生产商规模不经济的双渠道供应链协调策略选择

供应链协调是提高供应链整体利润、改善供应链各方利益的根本手段.旨在探讨生产商具有生产规模不经济特性的双渠道供应链协调策略.给出了集中决策下双渠道供应链的市场价格与整体利润,以及分散决策下批发价契约与收益共享契约的设计方法.提出了协调度的概念,即契约使供应链协调的程度.证明了批发价契约的协调度小于1,不能实现供应链的协调;而收益共享契约的协调度等于1,但相对于批发价契约,收益共享契约不能同时改善零售商和生产商的利润,导致收益共享契约不可实施.为找到双渠道供应链可实施的协调策略,提出了带固定补偿的收益共享契约的设计方法和用纳什讨价还价协商模型确定固定补偿的方法.结果表明,该契约的协调度等于1,并且相对于批发价契约能同时改善零售商和生产商的利润.另外,研究还表明:生产不经济的弹性系数与销售价格成正比、与供应链的整体利润成反比,并且不同渠道间的竞争越激烈,双渠道供应链的销售价格和需求反而越高,双渠道供应链的总利润也越高,并且,带固定补偿的收益共享契约同样可以协调线性成本下的双渠道供应链.最后用算例验证了本文结论的正确性和有效性.     

产品责任视角下零售商主导供应链质量激励策略研究

在制造商质量缺陷导致消费者效用损失,并使零售商承受产品责任的情形下,本文考虑了如何设计质量激励策略,以提升制造商产品质量水平。基于此,本文构建以零售商为核心的供应链博弈模型,考虑了事前质量成本分担策略和事后收益分享策略,及相应的零售商决策和纳什讨价还价决策两种模式,研究了供应链运作策略差异,揭示了如何设计更加有效的质量激励策略。研究表明,相对于分散化决策,质量激励策略促进了产品质量和供应链企业及系统业绩的提升,且事前质量成本分担策略比事后收益分享策略的效果要强;在不同决策模式下,零售商决策下的产品质量、市场需求和零售商利润相对较高,而纳什讨价还价决策下的制造商利润和供应链系统利润相对较高。这揭示,较低的产品质量和市场需求导致了相对较高的供应链系统利润;从供应链系统角度看,纳什讨价还价决策下的事前质量成本分担策略是最有效率的。此外,数值分析结果发现,责任成本提高了实施质量成本分担策略的动机,但降低了实施收益分享策略的动机;分担较多的质量成本,并不意味着较高的产品质量和供应链利润;要求供应链下游核心企业承担责任成本,并不会对产品质量和供应链业绩产生积极影响。     

考虑随机市场需求的双渠道供应链风险补偿策略研究——从质量与价格竞争视角

在由两个供应商和单个零售商构成的二级双渠道供应链系统中，分析了随机市场需求以及供应商之间同时存在价格与质量竞争的情形下的双渠道供应链协调问题。在供应链集中决策、无风险补偿及有风险补偿三种情境下，构建了基于质量和价格的风险补偿模型并求得纳什均衡解。研究结果表明：存在可行的需求风险补偿策略使得供应链达到协调水平，并且需求风险补偿策略对零售商更加有利；在需求风险补偿策略下，需求风险补偿价格与其对应批发价格正相关，与其产品质量水平负相关；价格竞争程度对零售商订货量具有负向影响作用，对供应链总利润影响作用随着竞争程度增加而减弱，质量竞争程度对零售商订货量和供应商产品质量水平具有正向影响作用，对供应链总利润影响呈倒U型关系；需求风险补偿策略能够激励零售商的订货行为，强化价格竞争程度和质量竞争程度对供应商订货量和供应链总利润的影响；在供应链系统中双渠道营销模式下的供应链总利润要优于单渠道营销模式下的供应链总利润。本文结论不仅详细剖析了风险补偿策略对双渠道供应链协调的影响关系，也理清了价格与质量竞争对各方行为策略的影响机理。     

考虑创新补偿的双渠道供应链协调机制研究

电子商务环境下,以创新降低物流、库存等分销成本是零售商赢得生存与发展的重要途径;在渠道和谐与利润最大化驱使下,策略性创新补偿成为制造商的首选策略.本文在传统分销与在线直销价格竞争下,构建模型并研究了上述双渠道供应链的协调问题.研究发现,单独利用创新补偿能实现制造商与零售商收益的Pareto改进,却无法达到供应链整体最优,设计了使渠道协调的两部定价合同.     

含交付时间不确定性的供应链协调策略研究

分析含有交付时间不确定性的季节性供应链管理问题.如果在供应链中共享供应商的交付时间不确定性信息,零售商可以调整订货量,减少销售损失,就有可能提高供应链的整体利润.分析指出,当交付时间不确定性程度大时,信息共享能提高改进供应链绩效的可能性,但不一定能提高供应链的整体绩效,因而,仅仅采用信息共享有应用价值,但有一定的局限性.进一步分析发现,存在能协调这种含有交付时间不确定性供应链策略,能使供应链达到整体绩效最大化,并且对供应链的利润进行分配,协调策略能有效地弥补信息共享的局限性.     

考虑顾客行为与零售商公平关切的易逝品定价与供应链协调研究

本文在公平中性的供应商、公平关切的零售商和策略型与短视型顾客并存的供应链下,研究了零售商的最优定价与订货量及供应链的协调.研究发现,公平关切的零售商有提高产品价格和压低订货量的倾向;批发价契约难以实现供应链的协调;收益共享契约能实现供应链的完美协调.论文还讨论了零售商的公平关切程度和利润分享比例对供应商利润、零售商效用及供应链效用的影响.     

具有两阶段生产模式和需求信息更新的供应链契约研究

针对二级供应链,在基于Stackelberg博弈的批发价契约中,分析了折扣价格与零售商最优提前订购量的关系及对契约双方期望收益的影响;设计了价格折扣与回购联合的协调契约,分析了供应链系统的最优提前生产量、供应链协调条件及协调契约下制造商和零售商的期望收益变化,并在协调契约不满足制造商的个体理性约束时,提出采用不对称Nash谈判模型设计两阶段生产与订购的回购契约,在保证供应链系统整体绩效最优的条件下,使制造商和零售商利润都能得到满意增长。研究表明,三级供应链实行两阶段订购的必要条件是分销商与零售商的最优提前订购量相等,与二级供应链相比,价格折扣契约下三级供应链效率更低,但价格折扣与回购联合的契约同样能协调三级供应链,该协调契约满足零售商与分销商激励相容约束。     

三级闭环供应链系统的定价、回购及协调策略

基于单一制造商、单一零售商和单一第三方物流服务提供商构成的三级供应链系统,将第三方物流引入到闭环供应链协调中来,在第三方物流服务提供商负责废品回收、正向物流服务价格由零售商承担的条件下,通过独立决策和联合决策两种决策方法,运用博弈理论进行求解,给出了两种不同决策方法下的定价策略;通过对两种定价方法的对比,认为联合决策下不仅系统利润明显提高,而且消费者也从中获益,并通过等利润增长率的方式对三者利润进行分配,实现共赢.最后通过实例进一步对结果进行验证.     

考虑损失厌恶—对多型供应链的收益共享契约

以一个两阶段的供应链系统为研究背景,建立了下游损失厌恶型零售商之间存在竞争的收益共享契约协调模型.研究发现,竞争性的多零售商之间存在唯一的纳什均衡总订货量使其期望效用实现最大化,且总订货量随零售商数目的增加而增加、随零售商风险厌恶程度的增加而减少.在此基础上,进一步分析了在整个供应链系统中存在唯一的“批发价一收益共享系数”比值能够使供应链利用收益共享契约达到协调,并推导了系统实现协调时最优契约参数之间的关系;最后结合数值实例验证了供应链收益共享契约机制的有效性.     

竞争环境下主导电子零售商的销售模式选择和信息共享策略

考虑由单一供应商、单一电子零售商和单一传统零售商组成的供应链，研究了占主导地位的电子零售商的在线销售模式(转售和代理销售)选择及信息共享策略。首先，针对两种在线销售模式，构建了信息共享和不共享情形下以供应链成员利润最大化为目标的优化模型，并通过求解模型得到了供应链成员的均衡定价和订货策略；然后，比较了不同策略组合下供应链各成员的期望利润，分析了各成员的信息共享策略偏好以及电子零售商的销售模式选择策略。研究表明：1)在转售模式下，当市场需求波动程度较小时，信息共享会增加电子零售商的利润，降低供应商和传统零售商的利润；当波动程度较大时，则相反；当市场需求波动处于中等水平时，电子零售商不共享信息能使供应商、电子零售商和传统零售商达成三赢局面。2)在代理销售模式下，信息共享能增加传统零售商的利润，但不一定能增加供应商和电子零售商的利润。只有佣金费率较低，高市场类型的概率较低且市场需求波动不大时，信息共享才是电子零售商的最优策略。3)主导电子零售商应该选择转售模式。本文的研究可为主导电商企业销售模式选择和信息分享决策提供理论依据;为相关零售企业在不同策略组合下定价和订货决策提供支持。     

Heuristic methods for the analysis of managerial problems

Problem analysis is a critical element of managerial problem solving that has not been included in or explicated by traditional stage models of the process. This paper argues that an analysis stage is needed to develop the implications of a problem's definition and to direct the selection and pursuit of solution strategies. Problem analysis is a beuristic activity. The paper explains and applies seven heuristic methods of analysis to a classic management case, generating a richer understanding and broader set of solution alternatives. Strategies for future research on problem analysis are discussed.     

On Disjunctive Cuts for Combinatorial Optimization

In the successful branch-and-cut approach to combinatorial optimization, linear inequalities are used as cutting planes within a branch-and-bound framework. Although researchers often prefer to use facet-inducing inequalities as cutting planes, good computational results have recently been obtained using disjunctive cuts, which are not guaranteed to be facet-inducing in general.A partial explanation for the success of the disjunctive cuts is given in this paper. It is shown that, for six important combinatorial optimization problems (the clique partitioning, max-cut, acyclic subdigraph, linear ordering, asymmetric travelling salesman and set covering problems), certain facet-inducing inequalities can be obtained by simple disjunctive techniques. New polynomial-time separation algorithms are obtained for these inequalities as a by-product.The disjunctive approach is then compared and contrasted with some other general-purpose frameworks for generating cutting planes and some conclusions are made with respect to the potential and limitations of the disjunctive approach.     

Problem Formulation and Solution Mechanisms: A Behavioral Study of Humanitarian Transportation Planning

When dealing with urgent, ill‐defined problems, such as rapidly evolving emergency situations, operations managers have little time for problem formulation or solution. While the mechanisms by which humans formulate and solve problems have been described, mechanisms for rapid, concurrent formulating and solving are not well understood. This study investigates these mechanisms through a field study of transportation planning in a humanitarian response setting. The findings show that the problem is solved through greedy search and formulated through sensemaking, in which search enables updates to an evolving problem formulation, and the formulation directs and limits the search process. This study explores the implications of these findings for the development of better problem formulation processes and problem‐solving strategies for urgent and ill‐defined operations management problems.     

Location and reliability problems on a line: Impact of objectives and correlated failures on optimal location patterns

In this paper we study a class of locations models where facilities are not perfectly reliable and failures may be correlated. We analyze problems with Median and Center objectives under complete and incomplete customer information regarding the state of facilities. The goal is to understand how failure probabilities, correlations, availability of information, and problem objective affect the optimal location patterns. In particular, we want to find analytical confirmations for location patterns observed in numerical experiments with network location models. To derive closed-form analytical results the analysis is restricted to a simple (yet classic) setting: a 2-facility problem on a unit segment, with customer demand distributed uniformly over the segment (results can be extended to other demand distributions as well). We derive explicit expressions for facility trajectories as functions of model parameters, obtaining a number of managerial insights. In addition we provide the decomposition of the optimal cost into the closed form components corresponding to the cost of travel, the cost of facility unreliability and the cost of incomplete information. Most of the theoretical insights are confirmed via numerical experiments for models with larger (3–5) number of facilities.     

From Linear to Semidefinite Programming: An Algorithm to Obtain Semidefinite Relaxations for Bivalent Quadratic Problems

In this paper, we present a simple algorithm to obtain mechanically SDP relaxations for any quadratic or linear program with bivalent variables, starting from an existing linear relaxation of the considered combinatorial problem. A significant advantage of our approach is that we obtain an improvement on the linear relaxation we start from. Moreover, we can take into account all the existing theoretical and practical experience accumulated in the linear approach. After presenting the rules to treat each type of constraint, we describe our algorithm, and then apply it to obtain semidefinite relaxations for three classical combinatorial problems: the K-CLUSTER problem, the Quadratic Assignment Problem, and the Constrained-Memory Allocation Problem. We show that we obtain better SDP relaxations than the previous ones, and we report computational experiments for the three problems.     

The fastest itinerary in time-dependent decentralized travel information systems

A travel information system (TIS) provides customers with information about transportation, transfers, and routings. A decentralized TIS is composed of autonomous subsystems and a central computer, where local subsystems have full control of their data and there is not a complete database in the central computer about the entire TIS. The transportation vehicles are scheduled. A travel itinerary from one spot to another contains the travel path and the schedule of transportations. The approach presented in this paper is for the central computer to identify the fastest itinerary across autonomous subsystems, based on the incomplete local information provided by the independent subsystems.     

The bandpass problem: combinatorial optimization and library of problems

A combinatorial optimization problem, called the Bandpass Problem, is introduced. Given a rectangular matrix A of binary elements {0,1} and a positive integer B called the Bandpass Number, a set of B consecutive non-zero elements in any column is called a Bandpass. No two bandpasses in the same column can have common rows. The Bandpass problem consists of finding an optimal permutation of rows of the matrix, which produces the maximum total number of bandpasses having the same given bandpass number in all columns. This combinatorial problem arises in considering the optimal packing of information flows on different wavelengths into groups to obtain the highest available cost reduction in design and operating the optical communication networks using wavelength division multiplexing technology. Integer programming models of two versions of the bandpass problems are developed. For a matrix A with three or more columns the Bandpass problem is proved to be NP-hard. For matrices with two or one column a polynomial algorithm solving the problem to optimality is presented. For the general case fast performing heuristic polynomial algorithms are presented, which provide near optimal solutions, acceptable for applications. High quality of the generated heuristic solutions has been confirmed in the extensive computational experiments. As an NP-hard combinatorial optimization problem with important applications the Bandpass problem offers a challenge for researchers to develop efficient computational solution methods. To encourage the further research a Library of Bandpass Problems has been developed. The Library is open to public and consists of 90 problems of different sizes (numbers of rows, columns and density of non-zero elements of matrix A and bandpass number B), half of them with known optimal solutions and the second half, without.     

Pareto mimic algorithm: An approach to the team orienteering problem

The team orienteering problem is an important variant of the vehicle routing problem. In this paper, a new algorithm, called Pareto mimic algorithm, is proposed to deal with it. This algorithm maintains a population of incumbent solutions which are updated using Pareto dominance. It uses a new operator, called mimic operator, to generate a new solution by imitating an incumbent solution. Furthermore, to improve the quality of a solution, it employs an operator, called swallow operator which attempts to swallow (or insert) an infeasible node and then repair the resulting infeasible solution. A comparative study supports the effectiveness of the proposed algorithm, especially, our algorithm can quickly find new better results for several large-scale instances. We also demonstrate that Pareto mimic algorithm can be generalized to solve other routing problems, e.g., the capacitated vehicle routing problem.     

多产品竞争环境中最优供货决策

在有多产品竞争的市场中,产品零售商首先需要了解每个产品在市场中的地位和市场份额,然后通过了解市场需求总量和销售支出情况来决定产品的供应数量,以达到利润的最大化的目标.怎样得到最优的供货量问题是现代库存管理的基础性问题之一.通过引进产品特征描述和消费者一般支出的概念,给出一种市场份额的分析方法和市场份额的一些数学性质及经济特点.然后引入管理中经典库存决策模型———“报童问题”模型,分析多产品竞争环境中零售商的最优供货决策问题.最后研究了在产品的某些特征描述改变的条件下,零售商最优供货量、期望利润和市场利润总量的敏感性分析及其经济含义解释.     

随机旅行时间局内车辆路径问题的模型及其算法

人们在生活中会经常遇到随机旅行时间的局内车辆路径问题,如现实物流配送中的交通堵塞现象.文章在Laporte等的研究基础上,提出了一个考虑堵塞点动态产生、一个个遇到,堵塞时间为随机变量的模型,并构造了求解该模型的算法.