A stochastic optimization model for integrated forward/reverse logistics network design

In this paper, we develop a stochastic programming model for an integrated forward/reverse logistics network design under uncertainty. First, an efficient deterministic mixed integer linear programming model is developed for integrated logistics network design to avoid the sub-optimality caused by the separate design of the forward and reverse networks. Then the stochastic counterpart of the proposed MILP model is developed by using scenario-based stochastic approach. Numerical results show the power of the proposed stochastic model in handling data uncertainty.     

A bi-objective reverse logistics network analysis for post-sale service

Reverse logistics, induced by various forms of return, has received growing attention throughout this decade. Reverse logistics network design is a major strategic issue. This paper addresses the analysis of reverse logistic networks that deal with the returns requiring repair service. A problem involving a manufacturer outsourcing to a third-party logistics (3PLs) provider for its post-sale services is proposed. First, a bi-objective optimization model is developed. Two objectives, minimization of the overall costs and minimization of the total tardiness of cycle time, are addressed. The facility capacity option at each potential location is treated as a discrete parameter. The purpose is to find a set of non-dominated solutions to the facility capacity arrangement among the potential facility locations, as well as the associated transportation flows between customer areas and service facilities. Then, a solution approach is designed for solving this bi-objective optimization model. The solution approach consists of a combination of three algorithms: scatter search, the dual simplex method and the constraint method. Finally, computational analyses are performed on trial examples. Numerical results present the trade-off relationship between the two objectives. The numerical results also show that the optimization for the first objective function leads to a centralized network structure; the optimization for the second objective function results in a decentralized network structure.     

A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs

Today's competitive business environment has resulted in increasing cooperation among individual companies as members of a supply chain. Accordingly, third party logistics providers (3PLs) must operate supply chains for a number of different clients who want to improve their logistics operations for both forward and reverse flows. As a result of the dynamic environment in which these supply chains must operate, 3PLs must make a sequence of inter-related decisions over time. However, in the past, the design of distribution networks has been independently conducted with respect to forward and reverse flows. Thus, this paper presents a mixed integer nonlinear programming model for the design of a dynamic integrated distribution network to account for the integrated aspect of optimizing the forward and return network simultaneously. Since such network design problems belong to a class of NP hard problems, a genetic algorithm-based heuristic with associated numerical results is presented and tested in a set of problems by an exact algorithm. Finally, a solution of a network plan would help in the determination of various resource plans for capacities of material handling equipments and human resources.     

A restricted dynamic model for refuse collection network design in reverse logistics

This study considers the problem of determining the locations of collection points as well as the allocations of refuses at demand points to collection points while satisfying the capacity and the maximum allowable collection distance constraints at each collection point. To consider fluctuating demands commonly occurred in refuse collection systems, we consider a restricted dynamic version of the problem in which the locations are fixed, but the allocations are changed over a given planning horizon. The problem is formulated as an integer programming model for the objective of minimizing the sum of fixed costs to open collection points and variable costs to transport refuses between demand and collection points, and then, due to the complexity of the problem, two heuristic algorithms are suggested. The heuristics, called the multi-stage branch and bound and the modified drop heuristics in this paper, are based on the decomposition of the entire problem into the static location and the dynamic allocation sub-problems. Computational experiments were done on various test instances, and the results are reported.     

A memetic algorithm for extending wireless sensor network lifetime

Extending the lifetime during which a wireless sensor network (WSN) can cover all targets is a key issue in WSN applications such as surveillance. One effective method is to partition the collection of sensors into several covers, each of which must include all targets, and then to activate these covers one by one. Therefore, more covers enable longer lifetime. The problem of finding the maximum number of covers has been modeled as the SET K-COVER problem, which has been proven to be NP-complete. This study proposes a memetic algorithm to solve this problem. The memetic algorithm utilizes the Darwinian evolutionary scheme and Lamarckian local enhancement to search for optima given the considerations of global exploration and local exploitation. Additionally, the proposed algorithm does not require an upper bound or any assumption about the maximum number of covers. The simulation results on numerous problem instances confirm that the algorithm significantly outperforms several heuristic and evolutionary algorithms in terms of solution quality, which demonstrate the effectiveness of the proposed algorithm in extending WSN lifetime.     

可持续逆向物流网络设计研究进展及趋势

随着可持续发展理念的深入践行以及逆向物流活动对环境与社会影响的日益显著,可持续逆向物流网络设计问题正成为研究的热点.首先系统总结了可持续逆向物流网络研究的总体现状,并对经济、环境、社会目标测度指标与方法、数学模型的决策变量、目标函数与约束条件、不确定因素考虑及处理方法、问题求解方法与工具以及研究应用情况进行概述.研究发现,经济、环境与社会影响测度指标不完善,尤其是环境与社会影响类决策变量考虑较少,社会可持续研究总体偏少且测度方法有待突破.同时,较少考虑多周期、多产品、越库作业、按质量等级回收、不确定环境等真实逆向物流运作场景,战略、战术与运作层面集成优化不够,以上这些均限制了模型的应用.面对复杂的数学模型,较少采用区间优化、模拟仿真等方法求解不确定问题,更为高效、可靠的元启发式算法或精确算法亟待设计,同时应用仿真优化技术、交叉熵等进行效能评估或算法间比较以提升网络设计的效率和科学性.最后展望了可持续逆向物流网络设计潜在的发展趋势.     

一种集成最后一公里的四方物流网络设计问题启发式算法

最后一公里分销网络可以帮助企业达成高响应性的供应链管理目标,集成最后一公里四方物流网络设计问题成为网络设计的一个重要研究方向.解决该问题需要对分销中心的位置,三方物流的选择、分配以及其车辆路径规划进行决策.在满足车辆路径规划、流守恒等约束条件下,以最小化网络构建费用为目标建立混合整数规划模型.由于该问题的NP-难特性,...     

Hybrid simulation-analytical modeling approaches for the reverse logistics network design of a third-party logistics provider

In this study, we consider a manufacturer that has strategically decided to outsource the company specific reverse logistics (RL) activities to a third-party logistics (3PL) service provider. Given the locations of the collection centers and reprocessing facilities, the RL network design of the 3PL involves finding the number and places of the test centers under supply uncertainty associated with the quantity of the returns. Hybrid simulation-analytical modeling, which iteratively uses mixed integer programming models and simulation, is a suitable framework for handling the uncertainties in the stochastic RL network design problem. We present two hybrid simulation-analytical modeling approaches for the RL network design of the 3PL. The first one is an adaptation of a problem-specific approach proposed in the literature for the design of a distribution network design of a 3PL. The second one involves the development of a generic approach based on a recently proposed novel solution methodology. In the generic approach instead of exchanging problem-specific parameters between the analytical and simulation model, the interaction is governed by reflecting the impact of uncertainty obtained via simulation to the objective function of the analytical model. The results obtained from the two approaches under different scenario and parameter settings are discussed.     

回收不确定下WEEE逆向物流网络鲁棒优化设计

电子电器废弃物（WEEE）存在对环境和人体健康的危害,有效对其进行回收能避免此类危害和提高资源的利用率。WEEE逆向物流回收网络的设计为实现这一目标起到了关键的作用。考虑WEEE逆向物流网络运作的不确定性,引入风险偏好系数和约束背离惩罚系数,建立了WEEE逆向物流网络的鲁棒优化模型。该模型能允许决策者对系统运作的鲁棒水平进行调节,同时能允许决策者对风险偏好进行调节。仿真结果表明建立的模型能有效抑制逆向物流系统运作的不确定性,使系统具有更低的风险。     

制造/再制造集成物流网络的优化设计研究

考虑到闭环物流系统中不同周期内各消费区域的产品需求数量、回收产品质量的不确定性,建立了多周期、有能力限制、正逆向物流集成运作的混合整数非线性规划模型。以总运营成本最少为目标,确定了制造/再制造集成物流系统中各种设施的数量、规模、位置以及各设施间的物流量分以配。通过算例验证了模型的有效性。     

集成三级物流系统的网络规划问题研究*

选址—路径问题是物流系统中的一个组合优化问题,启发式方法一般采用两阶段法将其分解为选址分派和车辆路径问题来顺序求解,但这两个阶段间的信息无法有效传递,因而往往不能得到集成问题的优化解。设计了具有能力约束的三级物流网络选址—路径问题模型,采用遗传算法整体求解该问题,避免了顺序求解带来的问题;设计了采用整数编码的三级染色体编码结构,采用禁忌搜索算法对交叉和变异操作作了改进,提高了算法的搜索效率,能够更适合集成问题的求解;最后通过算例分析,验证了本算法求解小规模选址路径问题的有效性。     

An optimization model for reverse logistics network under stochastic environment by using genetic algorithm

Recovery of used products has become increasingly important recently due to economic reasons and growing environmental or legislative concern. Product recovery, which comprises reuse, remanufacturing and materials recycling, requires an efficient reverse logistic network. One of the main characteristics of reverse logistics network problem is uncertainty that further amplifies the complexity of the problem. The degree of uncertainty in terms of the capacities, demands and quantity of products exists in reverse logistics parameters. With consideration of the factors noted above, this paper proposes a probabilistic mixed integer linear programming model for the design of a reverse logistics network. This probabilistic model is first converted into an equivalent deterministic model. In this paper we proposed multi-product, multi-stage reverse logistics network problem for the return products to determine not only the subsets of disassembly centers and processing centers to be opened, but also the transportation strategy that will satisfy demand imposed by manufacturing centers and recycling centers with minimum fixed opening cost and total shipping cost. Then, we propose priority based genetic algorithm to find reverse logistics network to satisfy the demand imposed by manufacturing centers and recycling centers with minimum total cost under uncertainty condition. Finally, we apply the proposed model to a numerical example.     

A differential memetic algorithm

Memetic algorithms have been devised to rectify the absence of a local search mechanism in evolutionary algorithms. This paper proposes a differential memetic algorithm (DMA). To this end, first we propose a differential bidirectional random search as a local search algorithm. Then, a randomized blending crossover (RBleX) is proposed which aimed to scatter the new born offspring more diversely in the whole search space. We devise our proposed DMA, by using the RBleX crossover in the GA, and including the DBRS local search algorithm. A comparison of the performance of the DMA and those of seven other evolutionary/memetic or hybrid algorithms reported in two different papers on numerous bechmark functions demonstrates better performance of proposed DMA algorithm in most of the cases.     

Strategic network design for reverse logistics and remanufacturing using new and old product modules

Establishment of reverse logistics (RL) networks for various original equipment manufacturers (OEM’s) is gaining significant importance. Various green legislations are forcing OEMs to take back their used, end-of-lease or end-of-life products, or products under warranty to minimize wastes and conserve resources. Therefore OEMs have turned to a better design of their products for maximum reuse and recycling and to retrieve back the used products through a network for reuse, remanufacture, recycle or disposal, so that maximum value can be achieved from their used products. However, designing of network points and assigning capacities to them depend not only on the volume of returned products but also on the demand for remanufactured products and the parts of used products. If OEMs are not able to add value to the used product, there would be no incentive to design a complex network.In this paper, a mathematical model for the design of a RL network is proposed. It is assumed that the returned products need to be consolidated in the warehouse before they are sent to reprocessing centres for inspection and dismantling. Dismantled parts are sent for remanufacturing or to the secondary market as spare parts. Recycling and disposal of these modules are also considered in the model. The use of the model is shown through its application in a numerical example.     

A linear relaxation-based heuristic approach for logistics network design

We address the problem of designing and planning a multi-period, multi-echelon, multi-commodity logistics network with deterministic demands. This consists of making strategic and tactical decisions: opening, closing or expanding facilities, selecting suppliers and defining the product flows. We use a heuristic approach based on the linear relaxation of the original mixed integer linear problem (MILP). The main idea is to solve a sequence of linear relaxations of the original MILP, and to fix as many binary variables as possible at every iteration. This simple process is coupled with several rounding procedures for some key decision variables. The number of binary decision variables in the resulting MILP is small enough for it to be solved with a solver. The main benefit of this approach is that it provides feasible solutions of good quality within an affordable computation time.     

Integrated multistage logistics network design by using hybrid evolutionary algorithm

Design and optimization of logistics is very important issue, which plans, implements and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption in order to meet customers’ requirements. In this paper, we formulate an integrated multistage logistics network model with considering the direct shipment and direct delivery of logistics and inventory. In addition, we propose an effective hybrid evolutionary algorithm (hEA) to solve this problem: (1) we employ an extended priority-based encoding method, (2) combine a local search (LS) technique and (3) proposed a new fuzzy logic control (FLC) to enhance the search ability of EA. Finally, numerical analysis of case study is carried out to show the effectiveness of the proposed approach.     

A bi-objective approach for shortest-path network interdiction

In the literature, solution approaches to the shortest-path network interdiction problem have been developed for optimizing a single figure-of-merit of the network configuration when considering limited amount of resources available to interdict network links. This paper presents a newly developed evolutionary algorithm that allows approximating the optimal Pareto set of network interdiction strategies when considering bi-objective shortest path problems. Thus, the paper considers the concurrent optimization of two objectives: (1) maximization of shortest-path length and (2) minimization of interdiction strategy cost. Also, the paper considers the transformation of the first objective into the minimization of the most reliable path reliability. To solve these multi-objective optimization problems, an evolutionary algorithm has been developed. This algorithm is based on Monte Carlo simulation, to generate potential network interdiction strategies, graph theory to analyze strategies’ shortest path or most reliable path and, an evolutionary search driven by the probability that a link will appear in the optimal Pareto set. Examples for different sizes of networks and network behavior are used throughout the paper to illustrate and validate the approach.     

A competent memetic algorithm for complex scheduling

We face the job shop scheduling problem with sequence dependent setup times and makespan minimization by memetic algorithm. This algorithm combines a classic genetic algorithm with a local searcher. The performance of the local searcher relies on the combination of a tabu search algorithm with a neighborhood structure termed N ^S that are thoroughly described and analyzed. Also, two evolution models are considered: Lamarckian and Baldwinian evolution. We report results from an experimental study across conventional benchmark instances showing that the proposed algorithm outperforms the current state-of-the-art methods and that Lamarckian evolution is better than Baldwinian evolution.     

基于混合概率解发掘算法的第四方物流弹复性网络设计

为了使第四方物流系统能够安全、有效地运作,研究基于弹复性的第四方物流网络设计问题。建立以网络构建成本为约束、弹复性为目标的优化模型,针对问题模型设计基于最优计算预算分配的混合概率解发掘算法。通过随机产生的不同规模问题,对混合概率解发掘算法进行性能评估,并将实验结果与传统的概率解发掘算法进行比较。实验结果表明了模型与算法的有效性。     

逆向物流多产品两阶段再生产网络鲁棒优化

考虑不同周期内逆向物流回收数量的不确定性和再生产设备的生产能力约束,将逆向物流回收数量、再生产成本和市场需求作为不确定参数,以成本最小为目标,建立了包含生产成本、设备运作成本、库存成本在内的多周期多产品两阶段逆向物流网络鲁棒优化模型。通过算例验证了模型的有效性。