基于相对风险分担的集群供应链协同创新收益分配机制研究

协同创新是集群供应链企业研发新技术和进行产品升级的重要手段。但是协同创新存在风险, 集群供应链中配套企业与核心企业由于在风险承受能力方面的差异, 使他们在面对相同创新风险时所承担的实际风险却不相同。针对该问题, 本文构建了配套企业与核心企业的协同创新模型, 设计了基于相对风险分担的收益分配机制, 并与现有分配方式进行比较。研究发现:基于相对风险分担的分配机制能够增强企业双方参与协同创新的意愿, 并且使企业所获收益与所承担的风险更加匹配, 达到激励配套企业积极投入的目的。     

供应链协同需求预测机制研究

如何实现供应链需求预测的有效协同是一个需要解决的难点问题。论文基于博弈论分析了协同需求预测的利益分配机制,定量分析了供应链协同需求预测的整体收益系数,提出了考虑收益和成本的动态激励机制,还定性分析了核心企业主导、信息共享以及信任等机制。提出的机制对提高供应链整体的预测能力,提升竞争力和整体绩效具有很好的价值。     

汽车行业供应链协同绩效评价及其应用

考虑汽车行业供应链协同绩效包含协同过程绩效、协同结果绩效和协同支持绩效,并结合客户服务和系统适应性,选择了30个汽车行业供应链协同评价指标,建立协同绩效评价指标体系.然后,选取以国内某汽车制造厂为核心企业的汽车行业供应链为例,运用模糊综合分析法对其供应链进行协同评价,实现了对该指标体系的量化,并对评价结果进行分析.结果表明,企业在过程、结果和支持等方面协同运作,可以提高供应链整体的协同绩效,从而进一步验证了评价指标体系的可行性和模糊综合评价方法的可操作性.     

带有乘积型多目标权函数的调度谈判问题

在供应链环境下的生产活动中,各成员对所辖资源具有独立的支配权,因此需要合理的机制使得协同调度方案得以实施,以提高供应链整体的效率.研究由具备不同讨价还价能力的成员所组成的供应链,建立了以纳什讨价还价公理体系为基础的调度谈判模型.在装配系统中,讨论两供应商关于交付顺序的协商.为求取纳什谈判解,提出了一类新的以多目标乘积项作为目标函数的调度问题.对于单机型供应商,新问题的计算复杂性尚未确定,设计了一种多项式时间的启发式算法以求得近优解,并通过数值算例进行验证.该谈判模型为供应链中各成员提供了一种合理的调度协调机制.     

基于约束放松的电子商务协同谈判模型

在众多的自动谈判模式中，协同谈判是一种新型的多线程谈判形式。文章分析了面向电子商务的协同谈判中的冲突问题，提出了解决谈判冲突的方法——满意度函数法。该方法在谈判模型中用约束网来表示谈判中的变量和约束关系，通过约束放松来解决谈判中的冲突。作者在对约束及约束网进行详细分析的基础上设计了约束传播算法，用它来求解约束网问题。文章通过实例验证了提出的论点和方法的正确性。采用这种基于约束放松的协同谈判模型可以有效地解决谈判中的冲突，提高谈判的效率。该方法的应用可为解决多个谈判的组合问题提供一种新的思路。     

基于IGA的供应链库存成本优化研究

随着市场竞争的加剧,企业之间的竞争已经演变为了供应链之间的竞争.传统的库存管理主要侧重于单个企业的库存最优,而供应链环境下的库存管理需要最大程度地实现供应链的库存整体最优.本文分析了供应链环境下的库存成本结构和供应链内成本,在此基础上,建立了多个供应商、一个核心制造企业、多个分销商的供应链库存成本模型,在分析免疫遗传算法(IGA)原理的基础上,详细说明基于免疫遗传算法的供应链库存成本模型的求解方法,最后通过算例仿真验证了模型和算法的可行性和有效性.     

供应链多层规划问题及其合作对策协商求解

针对由制造商、分销中心和零售商组成的多级分布式供应链协同计划问题,建立了三层规划生产-分销计划谈判模型,设计了基于合作对策的两步协商方法。首先,基于模糊隶属度原理,将三层规划问题转化为单层规划问题。接着,设计了基于合作对策的两步协商过程,描述了两步协商模型和交互协商算法步骤,并对算法进行了性能分析。最后,给出了基于多层规划的协同生产-分销计划模型,并得到了基于合作对策的协商算法的解决方案。并通过与其它优化策略的对比,验证该解决方案的优越性。     

产品需求预测的演化神经网络算法MLPES

在需求拉动型的供应链中，需求成为供应链的起点和动力源泉。由于制造商在供应链中的特殊地位，制造商成为供应链由需求驱动变为预测驱动的断耦点，以制造商为核心进行准确的需求预测可以在一定程度上减少需求不确定性的影响。本文在多层感知器的框架上，提出了基于演化策略的神经网络预测方法MLPES，改进了在多层感知器中普遍采用的BP算法，并设计了学习算法的流程，通过反复测试确定了模型的参数，最后对预测结果进行了分析。     

基于NSGAII的协同产品开发项目自动协商决策

供应链环境下的协同产品开发项目中,核心企业与合作企业之间,需要对项目工期、预算和质量进行协商。科学高效的协商决策方法有助于协同产品开发项目参与各方实现双赢的协商目标。采用多Agent系统建立了协商问题的框架,构建了协商Agent以自身效用最大化和与对方建议相似度最大化为目标的多目标反建议模型;提出了基于NSGAII的自动协商决策算法,算法采用了正整数和小数混合的实数编码方式,并在遗传操作中增加了约束限制,剔除不可行个体。计算机仿真实验结果表明了算法对提高协商成功率和协商效果的有效性,说明了协商Agent可接受的最低效用值对于协商结果的影响。     

产业集群背景下模糊联盟结构合作博弈的核心

运用模糊延拓等方法将核心理论扩展到模糊联盟结构合作博弈中,提供一种兼顾局中人的模糊参与度与联盟偏好的稳定分配方法,并且给出模糊联盟结构合作博弈的模糊Owen值稳定的充分条件.基于供应链协同创新中的不确定因素较多,将此跨供应链协作问题抽象为模糊联盟结构合作博弈模型,计算模糊信息下合作利益分配策略,在两个层次上分配额外收益:产业集群,供应链.模糊联盟结构合作博弈理论以及求解方法的研究,理论上拓展了经典合作博弈的应用范围,实证上又为供应链协同创新问题提供了一定分析思路,降低了由于收益分配不均导致的跨区域供应链破裂的概率.     

An agent-based negotiation model on price and delivery date in a fashion supply chain

Price and due-date negotiation between supply chain members is a critical issue. Motivated by industrial practice, we consider in this paper a make-to-order fashion supply chain in which the downstream manufacturer and the upstream supplier are cooperative on due-date and competitive on price. We propose a two-phase negotiation agenda based on such characteristics, and aim to find an optimal solution to deal with the negotiation problem considering production cost and mutual benefit. We build an analytical negotiation model for a manufacturer-supplier pair, discuss their utilities, and examine the Pareto efficiency frontier from the theoretical perspective. After that, from an application perspective, we build an agent-based two-phase negotiation system where agents are used to represent the two parties to enhance communication. In the cooperative phase, a simulated annealing based intelligent algorithm is employed to help the manufacturer agent and the supplier agent search tentative agreement on due dates which can minimize the total supply chain cost. In the competitive phase, the two parties bargain on the pricing issue using concession based methods. They adjust the reservation value and aspiration value for pricing accordingly based on the integrated utility and the result of the previous phase. Simulation results show that, the proposed negotiation approach can achieve optimal utility of agents and reach a win-win situation for the bilateral parties. Sensitivity analysis is conducted to further generate insights on how different parameters affect the performance of the proposed system.     

A simultaneous multi-issue negotiation through autonomous agents

Automated negotiation through autonomous agents has become increasingly important since the advent of e-marketplace. A deadlock may arise in which both negotiators refuse to disclose more information; then, mediation is required. We propose an agent-based sealed-bid design in which both agents simultaneously submit their respective offers to the mediate agent, and construct an efficient negotiation strategy which can reach an agreement aiming to maximize their owner’s utility. An important contribution of this paper is that we consider negotiators not necessarily to conflict over all issues, such as quantity. Our experimental results demonstrate that the proposed automated negotiation strategy is efficient in terms of the Pareto-efficiency of the negotiated contracts, the joint utility obtained, and the equality of both agents. In addition, since disclosure of information is reduced greatly, the design can discourage counter-speculation and effectively control fraud and misrepresentation to a certain extent. The method we proposed is simple and stable in the context of a game and the model could be well suited for practical agent applications.     

Optimizing maintenance service contracts under imperfect maintenance and a finite time horizon

When a company decides to outsource a service, the most important reasons for doing so usually are to focus on core business, to be able to access high‐quality services at lower costs, or to benefit from risk sharing. However, service contracts typically follow a structure whereby both owner and contractor attempt to maximize expected profits in a noncoordinated way. Previous research has considered supply chain coordination by means of contracts but is based on unrealistic assumptions such as perfect maintenance and infinite time‐span contracts. In this work, these limitations are overcome by defining the supply chain through a preventive maintenance strategy that maximizes the total expected profit for both parties in a finite time‐span contract. This paper presents a model to establish such conditions when maintenance is imperfect, and the contract duration is fixed through a number of preventive maintenance actions along a significant part of the asset life cycle under consideration. This formulation leads to a win–win coordination under a set of restrictions that can be evaluated a priori. The proposed contract conditions motivate stakeholders to continually improve their maintenance services to reach channel coordination in which both parties obtain higher rewards. Copyright © 2012 John Wiley & Sons, Ltd.     

需求预测更新条件下两阶段电子商务供应链契约协调

研究了一类在贝叶斯需求预测更新情形下,生命周期中采用B2C与C2B两阶段进行产品销售的供应链模型,研究发现无法使用单一契约来协调供应链。在此基础上,设计了具有供应商价格折扣契约与零售商承诺订购契约的两阶段供应链契约协调机制,证明了两阶段协调机制能够实现供应链的帕累托改进。算例分析表明了两阶段契约协调机制的有效性,并发现能够实现供应链完美协调的契约参数不一定在帕累托集合内,供应链不一定能够达到完美协调;且由于零售商与供应商的最优契约参数不同,需要通过零售商与供应商之间的谈判达到纳什均衡。     

需求受价格影响的三层供应链协调模型

针对由一个制造商、一个批发商和一个零售商构成的三层供应链系统,在随机市场需求且需求受商品零售价格影响的情况下,研究了零售商如何定价和确定订货量.在此基础上给出了一个能使供应链系统达到完美协调的收益分享合约策略,最后给出了数值例子.     

Optimizing inventory decisions in a two-level supply chain with order quantity constraints

A fundamental assumption in traditional inventory models is that all of the ordered items are of perfect quality. A two-level supply chain is considered consists of one retailer and a collection of suppliers that operate within a finite planning horizon, including multiple periods, and a model is formulated that simultaneously determines both supplier selection and inventory allocation problems in the supply chain. It is supposed that the ordered products dependent on the suppliers include a certain percentage of imperfect quality products and have different prices. In this paper, we study the impact of the retailer’s financial constraint. On the other hand, suppliers have restricted capacities and set minimum order quantity (MOQ) policy for the retailer’s order amount happened in each period. So, the problem is modeled as a mixed integer nonlinear programming. The purpose of this model is to maximize the total profit. The nutrients, fishery and fruitage industries give good examples for the proposed model. A numerical example is presented to indicate the efficiency of the proposed model. Considering the complexity of the model, a genetic algorithm (GA) is presented to solve the model. We demonstrate analytically that the proposed genetic algorithm is suitable in the feasible situations.     

A hybrid of genetic algorithm and particle swarm optimization for solving bi-level linear programming problem – A case study on supply chain model

The main goal of supply chain management is to coordinate and collaborate the supply chain partners seamlessly. On the other hand, bi-level linear programming is a technique for modeling decentralized decision. It consists of the upper level and lower level objectives. Thus, this paper intends to apply bi-level linear programming to supply chain distribution problem and develop an efficient method based on hybrid of genetic algorithm (GA) and particle swarm optimization (PSO). The performance of the proposed method is ascertained by comparing the results with GA and PSO using four problems in the literature and a supply chain distribution model.