模糊环境下可控提前期供应链库存优化的Stackelberg模型研究

为了有效地缩短提前期与降低库存成本,研究了模糊环境下可控提前期的供应链库存优化问题.利用三角形模糊数描述需求的不确定性,建立了一类模糊需求条件下可控提前期供应链库存优化的Stackelberg模型.利用三角形模糊数描述成本系数的不确定性,建立了模糊成本系数条件下可控提前期供应链库存优化的Stackelberg模型,并提出利用均值面积度量法来解模糊化.通过数值分析来验证两类模型的优化效果.     

基于量子粒子群算法的电力系统负荷多目标优化分配

针对电力系统经济负荷优化分配问题,提出了一种基于量子粒子群的多目标优化算法.该算法通过将改进后的量子进化算法融合到粒子群中,采用量子位对粒子的当前位置进行编码,用量子旋转门实现对粒子最优位置的搜索,用量子非门实现粒子位置的变异以避免早熟收敛.这种搜索机制能够遍历解空间,增强种群的多样性,并能用量子位的概率幅将最优解表述为解空间中的多种表述形式,从而增强全局最优的可能性.最后,通过算例进行仿真分析,结果表明算法的搜索能力和优化效率均优于普通粒子群算法.     

基于可信性理论的均值-熵-偏度投资组合模型及其算法求解

本文考虑到证券市场的投资者往往面临着随机和模糊两种不确定性的情形,在模糊随机环境下把证券的收益率视作三角模糊变量,在可信性理论基础上建立了带融资约束条件的均值-熵-偏度三目标投资组合决策模型,拓展了基于可信性理论的投资组合决策模型的研究内容,同时通过对约束条件处理方法,外部档案维护方法等关键算子的改良,提出了一种新的约束多目标粒子群算法。本文运用该算法对模型进行求解,把得到的最优解与传统的多目标粒子群算法得到的最优解进行对比,结果表明新算法得到的最优解的质量会显著地优于传统的多目标粒子群算法的最优解,从而验证了算法的有效性和准确性。该算法可以在三维空间中得到一个分布性和逼近性较好的Pareto最优曲面,满足投资者对不同目标的差异需求,为投资者提供合理的投资组合决策方案。     

具有模糊目标的多目标双矩阵对策集结与求解

针对不确定性多冲突环境,建立了多个具有模糊目标的多目标双矩阵对策的综合集结模型.在假定局中人各模糊目标的隶属函数为线性函数的情形下,基于总体模糊目标的可达度,给出了纳什均衡解的定义,并应用粒子群优化算法对集结模型求解.最后,给出一个军事例子说明了模型的实用有效性和粒子群优化算法求解的高效性.     

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。     

基于CUDA的边界变异量子粒子群优化算法

针对量子粒子群优化算法面对复杂优化问题时,临近最优解的搜索阶段存在收敛速度慢、在边界附近全局搜索性差的问题,提出了基于CUDA的边界变异量子粒子群优化算法.GPU(图形处理器)以多颗密集的计算核心模拟粒子的搜索过程,利用并发的优势提升粒子搜索速度;边界变异则通过以随机概率将边界粒子扩散到更大的搜索域,增加种群的多样性,提升粒子群的全局搜索性.对若干优化算法的仿真实验表明,所提出方法具有较好的全局收敛性,且同等目标精度下,取得了较高的有效加速比.     

改进的二进制粒子群算法求解车辆路径问题

设计了一种改进的二进制粒子群优化算法来求解车辆路径问题,算法基于粒子群算法的寻优模式充分考虑粒子之间的导向作用,改进二进制粒子群算法的位取值方式,减小了在进化过程中停滞于局部最优解的概率,并通过构造辅助函数处理优化问题的约束条件,基于分层次实现多个目标的思路来寻优,提高了算法的搜索效率和计算速度.实验测试结果验证了该算法对求解车辆路径问题的适用性和有效性.     

随机需求下多供应商和多零售商的生产-库存-运输联合优化模型

考虑随机需求下多供应商和多零售商的生产-库存-运输联合优化问题.在联合优化时,首先利用最近邻算法将各零售商分成不同区域,分区后问题转化为随机需求下单供应商对多零售商的生产-库存-运输联合优化问题.在每个分区内,由供应商统一决策其分区内各零售商的送货量和送货时间.利用粒子群算法和模拟退火算法相结合的两阶段算法求出最优送货量、最优运输路径和最大期望总利润.然后采用收入共享契约将增加的利润合理分配给各供应商和各零售商,使各方利润都得到增加,从而促使各方愿意合作.通过数值算例验证了联合优化模型优于独立决策模型.     

改进蝙蝠算法在云制造供应链中的应用

云制造环境下的供应链是新型的供应链,如何选择云制造平台中供应链节点的企业是需要解决的问题之一.针对使节点批次任务总完成时间最小的调度问题,由于蝙蝠算法容易陷入局部最优解,本文使用ROV编码对蝙蝠算法进行了重新编码和解码,并且对其进行了混沌序列初始化和自适应变步长的运算步长改进,提高了原蝙蝠算法的收敛速度和最优解的精度.通过仿真实验,结果表明改进的蝙蝠算法(IBA)较原蝙蝠算法(BA)具有更快的收敛速度、更好的稳定性,有效避免了原蝙蝠算法容易陷入局部最优解的状况,可较好地满足云制造环境下新型供应链动态性、复杂性的要求.     

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.     

基于实物期权的双源供应链柔性采购协调策略

在需求不确定下,由风险规避的供应商和占主导地位的零售商组成的二级供应链中,构建了在现货市场影响下基于实物期权的双源柔性采购协调模型,给出了实现供应链协调的最优期权参数的求解算法,探讨了现货市场价格不确定性和供应商风险规避特性对零售商采购策略和双方利润的影响。研究结果表明,在考虑现货市场影响和供应商风险规避的条件下,实物期权契约可以实现供应链协调。     

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.     

The research on optimization of auto supply chain network robust model under macroeconomic fluctuations

Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.     

Time traps in supply chains: Is optimal still good enough?

Operations Researchers support Supply Chain Management and Supply Chain Planning by developing adequate mathematical optimization models and providing suitable solution procedures. In this paper we discuss what adequate could mean. Therefore, we may ask several questions concerning “optimality” in Supply Chain Planning under causal and temporal uncertainty: What is an optimal solution? When is it optimal? For how long is it optimal? How should the design of a supply chain be changed when conditions and requirements ask for new structures? In particular, we discuss new approaches to Supply Chain Planning in order to give an optimal transformation from an initial solution over multiple periods to a desired one rather than just specifying an optimal snapshot solution. Time and uncertainty are the factors triggering the whole discussion. In particular, several flaws often found when dealing with these factors result in so-called “time traps”. We look at the impact of recent technological developments like the Internet of Things or Industry 4.0 on operational supply chain planning and control, and we show how online optimization can help to cope with real-time challenges. Moreover, we re-coin the concept of risk in the realm of Supply Chain Planning. Here the question is how to measure supply chain specific risks and how to incorporate them “adequately” into mathematical models.     

供需不确定条件下考虑双边努力的供应链组合契约设计

本文研究供需同时不确定条件下考虑双边努力的供应链契约设计问题,在一个供应商和一个零售商组成的二级供应链系统中,供应商的努力影响产量的不确定性,零售商的努力影响市场需求的不确定性。首先,利用Stackerberg主从博弈模型计算了分散决策时零售商、供应商的最优期望利润,并将计算结果与集中决策时供应链整体的最优期望利润进行对比。由于双重边际效应的存在,分散决策时供应链的最优期望利润小于集中决策时供应链的最优期望利润。基于此,本文设计了回购和成本分担组合契约来协调该供应链,计算了回购价格和成本分担系数的表达式,得到了回购和成本分担契约下供应商和零售商的最优期望利润。结果表明,在特定条件下,该组合契约不仅可以协调供应链,还可以实现供应链利润的任意分配。最后,通过数值计算,分析了供需双方的决策变量和期望利润随契约参数的变化情况。     

Particle swarm optimization for bi-level pricing problems in supply chains

With rapid technological innovation and strong competition in hi-tech industries such as computer and communication organizations, the upstream component price and the downstream product cost usually decline significantly with time. As a result, an effective pricing supply chain model is very important. This paper first establishes two bi-level pricing models for pricing problems with the buyer and the vendor in a supply chain designated as the leader and the follower, respectively. A particle swarm optimization (PSO) based algorithm is developed to solve problems defined by these bi-level pricing models. Experiments illustrate that this PSO based algorithm can achieve a profit increase for buyers or vendors if they are treated as the leaders under some situations, compared with the existing methods.     

求解不确定型二层线性规划模型的悲观决策方法

基于多参数线性规划理论，将不确定型二层线性规划问题转化为多个关于不确定参数的线性规划问题。利用不确定型决策方法中的悲观准则．从最不利的结果中选择最有利的结果，从而得到不确定型二层线性规划的最优解。数值实例的仿真结果表明，所提出的悲观决策方法对解决诸如不确定供应链的规划与运作等问题不失为一种有效的决策支持工具。     

A dynamic global and local combined particle swarm optimization algorithm

Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.     

风险规避供应商参与下基于实物期权的供应链协调

研究不确定性需求下,由风险规避的供应商和占主导地位的零售商组成的二级供应链的协调问题。构建了基于实物期权的协调机制,给出了实现协调的最优期权价格和期权执行价格的求解算法,分析了供应商风险规避特性对各方订货量和利润的影响。结果表明,在供应商风险规避条件下,基于实物期权的契约可以实现不确定需求下的供应链协调。     

A hybrid genetic algorithm for the finite horizon economic lot and delivery scheduling in supply chains

In this paper, we investigate the lot and delivery scheduling problem in a simple supply chain where a single supplier produces multiple components on a flexible flow line (FFL) and delivers them directly to an assembly facility (AF). It is assumed that all of parameters such as demand rates for the components are deterministic and constant over a finite planning horizon. The main objective is to find a lot and delivery schedule that would minimize the average of holding, setup, and transportation costs per unit time for the supply chain. We develop a new mixed integer nonlinear program (MINLP) and an optimal enumeration method to solve the problem. Due to difficulty of obtaining the optimal solution in medium and large-scaled problems, a hybrid genetic algorithm (HGA) is also developed. The proposed HGA incorporates a neighborhood search (NS) into a basic genetic algorithm that enables the algorithm to perform genetic search over the subspace of local optima. The two proposed solution methods are compared on randomly generated problems, and computational results show that the performance of HGA is very promising because it is able to find an optimal or near-optimal solution for majority of the test problems.