Applying modified NSGA-II for bi-objective supply chain problem

This paper minimizes the value of total cost and bullwhip effect in a supply chain. The objectives have been achieved through developing a new multi-objective formulation for minimizing the total cost and minimizing the bullwhip effect of a two-echelon serial supply chain. A new crossover algorithm for a fuzzy variable and a new mutation algorithm have also been proposed while applying Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to the proposed problem. The formulated problem has been simulated by Matlab software and the results of the modified NSGA-II have been compared with those of original NSGA-II. It is found from the results that the modified NSGA-II algorithm performs better than the original NSGA-II algorithm since the minimum values for both total cost and the bullwhip effect are obtained in case of the modified NSGA-II. The formulated bi-objective problem is new to the research community. The minimization of bullwhip effect has never been considered in a multi-objective optimization before. Besides crossover operator applied to the fuzzy variable and the mutation operator are newly introduced operators.     

基于混合方法的供应链协同计划研究

本文主要研究了供应链协同计划中的优化问题,所建数学模型中考虑了多计划期、多产品、多供应商、多制造商和多分销中心,以供应链系统总成本和总运行时间最小化为目标,采用整数规划和仿真相结合的混合方法来求解该模型。最后通过算例说明该混合方法对多目标供应链生产-分销计划模型求解的可行性和有效性。     

A bi-objective integrated procurement,production, and distribution problem of a multi-echelon supply chain network design: A new tuned MOEA

Efficient management of supply chain (SC) requires systematic considerations of miscellaneous issues in its comprehensive version. In this paper, a multi-periodic structure is developed for a supply chain network design (SCND) involving suppliers, factories, distribution centers (DCs), and retailers. The nature of the logistic decisions is tactical that encompasses procurement of raw materials from suppliers, production of finished product at factories, distribution of finished product to retailers via DCs, and the storage of raw materials and end product at factories and DCs. Besides, to make the structure more comprehensive, a flow-shop scheduling model in manufacturing part of the SC is integrated in order to obtain optimal delivery time of the product that consists of the makespan and the ship time of the product to DCs via factories. Moreover, to make the model more realistic, shortage in the form of backorder can occur in each period. The two objectives are minimizing the total SC costs as well as minimizing the average tardiness of product to DCs. The obtained model is a bi-objective mixed-integer non-linear programming (MINLP) model that is shown to belong to NP-Hard class of the optimization problems. Thus, a novel algorithm, called multi-objective biogeography based optimization (MOBBO) with tuned parameters is presented to find a near-optimum solution. As there is no benchmark available in the literature, the parameter-tuned multi-objective simulated annealing algorithm (MOSA) and the popular non-dominated sorting genetic algorithm (NSGA-II) are developed to validate the results obtained and to evaluate the performance of MOBBO using randomly generated test instances.     

Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic

Sustainability has been considered as a growing concern in supply chain network design (SCND) and in the order allocation problem (OAP). Accordingly, there still exists a gap in the quantitative modeling of sustainable SCND that consists of OAP. In this article, we cover this gap through simultaneously considering the sustainable OAP in the sustainable SCND as a strategic decision. The proposed supply chain network is composed of five echelons including suppliers classified in different classes, plants, distribution centers that dispatch products via two different ways, direct shipment, and cross-docks, to satisfy stochastic demand received from a set of retailers. The problem has been mathematically formulated as a multi-objective optimization model that aims at minimizing the total costs and environmental effect of integrating SCND and OAP, simultaneously. To tackle the addressed problem, a novel multi-objective hybrid approach called MOHEV with two strategies for its best particle selection procedure (BPSP), minimum distance, and crowding distance is proposed. MOHEV is constructed through hybridization of two multi-objective algorithms, namely the adapted multi-objective electromagnetism mechanism algorithm (AMOEMA) and adapted multi-objective variable neighborhood search (AMOVNS). According to achieved results, MOHEV achieves better solutions compared with the others, and also crowding distance method for BPSP outperforms minimum distance. Finally, a case study for an automobile industry is used to demonstrate the applicability of the approach.     

A supply chain network with product remanufacturing and carbon emission considerations: a two-phase design

Growing awareness of environmental issues is prompting the development of sustainable supply chain management. Closed-loop supply chains in which used products can be returned for remanufacture are becoming increasingly popular. This paper introduces a two-phase approach to the design of supply chain networks taking into account carbon emission and remanufacturing. In the first phase, a continuous approximation model is used to design the forward supply chain network. The objective is to minimize the total forward network cost by simultaneously determining the number and the service areas of distribution centers (DCs) and the replenishment cycle time for DCs. A nonlinear optimization technique is used to solve the forward supply chain network design problem. In the second phase, a reverse supply chain network is formulated based on the results of the first phase to determine the optimal number and service areas of remanufacturing centers (RCs) and the replenishment cycle time for RCs. Finally, numerical analyses are conducted to show the solution approach and provide some managerial insights.     

A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems

Cross-docking is a material handling and distribution technique in which products are transferred directly from the receiving dock to the shipping dock, reducing the need for a warehouse or distribution center. This process minimizes the storage and order-picking functions in a warehouse. In this paper, we consider cross-docking in a supply chain and propose a multi-objective mathematical model for minimizing the make-span, transportation cost and the number of truck trips in the supply chain. The proposed model allows a truck to travel from a supplier to the cross-dock facility and from the supplier directly to the customers. We propose two meta-heuristic algorithms, the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization (MOPSO), to solve the multi-objective mathematical model. We demonstrate the applicability of the proposed method and exhibit the efficacy of the procedure with a numerical example. The numerical results show the relative superiority of the NSGA-II method over the MOPSO method.     

不确定条件下配送回收中心选址配送问题研究

针对制造商、零售商、一个废弃处理中心和多个配送回收中心构成的闭环供应链,解决模糊随机环境下的配送回收中心选址配送问题。引用模糊随机理论处理产品回收率和可再利用率随机变量,以成本最低和碳排放最小为双重目标,以设施能力,设施间流量以及设施数量为约束,建立多目标闭环供应链配送回收中心选址配送模型。改进了全局-局部-邻域粒子群算法,设计了基于优先级的全局-局部-邻域粒子群算法方案,并用案例验证了模型及算法的有效性和先进性。     

Supply chain modeling in uncertain environment with bi-objective approach

Supply chain is viewed as a large-scale system that consists of production and inventory units, organized in a serial structure. Uncertainty is the main attribute in managing the supply chains. Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships among various entities exist in the SC. Uncertainty may result from customer’s demand variability or unreliability in external suppliers. In this paper we develop an inventory model for an assembly supply chain network (each unit has at most one immediate successor, but any number of immediate predecessors) which fuzzy demand for single product in one hand and fuzzy reliability of external suppliers in other hand affect on determination of inventory policy in SCM. External supplier’s reliability has determined using a fuzzy expert system. Also the performance of supply chain is assessed by two criteria including total cost and fill rate. To solve this bi-criteria model, hybridization of multi-objective particle swarm optimization and simulation optimization is considered. Results indicate the efficiency of proposed approach in performance measurement.     

相对公平关切下考虑产品设计的再制造合作模式与供应链协调

将相对公平关切和产品可再制造性设计同时纳入由一个制造商和一个再制造商构成的供应链中,构建外包和授权两种再制造合作模式下供应链的决策模型,研究制造商的再制造合作模式选择问题,分析最优再制造合作模式下再制造商相对公平关切和产品可再制造性设计对供应链生产定价决策及利润的影响,并设计相应的协调策略.研究结果表明:外包再制造是制造商的最优再制造合作模式;在外包再制造供应链中,再制造商的相对公平关切虽会降低制造商利润,但能提高供应链系统利润;产品可再制造性设计对再制造商总是有利的;外包价格契约能够实现外包再制造供应链的协调.     

基于EPR制度的企业再制造决策

考虑生产者延伸责任制度在我国再制造产业的实施,构建由一位制造商与一位再制造商组成的闭环供应链动态博弈模型,以成员企业的回收努力水平、废旧产品购买量、产品产量等为决策要素,分别探讨无政府参与的自由回收、基于回收率奖惩的政府规制以及政府规制下的基金调控3种模式下制造商与再制造商的最优决策,分析政府规制与基金调控对企业决策和环境绩效的影响.研究表明:制造商的回收策略与废旧产品的转移价格密切相关;基于回收率奖惩的政府规制对回收再制造活动具有积极的推进作用;再制造基金调控在一定程度上能够激励再制造商提高生产能力,有利于专业分工局面的形成,但不适于在再制造产业成熟阶段实施.研究结论对生产者延伸责任制度下企业再制造策略选择、政府环境规制设计具有一定的参考价值.     

第三方再制造外包模式选择与协调研究

研究第三方再制造外包选择与协调问题,将制造商外包和零售商外包两种模式进行对比分析.研究结果表明:制造商外包模式下新产品的数量、原始设备制造商的利润以及系统总利润更高,新产品的价格更低;零售商外包模式下再制品的数量、外包价格以及再制造商的利润更高.制造商外包模式具有较高的消费者剩余和较差的环境绩效.当再制造优势明显时,零售商外包模式的社会福利水平高于制造商外包模式.采用合作博弈方法对零售商外包模式下多主体闭环供应链进行协调,可为供应链管理决策提供理论依据与决策方法.     

复杂产品供应链质量控制方案递阶决策模型

提出一类供应链质量控制方案递阶决策模型,辅助复杂产品主制造商更好地推广和落实质量控制方案,以实现供应链整体质量的最优改善.首先搭建了一类新型的供应链质量屋分析平台,并将其表征为多目标规划模型,确定最优方案组合;然后,基于复杂产品供应链多层级结构设计质量屋网络,以控制资源为纽带构建多级多目标递阶决策模型,实现了质量控制方案的继承和衔接;最后,针对某型号商用飞机供应链开展案例研究,验证了所提出模型和方法的可行性和有效性.     

Designing an integrated multi-echelon agile supply chain network: a hybrid taguchi-particle swarm optimization approach

The present paper attempts to explore the integration of production, distribution and logistics activities at the strategic decision making level where, the objective is to design a multi-echelon supply chain network considering agility as a key design criterion. The design network conceived here addresses a class of five echelons of supply chains including suppliers, plants, distribution centers, cross-docks and customer zones. The problem has been mathematically formulated as a multi-objective optimization model that aims to minimize the cost (fixed and variable) and maximizes the plant flexibility and volume flexibility. The notion of cross-dock has been introduced as an intermediate level between distribution centers and customer zones to increase the profitability of manufacturing and service industries. In order to solve the underlying problem, a novel algorithm entitled hybrid taguchi-particle swarm optimization (HTPSO) has been proposed that incorporates the characteristics of statistical design of experiments and random search techniques. The main idea is to integrate the fundamentals of taguchi method i.e. orthogonal array and signal to noise ratio (SNR) in the PSO meta-heuristic to minimize the effect of the causes of variations. The proposed model has been authenticated by undertaking problem instances of varying size. Extensive computational experiments are conducted to validate the same and also the efficacy of the proposed HTPSO algorithm. The results obtained reveal that proposed solution methodology is an effective approach to solve the underlying problem.     

电网中断下基于DWOA的生鲜产品闭环供应链网络多目标模糊优化设计

针对生鲜产品供应链网络设计问题,建立了一种电网中断下的闭环生鲜供应链网络多目标模糊优化设计模型,以此解决供应链网络设计中的不确定性问题.首先,针对电网中断下生鲜产品闭环供应链网络结构设计,建立目标为成本最小、碳排放最少、中断时间最短的优化函数,采用Me测度和三角模糊数对该模型进行处理,将多目标问题转换为单目标问题;其次,在原有鲸鱼算法的基础上,引入差分算法的交叉和变异理念,增强其搜索能力,改善其局限性,得到改进差分鲸鱼优化算法(DWOA),并采用此方法对处理后的模型求解;最后,通过数值实例和敏感性分析表明,提出的算法和模型在处理生鲜产品供应链网络优化设计方面具有较强的求解能力,且计算时间较短.     

考虑政府补贴及环境设计的再制造闭环供应链生产决策

在外包及授权再制造模式下,研究考虑政府补贴及制造商环境设计的再制造闭环供应链生产决策问题.构建4种闭环供应链决策模型,分析政府补贴及环境设计水平对制造商与再制造商竞合关系、闭环供应链绩效以及环境的影响.研究发现,制造商可以通过调节单位再制造外包费或专利许可费实现与再制造商共享政府补贴,因此政府不同补贴策略不会对闭环供应链最优生产决策及绩效产生影响.政府补贴与消费者对再制造产品的认知程度,并不总是有利于提高制造商环境设计水平,但均有助于促进再制造产品销售.制造商总是有动机进行环境设计,而再制造商只有在环境设计能为再制造产品带来更多成本节约时,才有动力接受制造商的环境设计方案.相比于授权再制造,制造商及再制造商均在外包再制造下获得更多的利润.另外,政府补贴与环境设计并不一定总能起到减少产品环境总影响程度的作用.     

基于系统动力学的大型客机供应链质量管控契约优化

研究再制造视角下我国大型客机供应链质量控制问题.根据大型客机制造三级供应链中供应商、主制造商及销售商的运作特点,刻画三者内在的动力学参数特征;构建基于质量奖惩和成本分摊的供应链控制契约,建立主制造商质量控制契约协调优化规则;设计大型客机供应链质量控制仿真的系统动力学模型;以液压系统为例仿真分析管控策略,并通过对比分析得出管控策略的有效性.     

考虑质量价值水平的复杂产品供应链质量成本优化方法

优化复杂产品供应链质量成本是提高供应链质量管控能力、降低供应链质量风险的重要方法.综合考虑产品质量水平和产品研制成本,提出复杂产品质量价值概念,并详细分析以供应商网络为基础的复杂产品质量价值形成机理.建立复杂产品质量价值GERT网络模型,实现复杂产品质量价值水平的量化计算,并以此为基础,通过识别网络中的关键供应商建立优化模型,提出考虑质量价值水平的复杂产品供应链质量成本优化方法.最后通过算例验证模型方法的科学性和有效性.     

基于正交设计NSGA-II算法的制动器多目标优化*

针对汽车鼓式制动器,以制动效能因数最大、制动鼓体积最小和制动器温升最低为目标,建立了多目标优化模型。针对传统NSGA-II算法求解3目标优化问题的不足,引入正交设计策略,提出了改进的NSGA-II算法。将改进算法与目前三种经典的多目标优化算法在DTLZ系列测试函数上进行性能测试,结果表明改进算法在求解3目标优化问题上有更好的性能。用改进算法和NSGA-II两种算法同时求解制动器多目标优化设计实例,改进算法得到了分布更好的Pareto前端,表明改进算法对此类问题求解行之有效。     

具有风险规避型零售商的闭环供应链网络均衡分析

在随机需求条件下研究了由多个相互竞争的供应商、制造商、零售商和消费市场组成,且零售商具有风险规避特性的再制造闭环供应链网络均衡问题.通过对不同决策层级最优化行为的分析,分别得到各层级和整个供应链网络实现均衡的条件,并建立相关的变分不等式模型.运用拟牛顿算法对变分不等式进行求解,通过仿真分析了随机再制造率和零售商的风险规避程度对网络成员最优行为和闭环供应链网络均衡的影响.     

Order allocation for multiple supply-demand networks within a cluster

Manufacturers outside an industry cluster and suppliers within cluster form a multiple sourcing supply-demand network. Two different order allocation strategies, the production capacity-based strategy and the production load equilibrium-based strategy, are studied in this paper. The respective order allocation models of multiple manufacturers versus multiple suppliers are proposed. Considering the uncertainty of demand and enterprises’ production capacity, the discrete event system simulation is used to verify that the production load equilibrium-based strategy can not only guarantee the order’s on-time delivery from the perspective of the available manufacturing resources, but also lead to promote the whole supplier group’s operation level, so as to realize the optimization of the entire supply chain.