随机需求下的选址一库存配送系统集成规划模型及算法

研究了随机需求条件下由单供应商、候选分拨中心和分销点构成的选址-库存问题,分销点、分拨中心分别基于周期检查(R,s,Q)和连续检查(s,S)库存控制策略．综合考虑库存成本、运输成本和设施成本之间的均衡关系,建立了二级库存与无能力约束选址集成规划模型．给出了适合求解实际规模问题的拉格朗日松弛算法,提出了求解子问题的有效启发式方法,改进了次梯度优化方法．通过仿真试验验证了模型的正确性和算法的有效性．最后讨论了相对于传统规划方法,需求方差、服务水平、持有成本、提前期等关键库存控制参数对系统运营成本节约的影响规律．     

基于遗传算法的纺织企业机配件库存控制

合理压缩库存量,降低库存成本对于纺织企业来说意义重大.建立了在非平稳需求下的库存控制模型,并采用遗传算法求解,给出了满足库存成本最小的(s,S)控制策略.通过求解某配件的(s,S)控制策略,验证了算法的有效.     

粒子群算法在物流中心选址中的应用

物流中心选址是物流系统规划中的重要决策问题。为了快速得到合理的物流中心选址方案,针对问题的特点给出了选址问题的模型,提出了以最小化物流成本为目标函数的粒子群优化算法,开发了模型求解的MATLAB程序,并将算法应用于求解工厂仓库选址和废弃物回收中转站选址问题。实例求解结果表明,该算法求解选址问题的性能优于精确重心法,具有良好的搜索性能和实用性。     

.物流配送中心动态选址模型及算法研究*

针对传统物流配送中心动态选址模型没有充分考虑配送中心的可能状态和库存持有成本的问题,建立了一种新的模型。首先,利用两步骤近似法构建了在有库存和运输双重能力约束下,每一个周期配送中心的库存成本计算方法;然后,分别给出了配送中心在整个规划期内的打开、运营、关闭和再次打开的成本表达式;最后,分别用遗传算法、克隆选择算法、粒子群算法求解所建立的模型,并从算法的寻优能力、稳定性、运算速度和收敛性方面比较了三种算法的性能。算例测试结果表明,所建立的模型是有效的;从总体上看,遗传算法的适应性要强于克隆选择算法和粒子群算     

供应链生产—分销运作一体化研究

研究单工厂、多产品、多分销中心供应链网络的生产—分销运作一体化问题 ,利用混合整数规划方法 ,建立一体化的多周期模型 ,同步优化系统的生产批量、库存和车辆调度 .通过对模型的等价转换 ,设计了拉格朗日松弛启发式算法来求解模型 .数值实例的计算结果验证了算法的有效性 ,表明了一体化决策可以显著地降低供应链成本.     

考虑公众风险的多目标医疗废物选址路径问题及樽海鞘算法求解

针对医疗废物处理中心的选址路径问题,在考虑公众风险的情况下,构建多目标优化模型。首先,分别从政府、公众和处理中心承包商角度出发,构建了以运营成本、风险成本以及运输成本最小化的多目标选址路径模型;其次,针对所构建模型的特点,设计了一种改进的多目标樽海鞘算法对模型进行求解;最后,以四川省成都市的医疗废物处理中心的规划项目为例,对构建的模型和算法进行验证,通过优化结果的对比分析,验证了模型的可行性和算法的有效性。     

纺织企业的原料库存控制模型研究

合理控制库存量,降低原料库存成本对纺织企业提高市场竞争力具有重大的意义。通过建立原料库存的不定量不定期控制模型,并采用遗传算法求解,给出了满足平均库存成本最小的(s,S)控制策略,并通过实例验证算法的有效性。     

基于混合果蝇优化算法的选址-库存联合优化策略

针对装备维修保障仓库系统运营费用高、仓库点位布局不合理、备件库存结构不合理等问题,建立以多品种联合补货问题为基础的装备维修备件仓库选址-库存控制决策联合优化模型,模型可用于求解仓库的开设位置、维修活动需求点的指派情况、仓库补货时间以及库存水平等.根据模型的结构特点,利用多种群协同进化的方法改进传统果蝇优化算法的位置更新方式,设计一种内外两层搜索策略的混合果蝇优化算法,外层搜索策略作为算法的主程序用于搜索仓库选址决策变量,内层搜索策略采用改进的RAND算法用于搜索库存控制决策变量.仿真结果表明,混合果蝇优化算法具有良好的求解效率,能够确保库存系统在一定服务水平的基础上有效降低库存运营总成本.     

水上石油物流分拨中心选址模型及其遗传算法

我国水上石油物流网络错综复杂,合理配置和选择水上石油物流分拨中心具有重要的理论价值和现实意义。文中给出了我国水上石油物流分拨的网络架构,建立了相应的石油物流分拨中心选址的数学模型,针对模型的特点提出了遗传算法的解决策略,验证了该模型和方法的正确性,该方法也适用于其他种类大规模物流分拨中心的优化问题。     

改进的遗传神经网络优化选址方法

针对当前遗传神经网络在选址研究中缺少考虑影响因子权重的问题以及算法在求解时易于过早收敛的缺陷,提出一种利用模糊C均值聚类算法改进遗传神经网络模型的优化选址方法。通过建立选址中心决策矩阵,确定相应影响因子及其取值范围,得到所有影响因子的权重,进行迭代计算得到最优选址方案。实验以黑龙江省物流公司选址为例,分别采用BP(back propagation)算法、GA-BP(genetic algorithm and back propagation)算法和C-GA-BP(fuzzy C-means and genetic algorithm and back propagation)算法对选址的建设成本进行优化计算,经过验证,该方法提出的C-GA-BP算法在选址方面具有优化精度高、优化效果显著等特点。     

A dynamic closed-loop location-inventory problem under disruption risk

In this paper, a dynamic closed-loop location-inventory problem is addressed that optimizes strategic decisions (i.e., facility location in terms of contracting/selection of distribution centers and reworking centers) along with tactical ones (i.e., allocation of centers, inventory management) under facility disruption risks. The presented model seeks to minimize total cost as the first objective function, and time as the second one in the considered network. Due to the NP-Hard nature of the model, a hybrid meta-heuristic algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is presented to solve the problem in large scales. Finally, applicability of the proposed model is tested via a real case study and the results are analyzed in depth.     

Integrated optimization of inventory-distribution systems by random local search and a genetic algorithm

A new model and its solution procedure for the commodity distribution system consisting of distribution centers and consumer points are discussed. Demand is assumed to be a random variable that obeys a known, stationary probability distribution. An integrated optimization model is built where both the order-up-to-R policy, which is one of the typical inventory policies for periodic review models, and the transportation problem are considered simultaneously. The assignment of consumer points to distribution centers is not fixed. The problem is to determine the target inventory and the transportation quantity in order to minimize the expectation of the sum of inventory related costs and transportation costs. Simulation and linear programming are used to calculate the expected costs, and a random local search method is developed in order to determine the optimum target inventory. A genetic algorithm is also tested and compared with the proposed random local search method. The model and effectiveness of the proposed solution procedure are clarified by computational experiments.     

Production/inventory systems with a stochastic production rate under a continuous review policy

A single production facility is dedicated to producing one product with completed units going directly into inventory. The unit production time is a random variable. The demand for the product is given by a Poisson process and is supplied directly from inventory when available, or is backordered until it is produced by the production facility. Relevant costs are a linear inventory holding cost, a linear backorder cost, and a fixed setup cost for initiating a production run. The objective is to find a control policy that minimizes the expected cost per time unit.The problem may be modeled as an M/G/1 queueing system, for which the optimal decision policy is a two-critical-number policy. Cost expressions are derived as functions of the policy parameters, and based on convexity properties of these cost expressions, an efficient search procedure is proposed for finding the optimal policy. Computational test results demonstrating the efficiency of the search procedure and the behavior of the optimal policy are presented.     

一种求解Job-Shop调度问题的新型蚁群算法

Job-Shop调度问题是一类具有很高理论研究和工程应用价值的问题。针对使用蚁群算法求解Job-Shop调度问题时较难设置合适参数的问题,提出一种动态设置参数的新型蚁群求解算法。分析了蚁群算法中参数对求解结果的影响,给出了算法求解Job-Shop调度问题的关键技术和实现过程。最后对五个基本测试问题进行了仿真实验,并与遗传算法、模拟退火算法、基本蚁群算法进行了比较。结果表明,该算法能得到较优的结果,具有一定的应用价值。     

Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem

In this study a fuzzy c-means clustering algorithm based method is proposed for solving a capacitated multi-facility location problem of known demand points which are served from capacitated supply centres. It involves the integrated use of fuzzy c-means and convex programming. In fuzzy c-means, data points are allowed to belong to several clusters with different degrees of membership. This feature is used here to split demands between supply centers. The cluster number is determined by an incremental method that starts with two and designated when capacity of each cluster is sufficient for its demand. Finally, each group of cluster and each model are solved as a single facility location problem. Then each single facility location problem given by fuzzy c-means is solved by convex programming which optimizes transportation cost is used to fine-tune the facility location. Proposed method is applied to several facility location problems from OR library (Osman & Christofides, 1994) and compared with centre of gravity and particle swarm optimization based algorithms. Numerical results of an asphalt producer’s real-world data in Turkey are reported. Numerical results show that the proposed approach performs better than using original fuzzy c-means, integrated use of fuzzy c-means and center of gravity methods in terms of transportation costs.     

Design of Stochastic Distribution Networks Using Lagrangian Relaxation

This paper addresses the design of single commodity stochastic distribution networks. The distribution network under consideration consists of a single supplier serving a set of retailers through a set of distribution centers (DCs). The number and location of DCs are decision variables and they are chosen from the set of retailer locations. To manage inventory at DCs, the economic order quantity (EOQ) policy is used by each DC, and a safety stock level is kept to ensure a given retailer service level. Each retailer faces a random demand of a single commodity and the supply lead time from the supplier to each DC is random. The goal is to minimize the total location, shipment, and inventory costs, while ensuring a given retailer service level. The introduction of inventory costs and safety stock costs leads to a nonlinear NP-hard optimization problem. A Lagrangian relaxation approach is proposed. Computational results are presented and analyzed showing the effectiveness of the proposed approach.     

Semi-obnoxious single facility location in Euclidean space

This paper deals with the problem of determining within a bounded region the location for a new facility that serves certain demand points. For that purpose, the facility planners have two objectives. First, they attempt to minimize the undesirable effects introduced by the new facility by maximizing its minimum Euclidean distance with respect to all demand points (maximin). Secondly, they want to minimize the total transportation cost from the new facility to the demand points (minisum). Typical examples for such “semi-obnoxious” facilities are power plants that, as polluting agents, are undesirable and should be located far away from demand points, while cost considerations force planners to have the facility in close proximity to the customers. We describe the set of efficient solutions of this bi-criterion problem and propose an efficient algorithm for its solution.

Scope and purpose

It is becoming increasingly difficult to site necessary but potentially polluting (semi-obnoxious) facilities such as power plants, chemical plants, waste dumps, airports or train stations. More systematic decision-aid tools are needed to generate several options that balance the public's concerns with the interests of the developer or location planner. In this paper, a model is presented that generates the best possible sites (efficient solutions) with respect to two conflicting criteria: maximize distance from population centers and minimize total transportation costs. Having all efficient solutions at hand, the two sides can select one that best compromises their criteria. A very interesting property found is that most of these efficient solutions are on edges of a Voronoi diagram. An algorithm is developed that constructs the complete trajectory of efficient solutions.     

Economic Order Quantity model with Weibull deterioration distribution,shortage and ramp-type demand

A single-item single-period Economic Order Quantity model for deteriorating items with a ramp-type demand and Weibull deterioration distribution is considered. The shortages in inventory are allowed and backlogged completely. The model is developed over an infinite planning horizon and the optimal replenishment policy is derived by minimizing the total inventory cost per unit time. The numerical solution of the model is obtained, and the sensitivity of the parameters involved in the model is also examined.