Multi-objective optimization for sustainable supply chain network design considering multiple distribution channels

The emergence of Omni-channel has affected the practical design of the supply chain network (SCN) with the purpose of providing better products and services for customers. In contrast to the conventional SCN, a new strategic model for designing SCN with multiple distribution channels (MDCSCN) is introduced in this research. The MDCSCN model benefits customers by providing direct products and services from available facilities instead of the conventional flow of products and services. Sustainable objectives, i.e., reducing economic cost, enlarging customer coverage and weakening environmental influences, are involved in designing the MDCSN. A modified multi-objective artificial bee colony (MOABC) algorithm is introduced to solve the MDCSCN model, which integrates the priority-based encoding mechanism, the Pareto optimality and the swarm intelligence of the bee colony. The effect of the MDCSCN model are examined and validated through numerical experiment. The MDCSCN model is innovative and pioneering as it meets the latest requirements and outperforms the conventional SCN. More importantly, it builds the foundation for an intelligent customer order assignment system. The effectiveness and efficiency of the MOABC algorithm is evaluated in comparison with the other popular multi-objective meta-heuristic algorithm with promising results.     

A study on facility location–allocation problem in mixed environment of randomness and fuzziness

In logistics system, facility location–allocation problem, which can be used to determine the mode, the structure and the form of the whole logistics system, is a very important decision problem in the logistics network. It involves locating plants and distribution centers, and determining the best strategy for allocation the product from the plants to the distribution centers and from the distribution centers to the customers. Often uncertainty may be associated with demand, supply or various relevant costs. In many cases, randomness and fuzziness simultaneously appear in a system, in order to describe this phenomenon; we introduce the concept of hybrid variable and propose a mixed-integer programming model for random fuzzy facility location–allocation problem. By expected value and chance constraint programming technique, this model is reduced to a deterministic model. Furthermore, a priority-based genetic algorithm is designed for solving the proposed programming model and the efficacy and the efficiency of this method and algorithm are demonstrated by a numerical example. Till now, few has formulated or attacked the FLA problems in the above manner. Furthermore, the techniques illustrated in this paper can easily be applied to other SCN problems. Therefore, these techniques are the appropriate tools to tackle other supply chain network problems in realistic environments.     

Multi-objective biogeography-based optimization for supply chain network design under uncertainty

This paper proposes a new two-stage optimization method for multi-objective supply chain network design (MO-SCND) problem with uncertain transportation costs and uncertain customer demands. On the basis of risk-neutral and risk-averse criteria, we develop two objectives for our SCND problem. We introduce two solution concepts for the proposed MO-SCND problem, and use them to define the multi-objective value of fuzzy solution (MOVFS). The value of the MOVFS measures the importance of uncertainties included in the model, and helps us to understand the necessity of solving the two-stage multi-objective optimization model. When the uncertain transportation costs and customer demands have joined continuous possibility distributions, we employ an approximation approach (AA) to compute the values of two objective functions. Using the AA, the original optimization problem becomes an approximating mixed-integer multi-objective programming model. To solve the hard approximating optimization problem, we design an improved multi-objective biogeography-based optimization (MO-BBO) algorithm integrated with LINGO software. We also compare the improved MO-BBO algorithm with the multi-objective genetic algorithm (MO-GA). Finally, a realistic dairy company example is provided to demonstrate that the improved MO-BBO algorithm achieves the better performance than MO-GA in terms of solution quality.     

A steady-state genetic algorithm for multi-product supply chain network design

Supply chain network (SCN) design is to provide an optimal platform for efficient and effective supply chain management (SCM). The problem is often an important and strategic operations management problem in SCM. The design task involves the choice of facilities (plants and distribution centers (DCs)) to be opened and the distribution network design to satisfy the customer demand with minimum cost. This paper presents a solution procedure based on steady-state genetic algorithms (ssGA) with a new encoding structure for the design of a single-source, multi-product, multi-stage SCN. The effectiveness of the ssGA has been investigated by comparing its results with those obtained by CPLEX, Lagrangean heuristic, hyrid GA and simulated annealing on a set of SCN design problems with different sizes.     

A credibility-based fuzzy location model with Hurwicz criteria for the design of distribution systems in B2C e-commerce

Facility location problem is one of the most critical elements in the design of distribution systems, and numerous studies have focused on this issue. However, facility location theory and guidelines for B2C firms are sparse. In this paper, with regard to the customer characteristics peculiar to B2C e-commerce and the turbulence of the competitive market, a new fuzzy location model is proposed to optimize the distribution system design in B2C e-commerce. The model adopts a hierarchical agglomerative clustering method to classify customers and estimate the fuzzy delivery cost. At the same time, due to the turbulence of competitive market, both market supply and customer demand are treated as fuzzy variables in the model. Afterward, the credibility measure and Hurwicz criterion are introduced to convert the model into a crisp one which has NP-hard complexity. In order to solve the crisp model, an improved genetic algorithm with particle swarm optimization is developed. Finally, the computational results of some numerical examples are used to illustrate the application and performance of the proposed model and algorithm.     

A credibility-based fuzzy location model with Hurwicz criteria for the design of distribution systems in B2C e-commerce

Facility location problem is one of the most critical elements in the design of distribution systems, and numerous studies have focused on this issue. However, facility location theory and guidelines for B2C firms are sparse. In this paper, with regard to the customer characteristics peculiar to B2C e-commerce and the turbulence of the competitive market, a new fuzzy location model is proposed to optimize the distribution system design in B2C e-commerce. The model adopts a hierarchical agglomerative clustering method to classify customers and estimate the fuzzy delivery cost. At the same time, due to the turbulence of competitive market, both market supply and customer demand are treated as fuzzy variables in the model. Afterward, the credibility measure and Hurwicz criterion are introduced to convert the model into a crisp one which has NP-hard complexity. In order to solve the crisp model, an improved genetic algorithm with particle swarm optimization is developed. Finally, the computational results of some numerical examples are used to illustrate the application and performance of the proposed model and algorithm.     

预售策略下的快速时尚品物流分销网络双层规划模型

为解决预售策略下的快速时尚品物流分销网络的选址与分配问题,将最小化网络运作成本作为决策目标,构建了反映网络销售商与顾客决策行为的双层规划模型。模型充分考虑了决策双方的共同利益,并利用交互式模糊算法来确定中央仓库和第三方物流企业的选择、分配及服务方案。通过实例分析,既验证了算法的可行性,同时也表明了中央仓库的选址应尽量靠近需求点密集的区域,网络销售商应提供少量第三方物流服务企业供顾客选择。     

Solving fuzzy capacitated location routing problem using hybrid variable neighborhood search and evolutionary local search

A fuzzy capacitated location routing problem (FCLRP) is solved by using a heuristic method that combines variable neighborhood search (VNS) and evolutionary local search (ELS). Demands of the customer and travel times between customers and depots are considered as fuzzy and deterministic variables, respectively in FCLRP. Heterogeneous and homogeneous fleet sizes are performed together to reach the least multi-objective cost in a case study. The multi-objective cost consists of transportation cost, additional cost, vehicle waiting cost and delay cost. A fuzzy chance constrained programming model is added by using credibility theory. The proposed method reaches the solution by performing four stages. In the first stage, initial solutions are obtained by using a greedy heuristic method, and then VNS heuristic, which consists of seven different neighborhood structures, is performed to improve the solution quality in the second stage. In the third stage, a perturbation procedure is applied to the improved solution using ELS algorithm, and then VNS heuristic is applied again in the last stage. The combination of VNS and ELS is called VNSxELS algorithm and applied to a case study, which has fifty-seven customers and five distributing points, effectively in a reasonable time.     

基于时空聚类求解带容积约束的选址-路径问题

选址-路径问题是供应链管理和物流系统规划中的一个重要问题,对总成本具有十分重要的影响.对考虑配送中心容积约束的带时间窗的选址-路径问题进行研究,建立以总成本最小和客户满意度最大为目标的多目标规划模型,提出两阶段算法对其进行求解.首先,利用k-means聚类算法确定配送中心选址;然后,提出一种基于时间-空间双因素的客户划分方法以确定配送中心所服务客户;最后,利用粒子群算法对各配送中心的配送路径进行规划.数值算例表明,所提出的算法较其他已有算法,均能有效地降低物流运作总成本及总配送路径长度,为解决带容积约束及时间窗的选址-路径问题提供了一种新的解决思路.     

连锁门店选址与配送中心选择联合决策的f-MIGP模型

建立了连锁门店选址和配送中心选择联合决策问题的模糊多目标混合整数规划模型．针对该模型的特殊结构。提出一种适用的求解策略：首先确定每个模糊目标的隶属度函数；然后将模糊多目标混合整数规划模型转化为等价的清晰多目标混合整数规划模型，通过最大最小算子求出目标值；最后借助于两阶段算法，求出问题的最优解．通过应用算例进一步说明了该模型的有效性和可行性．     

基于改进GA的救援物资中转网点定位

针对大规模救援物资调运的多目标中转运输网点定位问题,考虑运输费用、中转网点的作业变动费用和运输时间,建立一个救援物资中转运输网点的非线性多目标混合整数规划模型。为有效求解该模型,提出一种基于矩阵编码的遗传算法,利用费用矩阵标杆的寻优导向信息提高遗传变异算子的局部搜索能力,提高全局收敛速度。通过算例分析验证该模型和算法的有效性。     

可持续闭环供应链网络设计的多目标模糊规划问题

为解决不确定条件下可持续闭环供应链网络设计的问题,以成本和环境影响最小、社会影响最大为目标,建立带有模糊参数的多目标闭环供应链网络规划模型.首先采用Me测度处理相关模糊目标和参数,并运用加权增广Epsilon-约束方法解决多目标问题,在此基础上设计一种基于[0, 1]随机数的双层编码遗传鲸鱼(GA–WOA)混合算法进行求解,然后构造多个不同规模算例,将混合算法求解结果与CPLEX、遗传算法的求解结果进行对比,结果证明该编码方式和混合算法具有合理性.最后针对模型的多个参数变化进行分析,以验证所建模型的可行性.     

Stochastic facility location with general long-run costs and convex short-run costs

This paper addresses the problem of minimizing the expected cost of locating a number of single product facilities and allocating uncertain customer demand to these facilities. The total costs consist of two components: firstly linear transportation cost and secondly the costs of investing in a facility as well as maintaining and operating it. These facility costs are general and non-linear in shape and could express both changing economies of scale and diseconomies of scale. We formulate the problem as a two-stage stochastic programming model where both demand and short-run costs may be uncertain at the investment time. We use a solution method based on Lagrangean relaxation, and show computational results for a slaughterhouse location case from the Norwegian meat industry.     

Multi-objective decision making model under fuzzy random environment and its application to inventory problems

In this paper, we concentrate on developing a fuzzy random multi-objective model about inventory problems. By giving some definitions and discussing some properties of fuzzy random variable, we design a method of solving solution sets of fuzzy random multi-objective programming problems. These are applied to numerical inventory problems in which all inventory costs, purchasing and selling prices in the objectives and constraints are assumed to be fuzzy random variables in nature, and then the impreciseness of fuzzy random variables in the above objectives and constraints are transformed into fuzzy variables which are similar trapezoidal fuzzy numbers. The exact parameters of fuzzy membership function and probability density function can be obtained through fuzzy random simulating the past dates. By comparing the results with those from the fuzzy multi-objective models, we believe that the proposed fuzzy random multi-objective model and hybrid intelligent algorithm provide significant solutions to construct other inventory models with fuzzy random variables in real life.     

Computation of probabilistic linear programming problems involving normal and log-normal random variables with a joint constraint

A method for solving single- and multi-objective probabilistic linear programming problems with a joint constraint is presented. It is assumed that the parameters in the probabilistic linear programming problems are random variables, and the probabilistic problem is converted to an equivalent deterministic mathematical programming problem. In this paper the parameters are generally considered as normal and log-normal random variables. A non-linear programming method is used to solve the single-objective deterministic problem, and a fuzzy programming method is used to solve the multi-objective deterministic problem. Finally, a numerical example is presented to illustrate the methodology.     

Two parameter tuned multi-objective evolutionary algorithms for a bi-objective vendor managed inventory model with trapezoidal fuzzy demand

This paper presents a bi-objective vendor managed inventory (BOVMI) model for a supply chain problem with a single vendor and multiple retailers, in which the demand is fuzzy and the vendor manages the retailers’ inventory in a central warehouse. The vendor confronts two constraints: number of orders and available budget. In this model, the fuzzy demand is formulated using trapezoidal fuzzy number (TrFN) where the centroid defuzzification method is employed to defuzzify fuzzy output functions. Minimizing both the total inventory cost and the warehouse space are the two objectives of the model. Since the proposed model is formulated into a bi-objective integer nonlinear programming (INLP) problem, the multi-objective evolutionary algorithm (MOEA) of non-dominated sorting genetic algorithm-II (NSGA-II) is developed to find Pareto front solutions. Besides, since there is no benchmark available in the literature to validate the solutions obtained, another MOEA, namely the non-dominated ranking genetic algorithms (NRGA), is developed to solve the problem as well. To improve the performances of both algorithms, their parameters are calibrated using the Taguchi method. Finally, conclusions are made and future research works are recommended.     

求解车辆路径问题的离散粒子群算法

考虑车辆行驶时间和顾客服务时间的不确定性,建立了以车辆配送总费用最小为目标的机会约束规划模型,将其进行清晰化处理,使之转化为一类确定性数学模型,并构造了求解该问题的一种离散粒子群算法。算法重新定义了粒子的运动方程及其相关离散量运算法则,并设计了排斥算子来维持群体的多样性。与标准遗传算法和粒子群算法比较,该算法能够有效避免算法陷入局部最优,取得了满意的结果。     

网上书店服务质量评价模型研究

影响网上书店服务质量的因素主要有网站可用性、网站易用性、网站完整性、网站信誉与评价、顾客个人倾向、顾客风险感知、执行费用、执行周期、网络安全技术和交易制度。基于这些影响因素,设计了网上书店服务质量评价指标体系,并对指标选择的合理性进行了检验。在此基础上,建立了网上书店服务质量模糊综合评价模型,并采集数据对服务质量评价模型进行了实证研究。     

多目标模糊机会约束规划的低碳多式联运路径优化

针对时间窗与需求量不确定性下的多式联运路径优化问题,运用梯形模糊数表示模糊需求量与模糊时间窗,并考虑碳排放成本、运输成本以及客户满意度,建立了多目标模糊机会约束模型。固定的交叉、变异概率会直接影响算法的收敛性,针对此问题,将自适应性与非支配排序遗传算法Ⅱ（NSGA-Ⅱ）结合,并通过与DOCPLEX和NSGA-Ⅱ的对比验证了所提模型与算法的有效性。最后,探究了碳税值与模糊需求量偏好值的变化对优化结果的影响。研究结果表明：碳税值的提出可有效促进“公转铁、公转水”,从而显著减少碳排放量,然而过高的碳税值并不一定意味着碳排放量的减少,还会对企业造成过高的成本;模糊需求量偏好值的提高会造成总成本的增加,意味着运输经济性与可靠性两者不可兼得。因此,合理设置碳税值与模糊需求量偏好值是提高多式联运环保效益与运输效益的有效方式。     

Applications of artificial intelligence for optimization of compressor scheduling

This paper presents a feasibility study of evolutionary scheduling for gas pipeline operations. The problem is complex because of several constraints that must be taken into consideration during the optimization process. The objective of gas pipeline operations is to transfer sufficient gas from gas stations to consumers so as to satisfy customer demand with minimum costs. The scheduling involves selection of a set of compressors to operate during a shift. The scheduling decision has to be made so as to satisfy the dual objectives of minimizing the sum of fuel cost, start-up cost, the cost of gas wasted due to oversupply, and satisfying minimal operative and inoperative time of the compressors. The problem was decomposed into the two subproblems of gas load forecast and selection of compressors. Neural networks were used for forecasting the load; and genetic algorithms were used to search for a near optimal combination of compressors. The study was conducted on a subsystem of the pipeline network located in southeastern Saskatchewan, Canada. The results are compared with the solutions generated by an expert system and a fuzzy linear programming model.