考虑渠道权力结构和产品双重差异的再制造闭环供应链最优定价决策

针对具有不同渠道权力结构和回收拆解产品质量差异、新产品与再制造产品价格差异的再制造闭环供应链,主要研究其成员企业最优定价决策问题。构建了包含制造/再制造商、零售商、消费者和回收拆解商的三级再制造闭环供应链决策模型;采用Stackelberg主从博弈确定不同渠道权力结构下各成员企业的最优定价策略及其利润,并重点探讨废旧产品回收率τ、产品替代系数δ两个关键参数对它们的影响;将该决策模型应用到汽车发电机再制造闭环供应链案例。研究结果表明：1） 对于3种渠道权力结构,当制造/再制造商和零售商为主导者时,其自身利润和再制造闭环供应链总利润均最大;而当回收拆解商为主导者时,其自身利润和总利润却最小;2） 随着τ和δ的提高,单位新产品与再制造产品的最优批发价、最优零售价均增加,单位废旧产品的最优回收价则降低;制造/再制造商和零售商的利润下降,回收拆解商的利润上升,导致再制造闭环供应链的总利润呈U形。研究能够为各成员企业的最优定价决策提供有效支持与参考。     

具有政府回收约束的闭环供应链回收再制造决策模型

在回收和再制造的闭环供应链中,一个第三方替制造方从用户中买回废旧产品。为了激励废旧产品的回收再制造,政府对制造商设定一个最低回收率,并且对制造商的回收进行奖惩;同时考虑第三方的回收努力,建立了基于第三方负责回收的多级再制造闭环供应链模型,设计了政府与制造商之间的奖惩函数;讨论比较了制造商不回收废旧产品、没有政府奖惩时回收再制造废旧产品和考虑政府约束时回收再制造废旧产品等3种情形下的最优策略,并且分析了参数变化对回收率和供应链成员企业收益的影响;结合国内某家电制造商废旧家电回收再制造的运作情况进行了仿真计算与分析。     

分类回收条件下闭环供应链的协调问题研究

根据制造商回收的废旧品是否具有可再造性,将废旧品分为可再造废旧品和不可再生废旧品两类,考虑到政府对制造商的激励作用,分别建立了分类回收条件下闭环供应链的集中决策利润模型和分散决策利润模型,并通过两种决策下的最优策略对比,得到了集中决策下闭环供应链系统的效率优于分散决策下的效率的结论。最后,用收益一费用分享契约对分散决策下的闭环供应链进行了协调,并用算例证明了模型的有效性。     

再制造工程的逆向物流管理初探

随着社会的发展和企业对经济效益的不断追求,再制造成为越来越多企业关注的焦点,也使再制造逆向物流成为物流体系的新的发展方向。按照再制造工程的观点,应对产品再制造进行全系统和全寿命过程的科学管理。再制造工程离不开再制造物流和一个良好的再制造物流网络。一个有效的再制造物流系统能够使废旧产品以较高的效率得到回收,给企业提供较为准确的信息使企业能够合理安排库存和生产计划,并使再制造后的产品能够及时地送达消费者,满足消费者的需求。     

制造商主导下差别主体努力的闭环供应链决策研究

针对再制造节约成本和努力水平对闭环供应链成员回收决策产生影响的问题,构建制造商主导下M模式、R模式和T模式3种不同主体回收模式,利用Stackelberg博弈知识,建立模型,采用数学优化法求解闭环供应链废旧品回收过程中单位价格、回收数量以及回收商利润的最优值,运用对比分析法得出闭环供应链混合回收的最优决策。研究表明,再制造节约成本较大对主体回收均有促进作用;回收努力成本系数的增加,使节点企业利润和闭环供应链系统整体利润均呈现下降趋势,最终利用算例求解,检验结论的合理性。研究为供应链各个主体的最佳定价策略提供有效的参考。     

再制造过程中再制造品的质量选择及其影响

在废旧品回收充足的静态环境下,假设可再制造部分的质量水平在再制造的过程中发生衰减,研究了制造商如何选择再制造品的质量以及如何有效地规划新产品和再制造品的价格。不同的不可再制造部分的质量水平会影响新产品和再制造品的市场需求量,研究表明,随着再制造品质量的不断提高,新产品需求不断降低,再制造品需求不断增加,再制造品对新产品的市场挤兑效应越来越明显,直至将新产品完全挤出市场,形成完全再制造品市场。在此过程中,制造商利润呈现出先凸后凹的增长趋势,其利润在完全再制造品市场时达到最大。     

不同市场主导者下的以旧换再回收模型

为探究闭环供应链的定价决策问题,基于市场上不同主导者与废旧品回收渠道,建立了4种以旧换再回收模型,并对比分析了各个模型中制造商与零售商的定价决策。研究表明,1) 企业权力越大,议价能力就越强,企业的利润就越大;2) 再制造产品的定价、废旧品的回收价仅与废旧品的回收渠道有关,与市场主导者无关;3) 制造商回收时,潜在消费者能获更多的以旧换再返利,但是会损害初始消费者的利益;4) 制造商可以通过适当的降低新产品的耐用性、提高消费者对于再制造产品的接纳度等方式促使更多的消费者参加以旧换再活动,增大废旧品的回收率,进一步的把再制造产品推向市场。     

授权模式下OEM和第三方的生产和回收定价竞争策略

假设第三方再制造商（第三方）需要向原始设备制造商（OEM）缴纳授权费才能进行回收再制造，OEM和第三方在正向供应链销售市场与逆向供应链回收市场均存在竞争。文章建立了以双方闭环供应链利润最大化为目标带约束条件的古诺博弈模型，通过K-T条件求出第三方部分再制造和第三方完全再制造两种情形下两企业最优的产品生产量以及废旧品回收价格。最后分别通过解析方法和数值算例分析了单位授权费等参数对决策变量和最优利润的影响。     

考虑再制造成本的闭环供应链回收渠道决策

当前考虑再制造产品成本对其回收渠道选择影响的研究相对缺乏。将再造品成本作为内生变量,分别讨论了制造商回收、零售商回收和回收商回收三种模式下新产品与再造品的产量以及废旧产品的回收量决策,并分别基于企业收益、消费者剩余和回收率视角展开回收渠道偏好分析。研究表明:从企业收益角度考虑,制造商的回收渠道偏好与再制品成本有关,而零售商始终偏好零售商回收渠道;从消费者剩余角度考虑,为满足消费者剩余最大化,不同的再制品成本会导致不同的回收渠道偏好;从回收率角度考虑,制造商回收渠道下的废旧产品回收率始终高于另外两种渠道。研究结论对再制造闭环供应链各方的定价决策以及回收渠道选择具有一定的参考价值。     

废旧汽车零部件再制造回收模型优化研究

建立废旧汽车零部件回收与再制造智能交易平台,对我国废车市场的建设和完善以及汽车零部件再制造产业链的发展具有重大的工程实用价值。为研究适用于第三方平台的回收模式和策略,本文构建了由制造商、再制造商、销售商、回收平台和激励政策共同组成的再制造闭环供应链回收模型,通过利润函数来揭示再制造供应链中各企业的定价策略和相关变量的关系。分别利用逆向归纳法和多目标遗传算法求解该模型,并以废旧汽车变速箱为案例,验证和比较了2种算法的可行性和优劣性。仿真分析结果表明,基于多目标遗传算法求解下制定的回收策略优于逆向归纳法,预期闭环供应链整体回收率和企业利润可得到全面优化,为再制造产业链各参与者的回收决策提供了具有现实意义的理论依据。     

Base stock versus WIP cap in single-stage make-to-stock production–inventory systems

A single-stage production-inventory system produces parts in a make-to-stock mode, and unsatisfied demand is backordered. The system operates under a so-called base stock with WIP cap replenishment policy, which works as follows. Whenever the Work-In-Process (WIP) plus finished goods inventory falls below a specified level, called base stock, a replenishment order for the production of a new part is issued. If the WIP inventory is below a different specified level, called WIP cap, the order goes through and a new part is released for production; otherwise, the order is put on hold until the WIP inventory drops below the WIP cap. First, it is shown that the optimal base stock that minimizes the long-run, average, inventory holding cost for a given minimum customer service level, is a non-increasing function of the WIP cap that reaches a minimum value, called minimum optimal base stock, at a finite WIP cap value, called critical WIP cap. Then, it is shown that the optimal WIP cap is less than or equal to the critical WIP cap and therefore the optima! base stock is greater than or equal to the minimum optimal base stock. More interestingly, however, it is conjectured that the optimal WIP cap is in fact exactly equal to the critical WIP cap and therefore the optimal base stock is exactly equal to the minimum optimal base stock. The minimum optimal base stock is none other than the optimal base slock of the same system operating under a classical base stock policy (with no WIP cap). Finally, the optimal parameters of a system operating under a base stock with WIP cap policy are related to the optimal parameter of the same system operating under a make-to-stock CONWIP policy.     

Coordinating quality,production and sales in manufacturing systems

We explore the benefits of jointly designing quality tolerances, customer admission and production control policies in manufacturing systems producing a single product to meet demand. These problems have been addressed separately in the past. We consider a simple admission/production control policy whereby the system produces until stock reaches a certain level and accepts orders until the backlog reaches another critical level. We model the system using queueing theory and propose an easily implementable procedure for selecting the optimal quality tolerances and the critical stock and backlog levels. From theoretical and numerical results, it appears that the proposed policy achieves a higher profit than other manufacturing practices, in which there is little or no coordination between the production, sales and quality control departments.     

Modelling on vendor-managed inventory policies with equal and unequal shipments under GHG emission-trading scheme

This paper studies five different stock control policies in the supply chain management. The lead time can be shortened by extra investment between two entities. The vendor produces a single product and delivers the order quantity in a number of unequal shipments to the buyer. The unit holding cost is divided into financial and storage components. The vendor takes care of financial component until the products are sold to the end customers to encourage them to buy more products. In order to reduce emissions from production and to protect the environment, some legislative actions have been taken such as implementing taxes and penalties. The cost function also includes these taxes and penalties. The optimal solutions of this constrained mixed integer non-linear programming problem are obtained by using the Genetic Algorithm (GA). Numerical examples are employed and comparison works are carried out with other existing literatures. Results show that the performance of the system is better when it is operated under unequal shipment policies and vendor-managed inventory (VMI) agreement.     

Approximate dynamic programming algorithms for multidimensional flexible production-inventory problems

An important issue in the manufacturing and supply chain literature concerns the optimisation of inventory decisions. Single-product inventory problems are widely studied and have been optimally solved under a variety of assumptions and settings. However, as systems become more complex, inventory decisions become more complicated for which the methods/approaches for optimising single inventory systems are incapable of deriving optimal policies. Manufacturing process flexibility provides an example of such a complex application area. Decisions involving the interrelated product inventories and production facilities form a highly multidimensional, non-decomposable system for which optimal policies cannot be readily obtained. We propose the methodology of approximate dynamic programming (ADP) to overcome the computational challenge imposed by this multidimensionality. Incorporating a sample backup simulation approach, ADP develops policies by utilising only a fraction of the computations required by classical dynamic programming. However, there are few studies in the literature that optimise production decisions in a stochastic, multi-factory, multi-product inventory system of this complexity. This paper aims to explore the feasibility and relevancy of ADP algorithms for this application. We present the results from numerical experiments that establish the strong performance of policies developed via temporal difference ADP algorithms in comparison to optimal policies and to policies derived from a deterministic approximation of the problem.     

SINGLE-STAGE, MULTI-PRODUCT PRODUCTION/INVENTORY SYSTEMS WITH BACKORDERS

A production/inventory system consisting of a single-stage facility producing multiple product types and a warehouse is considered. The demand process is assumed to be Poisson and the excess demand is backordered. Products have a priority structure and the processor is shared according to a switching rule. (R, r) continuous-review inventory control policies are used to start and stop production of each product. Production of a product continues until its target level is reached. Then, a switch-over takes place to the highest priority product among the ones that need the processor's attention. A set-up takes place every time a switch-over occurs. Both processing and set-up times are assumed to have arbitrary distributions. The number of switch-overs among the high priority products is restricted in case low priority products need attention. The objective is to obtain performance measures such as the average inventory and backorder levels as well as die probability of having a backorder upon arrival for each product. We develop an approximation procedure which emphasizes the characterization of the delay each product type experiences in receiving the processor's attention, once they place a request for it. The approximating arguments are based on independence assumptions.     

Development of joint maintenance and production strategies in a subcontracting environment

This article, inspired by an industrial problem, develops efficient maintenance and just-in-time production policies in a subcontracting environment according to two orientations. The first invokes subcontracting with the objective of satisfying a constant customer demand knowing that our production system, composed of a machine M ₁, cannot satisfy the totality of demand. Subcontracting is represented by a machine M ₂ which has a constant failure rate, while three maintenance policies for M ₁ are tested and evaluated. The second orientation takes the perspective of our production system as a supplier which is obliged to allocate part of its production capacity to subcontracting so as to satisfy a constant demand. We consider a production system made up of two machines, both of which produce a single type of product, are subject to breakdowns and can carry out subcontracting tasks. The objective of this part of the article is to prove the efficiency of the so-called integrated maintenance policy, which combines production and maintenance decisions in a subcontracting environment.     

DETERMINING THE NUMBER OF KANBANS IN MULTI-ITEM JUST-IN-TIME SYSTEMS

Kanbans (cards) are one means of controlling Just-in-time production systems. The kanbans serve as production authorization orders and essentially become the information system. The presence of a production ordering kanban for a specific part type constitutes an instruction to the workcenter to produce a “container” of that part. In this paper we assume workcenters produce multiple part types and address the issue of the number of kanbans needed for each part type. Our objective is to minimize the sum of inventory holding and backorder cost. A stochastic model is formulated. Steady-state results are derived for the cases of a few and many part types. Modifications for the case of expediting of backorders are also provided. Simulation results demonstrate the accuracy of the model for a large number of kanbans, but an investigation of a finite population queueing model for small numbers of kanbans is suggested as a worthwhile extension.     

Generating daily production plans for complex manufacturing facilities using multi-objective genetic algorithms

Modern production tracking and planning systems produce good inventory plans. Unfortunately, daily production problems often produce an inventory profile that is significantly different from that of the production plan. Several manufacturing objectives are available to remedy inventory profile problems, but a weighted combination of them can be more effective. We propose using genetic algorithms to search for best weight assignments in multi-objective daily production planning problems. This approach takes snapshots of a factory's inventory, equipment capacity and demands to generate a near optimal shop floor daily production plan. The implemented system runs on a daily basis at midnight for 40 min and generates the following day's production plan. Tests of this system in a large-scale semiconductor manufacturing facility show the proposed approach generates production plans of high quality in reasonable run times under many factory conditions.     

An optimal consignment stock production and replenishment policy with controllable lead time

This paper considers a single-vendor and single-buyer production system in which the lead-time is controllable with an extra investment under a long-term agreement between the two trading partners. The vendor produces at a finite rate, ships the outputs in lots of equal size within a production cycle, and delays those shipments for a certain period when the buyer’s inventory approaches the capacity limits. Therefore, the arrival of these shipments does not lead to an increase in the buyer’s inventory. Meanwhile, the buyer holds the payment until the complete consumption of the products. The holding cost consists of a storage component and a financial component. A joint EOQ/EPQ model is then established under cases where the buyer’s unit storage holding cost might be greater or less than that of the vendor to jointly determine the number of shipments, the size of each shipment, the number of delayed shipments, and the lead time that minimise the yearly joint total expected cost (JTEC) of the system. An efficient solution procedure is provided to solve the non-linear integer optimisation model that defined the system under consideration. A method to determine the integer global optima from the real global optima is also presented. Two numerical experiments are conducted to illustrate the procedure and the results show that considering the combined effect of adopting a consignment stock policy and lead time crashing opportunities may lead to a better result than any of these two policies considered separately.     

Optimal preventive maintenance in a production inventory system

We consider a production inventory system that produces a single product type, and inventory is maintained according to an (S, s) policy. Exogenous demand for the product arrives according to a random process. Unsatisfied demands are not back ordered. Such a make-to-stock production inventory policy is found very commonly in discrete part manufacturing industry, e.g., automotive spare parts manufacturing. It is assumed that the demand arrival process is Poisson. Also, the unit production time, the time between failures, and the repair and maintenance times are assumed to have general probability distributions. We conjecture that, for any such system, the down time due to failures can be reduced through preventive maintenance resulting in possible increase in the system performance. We develop a mathematical model of the system, and derive expressions for several performance measures. One such measure (cost benefit) is used as the basis for optimal determination of the maintenance parameters. The model application is explained via detailed study of 21 variants of a numerical example problem. The optimal maintenance policies (obtained using a numerical search technique) vary widely depending on the problem parameters. Plots of the cost benefit versus the system characteristic parameters (such as, demand arrival rate, failure rate, production rate, etc.) reveal the parameter sensitivities. The results show that the actual values of the failure and maintenance costs, and their ratio are significant in determining the sensitivities of the system parameters.