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Smart vending machine systems: Operation and performance

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Abstract

The operation of smart vending machine systems creates decision-making problems under vendor-managed inventory schemes involving product allocation to vending machine storage compartments, replenishment points of products and replenishment thresholds at vending machines, and vehicle routes for inventory replenishments, all of which have critical effects on system profit. This paper considers the operation of smart vending machine systems in which possible product substitutions occur when customers face stock-outs. A simple, intuitive procedure is presented to estimate substitution probabilities, construct an integrated optimisation mathematical model for the operation problem of smart vending machine systems, and develop a two-phase heuristic based on this mathematical model. The results of computational experiments demonstrate the substantial economic benefit of the smart vending machine system over the conventional system. Sensitivity analysis indicates that certain input variables significantly impact the effectiveness of the smart vending machine system.

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... As is known to us, the abilities to satisfy customer' demand decide businesses' service quality [5]. An effective location strategy of UVMs can help customers easily find their preferred products, and may directly translate into gained revenue [6]. ...
... The specific approach involves clustering candidate points with similar geographical locations and similar customer preferences into different "demand zones", UVMs in the same zone can transfer products and reassign customers when breakdown occurred in order to improve design reliability, which will be discussed in Sect. 5. In summary, this paper considers a reliable location design for UVMs with stochastic customers under different scenarios, with the goal to minimize total costs and maximize customer satisfaction. ...
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... A customer can buy products easily with the help of digital payment systems through the vending machine. Vending machines are extensively and frequently used in many technologically advanced nations like the USA, UK, China, Japan and more [7][8][9][10]. ...
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... A customer can buy products easily with the help of digital payment systems through the vending machine. Vending machines are extensively and frequently used in many technologically advanced nations like the USA, UK, China, Japan and more [7][8][9][10]. ...
Article
Full-text available
The advent of the Internet envisions a cashless society by enabling financial transactions through digital payments. Significantly, the emergence of coronavirus (COVID-19) disrupted our traditional cash handling means and triggered an inflexion point for switching towards contactless digital payments from physical cash payments. Furthermore, Internet of Things (IoT) technology escalates digital payments to the next level by enabling devices to render goods and services without requiring any human interaction. This research proposed an IoT-enabled cashless vending machine that incorporates both cloud computing and payment gateway for ordering and purchasing items through digital payment systems by using a mobile application. The system enables a pre-installed mobile application to scan the Quick Response (QR) code attached to the body of a vending machine, opens the portal of a web-based virtual machine through the code, allows user to choose and order items from the virtual vending, initiates and authorizes a digital payment through an IoT gateway installed inside the physical vending machine by establishing a connection between user's and vendor's financial entities, and finally, dispenses the ordered items by unlocking the shelves of the vending machine after the successful payment transaction. It operates in the Arduino platform with an ATmega 2560 Microcontroller and Esp8266 Wi-fi module as hardware components, mobile application software, and payment gateway API. The system performed an average response time of 14500 milliseconds to pick a product after running 150 consecutive API test calls. This result shows a satisfying time for enhancing customers' buying experiences with digital payment systems and a customizable and cost-effective IoT-based intelligent vending machine to introduce for mass production.
... A customer can buy product easily with the help of digital payment systems through the vending machine. Vending machine is extensively and frequently used machine in many technologically advanced nations like USA.UK, China, Japan and more [1][2][3][4] . ...
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... Literature pertinent to maritime transportation-inventory problems with certain stochastic characteristics is closely related to those of the stochastic inventory routing problems (IRPs) (see, e.g., Roldan et al. (2017)). For practical examples related to IRP problems, the reader may refer to Lmariouh et al. (2017), Park and Park (2015), and Ngoc and Nananukul (2017). An IRP simultaneously attempts to make the inventory management and routing decisions with the aim of minimizing the total cost via the implementation of a vendor-managed inventory (VMI) system (see, e.g., Cetinkaya and Lee (2000), Campbell et al. (2002), and Lee and Whang 2008)). ...
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We examine a maritime transportation-inventory problem under three daily demand distributions, namely gamma, exponential and uniform. This is essentially an extension of the problem of Soroush and Al-Yakoob (2018) in which case daily demands are assumed to be normally distributed. The principle thrust of this research effort is to find an optimal vessel schedule with the objective of minimizing the expected overall cost consisting of the vessels' operational expenses, expected penalties for violating some pre-specified lower and upper storage levels, and vessels' chartering expenses, while meeting the stochastic demand requirements at each destination with acceptable reliability levels. We formulate each problem scenario as a stochastic optimization model, which using chance-constrained programming, is converted into an exact mixed-integer nonlinear program. Our results show that different demand distributions lead to significantly different vessel schedules and associated costs. Sensitivity analyses are also performed.
... Overall, it is a decision making problem which includes multiple resolutions on product allocation to columns, determination of replenishment points and thresholds, and vehicle routing. Park and Park (2015) presented an integrated optimisation mathematical model for the operation problem of smart vending machine systems. In the addressed problem, stock-outs may occur and the customers can partially substitute products (we direct a reader interested in product substitution problems to a review paper by Shin, Park, Lee, & Benton (2015) ). ...
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... Many real-life applications arise in the maritime industry namely in the distribution of several types of fuel and gases by compartmentalised ships (Bausch et al., 1998;Bertazzi et al., 2002;Persson and Göthe-Lundgren, 2005;Christiansen et al., 2011;Engineer et al., 2012;Grønhaug et al., 2010;Qu et al., 1999;Ronen, 2002;Stålhane et al., 2012;Uggen et al., 2013). Non-maritime applications include the distribution of perishable products (Federgruen and Zipkin, 1984;Federgruen et al., 1986), the transportation of gases by tanker trucks (Bell et al., 1983), the automobile components industry (Alegre et al., 2007), the electrical products with multiple depots (Ramkumar et al., 2012), fuel delivery (Popović et al., 2012;Relvas et al., 2013), Smart vending machine systems (Park and Park, 2015) and the distribution of grocery and food products (Mateo et al., 2012;Liu and Chen, 2011;Hwang, 1999;Federgruen et al., 1986;Cai et al., 2013;Lahyani et al., 2015). A review of practical IRP applications can be found in Andersson et al. (2010). ...
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In this paper we solve a real-life distribution problem faced by a Moroccan bottled water company dealing with a combination of inventory and distribution decisions. To manage its distribution process, the company uses a vendor-managed inventory system, which means that the supplier controls the inventory at the customers. This problem is known as the inventory-routing problem (IRP) in which both transportation and inventory costs are simultaneously minimised. Our real-life problem contains several types of bottled water that must be shipped from a supplier to a set of regional depots and wholesalers. Inventory costs are paid at both the plant and at the customers, and shipments are performed by a fleet of homogeneous vehicles. We propose an ad-hoc modification of IRP models and branch-and-cut algorithms. Computational tests were carried out on fifteen real-life-based instances. The results show that significant savings of almost 10% can be obtained.
... Many real-life applications arise in the maritime industry namely in the distribution of several types of fuel and gases by compartmentalized ships (Bausch et al., 1998; Bertazzi et al., 2002; Persson and Göthe-Lundgren, 2005; Christiansen et al., 2011; Engineer et al., 2012; Grønhaug et al., 2010; Qu et al., 1999; Ronen, 2002; Stålhane et al., 2012; Uggen et al., 2013). Non-maritime applications include the distribution of perishable products (Federgruen and Zipkin, 1984; Federgruen et al., 1986), the transportation of gases by tanker trucks (Bell et al., 1983), the automobile components industry (Alegre et al., 2007), the electrical products with multiple depots (Ramkumar et al., 2012), fuel delivery (Popovi´cPopovi´c et al., 2012; Relvas et al., 2013), Smart vending machine systems (Park and Park , 2015) and the distribution of grocery and food products (Mateo et al., 2012; Liu and Chen, 2011; Hwang, 1999; Federgruen et al., 1986; Cai et al., 2013; Lahyani et al., 2015). A review of practical IRP applications can be found in Andersson et al. (2010). ...
... Many real-life applications arise in the maritime industry namely in the distribution of several types of fuel and gases by compartmentalized ships (Bausch et al., 1998;Bertazzi et al., 2002;Persson and Göthe-Lundgren, 2005;Christiansen et al., 2011;Engineer et al., 2012;Grønhaug et al., 2010;Qu et al., 1999;Ronen, 2002;Stålhane et al., 2012;Uggen et al., 2013). Non-maritime applications include the distribution of perishable products (Federgruen and Zipkin, 1984;Federgruen et al., 1986), the transportation of gases by tanker trucks (Bell et al., 1983), the automobile components industry (Alegre et al., 2007), the electrical products with multiple depots (Ramkumar et al., 2012), fuel delivery (Popović et al., 2012;Relvas et al., 2013), Smart vending machine systems (Park and Park , 2015) and the distribution of grocery and food products (Mateo et al., 2012;Liu and Chen, 2011;Hwang, 1999;Federgruen et al., 1986;Cai et al., 2013;Lahyani et al., 2015). A review of practical IRP applications can be found in Andersson et al. (2010). ...
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We develop a model that jointly optimizes a retailer's decisions for product prices, display facing areas, display orientations and shelf-space locations in a product category. Unlike the existing shelf-space allocation models that typically consider only the width of display shelves, our model considers both the width and height of each shelf, allowing products to be stacked. Furthermore, as demand is influenced by each product's two-dimensional facing area, we consider multiple product orientations that capture three-dimensional product packaging characteristics. That enables our model to not only treat shelf locations as decision variables, but also retailers’ stacking patterns in terms of product display areas and multiple display orientations. Further, unlike the existing studies which consider a retailer's shelf-space allocation decisions independent of its product pricing decisions, our model allows joint decisions on both and captures cross-product interactions in demand through prices. We show how a branch-and-bound based MINLP algorithm can be used to implement our optimization model in a fast and practical way.
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Vendor managed inventory (VMI) is an example of effective cooperation and partnering practices between up- and downstream stages in a supply chain. In VMI, the supplier takes the responsibility for replenishing his customers’ inventories based on their consumption data, with the aim of optimizing the over all distribution and inventory costs throughout the supply chain. This paper discusses the challenging optimization problem that arises in this context, known as the inventory routing problem (IRP). The objective of this IRP problem is to determine a distribution plan that minimizes average distribution and inventory costs without causing any stock-out at the customers. Deterministic constant customer demand rates are assumed and therefore, a long-term cyclical approach is adopted, integrating fleet sizing, vehicle routing, and inventory management. Further, realistic side-constraints such as limited storage capacities, driving time restrictions and constant replenishment intervals are taken into account. A heuristic solution approach is proposed, analyzed and evaluated against a comparable state-of-the-art heuristic.
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In the past decade, convenience stores have generally experienced low profit margins due to the intensive competition that exists in the industry. To reduce operating costs, these stores must be able to efficiently control their stock replenishment, especially for deteriorating items such as meal-boxes. To solve this problem, we employed a two-step model to determine the optimal amount of replenishment. In the first step, we obtained the basic reorder quantity by considering three inventory management methods involving the consideration of the probability forecast of demand, hypothesis testing and the newsboy method. In the second step of our model, a novel warning system is established by employing the support vector machine to modify the basic order quantity, which may be varied due to the effect of uncertain factors such as the weather, climate and economic prospects. Using actual data from a convenience store which was a part of the President Chain Store Corporation in Taiwan, the prediction accuracy of the two-step replenishment policies was evaluated. We also apply two methods to enhance accuracy and provide further insights into the model. The results show that the model is workable, and the results can be used as a valuable reference for future practical applications.
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The single-vehicle cyclic inventory routing problem (SV-CIRP) is concerned with a repeated distribution of a product from a single depot to a selected subset of retailers having stable demands. If a retailer is selected for replenishment, the supplier collects a retailer-related fixed reward. The objective is to determine the subset of retailers to cyclically replenish, the quantities to be delivered to each, and to design the vehicle delivery routes so that the expected total distribution and inventory cost is minimized while the total collected rewards from the selected retailers is maximized. The resulting distribution plan must prevent stockouts from occurring at each retailer. In this paper, the underlying optimization problem for the SV-CIRP is formulated as a mixed-integer program with linear constraints and a nonlinear objective function. An optimization approach combining DC-programming and Branch-and-Bound within a steepest descent hybrid algorithm, denoted by DCA-SDHA, is developed for its solution. The approach is tested on some randomly generated problems and the obtained results are compared with results from the standard steepest descent hybrid algorithm (SDHA). These encouraging results show that the proposed approach is indeed computationally more effective and is worth being further investigated for the solution of medium to large instances.
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Shelf space on which products are displayed is one of the most important resources in retail environment. This paper addresses problems of retailers who sell various brands of items through displaying on shelf space. We develop an integrated mathematical model for the shelf space design and item allocation problem with the objective of maximizing the retailer’s profit. To solve the model, genetic algorithms are proposed for two types of shelf space configurations: (1) slicing structure produced by ‘guillotine’ type cuts and (2) slicing structure by horizontal cuts only. The validity of the model is illustrated with example problems.
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We consider the infinite horizon inventory routing problem in a three-level distribution system with a vendor, a warehouse and multiple geographically dispersed retailers. In this problem, each retailer faces a demand at a deterministic, retailer-specific rate for a single product. The demand of each retailer is replenished either from the vendor through the warehouse or directly from the vendor. Inventories are kept at both the retailers and the warehouse. The objective is to determine a combined transportation (routing) and inventory strategy minimizing a long-run average system-wide cost while meeting the demand of each retailer without shortage. We present a decomposition solution approach based on a fixed partition policy where the retailers are partitioned into disjoint and collectively exhaustive sets and each set of retailers is served on a separate route. Given a fixed partition, the original problem is decomposed into three sub-problems. Efficient algorithms are developed for the sub-problems by exploring important properties of their optimal solutions. A genetic algorithm is proposed to find a near-optimal fixed partition for the problem. Computational results show the performance of the solution approach.
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We propose a static approximation of dynamic demand substitution behavior based on a fluid network model and a service-inventory mapping. This approximation greatly enhances our ability to analyze the interdependent inventory/service, price, and product assortment decisions in noncompetitive and competitive scenarios with demand substitution. We demonstrate that the approximation is well behaved and then apply it to two previously intractable applications. First, we study a price and service competition between single-product retailers. After establishing a unique pure-strategy Nash equilibrium, we find that competition results in lower price, higher demand, and a higher level of inventory. We also observe that the aggregate profit and inventory level increase to positive constants as the number of retailers goes to infinity. Second, we study a duopolistic competition on price, service, and product assortment. We establish a pure-strategy Nash equilibrium for the product assortment competition and identify a condition for uniqueness. We find that competition on both price and product assortment results in lower price and less variety for each competitor, but the total number of products and the aggregate inventory level in a duopoly market are both likely to be higher than in a monopolistic market.
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The optimum routing of a fleet of trucks of varying capacities from a central depot to a number of delivery points may require a selection from a very large number of possible routes, if the number of delivery points is also large. This paper, after considering certain theoretical aspects of the problem, develops an iterative procedure that enables the rapid selection of an optimum or near-optimum route. It has been programmed for a digital computer but is also suitable for hand computation.
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The problem of product assortment and inventory planning under customer-driven demand substitution is analyzed and a mathematical model for this problem is provided in this paper. Realistic issues in a retail context such as supplier selection, shelf space constraints, and poor quality procurement are also taken into account. The performance of three modified models, one that neglects customers' substitution behavior, another that excludes supplier selection decision, and one that ignores shelf space limitations, are analyzed separately with computational experiments. The results of the analysis demonstrate that neglecting customer-driven substitution or excluding supplier selection or ignoring shelf space limitations may lead to significantly inefficient assortments. The effects of demand variability and substitution cost on optimal assortment and supplier selection decisions as well as on the optimal revenue are also investigated. The main contribution of this paper is the development of a practical and flexible model to aid retailers in finding optimal assortments to maximize the expected profit.
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This paper addresses a geographically distributed vending machine inventory control problem where the multiple product choices in every vending machine and the varied demand for each product in every location's vending machine exist. The ordering quantity for each product and the desirable number of each product type in a vending machine must be simultaneously decided to maximize the total expected profit. Considering that the ordering costs are in piece-wise function and only one type of product is allocated in a slot of a vending machine, the proposed problem correlates to the piece-wise constrained nonlinear integer-programming problem. There is difficulty in deriving the exact optimal solutions to this problem since its computational complexity appears to be a nondeterministic polynomial problem. This paper presents a novel heuristic approach in finding (1) the ordering quantity for each product, (2) the product types, and (3) the corresponding quantity allocated in each vending machine. The numerical results show the effectiveness of the proposed methodology in solving the aforementioned problem. In this paper, the solutions found by this study's approach are as well as those found by the brute-force method, and if not equivalent, perform better than those by the Lingo software.Journal of the Operational Research Society (2005) 56, 307–316. doi:10.1057/palgrave.jors.2601811 Published online 18 August 2004