求解敏捷供应链调度优化问题的混合遗传算法

针对敏捷供应链调度决策中,需求的时间、数量约束和供应商生产能力、可用调度时段约束造成系统优化的复杂性,设计结合贪婪算法的混合遗传算法进行求解。算法以供应链系统库存成本和运输成本为适应度函数,以包含企业信息、部件信息和调度时段信息的时段编码作为遗传编码,以线性次序交叉LOX算子和逆序变异INV算子进行交叉和变异操作,在解码过程中结合贪婪算法进行调度决策和适应度计算,保证算法在满足约束条件的基础上快速收敛到系统Pareto最优解,通过算例验证算法的有效性。     

Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm

Manufacturing service supply chain (MSSC) optimization has been intensively studied to find an optimal service composition solution with the best quality of service (QoS) value. However, traditional MSSC optimization methods usually assume that candidate services are independent of one another. Therefore, potentially better MSSC solutions may have been neglected by not considering the positive influence of correlations between services on the QoS value. This study proposes a novel networked correlation-aware manufacturing service composition (NCMSC) mathematical model to characterize the influence of vertical and horizontal correlations between services on the QoS value of MSSC solution. To solve the NCMSC model, an extended artificial bee colony (ABC) algorithm is proposed to find a near-optimal solution with the best QoS value. The specific improvements to the original ABC algorithm include the following: (1) a new matrix-based encoding scheme is proposed to describe the MSSC solution in which each column contains a vertical composite structure and collaborative services for each subtask; (2) the migration operator of a biogeography-based optimization algorithm is combined with the original ABC algorithm to address the discrete MSSC optimization problem and improve the performance of the original ABC algorithm. The results of the experiments illustrate the importance of networked correlations between services, better practicality, effectiveness, and efficiency of the extended ABC algorithm in solving the optimization problem of MSSC.     

制造业供应链产销协同计划冲突协商模型

面对市场竞争日益剧烈和客户需求多样化的趋势,制造业供应链的制造商和经销商努力实现产销协同计划。然而在产销协同计划中时常出现冲突,及时有效消解冲突,能提高整个供应链的协作效率,改善供应链上企业间的合作关系;反之,会降低供应链的运作效率,削弱供应链的竞争力。针对这类冲突问题,引入让步协商策略,在有限信息共享条件下,建立供应链产销协同计划冲突协商模型;设计具有历史提议回顾特点的协商流程;通过文化基因算法,产生反提议生成策略;通过算例验证文化基因算法及冲突协商模型的有效性。     

Study of the performance of multi-behaviour agents for supply chain planning

In today's industrial context, competitiveness is closely associated to supply chain performance. Coordination between business units is essential to increase this performance, in order to produce and deliver products on time to customers, at a competitive price. While planning systems usually follow a single straightforward production planning process, this paper proposes that partners adapt together their local planning process (i.e. planning behaviours) to the different situations met in the supply chain environment. Because each partner can choose different behaviour and all behaviours will have an impact on the overall performance, it is difficult to know which is preferable for each partner to increase their performance. Using agent-based technology, simulation experiments have been undertaken to verify if multi-behaviour planning agents who can change planning behaviours to adapt to their environment can increase supply chain performance. These agents have been implemented in an agent-based planning platform, using a case study illustrating a lumber supply chain. The performance analysis shows that advanced planning systems can take advantage of using multiple planning processes, because of the dynamic context of supply chains.     

基于遗传算法的战略供应链集成研究

在战略供应链研究中,考虑供应链的三个主要阶段,采购、生产、配送和它们之间的相互作用,不同客户需求,设施配对关系,供应商优先权以及现有供应链设计模型的局限性,建立了混合整数非线性规划（MINLP）模型。为有效地解决这种大规模混合整数非线性规划模型的约束,采用自适应遗传算法（AGA）对该模型进行求解优化。实验结果表明,所提混合整数非线性规划模型能够有效解决战略供应链设计中的供应链协同优化问题,并能得到较优的供应链设计方案。     

A fast genetic algorithm for a critical protection problem in biomedical supply chain networks

In this paper, we present a new bilevel model for a biomedical supply chain network with capacity and budget constraint due to the protection and interdiction operations. The components considered in this model are biomedical devices, distribution centers (DCs), medical suppliers (MSs), and hospitals and patients as the demand points. On the other hand, two levels of decisions in the network planning is suggested: (1) the defender’s decision about protection operations of MSs and DCs, the assignment of clients to the DCs, and quantity of products shipped to DCs from MSs to minimize the demand-weighted traveling costs and transport costs and (2) the attacker’s decision about interdiction operations of MSs and DCs to maximize the capacity or service reduction and losses. Because of nondeterministic polynomial time (NP)-hardness of the problem under consideration, an efficient and fast approach based on a genetic algorithm and a fast branch and cut method (GA–FBC) was developed to solve the proposed model. Also, the efficiency via the comparison of results with the genetic algorithm based on CPLEX (GA-CPLEX) and decomposition method (DM) is investigated. In order to assess the performance of the presented GA–FBC, a set of 27 instances of the problem is used. Comprehensive analysis indicates that the proposed approach significantly solves the problem. In addition, the benefits and advantages of preference with running times and its accuracy is shown numerically. Simulation results clearly demonstrate that the defender’s objective effectively reduced and CPU time also within the large-sized instances of the problem in comparison with the GA-CPLEX and DM.     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

A multi-agent architecture for outsourcing SMEs manufacturing supply chain

In the present, global and competitive market customer's demands are very volatile. It is very difficult for small and medium size enterprises (SMEs) to satisfy their customer with quality product in reasonable price. Large firms are spending huge amount of their budget in generating technology development. To cope with this competitive market, usually, SMEs concentrate on their core expertise and outsource some non-core items or activities. The outsourcing decision (when, what, whom) is very crucial for small-scale industries. Realizing this scenario, in this article, an automated self-adaptive multi-agent system has been proposed. The proposed system will help SMEs to take an appropriate decision to mitigate the uncertainty in supply chain. The developed system exploits the “Outsourcing of operations” feature by its agents to conclude the manufacturing processes faster and reduce the idle time of certain machines with less human intervention. The communication protocol for agents has been described to give an insight of their communication. It was explained how various agents like outsourcing, material planning and supplier selection will come into action at different stages and take appropriate outsourcing decisions. Therefore, this multi-agent architecture will facilitate small scale manufacturing industries to execute their manufacturing processes and complex logistics issues efficiently. The execution of proposed architecture has been described in the simulated case study.     

多周期多原料的供应链网络成本优化及其混合遗传算法

建立并求解一个基于成本最小的供应链网络模型.与以往研究不同,在该模型中生产一种产品需要至少两种原料,每种原料都可以由备选供应商提供.根据模型的特点,用0、1代表对原材料供应商、工厂和分销中心的选择情况,以MATLAB 7.6为平台,运用Sheffield大学的遗传算法工具箱,将遗传算法与线性规划算法相结合,实现了模型的求解.算例结果表明,给出的染色体编码方案正确,混合遗传算法有效,能解决多周期、多原料的供应链网络成本优化问题.还探讨了需求和距离变化,以及需求随机时对最优成本和最优个体的影响.研究表明,需求变化的影响大于距离变化的影响,需求随机对最优成本和最优个体的影响不大.     

Heuristic factory planning algorithm for advanced planning and scheduling

This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPA's effectiveness and efficiency. This algorithm's power was demonstrated using computational and complexity analysis.     

基于协同计划的敏捷供应链系统

研究某大型汽车零部件公司的供应链业务集成,提出协同计划机制,并在敏捷供应链（Agile Supply Chain,ASC）的整体计划中通过核心企业控制和协调各节点企业的协同计划．采用Java和．NET技术构建基于协同计划的敏捷供应链系统体系及技术架构．     

多阶段煤炭供应链网络设计及其遗传算法

煤炭供应链网络设计旨在为大型煤炭集团选择合理的设施网络布局与最佳运量,以便提高效率并降低成本。考虑配煤加工与流量平衡等特有约束,建立了煤炭供应链网络混合整数规划模型,其优化目标是最小化固定设施成本、运输总成本与采购成本。考虑到模型求解的复杂度,设计了一种遗传算法,结合优先权与整数规则对染色体进行了编码与解码。实验算例表明所建立的模型能够真实地模拟煤炭供应链网络中设施布局与最佳运量的决策环境,其算法能够在允许的运算时间内获得稳定的满意解,随着算例规模的增大,其计算时间与优化结果均优于LINGO软件。     

A particle swarm optimization algorithm for solving unbalanced supply chain planning problems

This paper focuses on developing a decision methodology for the production and distribution planning of a multi-echelon unbalanced supply chain. In the supply chain system discussed here, multiple products, production loss, transportation loss, quantity discount, production capacity, and starting-operation quantity are considered simultaneously, and the system pattern is ascertained with based on appropriate partners and suitable transportation quantities. To make a quality decision in supply chain planning, we first propose an optimization mathematical model which integrates cost and time criteria. Then, a particle swarm optimization (PSO) solving method is proposed for obtaining acceptable results is called MEDPSO. The MEDPSO introduces the maximum possible quantity strategy into the basic procedure of PSO to generate the initial feasible population in a timely fashion and provides an exchange and disturbance mechanism to prevent particle lapse into the local solution. Finally, one case and two simulated supply chain structures are proposed to illustrate the effectiveness of the MEDPSO method by comparing the results of classical GA and PSO in solving multi-echelon unbalanced supply chain planning problems with quantity discount.     

A simulation-based genetic algorithm for inventory optimization in a serial supply chain

One of the important aspects of supply chain management is inventory management because the cost of inventories in a supply chain accounts for about 30% of the value of the product. The main focus of this work is to study the performance of a single‐product serial supply chain operating with a base‐stock policy and to optimize the inventory (i.e. base stock) levels in the supply chain so as to minimize the total supply chain cost (TSCC), comprising holding and shortage costs at all the installations in the supply chain. A genetic algorithm (GA) is proposed to optimize the base‐stock levels with the objective of minimizing the sum of holding and shortage costs in the entire supply chain. Simulation is used to evaluate the base‐stock levels generated by the GA. The proposed GA is evaluated with the consideration of a variety of supply chain settings in order to test for its robustness of performance across different supply chain scenarios. The effectiveness of the proposed GA (in terms of generating base‐stock levels with minimum TSCC) is compared with that of a random search procedure. In addition, optimal base‐stock levels are obtained through complete enumeration of the solution space and compared with those yielded by the GA. It is found that the solutions generated by the proposed GA do not significantly differ from the optimal solution obtained through complete enumeration for different supply chain settings, thereby showing the effectiveness of the proposed GA.     

Systems dynamics modelling of a manufacturing supply chain system

Supply chains are multifaceted structures focusing on the integration of all the factors involved in the overall process of production and distribution of end products to the customers. Growing interest in supply chain systems has highlighted the need to adopt appropriate approaches that can ensure the efficient management of their complexity, enormity and broadness of scope. With the main aim of supply chain management being to optimise the performance of supply chains, attention is mainly drawn to the development of modelling frameworks that can be utilised to analyse and comprehend the dynamic behaviour of supply chains. While there have been only a few supply chain modelling attempts reported in the literature, this paper proposes a modelling framework that is used to simulate the operation of a supply chain network of moderate complexity. The proposed model comprises four echelons and is build around a central medium-sized manufacturing company operating as a typical Make-to-Order (MTO) system. The developed model was built using a systems dynamics (SD) approach. The operations performed within a supply chain are a function of a great number of key variables which often seem to have strong interrelationships. The ability of understanding the network as a whole, analysing the interactions between the various components of the integrated system and eventually supplying feedback without de-composing it make systems dynamics an ideal methodology for modelling supply chain networks. The objective of the paper is to model the operation of the supply chain network under study and obtain a true reflection of its behaviour. The modelling framework is also used to study the performance of the system under the initial conditions considered and compare it with that obtained by running the system under eight different scenarios concerning commonly addressed real-life operational conditions. The modelling effort has focused on measuring the supply chain system performance in terms of key metrics such as inventory, WIP levels, backlogged orders and customer satisfaction at all four echelons. The study concludes with the analysis of the obtained results and the conclusions drawn from contrasting the system’s performance under each investigated scenario to that of the benchmark model.     

A heuristic relief transportation planning algorithm for emergency supply chain management

Emergency supply chain operations have to fulfil all the demands in a very short period of time, using the limited resources at its disposal. Mixed integer programming (MIP) is a popular method to solve emergency supply chain planning problems. However, as such problem increases in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. This study proposes a heuristic algorithm, called the Emergency Relief Transportation Planning Algorithm (ERTPA). ERTPA will group and sort demands according to the required products, the imposed due dates, the possible shared capacities, and the distances from the demand nodes to the depots. Then, ERTPA plans the demands individually, using a shortest travelling-time tree and a minimum cost production tree. To show the effectiveness and efficiency of the heuristic algorithm, a prototype was constructed and tested, using complexity and computational analyses.     

求解供应链优化问题的广义遗传算法

供应链优化研究是供应链管理中的一个重要问题,也是一个难题,针对该问题,提出了一个新型供应链优化模型,并且构造了广义遗传算法对其求解,该算法融入了特殊的演化规则,克服了遗传算法局部收敛的缺陷,提高了全局收敛的能力,实验表明对供应链优化问题的求解,广义遗传算法优于传统的遗传算法和分枝界定法。     

Integrated process planning and scheduling in a supply chain

This paper deals with the integration of process planning and scheduling, which is one of the most important functions in a supply chain to achieve high quality products at lower cost, lower inventory, and high level of performance. Solving the problem is essential for the generation of flexible process sequences with resource selection and for the decision of the operation schedules that can minimize makespan. We formulate a mixed integer programming model to solve this problem of integration. This model considers alternative resources: sequences and precedence constraints. To solve the model, we develop a new evolutionary search approach based on a topological sort. We use the topological sort to generate a set of feasible sequences in the model within a reasonable computing time. Since precedence constraints between operations are handled by the topological sort, the developed evolutionary search approach produces only feasible solutions. The experimental results using various sizes of problems provide a way to demonstrate the efficiency of the developed evolutionary search approach.     

基于多代理的供应链协同生产计划模型

供应链是由分布在全球的供应商、制造商、仓库、分销中心和零售商组成的复杂网络。其生产计划具有分布性,自治性,同步性和开放性等特点,使得传统的生产计划方法已经不能适应供应链的计划需求。在分析了多代理技术和供应链系统生产计划特点的基础上,采用智能代理封供应链系统的功能实体和物理实体进行封装,提出了一个基于多代理的供应链系统网络模型,并构建了基于多智能代理的生产计划运行模式。该生产计划模型分为三层：全局生产计划,企业内部子生产计划和各个任务的详细生产计划。它突破了传统生产计划的局限性,从全局规划的角度来整合供应链上的所有资源,消除了不同企业子生产计划所产生的冲突和差异。很好的体现了供应链系统信息共享和资源共享的原则。     

Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate

The paper considers a closed-loop serial supply chain consisting of a raw material supplier, a manufacturer, a retailer and a collector who collects the used product from consumers. The retailer's demand is met up by both manufacturing and remanufacturing. The manufacturing process is assumed to be imperfect as it can produce some defectives which are reworked in the same cycle itself. The remanufacturing of used items solely depends on the quality level of collected items. Two mathematical models are developed. The first model considers a single manufacturing–remanufacturing cycle, while the second model considers multiple manufacturing and remanufacturing cycles. Both the models are solved using algorithms developed for sequential and global optimizations. Numerical studies show that (i) the acceptance quality level of returned items and the length of the replenishment cycle for the retailer are lower in case of sequential optimization than those in global optimization, (ii) integration among supply chain members results in less number of shipments from the manufacturer to the retailer, and (iii) the joint total profit is higher when the integrated approach is adopted. The percentage increase in joint total profit with the integrated policy is 1.24% in the first model while it is 0.544% in the second model.