考虑中断情况的三级供应链利润分配优化策略

对于由制造商,分销商和零售商所构成的二层渠道三级供应链利润分配问题,在供应链正常及中断两种情况下构建了基于Stackelberg的利润独立分配模型和企业合作时的利润分配模型,进行了博弈分析并比较了不同分配策略对供应链节点企业利润及总利润的影响.结果表明,无论供应链有无中断,供应链节点企业间合作时的总利润要明显高于独立决策时节点企业的总利润.供应链中断引起需求增加时,制造商是否在原有基础上扩大生产取决于m与βc11大小的比较;需求减少时,制造商是否在原有基础上对原生产计划和价格进行调整取决于m与βc1大小的比较;无论是需求增加还是需求减少,合作时市场容量变动量m对βc11(或βc1)的依赖程度均远低于独立决策的情况.在供应链中断利润独立分配优化策略中,提高(或降低)的价格分配份额的大小取决于决策顺序.供应链节点企业可以通过讨价还价能力的大小来选择适当的分配因子进行利润分配.通过引入Shapley值法及其算例,进一步证明了供应链节点企业间的合作能有效提高利润水平,并保证供应链利润分配的合理性与有效性.     

随机需求条件下不同合作方式的供应链利润分配研究

考虑由一个制造商和一个分销商组成的二级供应链,引入共享契约作为利润再分配的手段,建立了随机需求条件下供应链的Stackberg博弈和张伯伦利润分配模型,研究表明:核心企业制造商可以通过调整利润分配参数使供应链各节点实现共赢,同时在供应链节点企业竞争地位不对等的情况下,采用张伯伦模型的合作方式会使节点利润更优.     

基于销售商串货的供应链利润模型

为分析供应商采用数量折扣作为激励机制后,销售商串货对供应链整体利润及成员利润的影响,研究一个供应商和两个销售商组成的供应链,构造销售商无串货和串货两个Stackelberg博弈利润模型,通过模型推导确定供应商和销售商的最优决策,并用数值实验分析折扣率、销售成本与制造成本等参数的变化对无串货和串货两种情形下供应链整体利润差值和成员利润差值的影响.研究结果表明:在一定折扣率区间内串货能同时提高供应链整体利润及成员利润.因此,供应商作为供应链主导方,可通过设置不同折扣率调控销售商的串货行为,实现供应链整体利润及成员利润的提高.     

激励上游企业创新投入的供应链协调研究

建立了基于上游企业创新投入降低运作成本的供应链博弈模型,研究发现上游企业创新投入与市场性质、上下游企业在供应链中的地位、上游企业的保留利润都有关,比较了不同市场性质下供应链整体利润之间的关系,提出了基于不同决策者下使上游企业的创新投入达到供应链最优的协调契约,最后通过具体算例分析证实了结论.     

基于回购契约的闭环供应链系统动力学模型构建及仿真分析

为了探索回购契约对闭环供应链系统的动态影响,构建了基于回购契约的闭环供应链系统动力学模型,在不同回购契约参数条件下进行仿真,系统地分析了闭环供应链整体及各节点企业的利润变动情况.研究表明:1)上游成员承诺的回购率越高,闭环供应链整体及各节点企业的利润越大.2)在闭环供应链中,制造商所需的协调时间最长,销售商利润最大,回收商利润最小,但随着回购率增加至阈值后,回收商的利润增长率最高.     

具有公平偏好成员的供应链定价决策与利润分析

将公平偏好因素引入供应链定价决策中,以零售商和制造商均为公平中性时的决策结果为参照,基于FS公平偏好效用函数分析了仅零售商具有不利不公平厌恶偏好、仅制造商具有有利不公平厌恶偏好和二者均具有公平偏好三种情形下公平偏好对供应链双方的均衡定价策略及利润的影响.结果表明:在仅零售商具有不利不公平厌恶偏好情形下,制造商降低批发价格,零售商最优零售价格保持不变,供应链整体利润保持不变;另外两种情形下,制造商和零售商均采取降价策略,供应链整体利润大于双方均为公平中性情形;三种情形下,零售商将获得与公平中性情形相比更多的供应链利润.     

线性需求供应链中供应商参股制造商的定价与协调

企业之间通过相互参股组成股权联盟提升收益和竞争力来应对多变的环境成为企业股权改革的趋势。在实际管理实践中,供应链各节点企业通过参股战略以改善各自绩效从而提升供应链效率。本文考虑在线性市场需求环境下,构建了由供应商A与制造商B组成的二级供应链,供应商A对制造商B实施参股战略的供应链模型。引入Stackelberg博弈模型,分别讨论分散式与集中式情况下各节点企业的最优决策。研究表明,供应商A对制造商B参股不能消除供应链双重边际效应,基于此设计了收益共享和线性转移支付的协调契约。该契约通过调整批发价使供应商A与制造商B以任意比例分配系统利润,使供应链完美协调。     

不公平厌恶条件下的闭环供应链收人费用共享契约协调北大核心

在不公平厌恶条件下研究闭环供应链收入费用共享契约协调问题实质是在模拟现实条件的基础上,探讨实现供应链整体最优化.在前人的研究基础上,构建基于不公平厌恶的二级闭环供应链利润模型,对比集中决策与独立决策的利润差距,得出不公平厌恶条件下的独立决策效用水平低于集中决策,再通过收入费用再分配契约改变独立决策效用,得出不公平厌恶条件下的收入费用共享契约有利于提高闭环供应链整体效用的结论.最后,提出建立收入费用共享契约约束供应链主体行为,扩大整体供应链效用的管理学建议.     

具强零售商和广告影响的闭环供应链模型

构建了一个具强零售商和广告影响的闭环供应链博弈模型.首先在集中决策和分散决策两种决策模式下,分别探讨了闭环供应链的定价,广告投入和回收率决策,并分析了广告效应对节点企业各决策变量和利润的影响,以及对比了强零售商下不同回收渠道的优劣.其次,考察了通道费契约对具强零售商的闭环供应链的协调作用.主要结论为: 1)与无广告效应...     

不公平厌恶条件下的闭环供应链收人费用共享契约协调

在不公平厌恶条件下研究闭环供应链收入费用共享契约协调问题实质是在模拟现实条件的基础上,探讨实现供应链整体最优化.在前人的研究基础上,构建基于不公平厌恶的二级闭环供应链利润模型,对比集中决策与独立决策的利润差距,得出不公平厌恶条件下的独立决策效用水平低于集中决策,再通过收入费用再分配契约改变独立决策效用,得出不公平厌恶条件下的收入费用共享契约有利于提高闭环供应链整体效用的结论.最后,提出建立收入费用共享契约约束供应链主体行为,扩大整体供应链效用的管理学建议.     

Fair transfer price and inventory holding policies in two-enterprise supply chains

A key issue in supply chain optimisation involving multiple enterprises is the determination of policies that optimise the performance of the supply chain as a whole while ensuring adequate rewards for each participant.In this paper, we present a mathematical programming formulation for fair, optimised profit distribution between echelons in a general multi-enterprise supply chain. The proposed formulation is based on an approach applying the Nash bargaining solution for finding optimal multi-partner profit levels subject to given minimum echelon profit requirements.The overall problem is first formulated as a mixed integer non-linear programming (MINLP) model. A spatial and binary variable branch-and-bound algorithm is then applied to the above problem based on exact and approximate linearisations of the bilinear terms involved in the model, while at each node of the search tree, a mixed integer linear programming (MILP) problem is solved. The solution comprises inter-firm transfer prices, production and inventory levels, flows of products between echelons, and sales profiles.The applicability of the proposed approach is demonstrated by a number of illustrative examples based on industrial processes.     

Decentralized Dual-Based Algorithm for Computing Optimal Flows in a General Supply Chain

This paper addresses the issue of the optimal flow allocation in general supply chains. Our basic observation is that a distribution channel involving several reselling steps for a particular product can be viewed as a route in a supply chain network. The flow of goods or services along each route is influenced by the customer's demand, described by the corresponding utility functions, and prices charged at each node. We develop an optimization algorithm based on the primal-dual framework and the Newton's step that computes optimal prices at each node (dual problem) and then computes the optimal flow allocation (primal problem) based on these prices. Our main contribution is a discovery that the Newton's step leads to a partially decentralized algorithm which is a first step toward a decentralization schema for computing optimal prices.     

Modeling a supply chain using a network of queues

In this paper, a supply chain is represented as a two-input, three-stage queuing network. An input order to the supply chain is represented by two stochastic variables, one for the occurrence time and the other for the quantity of items to be delivered in each order. The objective of this paper is to compute the minimum response time for the delivery of items to the final destination along the three stages of the network. The average number of items that can be delivered with this minimum response time constitute the optimum capacity of the queuing network. After getting serviced by the last node (a queue and its server) in each stage of the queuing network, a decision is made to route the items to the appropriate node in the next stage which can produce the least response time.     

A flexible and generic approach to dynamic modelling of supply chains

In this paper, we present a new modelling approach for realistic supply chain simulation. The model provides an experimental environment for informed comparison between different supply chain policies. A basic simulation model for a generic node, from which a supply chain network can be built, has been developed using an object-oriented approach. This generic model allows the incorporation of the information and physical systems and decision-making policies used by each node. The object-oriented approach gives the flexibility in specifying the supply chain configuration and operation decisions, and policies. Stochastic simulations are achieved by applying Latin Supercube Sampling to the uncertain variables in descending order of importance, which reduces the number of simulations required. We also present a case study to show that the model is applicable to a real-life situation for dynamic stochastic studies.     

基于改进的粒子群算法求解供应链网络均衡问题

传统的供应链求解方法为投影法,针对其要对投影进行计算,十分复杂的缺点,提出用改进的粒子群算法求解供应链均衡问题,利用动态异步调整学习因子来有效的提高了算法搜索能力与精度。本文介绍了供应链网络均衡问题转变为无约束优化问题的方法,然后用改进的粒子群优化算法进行求解。通过四个数值算例,将实验结果与标准粒子群算法、蜂群算法、学习因子同步变化的粒子群算法进行比较,验证了改进的粒子群优化算法在解决供应链网络均衡问题中的有效性与优越性,为供应链网络求解提供了一种新的方法。     

An exact algorithm for a cross-docking supply chain network design problem

This paper proposes a branch-and-price algorithm as an exact algorithm for the cross-docking supply chain network design problem introduced by one of the authors of this paper. The objective is to optimally locate cross-docking (CD) centres and allocate vehicles for direct transportation services from the associated origin node to the associated CD centre or from the associated CD centre to the associated destination node so as to satisfy a given set of freight demands at minimum cost subject to the associated service (delivery) time restriction. A set-partitioning-based formulation is derived for the problem for which some solution properties are characterized. Based on the properties, a branch-and-price algorithm is derived. The properties can also be used in deriving any efficient local search heuristics with the move operation (neighbourhood search operation) of modifying assignment of some freight demands from current CD centres to other CD centres. Computational experiments show that the branch-and-price algorithm is effective and efficient and also that the solution properties contribute to improve the efficiency of the local search heuristics.     

A logistics problem: a non-markovian queueing network model and algorithm

In this paper, a stochastic version of the classical deterministic balanced single commodity capacitated transportation network problem is presented. In this model, each arc of the network connects a supply node to a demand node and the flow of units forming along each arc of the network forms a stochastic process (i.e.G/M/1 queueing system with generally distributed interarrival time, a Markovian server, a single server, infinite capacity, and the first come first served queueing discipline). In this model, the total transportation cost is minimized such that the total supply rate is equal to the total demand rate, and the resulting probability of finding excessive congestion along each arc (i.e., the resulting probability of finding congestion inside the queueing system formed along each arc in excess of a fixed number) is equal to a desirable value     

A heuristic for BILP problems: The Single Source Capacitated Facility Location Problem

In the Single Source Capacitated Facility Location Problem (SSCFLP) each customer has to be assigned to one facility that supplies its whole demand. The total demand of customers assigned to each facility cannot exceed its capacity. An opening cost is associated with each facility, and is paid if at least one customer is assigned to it. The objective is to minimize the total cost of opening the facilities and supply all the customers. In this paper we extend the Kernel Search heuristic framework to general Binary Integer Linear Programming (BILP) problems, and apply it to the SSCFLP. The heuristic is based on the solution to optimality of a sequence of subproblems, where each subproblem is restricted to a subset of the decision variables. The subsets of decision variables are constructed starting from the optimal values of the linear relaxation. Variants based on variable fixing are proposed to improve the efficiency of the Kernel Search framework. The algorithms are tested on benchmark instances and new very large-scale test problems. Computational results demonstrate the effectiveness of the approach. The Kernel Search algorithm outperforms the best heuristics for the SSCFLP available in the literature. It found the optimal solution for 165 out of the 170 instances with a proven optimum. The error achieved in the remaining instances is negligible. Moreover, it achieved, on 100 new very large-scale instances, an average gap equal to 0.64% computed with respect to a lower bound or the optimum, when available. The variants based on variable fixing improved the efficiency of the algorithm with minor deteriorations of the solution quality.     

Near-perfect token distribution

Suppose that n tokens are arbitrarily placed on the n nodes of a graph. At each parallel step one token may be moved from each node to an adjacent node. An algorithm for the near-perfect token distribution problem redistributes the tokens in a finite number of steps, so that, at the end, no more than O(1) tokens reside at each node. (In perfect distribution, at the end, exactly one token resides at each node.) In this paper we present a simple algorithm that works for all extrovert graphs, a new property which we define and study. In terms of connectivity requirements, extrovert graphs are roughly in-between expanders and compressors. Our results lead to an optimal solution for the near-perfect token distribution problem on almost all cubic graphs. The new solution is conceptually simpler than previous algorithms, and applies to graphs of minimum possible degree. © 1994 John Wiley & Sons, Inc.