A periodic-review inventory control policy for a two-level supply chain with multiple retailers and stochastic demand

We consider a time-based inventory control policy for a two-level supply chain with one warehouse and multiple retailers in this paper. Let the warehouse order in a fixed base replenishment interval. The retailers are required to order in intervals that are integer-ratio multiples of the base replenishment interval at the warehouse. The warehouse and the retailers each adopt an order-up-to policy, i.e. order the needed stock at a review point to raise the inventory position to a fixed order-up-to level. It is assumed that the retailers face independent Poisson demand processes and no transshipments between them are allowed. The contribution of the study is threefold. First, we assume that when facing a shortage the warehouse allocates the remaining stock to the retailers optimally to minimize system cost in the last minute before delivery and provide an approach to evaluate the exact system cost. Second, we characterize the structural properties and develop an exact optimal solution for the inventory control system. Finally, we demonstrate that the last minute optimal warehouse stock allocation rule we adopt dominates the virtual allocation rule in which warehouse stock is allocated to meet retailer demand on a first-come first-served basis with significant cost benefits. Moreover, the proposed time-based inventory control policy can perform equally well or better than the commonly used stock-based batch-ordering policy for distribution systems with multiple retailers.     

Allocation of warehouse inventory with electronic data interchange and fixed order intervals

This paper considers the problem of allocating warehouse inventory to retailers where retailer orders and the replenishment of warehouse inventory occur periodically on a fixed schedule. We assume that the warehouse and the retailers have the opportunity to exchange demand information through Electronic Data Interchange (EDI). At the warehouse level, for instance, the available information on the retailer's demand may be utilized in determining the shipment quantities needed to meet the desired service level to the retailers. Unlike similar models focusing primarily on optimizing systems wide performance measures, in this paper we focus on the service level furnished to the retailers by the warehouse. To this end, three different allocation policies are considered: static, myopic, and dynamic rules characterizing the impact of available demand information on the resulting service levels. Numerical illustrations exemplify the allocation rules considered. An interesting though counter intuitive observation is that the existence of additional demand information cannot, a prior, be assumed superior.     

A partition approach to the inventory/routing problem

In this study we focus on the integration of inventory control and vehicle routing schedules for a distribution system in which the warehouse is responsible for the replenishment of a single item to the retailers with demands occurring at a specific constant (but retailer-dependent) rate, combining deliveries into efficient routes. This research proposes a fixed partition policy for this type of problem, in which the replenishment interval of each of the retailers’ partition region as well as the warehouse is accorded the power of two (POT) principle. A lower bound of the long-run average cost of any feasible strategy for the considered distribution system is drawn. And a tabu search algorithm is designed to find the retailers’ optimal partition regions under the fixed partition policy proposed. Computational results reveal the effectiveness of the policy as well as of the algorithm.     

Stock Rationing in a Continuous Review Two-Echelon Inventory Model

In this paper we consider a 1-warehouse, N-retailer inventory system where demand occurs at all locations. We introduce an inventory model which allows us to set different service levels for retailers and direct customer demand at the warehouse. For each retailer a critical level is defined, such that a retailer replenishment order is delivered from warehouse stock if and only if the stock level exceeds this critical level. It is assumed that retailer replenishment orders, which are not satisfied from warehouse stock, are delivered directly from the outside supplier, instead of being backlogged. We present an analytical upper bound on the total cost of the system, and develop a heuristic method to optimize the policy parameters. Numerical experiments indicate that our technique provides a very close approximation of the exact cost. Also, we show that differentiating among the retailers and direct customer demand can yield significant cost reductions.     

A two-echelon inventory model with lost sales

This paper considers a single-item, two-echelon, continuous-review inventory model. A number of retailers have their stock replenished from a central warehouse. The warehouse in turn replenishes stock from an external supplier. The demand processes on the retailers are independent Poisson. Demand not met at a retailer is lost. The order quantity from each retailer on the warehouse and from the warehouse on the supplier takes the same fixed value Q, an exogenous variable determined by packaging and handling constraints. Retailer i follows a (Q, R_i) control policy. The warehouse operates an (SQ, (S − 1)Q) policy, with non-negative integer S. If the warehouse is in stock then the lead time for retailer i is the fixed transportation time L_i from the warehouse to that retailer. Otherwise retailer orders are met, after a delay, on a first-come first-served basis. The lead time on a warehouse order is fixed. Two further assumptions are made: that each retailer may only have one order outstanding at any time and that the transportation time from the warehouse to a retailer is not less than the warehouse lead time. The performance measures of interest are the average total stock in the system and the fraction of demand met in the retailers. Procedures for determining these performance measures and optimising the behaviour of the system are developed.     

Model and algorithm for inventory/routing decision in a three-echelon logistics system

This paper addresses an integrated inventory and routing problem in a three-echelon logistics system, which consists of a supplier, a central warehouse and a group of retailers. The inventory decision of each member and the routing decision among members of the system are made simultaneously, with the objective of minimizing the overall average cost of the system. A strategy named fixed partition and power-of-two (FP–POT) is proposed for the considered problem and a variable large neighborhood search (VLNS) algorithm, which is a special case of variable neighborhood search (VNS) algorithm, is developed. The efficiency of the strategy as well as the algorithm is illustrated by comparing computational results with a lower bound. The advantage of the proposed VLNS algorithm is further shown by getting better results for the problems in a two-echelon logistics system, which have been solved by a Tabu Search algorithm recently.     

求解硬时间窗约束下随机需求库存-路径问题的优化算法

随机需求库存-路径问题(Stochastic Demand Inventory Routing Problem, SDIRP)即考虑随机需求环境下供应链中库存与配送的协调优化问题,是实施供应商管理库存策略过程中的关键所在,也是典型的NP难题之一。文章以具有硬时间窗约束的随机需求库存-路径问题(Stochastic Demand Inventory Routing Problem with Hard Time Windows, SDIRPHTW)为研究对象,将SDIRPHTW分解为直接配送的随机库存-路径问题和具有硬时间窗约束的路径优化问题两个子问题,并以最小化系统运行成本和用车数量为目标,设计了一个基于(s,S)库存策略和修正C-W节约法的启发式算法。最后,通过相应的数值算例验证了算法的有效性。     

Two warehouse inventory models for single vendor multiple retailers with price and stock dependent demand

Here a single vendor multiple retailer inventory model of an item is developed where demand of the item at every retailer is linearly dependent on stock and inversely on some powers of selling price. Item is produced by the vendor and is distributed to the retailers following basic period policy. According to this policy item is replenished to the retailers at a regular time interval (T₁) called basic period (BP) and replenishment quantity is sufficient to last for the period T₁. Due to the scarcity of storage space at market places, every retailer uses a showroom at the market place and a warehouse to store the item, little away from the market place. Item is sold from the showroom and is filled up from the warehouse in a bulk release pattern. Some of the inventory parameters are considered as fuzzy in nature and model is formulated to maximize the average profit from the whole system. Imprecise objective is transformed to equivalent deterministic ones using possibility/necessity measure of fuzzy events with some degree of optimism/pessimism. A genetic algorithm (GA) is developed with roulette wheel selection, arithmetic crossover and random mutation and is used to solve the model. In some complex cases, with the help of above GA, fuzzy simulation process is used to derive the optimal decision. The model is illustrated through numerical examples and some sensitivity analyses are presented.     

Synchronized routing of seasonal products through a production/distribution network

This paper presents a multi-period vehicle routing problem for a large-scale production and distribution network. The vehicles must be routed in such a way as to minimize travel and inventory costs over a multi-period horizon, while also taking retailer demands and the availability of products at a central production facility into account. The network is composed of one distribution center and hundreds of retailers. Each retailer has its demand schedule representing the total number of units of a given product that should have been received on a given day. Many high value products are distributed. Product availability is determined by the production facility, whose production schedule determines how many units of each product must be available on a given day. To distribute these products, the routes of a heterogeneous fleet must be determined for a multiple period horizon. The objective of our research is to minimize the cost of distributing products to the retailers and the cost of maintaining inventory at the facility. In addition to considering product availability, the routing schedule must respect many constraints, such as capacity restrictions on the routes and the possibility of multiple vehicle trips over the time horizon. In the situation studied, no more than 20 product units could be carried by a single vehicle, which generally limited the number of retailers that could be supplied to one or two per route. This article proposes a mathematical formulation, as well as some heuristics, for solving this single-retailer-route vehicle routing problem. Extensions are then proposed to deal with the multiple-retailer-route situation.     

An inventory-transportation system with stochastic demand

We study a logistic system in which a supplier has to deliver a set of products to a set of retailers to face a stochastic demand over a given time horizon. The transportation from the supplier to each retailer can be performed either directly, by expensive and fast vehicles, or through an intermediate depot, by less expensive but slower vehicles. At most one time period is required in the former case, while two time periods are needed in the latter case. A variable transportation cost is charged in the former case, while a fixed transportation cost per journey is charged in the latter case. An inventory cost is charged at the intermediate depot. The problem is to determine, for each time period and for each product, the quantity to send from the supplier to the depot, from the depot to each retailer and from the supplier to each retailer, in order to minimize the total expected cost. We first show that the classical benchmark policy, in which the demand of each product at each retailer is set equal to the average demand, can give a solution which is infinitely worse with respect to the optimal solution. Then, we propose two classes of policies to solve this problem. The first class, referred to as Horizon Policies, is composed of policies which require the solution of the overall problem over the time horizon. The second class, referred to as Reoptimization Policies, is composed of a myopic policy and several rolling-horizon policies in which the problem is reoptimized at each time period, once the demand of the time period is revealed. We evaluate the performance of each policy dynamically, by using Monte Carlo Simulation.     

Minimizing the Total Cost in an Integrated Vendor—Managed Inventory System

In this paper we consider a complex production-distribution system, where a facility produces (or orders from an external supplier) several items which are distributed to a set of retailers by a fleet of vehicles. We consider Vendor-Managed Inventory (VMI) policies, in which the facility knows the inventory levels of the retailers and takes care of their replenishment policies. The production (or ordering) policy, the retailers replenishment policies and the transportation policy have to be determined so as to minimize the total system cost. The cost includes the fixed and variable production costs at the facility, the inventory costs at the facility and at the retailers and the transportation costs, that is the fixed costs of the vehicles and the traveling costs. We study two different types of VMI policies: The order-up-to level policy, in which the order-up-to level quantity is shipped to each retailer whenever served (i.e. the quantity delivered to each retailer is such that the maximum level of the inventory at the retailer is reached) and the fill-fill-dump policy, in which the order-up-to level quantity is shipped to all but the last retailer on each delivery route, while the quantity delivered to the last retailer is the minimum between the order-up-to level quantity and the residual transportation capacity of the vehicle. We propose two different decompositions of the problem and optimal or heuristic procedures for the solution of the subproblems. We show that, for reasonable initial values of the variables, the order in which the subproblems are solved does not influence the final solution. We will first solve the distribution subproblem and then the production subproblem. The computational results show that the fill-fill-dump policy reduces the average cost with respect to the order-up-to level policy and that one of the decompositions is more effective. Moreover, we compare the VMI policies with the more traditional Retailer-Managed Inventory (RMI) policy and show that the VMI policies significantly reduce the average cost with respect to the RMI policy.     

Coordinated control of distribution supply chains in the presence of fuzzy customer demand

This paper considers a single product inventory control in a Distribution Supply Chain (DSC). The DSC operates in the presence of uncertainty in customer demands. The demands are described by imprecise linguistic expressions that are modelled by discrete fuzzy sets. Inventories at each facility within the DSC are replenished by applying periodic review policies with optimal order up-to-quantities. Fuzzy customer demands imply fuzziness in inventory positions at the end of review intervals and in incurred relevant costs per unit time interval. The determination of the minimum of defuzzified total cost of the DSC is a complex problem which is solved by applying decomposition; the original problem is decomposed into a number of simpler independent optimisation subproblems, where each retailer and the warehouse determine their optimum periodic reviews and order up-to-quantities. An iterative coordination mechanism is proposed for changing the review periods and order up-to-quantities for each retailer and the warehouse in such a way that all parties within the DSC are satisfied with respect to total incurred costs per unit time interval. Coordination is performed by introducing fuzzy constraints on review periods and fuzzy tolerances on retailers and warehouse costs in local optimisation subproblems.     

Single-manufacturer,multi-retailer consignment policy for retailers’ generalized demand distributions

Consignment policy (CP) is a novel approach to the inventory management in supply chains. It is based on strong interaction and reliable collaboration between vendor(s) and buyer(s), which is acquiring growing importance in today's industrial reality. Unlike most literature focusing on single-vendor single-buyer models and deterministic customer demand, a single-manufacturer (vendor) multi-retailer (buyer) generic model is developed under stochastic customer demand in this study. In order to understand the potential benefits of CP, it is compared with a traditional policy (TP) model developed in the similar approach. The models are tested with two scenarios of uniform and exponential demand distributions of the retailers. The results show how CP works better than the traditional uncoordinated optimization. It not only helps the manufacturer to generate higher profit, but also coordinates retailers to achieve a higher supply chain profit. At the same time, each retailer earns at least as much as they do in TP. Further price discount sensitivity analysis demonstrates the efficiency of CP when facing price-demand fluctuation.     

Linear programming based decomposition methods for inventory distribution systems

We consider an inventory distribution system consisting of one warehouse and multiple retailers. The retailers face random demand and are supplied by the warehouse. The warehouse replenishes its stock from an external supplier. The objective is to minimize the total expected replenishment, holding and backlogging cost over a finite planning horizon. The problem can be formulated as a dynamic program, but this dynamic program is difficult to solve due to its high dimensional state variable. It has been observed in the earlier literature that if the warehouse is allowed to ship negative quantities to the retailers, then the problem decomposes by the locations. One way to exploit this observation is to relax the constraints that ensure the nonnegativity of the shipments to the retailers by associating Lagrange multipliers with them, which naturally raises the question of how to choose a good set of Lagrange multipliers. In this paper, we propose efficient methods that choose a good set of Lagrange multipliers by solving linear programming approximations to the inventory distribution problem. Computational experiments indicate that the inventory replenishment policies obtained by our approach can outperform several standard benchmarks by significant margins.     

Strategic information sharing in a supply chain

We consider a two-member supply chain that manufactures and sells newsboy-type products and comprises a downstream retailer and an upstream vendor. In this supply chain, the vendor is responsible for making stock-level decisions and holding the inventory, and the retailer is better informed about market demand. In each period, the retailer receives a signal about market demand before the actual demand is realized, and must decide whether to reveal the information to the vendor, at a cost, before the vendor starts production. We assume that any information that the retailer reveals is truthful. We model the situation as a Bayesian game, and find that, in equilibrium, whether the retailer reveals or withholds the information depends on two things—the cost of revealing the information and the nature of market demand signal that the retailer receives. If the cost of sharing the information is sufficiently large, then the retailer will withhold the information from the vendor regardless of the type of signal that is received. If the cost of sharing the information is small, then the retailer will reveal the information to the vendor if a high demand is signaled, but will withhold it from the vendor if a low demand is signaled. In general, reducing the cost of sharing information and increasing the profit margin of either the retailer or the vendor (or reducing the cost of the vendor or retailer) will facilitate information sharing.     

随机需求下多供应商和多零售商的生产-库存-运输联合优化模型

考虑随机需求下多供应商和多零售商的生产-库存-运输联合优化问题.在联合优化时,首先利用最近邻算法将各零售商分成不同区域,分区后问题转化为随机需求下单供应商对多零售商的生产-库存-运输联合优化问题.在每个分区内,由供应商统一决策其分区内各零售商的送货量和送货时间.利用粒子群算法和模拟退火算法相结合的两阶段算法求出最优送货量、最优运输路径和最大期望总利润.然后采用收入共享契约将增加的利润合理分配给各供应商和各零售商,使各方利润都得到增加,从而促使各方愿意合作.通过数值算例验证了联合优化模型优于独立决策模型.     

Controlling distribution inventory systems with shipment consolidation and compound Poisson demand

We consider a one-warehouse-multiple-retailer inventory system where the retailers face stochastic customer demand, modelled as compound Poisson processes. Deliveries from the central warehouse to groups of retailers are consolidated using a time based shipment consolidation policy. This means that replenishment orders have to wait until a vehicle departures, which increases the lead time for the retailers and therefore also the safety stock. Thus, a trade-off exists between expected shipment costs and holding costs. Our aim is to determine the shipment intervals and the required amount of safety stock for each retailer and the warehouse to minimize total cost, both for backorder costs and fill rate constraints. Previous work has focused on exact solutions which are computationally demanding and not applicable for larger real world problems. The focus of our present work is on the development of computationally attractive heuristics that can be applied in practice. A numerical study shows that the proposed heuristics perform well compared to the exact cost minimizing solutions. We also illustrate that the approaches are appropriate for solving real world problems using data from a large European company.     

A two-echelon base-stock inventory model with Poisson demand and the sequential processing of orders at the upper echelon

We consider a two-echelon inventory system with a number of non-identical, independent ‘retailers’ at the lower echelon and a single ‘supplier’ at the upper echelon. Each retailer experiences Poisson demand and operates a base stock policy with backorders. The supplier manufactures to order and holds no stock. Orders are produced, in first-come first-served sequence, with a fixed production time. The supplier therefore functions as an M/D/1 queue. We are interested in the performance characteristics (average inventory, average backorder level) at each retailer. By finding the distribution of order lead time and hence the distribution of demand during order lead time, we find the steady state inventory and backorder levels based on the assumption that order lead times are independent of demand during order lead time at a retailer. We also propose two alternative approximation procedures based on assumed forms for the order lead time distribution. Finally we provide a derivation of the steady state inventory and backorder levels which will be exact as long as there is no transportation time on orders between the supplier and retailers. A numerical comparison is made between the exact and approximate measures. We conclude by recommending an approach which is intuitive and computationally straightforward.     

Determining the Pareto set in a bicriteria two-echelon inventory/distribution system

This article concerns two-echelon inventory/distribution system, consisting of a warehouse and a retailer. We assume that the demand is deterministic and stockouts are not permitted. Two criteria are considered: to minimize the annual inventory cost and the annual total number of damaged items by improper shipment handling. The problem consists of determining the non-dominated inventory policies in such a way that the trade-off between both criteria is achieved. We present the characterization of the non-dominated optimal solution set and we use this result to correct the solution method previously proposed by other authors for a problem with identical cost structure. An efficient algorithm to calculate the non-dominated solution set is introduced. Computational results on several randomly generated problems are reported.     

Performance evaluation of distribution strategies for the inventory routing problem

Direct shipping strategy is an easy-to-implement distribution strategy frequently used in industrial distribution systems. In this paper, an analytic method is developed for performance evaluation of the strategy for the infinite horizon inventory routing problem with delivery frequency constraint. With the method, the effectiveness of direct shipping strategy can be represented as a function of some system parameters. We demonstrate that the effectiveness of direct shipping is at least the square root of the smallest utilization ratio of vehicle capacity. This implies that the effectiveness of the strategy can reach 100% (respectively, 94.86%) whenever the demand rate of each retailer is 100% (respectively, 90%) of the vehicle capacity multiplied by the upper bound of the delivery frequency. This insight can help a firm answer questions such as: under what conditions direct shipping strategy is effective and why, and how effective the strategy is under a specific condition? In case direct shipping strategy is proven ineffective, a more general Fixed Partition Policy (FPP) that combines direct shipping strategy and multiple-stop shipping strategy must be used. An analytic method is also developed for performance evaluation of general FPPs. We demonstrate that the effectiveness of an FPP depends on the total demand rate of the retailers in each partition (each retailer set) and their closeness level. This insight provides a useful guideline to the design of effective FPPs. The analytic methods make the performance improvement of a distribution system possible through adjusting its system parameters.