退货情形下基于替代率的两产品联合订货决策研究

研究了一类在产品订货模型中考虑客户退货和产品间替代的两产品联合最优订货决策问题.假定客户以一概率退回其所购产品,退货产品经修复后可重新在市场上进行销售,销售期末未售出的商品只能在二级市场上降价处理,且不同产品间存在替代关系,销售商以自身利润最大化为目标确定两产品的订货量,得到其最优订货量及一些性质.研究结果表明:在允许退货的背景下,替代策略的采用将会进一步提高销售商利润.     

Last time buy decisions for products sold under warranty

Manufacturers supplying products under warranty need a strategy to deal with failures during the warranty period: repair the product or replace it by a new one, depending on e.g. age and/or usage of the failed product. An (implicit) assumption in virtually all models is that new products to replace the failed ones are immediately available at given replacement costs. Because of the short life cycles of many products, manufacturing may be discontinued before the end of the warranty period. At that point in time, the supplier has to decide how many products to put on the shelf to replace failed products under warranty that will be returned from the field (the last time buy decision). This is a trade-off between product availability for replacement and costs of product obsolescence. In this paper, we consider the joint optimization of repair-replacement decisions and the last time buy quantity for products sold under warranty. We develop approximations to estimate the total relevant costs and service levels for this problem, and show that we can easily find near-optimal last time buy quantities using a numerical search. Comparison to discrete event simulation results shows an excellent performance of our methods.     

Finite and infinite-horizon single vehicle routing problems with a predefined customer sequence and pickup and delivery

We consider the problem of finding the optimal routing of a single vehicle that starts its route from a depot and picks up from and delivers K different products to N customers that are served according to a predefined customer sequence. The vehicle is allowed during its route to return to the depot to unload returned products and restock with new products. The items of all products are of the same size. For each customer the demands for the products that are delivered by the vehicle and the quantity of the products that is returned to the vehicle are discrete random variables with known joint distribution. Under a suitable cost structure, it is shown that the optimal policy that serves all customers has a specific threshold-type structure. We also study a corresponding infinite-time horizon problem in which the service of the customers is not completed when the last customer has been serviced but it continues indefinitely with the same customer order. For each customer, the joint distribution of the quantities that are delivered and the quantity that is picked up is the same at each cycle. The discounted-cost optimal policy and the average-cost optimal policy have the same structure as the optimal policy in the finite-horizon problem. Numerical results are given that illustrate the structural results.     

Postponed two-pricing and ordering opportunity for selling a single season inventoried product

Postponement strategies are becoming increasingly important in light of a global trend in which products’ life-cycles are decreasing, such that even products that are not traditionally considered seasonal become “obsolete” within a short period of time (e.g., electronic devices, new cars). Our work addresses postponed-pricing and ordering decisions for a retailer who sells a newsvendor-type inventoried product, in a selling season that is divided into two sub-periods. The division of the selling season enables the retailer to on-line adjust her decisions when faced with a scenario (one that is highly prevalent in reality) in which potential demand changes (increases or decreases) following consumers’ experiences of the product in early stages of the selling season. We assume that the retailer has two opportunities for receiving shipments: prior to the first sub-period and prior to the second one. The retailer determines each order quantity (base-stock level) on the basis of the demand distribution for the corresponding sub-period. In each sub-period, after observing additional market signals, the retailer determines the price of the product for that sub-period. With the aid of a stochastic programming approach, we develop optimization problems and solution methods in order to obtain pricing and ordering decisions that maximize the expected profit of the retailer. We present an extensive numerical example that compares the suggested strategy to three alternative strategies, and conclude that price postponement and responsiveness to demand changes can each reduce leftovers and lost sales as well as substantially increase expected profit.     

再制造能力有限的多产品混合系统库存决策

为确定各产品的制造与再制造策略,对再制造能力有限的多产品混合系统进行研究.在系统中,对多种产品进行制造和再制造.每种产品在顾客使用后都会以恒定速率返回,但因再制造能力有限,有些产品无法用于再制造而被处置.每种产品需求恒定且由服务性产品来满足,服务性产品由制造品和再制造品组成,不允许缺货.在一次制造准备和至少一次再制造准备策略下构建了库存决策模型,利用拉格朗日乘数法和贪婪算法分别确定了各产品的再制造顺序和再制造比率.并当再制造比率一定时,给出了再制造准备次数为正整数时各产品制造与再制造策略的求解程序,得到了各产品制造和再制造批量、再制造准备次数等求解公式.最后,应用算例对模型及求解方法进行了验证.     

Inventory policy for products with price and time-dependent demands

This paper investigates the problem of jointly determining the order size and optimal prices for a perishable inventory system under the condition that demand is time and price dependent. It is assumed that a decision-maker has the opportunity to adjust prices before the end of the sales season to influence demand and to improve revenues. A mathematical model is developed to find the optimal number of prices, the optimal prices and the order quantity. Analytical results show that a stationary solution to the Kuhn–Tucker necessary conditions can be found and it is shown to be the optimal solution. The analytical results lead us to derive a solution procedure for determining the optimal order size and prices.     

Supply chain coordination for newsvendor-type products with two ordering opportunities and demand information update

This paper studies a coordination issue with two ordering opportunities in a two-echelon supply chain, where one manufacturer sells a single newsvendor-type product through one buyer. The manufacturer does not hold inventory and activates production or order with an infinite capacity and a fixed setup cost in response to the buyer’s order. The buyer places two orderings during the selling period of the product: one happens at the beginning of the period and the other at some specified time within the selling period. The whole selling period is divided into two stages or sub-periods by the buyer’s second order. The stochastic demands in the two sub-periods are assumed to be auto-correlated. The excess demand before the second order is partially backordered, whereas the excess demand at the end of the selling season is utterly lost. Under both the centralized and decentralized settings, we develop the models of how the buyer determines his two-ordering policies. We analyse the existence and uniqueness of the optimal solutions to the models and present the corresponding analytical solutions. Furthermore, we propose an improved revenue-sharing contract that can realize the perfect coordination of the supply chain and study how the revenue-sharing policies affect the supply chain members’ decisions. Finally, we show the superiority of the presented two-ordering strategy through numerical examples.     

On the optimal control of manufacturing and remanufacturing activities with a single shared server

We consider a single-product make-to-stock manufacturing–remanufacturing system. Returned products require remanufacturing before they can be sold. The manufacturing and remanufacturing operations are executed by the same single server, where switching from one activity to another does not involve time or cost and can be done at an arbitrary moment in time. Customer demand can be fulfilled by either newly manufactured or remanufactured products. The times for manufacturing and remanufacturing a product are exponentially distributed. Demand and used products arrive via mutually independent Poisson processes. Disposal of products is not allowed and all used products that are returned have to be accepted. Using Markov decision processes, we investigate the optimal manufacture–remanufacture policy that minimizes holding, backorder, manufacturing and remanufacturing costs per unit of time over an infinite horizon. For a subset of system parameter values we are able to completely characterize the optimal continuous-review dynamic preemptive policy. We provide an efficient algorithm based on quasi-birth–death processes to compute the optimal policy parameter values. For other sets of system parameter values, we present some structural properties and insights related to the optimal policy and the performance of some simple threshold policies.     

Heuristics with guaranteed performance bounds for a manufacturing system with product recovery

We consider a manufacturing system with product recovery. The system manufactures a new product as well as remanufactures the product from old, returned items. The items remanufactured with the returned products are as good as new and satisfy the same demand as the new item. The demand rate for the new item and the return rate for the old item are deterministic and constant. The relevant costs are the holding costs for the new item and the returned item, and the fixed setup costs for both manufacturing and remanufacturing. The objective is to determine the lot sizes and production schedule for manufacturing and remanufacturing so as to minimize the long-run average cost per unit time. We first develop a lower bound among all classes of policies for the problem. We then show that the optimal integer ratio policy for the problem obtains a solution whose cost is at most 1.5% more than the lower bound.     

Supply planning models for a remanufacturer under just-in-time manufacturing environment with reverse logistics

We study a supply planning problem in a manufacturing system with two stages. The first stage is a remanufacturer that supplies two closely-related components to the second (manufacturing) stage, which uses each component as the basis for its respective product. The used products are recovered from the market by a third-party logistic provider through an established reverse logistics network. The remanufacturer may satisfy the manufacturer’s demand either by purchasing new components or by remanufacturing components recovered from the returned used products. The remanufacturer’s costs arise from product recovery, remanufacturing components, purchasing original components, holding inventories of recovered products and remanufactured components, production setups (at the first stage and at each component changeover), disposal of recovered products that are not remanufactured, and coordinating the supply modes. The objective is to develop optimal production plans for different production strategies. These strategies are differentiated by whether inventories of recovered products or remanufactured components are carried, and by whether the order in which retailers are served during the planning horizon may be resequenced. We devise production policies that minimize the total cost at the remanufacturer by specifying the quantity of components to be remanufactured, the quantity of new components to be purchased from suppliers, and the quantity of recovered used products that must be disposed. The effects of production capacity are also explored. A comprehensive computational study provides insights into this closed-loop supply chain for those strategies that are shown to be NP-hard.     

双重价格规制下三渠道季节性产品供应链跨期风险共担机制研究

以双重价格规制下三渠道季节性产品供应链为研究对象,首先通过构建序贯博弈模型,研究供应链的淡季和旺季的最优决策和利润,在此基础上构建跨期风险共担协调契约和模型,以实现供应链总利润最大化和供应链企业的双赢。研究表明,由于渠道1的原材料价格受到上限规制,旺季供应商不能完全兑现制造商的订购量;由于最终产品的价格受到严格规制,旺季产成品的供应短缺的概率增加;跨期风险共担机制使得供应商和制造商的决策与集成供应链的决策完全一致,供应链总利润达到集成决策时的利润水平;研究还表明,供应商和制造商的渠道集成决策下,旺季产成品的供应缺货的概率进一步加大。     

退款保证对竞争供应链的影响:基于顾客退货行为视角

为研究退款保证对竞争供应链的影响,从顾客退货行为视角构建消费者效用函数,建立竞争制造商与在线零售商之间的博弈模型,分析退款保证对供应链均衡的影响。研究发现:高质量产品的批发价格和零售价格总是更高,但高质量产品制造商可能并不能获得更多利润;退款保证会影响消费者的产品购买选择,对低质量产品需求有利。然而,从利润最大化的角度,在线零售商只有在退货损失足够低时,才会有动机提供退款保证,而退款保证对制造商利润的影响取决于退货产品残值。     

考虑消费者后悔行为的零售商预售及退货策略研究

本文考虑消费者预购后悔行为,研究需求和消费者估值均不确定情况下,零售商的三种预售策略:不提供退货、退货不再销售和退货再销售的预售策略;探讨消费者后悔行为对零售商的预售价、订购量、退货额和预售策略选择的影响。研究发现:行动后悔越强,预售价格越低,对零售商的收益越不利;而等待后悔越强,预售价格越高,对零售商的收益越有利,因而零售商可以在现售期保持一定的产品缺货率,增强消费者的等待后悔行为。零售商提供退货服务总是优于不提供退货策略,零售商是否对被退回产品再处理进行二次销售主要依赖于被退回产品的再处理成本大小。     

产能约束对闭环供应链回收渠道选择的影响

在制造商存在产能约束下,建立了再制造闭环供应链回收渠道决策的Stackelberg博弈模型,得出了三种回收渠道下的回收率、零售价以及制造商、零售商和供应链的利润,分析了制造商的生产能力对回收渠道决策的影响。研究表明:存在产能约束时,制造商回收渠道中的回收率总是最高的,这与无产能约束时零售商回收渠道中回收率最高的情况明显不同;同时,若废旧产品回收转移支付价格较低,制造商会选择自己回收,此时的零售价最低,制造商、零售商和供应链利润最高;若回收转移支付价格较高,制造商会选择零售商回收,此时的零售价最低,制造商和供应链利润最高。     

Multiple-buyer multiple-vendor multi-product multi-constraint supply chain problem with stochastic demand and variable lead-time: A harmony search algorithm

In this paper, a multi-buyer multi-vendor supply chain problem is considered in which there are several products, each buyer has limited capacity to purchase products, and each vendor has warehouse limitation to store products. In this chain, the demand of each product is stochastic and follows a uniform distribution. The lead-time of receiving products from a vendor to a buyer is assumed to vary linearly with respect to the order quantity of the buyer and the production rate of the vendor. For each product, a fraction of the shortage is backordered and the rest are lost. The ordered product quantities are placed in multiple of pre-defined packets and there are service rate constraints for the buyers. The goal is to determine the reorder points, the safety stocks, and the numbers of shipments and packets in each shipment of the products such that the total cost of the supply chain is minimized. We show that the model of this problem is of an integer nonlinear programming type and in order to solve it a harmony search algorithm is employed. To validate the solution and to compare the performance of the proposed algorithm, a genetic algorithm is utilized as well. A numerical illustration and sensitivity analysis are given at the end to show the applicability of the proposed methodology in real-world supply chain problems.     

Optimizing multi-item multi-period inventory control system with discounted cash flow and inflation: Two calibrated meta-heuristic algorithms

A mixed binary integer mathematical programming model is developed in this paper for ordering items in multi-item multi-period inventory control systems, in which unit and incremental quantity discounts as well as interest and inflation factors are considered. Although the demand rates are assumed deterministic, they may vary in different periods. The situation considered for the problem at hand is similar to a seasonal inventory control model in which orders and sales happen in a given season. To make the model more realistic, three types of constraints including storage space, budget, and order quantity are simultaneously considered. The goal is to find optimal order quantities of the products so that the net present value of total system cost over a finite planning horizon is minimized. Since the model is NP-hard, a genetic algorithm (GA) is presented to solve the proposed mathematical problem. Further, since no benchmarks can be found in the literature to assess the performance of the proposed algorithm, a branch and bound and a simulated annealing (SA) algorithm are employed to solve the problem as well. In addition, to make the algorithms more effective, the Taguchi method is utilized to tune different parameters of GA and SA algorithms. At the end, some numerical examples are generated to analyze and to statistically and graphically compare the performances of the proposed solving algorithms.     

Forecasting demand for single-period products: A case study in the apparel industry

The problem considered is that of forecasting demand for single-period products before the period starts. We study this problem for the case of a mail order apparel company that needs to order its products pre-season. The lack of historical demand data implies that other sources of data are needed. Advance order data can be obtained by allowing a selected group of customers to pre-order at a discount from a preview catalogue. Judgments can be obtained from purchase managers or other company experts. In this paper, we compare several existing and new forecasting methods for both sources of data. The methods are generic and can be used in any single-period problem in the apparel or fashion industries. Among the pre-order based methods, a novel ‘top-flop’ approach provides promising results. For a small group of products from the case company, expert judgment methods perform better than the methods based on advance demand information. The comparative results are obviously restricted to the specific case study, and additional testing is required to determine whether they are valid in general.     

Capacitated dynamic lot sizing problems in closed-loop supply chain

In this paper, we address the capacitated dynamic lot sizing problem arising in closed-loop supply chain where returned products are collected from customers. These returned products can either be disposed or be remanufactured to be sold as new ones again; hence the market demands can be satisfied by either newly produced products or remanufactured ones. The capacities of production, disposal and remanufacturing are limited, and backlogging is not allowed. A general model of this problem is formulated, and several useful properties of the problem are characterized when cost functions are concave. Moreover, this problem is analyzed and solved to optimality using dynamic programming algorithms under different scenarios. It is shown that the problem with only disposal or remanufacturing can be converted into a traditional capacitated lot sizing problem and be solved by a polynomial algorithm if the capacities are constant. A pseudo-polynomial algorithm is proposed for the problem with both capacitated disposal and remanufacturing. The problem with capacitated production and remanufacturing and the problem with uncapacitated production and capacitated remanufacturing are also analyzed and solved. Through numerical experiments we show that the proposed algorithms perform well when solving problems of practical sizes. From the experimental results also indicates that it is worthwhile to expand the remanufacturing capacity only when returned products exist in a relatively long planning horizon, and production capacities have little effect on the remanufacturing plan when the demand is mainly satisfied by the production.     

The effect of lifetime buys on warranty repair operations

Lifetime buys are a common practice in the electronics and telecommunication industries. Under this practice, manufacturers procure their repair parts inventory in one order to support the spare part needs of a product for the duration of its warranty repair period. In this paper, we consider a repair operation in which defective items under warranty are returned to a manufacturer who either repairs these items using its spare parts inventory or replaces each defective unit with a new product. We show how fixed repair capability costs, variable repair costs, inventory holding costs, and replacement costs affect a firm's optimal repair and replacement decisions. The model is used to gain insights for products from a major mobile device manufacturer in the United States.     

The effect of risk aversion on a supply chain with postponed pricing

We consider a supply chain consisting of a supplier and a risk-averse retailer operating under endogenous demand in retail pricing. The demand potential is uncertain and is revealed at the beginning of the selling season when it is too late to order products. The product price, on the other hand, is not determined in advance and can be postponed until the demand is revealed. The goal is to study the effect of risk-aversion and postponed pricing on both the retailer’s decisions and the overall supply chain. We find that the risk-averse retailer does not necessarily order less than the risk-neutral one and may introduce a bias by choosing a specific demand distribution. We contrast two specific choices. One is symmetric (balanced) with respect to the mean demand potential. The other is skewed (pessimistic) with most observations expected below the mean demand potential. Our numerical results show that the binding downside risk constraint deteriorates the supply chain performance when the forecast is balanced and improves it when the forecast is pessimistic.