An analysis of production-inventory models with deteriorating items in a two-echelon supply chain

This paper revisits two previous studies that addressed the integrated production–inventory problem for deteriorating items in a two-echelon supply chain, where the item’s deterioration rate is a constant or follows a continuous probability distribution function. The aim of this study is to present an improved solution procedure to determine the delivery lot size and the number of deliveries per production batch cycle that minimizes the total cost of the entire supply chain. The performance of the proposed methodology is illustrated analytically and numerically.     

A fuzzy goal programming and meta heuristic algorithms for solving integrated production: distribution planning problem

Integrated production–distribution planning is one of the most important issues in supply chain management (SCM). We consider a supply chain (SC) network to consist of a manufacturer, with multiple plants, products, distribution centers (DCs), retailers and customers. A multi-objective linear programming problem for integrating production–distribution, which considers various simultaneously conflicting objectives, is developed. The decision maker’s imprecise aspiration levels of goals are incorporated into the model using a fuzzy goal programming approach. Due to complexity of the considered problem we propose three meta-heuristics to tackle the problem. A simple genetic algorithm and a particle swarm optimization (PSO) algorithm with a new fitness function, and an improved hybrid genetic algorithm are developed. In order to show the efficiency of the proposed methods, two classes of problems are considered and their instances are solved using all methods. The obtained results show that the improved hybrid genetic algorithm gives us the best solutions in a reasonable computational time.     

A dynamic inventory model with supplier selection in a serial supply chain structure

Considering the inherent connection between supplier selection and inventory management in supply chain networks, this article presents a multi-period inventory lot-sizing model for a single product in a serial supply chain, where raw materials are purchased from multiple suppliers at the first stage and external demand occurs at the last stage. The demand is known and may change from period to period. The stages of this production–distribution serial structure correspond to inventory locations. The first two stages stand for storage areas for raw materials and finished products in a manufacturing facility, and the remaining stages symbolize distribution centers or warehouses that take the product closer to customers. The problem is modeled as a time-expanded transshipment network, which is defined by the nodes and arcs that can be reached by feasible material flows. A mixed integer nonlinear programming model is developed to determine an optimal inventory policy that coordinates the transfer of materials between consecutive stages of the supply chain from period to period while properly placing purchasing orders to selected suppliers and satisfying customer demand on time. The proposed model minimizes the total variable cost, including purchasing, production, inventory, and transportation costs. The model can be linearized for certain types of cost structures. In addition, two continuous and concave approximations of the transportation cost function are provided to simplify the model and reduce its computational time.     

Supply chain network design: partner selection and production/distribution planning using a systematic model

In this paper, a novel multi-phase mathematical approach is presented for the design of a complex supply chain network. From the point of network design, customer demands, and for maximum overall utility, the important issues are to find suitable and quality companies, and to decide upon an appropriate production/distribution strategy. The proposed approach is based on the genetic algorithm (GA), the analytical hierarchy process (AHP), and the multi-attribute utility theory (MAUT) to satisfy simultaneously the preferences of the suppliers and the customers at each level in the network. A case study with a good quality solution is provided to confirm the efficiency and effectiveness of the proposed approach. Finally, to demonstrate the performance of the proposed approach, a comparative numerical experiment is performed by using the proposed approach and the common single-phase genetic algorithm (SGA). Empirical analysis results demonstrate that the proposed approach can outperform the SGA in partner selection and production/distribution planning for network design.     

Competitive facility location on decentralized supply chains

This paper addresses a novel competitive facility location problem about a firm that intends to enter an existing decentralized supply chain comprised of three tiers of players with competition: manufacturers, retailers and consumers. It first proposes a variational inequality for the supply chain network equilibrium model with production capacity constraints, and then employs the logarithmic-quadratic proximal prediction–correction method as a solution algorithm. Based on this model, this paper develops a generic mathematical program with equilibrium constraints for the competitive facility location problem, which can simultaneously determine facility locations of the entering firm and the production levels of these facilities so as to optimize an objective. Subsequently, a hybrid genetic algorithm that incorporates with the logarithmic-quadratic proximal prediction–correction method is developed for solving the proposed mathematical program with an equilibrium constraint. Finally, this paper carries out some numerical examples to evaluate proposed models and solution algorithms.     

灾情巡视问题的蚁群两目标优化算法研究

分析目前灾情巡视问题求解方法存在的缺陷,归纳出灾情巡视问题两目标优化模型.针对灾情巡视问题模型特点,引入蚁群算法和多目标优化理论,提出两个灾情巡视问题的蚁群两目标优化算法:算法1将灾情巡视问题的道路网络转化为完全图,增加m-1个(m为巡视组数)虚拟巡视起点,将灾情巡视两目标优化问题转化为单旅行商两目标优化问题,然后使用蚁群算法和多目标优化理论进行迭代求解.算法2使用一只蚂蚁寻找一个子回路,m个子回路构成一个灾情巡视可行方案,采用罚函数法和多目标优化理论构建增广两目标优化评价函数,使用g组,共g×m只蚂蚁共同协作来发现灾情巡视问题的最优解.算法特点:①算法1将灾情巡视两目标优化问题转化为单旅行商两目标优化问题,可以充分利用已有蚁群算法求解单旅行商问题的研究成果;②两个算法引入蚁群算法,提高了算法效率;③两个算法克服目前灾情巡视问题的求解方法不严密性缺陷;④两目标优化算法可以为用户提供多个满足约束条件的Pareto组合解,扩大了用户选择范围,增强了算法的适用性.算法测试表明:灾情巡视问题的蚁群两目标优化算法是完全可行和有效的.     

Collaborative production planning of supply chain under price and demand uncertainty

This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.     

Real time disruption management for a two-stage batch production–inventory system with reliability considerations

In this research, a two-stage batch production–inventory system is introduced. In this system, the production may be disrupted, for a given period of time, either at one or both stages. In this paper, firstly, a mathematical model has been developed to suggest a recovery plan for a single occurrence of disruption at either stage. Secondly, multiple disruptions have been considered, for which a new disruption may or may not affect the recovery plan of earlier disruptions. We propose a new approach that deals with a series of disruptions over a period of time, which can be implemented for disruption recovery on a real time basis. In this approach, the model formulated for single disruption has been integrated to generate initial solutions for individual disruptions and the solutions have been revised for multiple dependent disruptions with changed parameters. With the proposed approach, an optimal recovery plan can be obtained in real time, whenever the production system experiences either a sudden disruption or a series of disruptions, at different points in time. Some numerical examples and a real-world case study are presented to explain the benefits of our proposed approach.     

A Hybrid Taguchi–Immune approach to optimize an integrated supply chain design problem with multiple shipping

Supply chain network design is considered a strategic decision level problem that provides an optimal platform for the effective and efficient supply chain management. In this research, we have mathematically modeled an integrated supply chain design. To ensure high customer service levels, we propose the inclusion of multiple shipping/transportation options and distributed customer demands with fixed lead times into the supply chain distribution framework and formulated an integer-programming model for the five-tier supply chain design problem considered. The problem has been made additionally complex by including realistic assumptions of nonlinear transportation and inventory holding costs and the presence of economies of scale. In the light of aforementioned facts, this research proposes a novel solution methodology that amalgamates the features of Taguchi technique with Artificial Immune System (AIS) for the optimum or near optimum resolution of the problem at hand. The performance of the proposed solution methodology has been benchmarked against a set of test instances and the obtained results are compared against those obtained by Genetic Algorithm (GA), Hybrid Taguchi–Genetic Algorithm (HTGA) and AIS. Simulation results indicate that the proposed approach can not only search for optimal/near optimal solutions in large search spaces but also has good repeatability and convergence characteristics, thereby proving its superiority.     

基于改进蚁群算法的最短路径问题研究及应用

蚁群系统作为一种蚁群算法是解决最短路径问题的一种行之有效的方法.然而,它自身也存在着一些缺陷,主要针对基本蚁群算法易陷入局部最优这一缺陷对其进行改进,集中体现在初始信息素求解和信息素更新这两方面.为了进一步了解改进蚁群算法的优点,进行了实验仿真:将改进的蚁群算法应用子模拟医疗救护GIS中,利用GIS的网络分析功能对城市道路网络的最短路径选择算法进行了深入地探讨研究,并以山西省太原市的交通路线作为实例进行研究.计算机仿真结果表明,改进的蚁群算法在解决最短路径问题时较基本蚁群算法的性能好,它具有一定的理论参考价值和现实意义.     

A particle swarm optimization-based hybrid algorithm for minimum concave cost network flow problems

Traditionally, minimum cost transshipment problems have been simplified as linear cost problems, which are not practical in real applications. Some advanced local search algorithms have been developed to solve concave cost bipartite network problems. These have been found to be more effective than the traditional linear approximation methods and local search methods. Recently, a genetic algorithm and an ant colony system algorithm were employed to develop two global search algorithms for solving concave cost transshipment problems. These two global search algorithms were found to be more effective than the advanced local search algorithms for solving concave cost transshipment problems. Although the particle swarm optimization algorithm has been used to obtain good results in many applications, to the best of our knowledge, it has not yet been applied in minimum concave cost network flow problems. Thus, in this study, we employ an arc-based particle swarm optimization algorithm, coupled with some genetic algorithm and threshold accepting method techniques, as well as concave cost network heuristics, to develop a hybrid global search algorithm for efficiently solving minimum cost network flow problems with concave arc costs. The proposed algorithm is evaluated by solving several randomly generated network flow problems. The results indicate that the proposed algorithm is more effective than several other recently designed methods, such as local search algorithms, genetic algorithms and ant colony system algorithms, for solving minimum cost network flow problems with concave arc costs.     

基于径向基函数神经网络的流程企业供应链预测仿真

本文在比较预测方法的基础上，采用径向基函数（RBF）神经网络技术建立流程企业供应链预测模型，进行了实例预测仿真，并将预测结果与BP网络的预测结果进行了比较。结果表明，RBF网络误差小于BP网络，其中平方根RBF网络的预测仿真误差最小，而BP网络的误差最大。     

An analytical method for cost analysis in multi-stage supply chains: A stochastic network model approach

This paper studies the cost distribution characteristics in multi-stage supply chain networks. Based on the graphical evaluation and review technique, we propose a novel stochastic network mathematical model for cost distribution analysis in multi-stage supply chain networks. Further, to investigate the effects of cost components, including the procurement costs, inventory costs, shortage costs, production costs and transportation costs of supply chain members, on the total supply chain operation cost, we propose the concept of cost sensitivity and provide corresponding algorithms based on the proposed stochastic network model. Then the model is extended to analyze the cost performance of supply chain robustness under different order compensation ability scenarios and the corresponding algorithms are developed. Simulation experiment shows the effectiveness and flexibility of the proposed model, and also promotes a better understanding of the model approach and its managerial implications in cost management of supply chains.     

On the optimization of supply chain networking decisions

Companies strive to position themselves to maximize the value they add to the supply chains in which they are embedded. This raises strategic questions such as: Which durable resources should be developed to enhance current core competencies? Which activities should be externalized and to which potential partner should they be given? Which internal activities should be preserved and developed? How should the resources of the enterprise be allocated to activities? The aim of this paper is to propose a mathematical programming model of the extended enterprise which can be used to investigate this type of strategic networking issues. A number of general network modeling constructs are first proposed. A model to optimize the supply chain structure under specific assumptions on the nature of production, cost and value functions in typical production/distribution companies is then derived. A heuristic to obtain solutions from the model is also presented. Finally, an example based on a refrigerator company is used to illustrate the usefulness of the approach.     

Successive Linear Approximation Procedure for Generalized Fixed-Charge Transportation Problems

In this paper, we develop a new heuristic procedure for solving a generalization of the fixed-charge transportation problem in which there are resource losses in addition to the fixed charges. The losses may be evaporation losses when the commodity is a liquid, heat losses in an electrical distribution network, or deterioration losses in distribution networks involving perishable commodities such as, for example, food items. The proposed procedure consists of solving a sequence of pro-rated problems. It is different from heuristic procedures that have been developed for solving the standard fixed charge transportation problem, in that it is not based on extreme point enumeration. We experiment with problems involving up to 2100 arcs with fixed charges and resource losses. The results show that the proposed approach is viable for solving medium-to-large sized problems.     

Optimizing the economic lot size of a three-stage supply chain with backordering derived without derivatives

Multi-stage supply chain management integration provides a key to successful international business operations. This is because the integrated approach improves the global system performance and cost efficiency. The integrated production inventory models using differential calculus to solve the multi-variable problems are prevalent in operational research. This paper extends the integrated vendor–buyer inventory problem by Yang and Wee [Yang, P.C., Wee, H.M., 2002. The economic lot size of the integrated vendor–buyer system derived without derivatives. Optimal Control Applications and Methods 23, 163–169] to solve the multi-variable problems in the supply chain, and simplifies the solution procedure using a simple algebraic method. As a result, the solution procedure may be easily understood and applied so as to derive the optimal solution.     

军事物流系统中定位-运输路线安排模型及算法

考虑到战时物资需求的紧迫性和保障资源的有限性,从决策者的角度出发,以军事物流系统总体供应时间最短为目标,构建了两级军事配送网络的定位-运输路线安排模型,并给出一种启发式算法.算法分为两个阶段,首先利用蚁群算法和线性规划的方法解决运输路线安排问题,然后运用贪婪搜索算法解决军事物流配送中心选址问题.最终,将两种算法结合起来进行逐步搜索,从而得到模型的解,并运用实例说明了算法的有效性和可行性.     

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。     

A facility location model for global closed-loop supply chain network design

Forward and reverse supply chains form a closed-loop supply chain. In this paper, a mathematical model is proposed for a closed-loop supply chain network by considering global factors, including exchange rates and customs duties. The model is a multi-objective mixed-integer linear programming model under uncertain demand. A solution approach based on fuzzy programming is developed for solving the optimization problem. The model is then applied in a network, which is located in Southwestern Ontario, Canada. A sensitivity analysis is provided to validate the model. This model considers global factors, multi-objectives, and uncertainty simultaneously in a closed-loop supply chain network.