Simulation-based heuristic method for container supply chain network optimization

With the emerging of free trade zones (FTZs) in the world, the service level of container supply chain plays an important role in the efficiency, quality and cost of the world trade. The performance of container supply chain network directly impacts its service level. Therefore, it is imperative to seek an appropriate method to optimize the container supply chain network architecture. This paper deals with the modeling and optimization problem of multi-echelon container supply chain network (MCSCN). The problem is formulated as a mixed integer programming model (MIP), where the objective is subject to the minimization of the total supply chain service cost. Since the problem is well known to be NP-hard, a novel simulation-based heuristic method is proposed to solving it, where the heuristic is used for searching near-optimal solutions, and the simulation is used for evaluating solutions and repairing unfeasible solutions. The heuristic algorithm integrates genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, where the GA is used for global search and the PSO is used for local search. Finally, computational experiments are conducted to validate the performance of the proposed method and give some managerial implications.     

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.     

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.     

A cooperation model based on CVaR measure for a two-stage supply chain

In this paper, we introduce a cooperation model (CM) for the two-stage supply chain consisting of a manufacturer and a retailer. In this model, it is supposed that the objective of the manufacturer is to maximise his/her profit while the objective of the retailer is to minimise his/her CVaR while controlling the risk originating from fluctuation in market demand. In reality, the manufacturer and the retailer would like to choose their own decisions as to wholesale price and order quantity to optimise their own objectives, resulting the fact that the expected decision of the manufacturer and that of the retailer may conflict with each other. Then, to achieve cooperation, the manufacturer and the retailer both need to give some concessions. The proposed model aims to coordinate the decisions of the manufacturer and the retailer, and balance the concessions of the two in their cooperation. We introduce an s* ? optimal equilibrium solution in this model, which can decide the minimum concession that the manufacturer and the retailer need to give for their cooperation, and prove that the s* ? optimal equilibrium solution can be obtained by solving a goal programming problem. Further, the case of different concessions made by the manufacturer and the retailer is also discussed. Numerical results show that the CM is efficient in dealing with the cooperations between the supplier and the retailer.     

A mixed integer nonlinear programming model and heuristic solutions for location,inventory and pricing decisions in a closed loop supply chain

We analyze a network design problem for a closed-loop supply chain that integrates the collection of the used products with the distribution of the new products. We present a mixed integer nonlinear facility location-inventory-pricing model to decide on the optimal locations of the facilities, inventory amounts, prices for new products and incentive values for the collection of right amount of used products in order to maximize the total supply chain profit. We develop heuristics for the solution of this model and analyze the effectiveness of these heuristics and the effects of the parameters on this system through numerical experiments.     

一种供应链绩效评价新方法及其应用

利用平衡记分法建立供应链绩效评价指标体系,利用粗糙集理论对绩效评价体系进行约简,得到约简后的指标集后,使用基于信息熵的多属性评价方法对供应链绩效进行评价和排序,以便决策者参考。经过实证分析,证明该方法是有效的供应链绩效评价方法。     

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 solution method for a two-layer sustainable supply chain distribution model

This article presents an effective solution method for a two-layer, NP-hard sustainable supply chain distribution model. A DoE-guided MOGA-II optimiser based solution method is proposed for locating a set of non-dominated solutions distributed along the Pareto frontier. The solution method allows decision-makers to prioritise the realistic solutions, while focusing on alternate transportation scenarios. The solution method has been implemented for the case of an Irish dairy processing industry׳s two-layer supply chain network. The DoE generates 6100 real feasible solutions after 100 generations of the MOGA-II optimiser which are then refined using statistical experimentation. As the decision-maker is presented with a choice of several distribution routes on the demand side of the two-layer network, TOPSIS is applied to rank the set of non-dominated solutions thus facilitating the selection of the best sustainable distribution route. The solution method characterises the Pareto solutions from disparate scenarios through numerical and statistical experimentations. A set of realistic routes from plants to consumers is derived and mapped which minimises total CO₂ emissions and costs where it can be seen that the solution method outperforms existing solution methods.     

考虑偏好与合作的供应链网络设计与优化

提出了考虑核心企业决策偏好与成员企业合作意愿的双目标供应链网络设计与优化问题。应用模糊多目标规划方法,将决策者对各个目标的不精确期望水平考虑到供应链网络的设计与优化模型中。研究了集中决策模式下核心决策者的不同目标偏好与分散决策模式下各组织成员群体决策的合作意愿,对供应链的各个目标及网络优化配置的影响。结果表明,“极大极小”算子体现了核心决策者对各个目标的偏好,但大权重目标以牺牲小权重目标的值为代价的。新的“模糊与”补偿算子能使供应链的各个目标值获得理想的均衡效果,体现了供应网络中核心企业与其他成员企业群体协商决策的管理思想与行为特征。     

Economic selection of process mean and lot‐sizing decisions for multi‐newsvendor supply chain

Two related problems are integrated in this paper, the first is the targeting problem and another is production/inventory decisions in a supply chain. The supply chain under consideration consists of a supplier of raw material, a single producer, and multiple newsvendors. The producer can adjust the process mean before starting the production run. Once set to a certain target value, the process mean is not changed until the production lot is completed. At the end of a production run, the producer screens the lot and uses specification limits to evaluate the quality of the item. Nonconforming items are scrapped with no salvage value; however, conforming items are delivered to newsvendors who are subjected to random demand. If demand on a newsvendor in a season is lower than anticipated, surplus items will be returned to the producer at a certain transportation cost. We first develop a mathematical model that maximizes the expected total profit of the supply chain. Then, a table for two special functions is devised to simplify the solution method and is used to find the optimal solution of the proposed model. We also study the significance of this integration by comparing the performance of the proposed model with an independent model where the process mean selection and lot‐sizing decisions are found separately.     

Adaptive genetic algorithm for advanced planning in manufacturing supply chain

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times.The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach. Received: June 2005 / Accepted: December 2005     

Process planning for closed-loop aerospace manufacturing supply chain and environmental impact reduction

A considerable amount of work has recently been applied to the development of processes to reduce negative environmental impacts of disposal products. Different waste reduction options such as direct reuse, repair, refurbishing, cannibalization, and remanufacturing were introduced to overcome these shortages. This paper studies an integrated system of manufacturing and remanufacturing using a capacitated facility in the aerospace industry, where products are returned after certain flight hours or cycles for overhaul. A mixed integer linear programming model is developed to maximize profit considering manufacturing, remanufacturing set-up, refurbishing, and inventory carrying costs. The model was tested through a set of experimental data. Further sensitivity analysis was conducted aiming at revealing the effects of certain factors on inventory carrying cost, profit, amount of scrap, and inventory turnover ratio.     

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.     

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。     

敏捷供应链插单调度优化的启发式算法

针对在敏捷供应链已有生产计划基础上进行的插单调度问题,以单工厂和多供应商组成的两级供应链系统为研究对象,采用时间槽表示可用调度时间,以最小化供应链总成本为目标,建立了调度优化问题的整数规划(IP)模型,设计了求解该模型的逐批选优启发式(OOSH)算法。通过算例将其与距离优先和作业周期优先决策算法比较,验证了启发式算法的有效性,显示了敏捷供应链协同调度可以有效提高供应链的市场竞争力。     

A simulation model of a coordinated decentralized supply chain

This paper presents a simulation‐based study of a coordinated, decentralized linear supply chain (SC) system. In the proposed model, any supply tier considers its successors as part of its inventory system and generates replenishment orders on the basis of its partners’ operational information. We show that the proposed coordinated decision‐making process creates a reduction in information distortion along an SC compared with a traditional, noncoordinated strategy. A novel result is that we show how a coordinated SC can avoid the detrimental consequences of demand amplification in terms of penalty costs due to the stockout phenomenon in upstream stages.     

Pricing and collecting decisions in a closed-loop supply chain with symmetric and asymmetric information

The optimal decision problem of a closed-loop supply chain with symmetric and asymmetric information structures is considered using game theory in this paper. The paper aims to explore how the manufacturer and the retailer make their own decisions about wholesale price, retail price, and collection rate under symmetric and asymmetric information conditions. Four game models are established, which allow one to examine the strategies of each firm and explore the role of the manufacturer and the retailer in four different game scenarios under symmetric and asymmetric information structures. The optimal strategies in closed form are given under the decision scenarios with symmetric information; moreover, the first order conditions that the optimal retail price, optimal wholesale price, and optimal collection rate satisfy are given under the decision scenarios with asymmetric information. The results obtained under symmetric and asymmetric information conditions and some key model parameters used in this paper are analyzed using a numerical approach by which some managerial analysis are given.     

基于粗糙集的供应链绩效改进决策研究

从知识发现和数据挖掘的角度,利用粗糙集的理论和方法,建立了基于粗糙集的供应链绩效改进决策模型框架,并给出其中的指标约简方法和基于分辨矩阵计算指标权重的数学模型。最后结合一个制造业供应链绩效评价实例,首先对其基于平衡记分卡的指标体系进行了约简,然后建立了基于粗糙集的供应链绩效改进决策模型,确定了指标的客观权重。并对结果进行了分析,提出了绩效改进决策意见和措施。     

Joint optimization of pricing and planning decisions in divergent supply chain

In divergent supply chains, such as in the oil industry, processing raw materials results in an outflow of multiple products. Final products are stored at international depots, from where they are ready to be shipped to the markets. Even if one company controls the entire chain, when production and sales organizations are decoupled, a relevant problem is to determine the internal prices of products at depots for achieving coordination. We propose an optimization model involving pricing and production decisions, and several constraints commonly used in divergent chains. In our approach, the producer incorporates the sellers’ behavior by expressing demand as a function of the internal price. As a result, our model serves as a coordination mechanism in trying to get an overall coordinated integrated solution in a decoupled reality. Numerical examples in single and multiple periods problems show the advantages of our approach over cost‐based methods.