供应链环境下带越库配送的车辆路径问题

从零售业纵向供应链整合入手,考虑供应商、零售商和配送中心构成的协同配送网络,研究带越库配送的车辆路径问题。分析越库配送实施要求和操作准则,设置协同到达作业时间,将配送过程分为集货、分拣和送货三个阶段,建立最小化车辆运输成本和固定成本为目标的越库配送路径优化模型。考虑模型的复杂性,设计改进遗传算法进行求解。最后由仿真实例验证模型可行性和算法有效性。结果表明,越库配送模式能有效服务城市区域零售门店的及时供货情况,在配送时间和运输成本方面有着显著优势。     

供应链环境下带越库配送的多配送中心车辆路径问题

从零售业纵向供应链管理入手,考虑由供应商、零售商和多个配送中心构成的城市物流协同配送网络,研究带有越库配送的多配送中心车辆路径问题.分析越库配送的实施要求和操作准则,将配送过程分为集货、送货阶段.应对产品种类多样化需求,设置集货过程车辆协同作业返回配送中心,送货过程需求可拆分的运作机制.以最小化车辆运输成本和操作成本为目标,建立多配送中心车辆路径问题优化模型.针对模型特性设计改进遗传算法进行求解.最后通过仿真实例验证模型的可行性和算法的有效性, 结果表明,越库配送模式能有效服务城市区域零售门店的及时供货,在配送时间和运输成本方面具有显著优势.     

考虑随机旅行时间与二维装载约束的越库配送车辆路径优化

面向越库配送模式下二维装载和车辆路径联合优化,考虑现实配送过程的不确定性因素,提出考虑随机旅行时间和二维装载约束的越库配送车辆路径问题.基于蒙特卡洛模拟与场景分析方法,建立以运输成本、车辆固定成本以及时间窗期望惩罚成本之和最小化为目标的带修正随机规划模型.继而根据问题特征,设计改进的自适应禁忌搜索算法和基于禁忌搜索的多重排序最佳适应装箱算法进行求解.其中,改进的自适应禁忌搜索算法在禁忌搜索算法的基础上引入自适应机制,对不同邻域算子进行动态选择,并提出基于移除-修复策略的多样性机制以增强算法的寻优能力.数值实验表明,所提出的模型与方法能够有效求解考虑随机旅行时间和二维装载约束的越库配送车辆路径问题,自适应与多样性机制能一定程度上增强算法的全局搜索能力.     

An integrated supply chain network design problem for bidirectional flows

Coordination among supply chains has elicited considerable attention in both academia and industry. This paper investigates an integrated supply chain network design problem that involves the determination of the locations for distribution centers and the assignment of customers and suppliers to the corresponding distribution centers. The problem simultaneously involves the distribution of products from the manufacturer to the customers and the collection of components from the suppliers to the manufacturer via cross-docking at distribution centers. The co-location of different types of distribution centers and coordinated transportation are introduced to achieve cost savings. A Lagrangian relaxation-based algorithm is then developed. Extensive computational experiments show that the proposed algorithm has stable performance and outperforms CPLEX for large-scale problems. An industrial case study is considered and sensitivity analysis is conducted to explore managerial insights. Finally, conclusions are drawn, and future research directions are outlined.     

Analysing the factors that influence the Pareto frontier of a bi‐objective supply chain design problem

In this paper a bi‐objective multi‐product model for the design of a production/distribution supply chain logistic network with four echelons is considered. The proposed optimization model minimizes the total cost of the network (including the fixed cost to open facilities and the transportation costs between them) and the total CO₂ emissions. Five factors (network size, product complexity, cost variability, CO₂ emissions generation and over‐capacity) are considered for the experimental framework. The problem is solved using the ε‐constraint method and the resulting Pareto frontiers (PF) are characterized using five new metrics specifically developed for analysing how those factors affect the resulting optimal configurations. The results show that over‐capacity and product complexity are the two most influential factors regarding the characteristics of the PF, and that their effects are in the same direction: more complexity and capacity mean a wider set of optima alternatives, some close to the ideal point, and in general with a smaller number of links used.     

A novel learning based approach for a new integrated location-routing and scheduling problem within cross-docking considering direct shipment

One of the most important problem in supply chain management is the design of distribution systems which can reduce the transportation costs and meet the customer's demand at the minimum time. In recent years, cross-docking (CD) centers have been considered as the place that reduces the transportation and inventory costs. Meanwhile, neglecting the optimum location of the centers and the optimum routing and scheduling of the vehicles mislead the optimization process to local optima. Accordingly, in this research, the integrated vehicle routing and scheduling problem in cross-docking systems is modeled. In this new model, the direct shipment from the manufacturers to the customers is also included. Besides, the vehicles are assigned to the cross-dock doors with lower cost. Next, to solve the model, a novel machine-learning-based heuristic method (MLBM) is developed, in which the customers, manufacturers and locations of the cross-docking centers are grouped through a bi-clustering approach. In fact, the MLBM is a filter based learning method that has three stages including customer clustering through a modified bi-clustering method, sub-problems’ modeling and solving the whole model. In addition, for solving the scheduling problem of vehicles in cross-docking system, this paper proposes exact solution as well as genetic algorithm (GA). GA is also adapted for large-scale problems in which exact methods are not efficient. Furthermore, the parameters of the proposed GA are tuned via the Taguchi method. Finally, for validating the proposed model, several benchmark problems from literature are selected and modified according to new introduced assumptions in the base models. Different statistical analysis methods are implemented to assess the performance of the proposed algorithms.     

A genetic algorithm for solving the fixed-charge transportation model: Two-stage problem

Transportation of goods in a supply chain from plants to customers through distribution centers (DCs) is modeled as a two-stage distribution problem in the literature. In this paper we propose genetic algorithms to solve a two-stage transportation problem with two different scenarios. The first scenario considers the per-unit transportation cost and the fixed cost associated with a route, coupled with unlimited capacity at every DC. The second scenario considers the opening cost of a distribution center, per-unit transportation cost from a given plant to a given DC and the per-unit transportation cost from the DC to a customer. Subsequently, an attempt is made to represent the two-stage fixed-charge transportation problem (Scenario-1) as a single-stage fixed-charge transportation problem and solve the resulting problem using our genetic algorithm. Many benchmark problem instances are solved using the proposed genetic algorithms and performances of these algorithms are compared with the respective best existing algorithms for the two scenarios. The results from computational experiments show that the proposed algorithms yield better solutions than the respective best existing algorithms for the two scenarios under consideration.     

Applied p-median and p-center algorithms for facility location problems

Facility location problems with the objective to minimize the sum of the setup cost and transportation cost are studied in this paper. The setup and transportation costs are considered as a function of the number of opened facilities. Three methods are introduced to solve the problem. The facility location model with bounds for the number of opened facility is constructed in this work. The relationship between setup cost and transportation cost is studied and used to build these methods based on greedy algorithm, p-median algorithm and p-center algorithm. The performance of the constructed methods is tested using 100 random data sets. In addition, the networks representing the road transportation system of Chiang Mai city and 5 provinces in Northern Thailand are illustrated and tested using all presented methods. Simulation results show that the method developed from greedy algorithm is suitable for solving problems when the setup cost is higher than transportation cost while the opposite cases are more efficiently solved with the method developed by the p-median problem.     

基于改进神经网络的非满载车辆路线优化模型

货物流通过程中,目前流行的车辆调度方式--基于简单的神经网络模型设计,造成运输成本的浪费。提出了一种基于改进神经网络的非满载车辆路线优化挖掘模型,来解决运输过程中的非满载车辆调度优化问题。改进的模型通过对非满载车辆时域长度和空域概率的加权、对神经网络稳定状态进行约束、建立非满载车辆起点和终点函数方程生成改进算法配送模型,并通过对新模型进行时间窗加权,合成了改进神经网络非满载车辆挖掘模式。仿真结果表明,该挖掘模型与传统的神经网络计算方法相比,能够提高非满载车辆路线选择效率和正确性,取得了较好的效果。     

Positioning of goods in a cross-docking environment

Cross docking is one of the options to reduce lead times and inventories and to improve customer response time in supply chains. Cross-docking centres are dynamic environments where products arrive, are regrouped, and leave the same day. In this paper we focus on the process of short-term storage of unit-loads in a cross-docking environment. The goal is to determine temporary storage locations for incoming unit loads such that the travel distances of the forklift trucks with these unit loads are minimised. We model this problem as a novel application of the minimum cost flow problem and show the applicability of the model for different types of layouts and priorities. We demonstrate both the efficiency and effectiveness of the method in the operational and design phase at cross-docking environments by applying it to practice-oriented examples. Furthermore, we show that the approach is superior to a commonly used heuristic method.     

A hybrid simulated annealing algorithm for location and routing scheduling problems with cross-docking in the supply chain

The location and routing scheduling problems with cross-docking can be regarded as new research directions for distribution networks in the supply chain. The aims of these problems are to concurrently design a cross-docking center location and a vehicle routing scheduling model, known as NP-hard problems. This paper presents a two-stage mixed-integer programming (MIP) model for the location of cross-docking centers and vehicle routing scheduling problems with cross-docking due to potential applications in the distribution networks. Then, a new algorithm based on a two-stage hybrid simulated annealing (HSA) with a tabu list taken from tabu search (TS) is proposed to solve the presented model. This proposed HSA not only prevents revisiting the solution but also maintains the stochastic nature. Finally, small and large-scale test problems are randomly generated and solved by the HSA algorithm. The computational results for different problems show that the proposed HSA performs well and converges fast to reasonable solutions.     

Efficient solution of a class of location–allocation problems with stochastic demand and congestion

We consider a class of location–allocation problems with immobile servers, stochastic demand and congestion that arises in several planning contexts: location of emergency medical clinics; preventive healthcare centers; refuse collection and disposal centers; stores and service centers; bank branches and automated banking machines; internet mirror sites; web service providers (servers); and distribution centers in supply chains. The problem seeks to simultaneously locate service facilities, equip them with appropriate capacities, and allocate user demand to these facilities such that the total cost, which consists of the fixed cost of opening facilities with sufficient capacities, the access cost of users׳ travel to facilities, and the queuing delay cost, is minimized. Under Poisson user demand arrivals and general service time distributions, the problem is set up as a network of independent M/G/1 queues, whose locations, capacities and service zones need to be determined. The resulting mathematical model is a non-linear integer program. Using simple transformation and piecewise linear approximation, the model is linearized and solved to ϵ-optimality using a constraint generation method. Computational results are presented for instances up to 400 users, 25 potential service facilities, and 5 capacity levels with different coefficients of variation of service times and average queueing delay costs per customer. The results indicate that the proposed solution method is efficient in solving a wide range of problem instances.     

The congested facility location problem

Consider a network facility location problem where congestion arises at facilities, and is represented by delay functions that approximate the queueing process. We strive to minimize the sum of customers' transportation and waiting times, and facilities' fixed and variable costs. The problem is solved using a column generation technique within a branch-and-bound scheme. Numerical results are reported and a bilevel (user-optimized) formulation considered, among other extensions.     

Sustainability SI: Optimal Prices of Electricity at Public Charging Stations for Plug-in Electric Vehicles

With an increasing deployment of plug-in electric vehicles, evaluating and mitigating the impacts of additional electrical loads created by these vehicles on power distribution grids become more important. This paper explores the use of prices of electricity at public charging stations as an instrument, in couple of road pricing, to better manage both power distribution and urban transportation networks. More specifically, a multi-class combined distribution and assignment model is formulated to capture the spatial distribution of plug-in electric vehicles across the transportation network and estimate the electrical loads they impose on the power distribution network. Power flow equations are subsequently solved to estimate real power losses. Prices of electricity at public charging stations and road tolls are then optimized to minimize both real power losses in the distribution grid and total travel time in the urban transportation network. The pricing model is formulated as a mathematical program with complementarity constraints and solved by a manifold suboptimization algorithm and a pattern search method. Numerical examples are presented to demonstrate the proposed model and solution algorithms.     

Bicriteria location of a semi-obnoxious facility

The problem of locating a new semi-obnoxious facility in an existing layout is considered. The facility interacts with the existing facilities, so that on one hand, it is desired to be placed close to them in order to minimize total transportation cost, but on the other hand it is not desired to be placed too close to them because it has certain undesirable effects. For this problem, a maximin–minisum bicriteria location model with rectilinear distances is developed. The resulting nonconvex bicriteria problem is decomposed into a series of linear bicriteria problems which are solved by an adaptation of the Fourier–Motzkin Elimination Method. An algorithm that constructs the entire nondominated and efficient sets is presented and it is illustrated in an example problem.     

A class of multi-objective supply chain networks optimal model under random fuzzy environment and its application to the industry of Chinese liquor

An important issue, when shipping cost and customers demand are random fuzzy variables in supply chain network (SCN) design problem, is to find the network strategy that can simultaneously achieve the objectives of minimization total cost comprised of fixed costs of plants and distribution centers (DCs), inbound and outbound distribution costs, and maximization customer services that can be rendered to customers in terms of acceptable delivery time. In this paper, we propose a random fuzzy multi-objective mixed-integer non-linear programming model for the SCN design problem of Luzhou Co., Ltd. which is representative in the industry of Chinese liquor. By the expected value operator and chance constraint operator, the model has been transformed into a deterministic multi-objective mixed-integer non-linear programming model. Then, we use spanning tree-based genetic algorithms (st-GA) by the Prüfer number representation to find the SCN to satisfy the demand imposed by customers with minimum total cost and maximum customer services for multi-objective SCN design problem of this company under condition of random fuzzy customers demand and transportation cost between facilities. Furthermore, the efficacy and the efficiency of this method are demonstrated by the comparison between its numerical experiment results and those of tradition matrix-based genetic algorithm.     

A new stochastic approach for a reliable p-hub covering location problem

A hub location problem (HLP) is a fertile research field, in the aspect of interdisciplinary studies, such as transportation, operation research, network design, telecommunication and economics. The location of hub facilities and allocation of non-hub nodes to hubs configure the backbone of HLPs. This study presents a new mathematical model for a reliable HLP by a new stochastic approach to minimize the total transportation cost and obtain maximum flows that the network can carry, when its link capacities are subject to stochastic degradations, as in a form of daily traffic, earthquake, flood, etc. We consider the road capacity reliability as a probability that ensures the maximum network capacity is greater than or equal to the total incoming flow to the network by considering the road capacity as random variable. As a result, this paper assumes that link capacities satisfy in a Truncated Erlang $(\mathit{TErl})$ distribution function. Due to complexity of the HLP, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is applied on the problem in order to achieve near-optimal solutions. Furthermore, the performance of the proposed algorithm (i.e., DE) is evaluated by the performance of the genetic algorithm (GA) applied on the given problem. Some computational experiments are presented to illustrate the effectiveness of the presented model and proposed algorithm. Finally the conclusion is provided.     

Cross-docking and milk run logistics in a consolidation network: A hybrid of harmony search and simulated annealing approach

In this paper, we develop a novel integer programming model for the transportation problem of a consolidation network where a set of vehicles are used to transport goods from suppliers to their corresponding customers via three transportation systems: direct shipment, shipment through cross-dock (indirect shipment) and milk run. Since the proposed problem formulation is NP-hard, we offer a hybrid of harmony search (HS) and simulated annealing (SA) based heuristics (HS-SA algorithm) in order to solve the problem. The objective of this problem is to minimize the total shipping cost in the network, so it is tried to reduce the number of required vehicles using an efficient vehicle routing strategy in the algorithm. Solving several numerical examples demonstrates that our solving approach performs much better than GAMS/CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for large size problem instances.     

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.     

考虑供应商选择的选址一库存一路径的联合优化

针对装配型制造企业供应链集成优化问题,建立了随机需求情形下整合供应商选择和各层级之间运输方式选择的多层级选址—库存模型。该模型通过对供应商的选择,装配厂和分销中心的选址,相邻两层级之间的分配服务关系及运输方式的确定,实现整体供应链网络成本最小化。为求解此混合整数非线性规划模型,设计了一种矩阵编码的改进自适应遗传算法。仿真实验表明,该算法的解的寻优能力明显优于标准遗传算法,得出了供应链总成本与装配厂的最大提前期存在一定规律性的结论。