static model are given. These extensions involve costs varying over time, economies of scale in capital expenditure and/or operating cost and stochastic version. The dynamic warehouse sizing problem is shown to be a network flow problem which could be solved by using network flow algorithms. The structure of an optimal solution is also given. The concave cost version of the dynamic warehouse sizing problem is also discussed and it is shown that this problem can be solved efficiently using dynamic programming.
The Self Storage Association UK Annual Survey
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Self Storage Association Fact Sheet. Alexandria, VA: Self Storage Association
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Talluri, K. T., and G. J. Van Ryzin. 2004a. The Theory and Practice of Revenue Management. Boston: Kluwer.