需求双向替代与生产转换下动态批量决策及预测时阈研究

本文研究了存在需求双向替代和生产转换情形下的两产品动态批量决策和预测时阈问题。构建了包含时变的生产转换成本、替代成本以及两产品的生产成本和库存成本在内的成本最小化模型。在最优解性质的基础上，设计出前向动态规划算法求解问题。利用边际成本分析方法得出两产品生产点及再生点的单调性特征，并建立了相应的两产品再生集，最后给出求解预测时阈和相应的决策时阈的充分条件。运用数值实验分析了需求单向替代和双向替代情形下预测时阈的相对大小、同一周期最多发生一次生产转换与可以发生多次转换情形下预测时阈的相对大小，需求增长性与波动性对预测时阈的影响以及生产转换成本与替代成本同时变动时对预测时阈的影响。发现需求双向替代将增加预测时阈的长度，而同一周期可以发生多次生产转换将降低预测时阈的长度。预测时阈随着生产转换成本的增加而显著递增，随着需求增长参数和需求波动参数以及替代成本的增加而递减。     

服务多类需求串行供应链的最优控制策略

研究由一系列生产环节采用串行方式组成多级供应链的最优控制策略.在此供应链中,原材料经过各级生产环节顺序加工形成最终产品.各级生产环节的加工时间服从随机分布.对最终产品存在多类随机需求.在每个时刻,管理者需要决定:1)是否该启动某个生产环节的生产;2)当有需求到达时,是否该满足此需求.管理者期望系统运行的总期望折扣成本最小.构造了该系统的马尔可夫决策模型并深入研究了其最优控制策略及其动态协同特性.在生产策略方面,证明系统的最优生产策略就是对各级生产环节采用动态的基本库存策略.该策略的动态协同特性主要体现在各级生产环节的最优基本库存水平受其他生产环节的库存水平影响.在产品分配方面,证明系统的最优分配策略是动态配给策略.该策略的动态协同特性主要体现在每类随机需求最优配给水平受各级生产环节的库存水平影响.     

废旧产品再制造环境下的产品定价和再制造方式分配

原材料价格的不断上涨,使得制造企业开始通过废旧产品的回收和再制造来缓解制造成本的上涨.然而,制造企业可以采用较昂贵的技术生产与新产品无差异的再制造品,也可以采用较廉价的技术生产在低端市场销售的再制造品,不同的再制造方式会给制造商带来不同的利润;另一方面废旧品的边际回收成本随着回收率的上升而上升,使得制造企业必须小心选择废旧品的回收数量.文中构建了高端市场和低端市场之间存在需求的线性替代关系时,再制造品和新产品的定价问题,并在优化计算的基础上得到了成本结构对于最优利润、最优价格、最优回收率和不同再制造方式比重的影响.     

风险质检行为下的模糊生产-库存决策

本文研究在风险质检行为下需求与生产率具有模糊属性的由一个制造商与一个零售商构成的生产-库存问题。在假设制造商生产的产品存在质量缺陷,零售商在质检过程中存在质检风险的基础上,分别建立了需求模糊下的生产-库存总成本模型,需求与生产率模糊下的总成本模型,运用符号距离法进行了逆模糊化处理,将模糊化的生产-库存总成本转化为确定性成本。证明了两类情形下的总成本均是关于最优订购量与最优缺货量的联合凸函数。数值分析结果表明：随着期望缺陷率的增加,最优订购量增加,最优缺货量减少,最优成本增加,且最优成本增加的速度越来越快。一类质检风险增大引起总成本增加,二类质检风险增大引起总成本减少。最优订购策略对质检一类风险敏感,对质检二类风险不敏感。     

面向批量订货和多需求类的生产与库存配给

针对一个面向两个需求类的生产企业,根据客户每次订货是否可分批交货,提出了当客户订货可分割和不可分割时供应商的最优生产和库存配给策略.分析表明,供应商的最优生产控制策略可用一个取决于系统状态的基准库存水平表示,最优的库存配给策略则用一个多层的取决于状态的配给水平向量表示.随后,该结论被推广至包含任意多个需求类的生产系统.数值分析验证了文中最优策略的有效性.     

原材料价格波动下的库存与生产联合决策

价格波动的情况下如何进行原材料采购,并在产品的生产决策中如何权衡市场需求、产品价格以及采购与库存成本等问题是当前很多企业面临的普遍难题.对此类问题,假定原材料的价格波动服从马尔可夫过程,成品的市场需求为与价格相关的随机变量,进而建立了多周期库存采购与生产联合决策模型,并分析了系统的动态最优策略.模型同时考虑了原材料买进或者卖出决策和成品生产决策.研究结果表明,模型的最优策略,即原材料库存决策与成品生产决策均满足动态基库存策略.同时证明了一系列性质,这些性质展示了原材料价格波动下最优库存策略的特征.数值实验进一步验证了模型的最优策略,并研究了参数变化时最优策略的变化.     

基于成本-收益分析的逆向供应链网络演化机制分析

不同于发达国家TPT(第三方回收)的逆向供应链模式,我国逆向供应链具有无标度网络特征,其中大多数逆向供应链下游企业是走街串巷的个体商贩及其家庭作坊.本文提出基于成本-收益分析的我国逆向供应链网络演化机制.其中,废品回收站点需要支付固定的生产场所租金和投资购买专业生产设备,该固定成本可以在回收废品数量中均摊.当回收站点选择下游家庭作坊(或流动商贩)的最优数量时,将依据成本-收益分析获得最大化利益.最后,广东清远市电子产品废弃物回收网络的案例,以及NetLogo平台下数值仿真结果,较好地验证了理论分析结果.     

考虑不同回收质量等级的再制造系统回收生产决策

废旧产品的回收再制造过程往往在回收质量、再制造成本、再制造产出率、再制造产品需求等方面存在不确定性因素,极大地增加了再制造生产管理决策的复杂性。本文在一个回收再制造系统中研究了存在多种回收质量等级时的两阶段回收—再制造联合优化决策问题,并扩展到需求与价格相关和再制造产出率随机两种情形。在最大化再制造商期望利润的决策目标下,基于每种回收质量等级的单位回收和再制造成本构造出再制造系统的有效生产前沿面,给出了不同决策情形下再制造商的最优回收数量、销售定价的解析解,并且分析了一些主要的参数对再制造商最优决策的影响。本文的研究结果表明：（1）含有多种回收质量等级的再制造系统中存在一个下凸的有效生产前沿面,不在该前沿面上的任何质量等级的回收产品都将不会被用于再制造;（2）在同等的政府补贴额度下,回收补贴方式对再制造商决策的影响程度大于再制造补贴方式;（3）当再制造品的市场需求与价格相关时,最优销售价格至少大于第一种被使用的回收质量等级的边际回收和再制造成本;（4）任意两种回收质量等级之间存在着替代或互补效应,由其成本差异决定,并且这种效应随着需求不确定性的增大而增大;（5）再制造产出率的不确定性和再制造品需求的不确定性之间存在"对冲"效应,这种效应随着再制造产出率不确定性的降低而减弱。本文的研究可为不确定性环境下再制造企业的回收、生产管理决策提供有益的管理启示。     

生产投入、政府激励与企业生产规模决策

在同时面临外生性和内生性因素影响的需求不确定环境下,通过建立生产投入、政府激励和企业生产规模的决策模型,在研究企业生产投入的价值表达式的基础上,分析了生产投入、政府激励与企业生产规模决策之间的作用机理,对临界值方程、最优生产规模和最优政府激励作了分析.并通过模拟赋值对数理模型进行了数值分析.研究表明:1)灵活的企业可依据市场需求的变化,参考资本化未来生产规模、政府激励现金流、放弃期权价值和需求变化期权价值等对生产规模进行最优化调整;2)当企业单位时间收益流满足一定水平时,存在最优生产规模方程和最优政府激励方程及其相应的阈值方程;3)生产投入、政府激励及企业生产规模之间联系密切,灵活的企业可据此对生产进行动态调整.此外,还与外生性模型作比较验证了该模型的适应性等.由此,可作为需求不确定环境下加强企业生产规模决策的一种参考.     

生产投入、政府激励与企业生产规模决策北大核心CSCDCSSCI

在同时面临外生性和内生性因素影响的需求不确定环境下,通过建立生产投入,政府激励和企业生产规模的决策模型,在研究企业生产投入的价值表达式的基础上,分析了生产投入,政府激励与企业生产规模决策之间的作用机理,对临界值方程、最优生产规模和最优政府激励作了分析.并通过模拟赋值对数理模型进行了数值分析.研究表明:1)灵活的企业可依据市场需求的变化,参考资本化未来生产规模、政府激励现金流、放弃期权价值和需求变化期权价值等对生产规模进行最优化调整;2)当企业单位时间收益流满足一定水平时,存在最优生产规模方程和最优政府激励方程及其相应的阈值方程;3)生产投入,政府激励及企业生产规模之间联系密切,灵活的企业可据此对生产进行动态调整.此外,还与外生性模型作比较验证了该模型的适应性等.由此,可作为需求不确定环境下加强企业生产规模决策的一种参考.     

A competitive multiple-product newsboy problem with partial product substitution

This paper studies a multi-product competitive newsboy problem with shortage penalty cost and partial product substitution. We characterize the unique Nash equilibrium of the competitive model and analyze some properties of the equilibrium. An iterative algorithm is developed on the basis of approximating the effective demand as well as the expected profit function for each product. Numerical experiments are conducted to illustrate the impacts of product substitution, demand correlation and demand variation on the optimal order quantities and the corresponding expected profits, and to compare the total optimal inventory level of the competitive case with that of the centralized case. The conclusion that competition always results in a higher total inventory level, even under the effect of product substitution is drawn in the symmetric case.     

具有最小交易量限制的多阶段均值-半方差投资组合优化

考虑交易成本,借款约束和阈值约束,文章提出了具有最小交易量限制的多阶段均值-半方差投资组合模型。该模型是具有路径依赖性的混合整数动态优化问题,还是NP完全问题。文章提出了前向动态规划方法求解。最后,通过一个算例比较不同风险约束下的最优投资策略,从而验证模型和算法的有效性。     

The two-level lot-sizing problem with outbound shipment

We consider the two-level lot-sizing problem for the integrated replenishment and dispatch plan of a third-party logistics company and distribution center. Each outbound shipment for a dispatch involves cargos that incur a stepwise transportation cost. To increase the effect of shipment consolidation, the distribution center allows backlogging. This problem has the characteristics of capacitated and uncapacitated lot-sizing. A forward dynamic program is applied to exploit the beneficial characteristic of the uncapacitated problem. The forward dynamic program is implemented using Zangwill partitions to decrease the number of computational parameters with legitimacy checks to guarantee optimality. Employing these techniques, we provide an O(T⁴) algorithm, which serves as an improvement on the O(T⁶) algorithm of previous research. Moreover, computational results from actual implementations show that our algorithm is significantly faster than that of prior research.     

Approximate event detection over multi-modal sensing data

Composite event detection is one of the fundamental tasks for heterogeneous wireless sensor networks (WSNs). Multi-modal data generated by heterogeneous sensors bring new challenges for composite event monitoring in heterogeneous WSNs. By exploiting the correlations between different types of data, the approximate composite event detection problem is investigated in this paper. The optimal transmitting scheme problem is proposed to calculate the optimal transmitting scheme with minimum cost on the constraint that the confidence of the composite event must exceed the threshold. The optimal transmitting scheme problem is proved to belong to NP-complete. A dynamic programming based algorithm is presented for simple linear confidence combination operators, which runs in pseudo-polynomial time. The greedy based approximate algorithm is also designed for general confidence combination operators and the approximate ratio is proved to be 2 for “+” as the confidence combination operator. The simulation results show that our algorithms can reduce energy consumption significantly.     

DYNAMIC LOT SIZING FOR A SINGLE-FACILITY MULTIPRODUCT PROBLEM IN A ROLLING-HORIZON ENVIRONMENT

This paper considers a production planning model for a single-facility multiproduct problem where backlogging is not allowed. A planning-horizon theorem is derived. From that theorem, a forward algorithm for finding an optimal solution over a finite horizon and a procedure for selecting the first-period production in a rolling-horizon environment are developed. Computational results from a set of simulation experiments designed to investigate the cost effectiveness of the procedure demonstrate its effectiveness.     

Managing stochastic demand in an Inventory Routing Problem with transportation procurement

We study an Inventory Routing Problem in which the supplier has a limited production capacity and the stochastic demand of the retailers is satisfied with procurement of transportation services. The aim is to minimize the total expected cost over a planning horizon, given by the sum of the inventory cost at the supplier, the inventory cost at the retailers, the penalty cost for stock-out at the retailers and the transportation cost. First, we show that a policy based just on the average demand can have a total expected cost infinitely worse than the one obtained by taking into account the overall probability distribution of the demand in the decision process. Therefore, we introduce a stochastic dynamic programming formulation of the problem that allows us to find an optimal policy in small size instances. Finally, we design and implement a matheuristic approach, integrating a rollout algorithm and an optimal solution of mixed-integer linear programming models, which is able to solve realistic size problem instances. Computational results allow us to provide managerial insights concerning the management of stochastic demand.     

A tardiness-augmented approximation scheme for rejection-allowed multiprocessor rescheduling

In the multiprocessor scheduling problem to minimize the total job completion time, an optimal schedule can be obtained by the shortest processing time rule and the completion time of each job in the schedule can be used as a guarantee for scheduling revenue. However, in practice, some jobs will not arrive at the beginning of the schedule but are delayed and their delayed arrival times are given to the decision-maker for possible rescheduling. The decision-maker can choose to reject some jobs in order to minimize the total operational cost that includes three cost components: the total rejection cost of the rejected jobs, the total completion time of the accepted jobs, and the penalty on the maximum tardiness for the accepted jobs, for which their completion times in the planned schedule are their virtual due dates. This novel rescheduling problem generalizes several classic NP-hard scheduling problems. We first design a pseudo-polynomial time dynamic programming exact algorithm and then, when the tardiness can be unbounded, we develop it into a fully polynomial time approximation scheme. The dynamic programming exact algorithm has a space complexity too high for truthful implementation; we propose an alternative to integrate the enumeration and the dynamic programming recurrences, followed by a depth-first-search walk in the reschedule space. We implemented the alternative exact algorithm in C and conducted numerical experiments to demonstrate its promising performance.

     

Traffic regulation with single- and dual-homed ISPs under a percentile-based pricing policy

We investigate how a customer (an enterprise or a large organization), when facing a percentile-based pricing policy, can optimally balance the Internet access cost and the traffic buffering delay penalty by traffic regulation. The problem is referred to as the Optimal Traffic Regulation (OTR) problem. Solutions to various cases of the OTR problem are provided. For a customer with a single-homed ISP, we present optimal solutions to the OTR problem based on dynamic programming for the offline case with a known traffic demand pattern. A real-time traffic scheduling algorithm is proposed to deal with the online case where the traffic demands are different from a given demand pattern. We further extend the dynamic programming model to the case of dual-homed ISPs. Experimental results on the data from an Internet trace confirm the effectiveness of our solutions. Part of the results has been presented in the First International Conference on Scalable Information Systems.     

DETERMINISTIC TIME‐VARYING DEMAND LOT‐SIZING MODELS WITH LEARNING AND FORGETTING IN SETUPS AND PRODUCTION

We study the deterministic time‐varying demand lot‐sizing problem in which learning and forgetting in setups and production are considered simultaneously. It is an extension of Chiu's work. We propose a near‐optimal forward dynamic programming algorithm and suggest the use of a good heuristic method in a situation in which the computational effort is extremely intolerable. Several important observations obtained from a two‐phase experiment verify the goodness of the proposed algorithm and the chosen heuristic method.     

Single-machine batch delivery scheduling with an assignable common due window

This paper addresses a batch delivery single-machine scheduling problem in which jobs have an assignable common due window. Each job will incur an early (tardy) penalty if it is early (tardy) with respect to the common due window under a given schedule. There is no capacity limit on each delivery batch, and the cost per batch delivery is fixed and independent of the number of jobs in the batch. The objective is to find the optimal size and location of the window, the optimal dispatch date for each job, as well as an optimal job sequence to minimize a cost function based on earliness, tardiness, holding time, window location, window size, and batch delivery. We show that the problem can be optimally solved in O(n⁸)$O(n^8)$ time by a dynamic programming algorithm under a reasonable assumption on the relationships among the cost parameters. A computational experiment is also conducted to evaluate the performance of the proposed algorithm. We also show that some special cases of the problem can be optimally solved by lower order algorithms.