关于物流多目标选址规划仿真研究

设施选址问题作为物流网络规划的基础,针对现实环境下物流设施选址的相关影响因素,并对选址点与客户需求市场之间的运输时间进行把握.综合考虑设施选址成本的最小化,和物流服务覆盖范围的最大化涉及到多个NP难问题的综合多目标规划,是物流设施选址的难点.为解决上述问题,在分析物流设施选址问题与区域已存设施点、上下游设施点、人员密集区域、待选址区域地理环境和交通环境等的关系基础上,将覆盖问题中距离要素构建为更为符合现实的时间要素,应用道路阻抗函数构建设施点与需求点之间的时间距离;综合构建基于最小化物流设施选址成本的物流设施选址多目标规划模型;设计算法对选址空间进行预处理,松弛子问题目标函数,将多目标求解空间转化为单目标求解空间.仿真结果表明,以时间替代空间距离的处理更为符合物流活动实际,考虑多个具体因素的物流多目标选址规划更为接近现实,选址结果在最小化选址成本基础上能在限定时间内响应客户需求,对实践有较好的指导性.     

考虑设施失效的军事物流配送中心选址模型

为了提高军事配送系统的经济性,针对物资配送中后勤设施失效时进行支援保障和越级保障的情况,将军事物流配送中心的属性分为“首选”与“备选”两种,并把设施失效时的应急配送成本作为决策目标的一部分,建立了最小化设施固定成本、正常配送成本、应急配送成本之和的军事物流配送中心选址模型,采用贪婪取走的启发式算法进行了模型求解,比较了考虑设施失效情况的选址方案与未考虑设施失效情况的选址方案之间的差别。仿真算例结果表明,虽然由模型所得选址方案的设施固定成本与正常配送成本之和高于未考虑设施失效情况的选址方案,但平均期望成本增加值低于后者。     

需求不确定的设施选址与多阶段生产鲁棒模型研究

选址决策是长期的战略性问题,在选址问题中考虑不确定性因素至关重要。假设需求取值于有界的对称区间,在设施选址与多阶段生产问题中提出一种新的鲁棒性方法,通过调节不确定预算,来权衡解的鲁棒性与系统成本之间的关系。该鲁棒性问题不仅能够转化成线性规划,而且可以计算出设施的最低服务水平。最后,通过随机生成数值算例,得出不同鲁棒性水平下拓扑结构截然不同的设施网络,并分析了服务水平与成本之间的权衡关系,同时对需求的不确定水平作了敏感性分析。     

中断情境下可靠性应急设施选址-分配多目标优化模型

应急设施选址是长期战略性决策布局问题,选址-分配网络面临潜在的中断风险.在中断情境下构建以成本经济性、覆盖质量均衡性及公平性为核心的多目标体系.以最小化系统成本为目标反映经济性,以覆盖服务质量最大化为目标反映均衡性,以最大化最小需求覆盖水平为目标反映公平性,建立中断情境下服务能力有限的可靠性应急设施选址-分配多目标优化模型.采用带精英策略的快速非支配排序遗传算法(NSGA-II)对模型予以求解,获得经济成本、覆盖服务质量均衡性与公平性之间的Pareto解集,给出Pareto最优解集在三维空间的分布及应急设施选址布局网络的拓扑结构.研究成果将为决策者在中断环境下设计可靠的选址-分配网络提供决策支持.     

基于改进遗传算法的省级医疗中心选址研究

针对公共设施选址问题中因多目标约束条件造成的复杂空间搜索问题,提出了一种基于遗传算法的P-中值模型,以设施点与供应点间的分配关系作为基因序列,将出行时间消耗、建设投入成本、容量限制条件等因素构成目标函数,用于设施供需分配过程中的优化求解。同时从初始种群构成方式和变异率两方面对遗传算法进行改进以提高求解准确性。实验将该模型运用于河南省省级医疗中心选址,并结合多种评价指标得出多样化的医疗中心布局方案,验证了模型的有效性和可行性。     

农垦系统应急物资设施选址问题研究磁

论文对农垦系统应急物资储备库选址问题进行研究,综合了 P-中值模型、P-中心模型、覆盖模型等一般选址模型的优缺点,同时考虑到应急设施选址的效率性、公平性和成本等多方面因素,建立了一个多目标决策模型。该模型采用线性加权和法求解,得出应急设施的最优选址点。经齐齐哈尔垦区应急行动检验,多目标决策模型的计算结果科学合理、经济可行,为应急设施选址决策提供了有效依据。     

基于遗传模拟退火算法的多层设施选址方法

逆向物流网络是逆向物流系统高效运作的基础和前提,而设施的选址定位是逆向物流网络设计的核心问题.为此,提出一个多层设施选址模型,旨在构建由回收点、回收中心和生产点相结合的最佳逆向物流回收网络.根据模型特点,提出基于遗传模拟退火算法的求解方法,个体采用二进制十进制混合编码;提出基于Metropolis准则的特定遗传进化操作;设计顾客对回收点、回收点对回收中心的两个子分配算法保证所有约束的满足性.最后通过仿真实验,得到满意的设施选址方案.可见,选址模型和算法是一种有效的设施选址方法,具有一定的应用前景.     

Hadoop下并行遗传算法研究及在应急设施选址中的应用

随着云计算的出现,大数据的概念也随之产生。自然灾害日趋增多,要求应急设施的部署规模不断扩大,这时,如何有效进行大规模应急设施的选址成为应急管理系统的关键。因此,提出一种改进的并行遗传算法并在Hadoop平台上编程实现,并应用于求解应急设施选址问题的集合覆盖模型,达到求解应急设施选址的目的。试验结果表明,改进的并行遗传算法不管在获取全局最优解上还是在求解大规模应急设施选址的时效性上都优于原有算法,是一种云计算环境下有效的应急设施选址问题求解算法。     

突发疫情下基于机会约束的应急医疗设施建设选址与公平配置优化

针对要求迅速处置突发公共卫生事件的需求,作为有效隔离控制疫情扩散的关键举措之一,提前规划应急医疗设施选址及规模,对于快速响应、节约建设成本和社会资源具有重要意义.对此,针对需求不确定的突发疫情状况,研究应急医疗设施的建设选址以及床位规模的优化决策问题,以最小化建设选址与运营成本以及床位资源供求比差异为优化目标,建立双目标整数规划模型.根据模型特点,设计epsilon约束精确算法和基于就近原则思想的启发式算法进行求解,同时运用非支配排序遗传算法(NSGA-II)进行对比分析.数值实验结果验证了所设计算法的运行效率.研究结论对于应急医疗设施建设选址及规模决策具有良好的应用指导价值,可为管理者决策提供一定的理论依据.     

韦伯型多点设施优化选址的组合算法研究

韦伯型设施选址问题是组合优化领域中的一类重要问题,其核心内容是如何在离散的需求空间域内,寻找到最优决策关注点,即设施点。对于单点设施最优规划问题,由于不存在设置点之间的作用,仅考虑设施点与需求点之间的引力作用问题即可。对于多点设施的最优规划问题,不仅存在着设施点与需求点之间的引力作用问题,而且从资源优化配置的角度,还存在着设施点之间的斥力问题。因此,需要从系统整体优化的角度进行选择规划。目前解决韦伯型设施多点的优化选址问题,一般是通过寻找局部最优解的逐次递阶法来确定最优设施点。但由于该方法没有考虑到设施点间的斥力问题,容易导致设施点间的粘连。针对此问题,提出了一种PGSA-GA组合算法,通过建立模拟植物生长算法得到全局最优解的单点坐标,将其与需求点结合构建遗传算法优化的多目标规划多点设施选址模型求出Pareto最优解,并依此推广到多次选址方案。     

Discrete models for competitive location with foresight

We adapt the competitive location model based on maximal covering to include the knowledge that a competitor will enter the market later with a single new facility. The objective is to locate facilities under a budget constraint in order to maximise the remaining market share after the competitor's later entry.     

Facility location in anticipation of future competition

In this paper, we consider locating a new facility in a competitive environment. A future competitor is expected to enter the market and locate his facility at its best site. The best location for one's own facility is to be found such that the market share captured following the competitor's entry is maximized. The problem is complicated because the best location for the competitor depends on the selected location for one's own facility. The problem is formulated using the gravity model for the estimation of market share. Three heuristic solution procedures are proposed. Computational experiments with these heuristics are presented.     

Sequential versus simultaneous approach in the location and design of two new facilities using planar Huff-like models

Companies frequently decide on the location and design for new facilities in a sequential way. However, for a fixed number of new facilities, the company might be able to improve its profit by taking its decisions for all the facilities simultaneously. In this paper we compare three different strategies: simultaneous location and independent design of two facilities in the plane, the same with equal designs, and the sequential approach of determining each facility in turn. The basic model is profit maximization for the chain, taking market share, location costs and design costs into account. The market share captured by each facility depends on the distance to the customers (location) and its quality (design), through a probabilistic Huff-like model. Recent research on this type of models was aimed at finding global optima for a single new facility, holding quality fixed or variable, but no exact algorithm has been proposed to find optimal solutions for more than one facility. We develop such an exact interval branch-and-bound algorithm to solve both simultaneous location and design two-facility problems. Then, we present computational results and exhibit the differences in locations and qualities of the optimal solutions one may obtain by the sequential and simultaneous approaches.     

Constrained location of competitive facilities in the plane

This paper examines a competitive facility location problem occurring in the plane. A new gravity-based utility model is developed, in which the capacity of a facility serves as its measure of attractiveness. A new problem formulation is given, having elastic gravity-based demand, along with capacity, forbidden region, and budget constraints. Two solution algorithms are presented, one based on the big square small square method, and the second based on a penalty function formulation using fixed-point iteration. Computational testing is presented, comparing these two algorithms along with a general-purpose nonlinear solver.Scope and purposeIn a competitive business environment where products are not distinguishable, facility location plays an important role in an organization's success. This paper examines a firm's problem of selecting the locations in the plane for a set of new facilities such that market capture is maximized across all of the firm's facilities (both new and pre-existing). Customers are assumed to divide their demand among all competing facilities according to a utility function that considers facility attractiveness (measured by facility capacity for satisfying demand) and customer-facility distance. The level of customer demand is assumed to be a function of the facility configuration. Three types of constraints are introduced, involving facility capacity, forbidden regions for new facility location, and a budget function. Two solution algorithms are devised, one based on branch-and-bound methods and the other based on penalty functions. Computational testing is presented, comparing these two algorithms along with a general-purpose nonlinear solver.     

Capacity constrained maximizing bichromatic reverse nearest neighbor search

When planning a new development (facility/service site), location decisions are always the major issue. In this paper we introduce a novel query capacity constraint MaxBRNN, which can solve the facility location selection problem efficiently.The MaxBRNN (maximizing BRNN) query is based on bichromatic reverse nearest neighbor (BRNN) query which uses the number of reverse nearest customers to model the influence of a facility location. The MaxBRNN query has been appreciated extensively in spatial database studies because of its great potential in real life applications, such as, markets decision, sensor network clustering and the design of GSM (global system for mobile communication). The existing researches mostly suppose that the service facility's capacity is unlimited. However, in real cases, facilities are inevitably constrained by designed capacities. For example, if the government wants to select a new place to set up an emergency center to share the existing centers’ patients, they need to know the current emergency centers’ capacity so that they can estimate the new center's scale. Thus, the capacity constrained MaxBRNN query is significantly important in planning a new development. As far as we know, the capacity constrained MaxBRNN query has not been studied yet, so, we formulate this problem, propose a basic solution and develop some efficient algorithms for the query.Our major contributions are as follows: (1) we propose a novel query capacity constraint MaxBRNN which can solve the facility location selection problem effectively and efficiently; (2) we develop a basic algorithm CCMB and two improved algorithms which can find out the optimal region in terms of building a new facility, maximize its impact and deal with the complicated reassignment when adding new facilities into the dataset; (3) we prove the algorithms’ effectiveness and efficiency by extensive experiments using both real and synthetic data sets.     

A leader–follower model for discrete competitive facility location

In this paper we investigate a leader–follower (Stackelberg equilibrium) competitive location model. The competitive model is based on the concept of cover. Each facility attracts consumers within a “sphere of influence” defined by a “radius of influence.” The leader and the follower, each has a budget to be spent on the expansion of their chains either by improving their existing facilities or constructing new ones. We find the best strategy for the leader assuming that the follower, knowing the action taken by the leader, will react by investing his budget to maximize his market share. The objective of the leader is to maximize his market share following the follower׳s reaction.     

Optimal location policy for three competitive facilities

Competitive facility location problems have been investigated in many papers. In most, authors have applied location models with two competitors. In this paper three companies, which are mutually competitive, intend to locate their facilities in a linear market. It is well-known that Nash equilibrium solution for location problem does not include three competitive facilities. In this paper we present the optimal location strategies for three facilities. In our model we assume that the demands are continuously distributed in a linear market and the facilities are locating according to a specific order of sequence, A, B and C. We apply the Stackelberg equilibrium solutions for competitive location problems with three facilities. In our model, we consider the decision problems in three stages. In the first stage, we decide the optimal location of facility A, which is located optimally in respect to the remaining two facilities B and C. In the second stage, we determine the optimal location of facility B which is optimally located in respect to facility C, by utilizing the information on the location of facility A. Finally in the third stage problem we decide the location of facility C, optimally located by utilizing the information on the location of A and B. In the first stage, we need the optimal solutions of the second and third stages. In the second stage we need the optimal solution of the third stage problem. Therefore, first we solve the third stage problem which is the simplest. After that, we solve the second stage problem utilizing the optimal solution strategy of the third stage problem. In this paper we present the optimal location strategies for three facilities.     

基于竞争环境的截流设施选址与车辆路径问题

研究竞争环境下截流设施选址与带时间窗的多中心车辆路径问题。首先,在考虑设施覆盖范围衰退的情况下,利用阶梯型效用函数和偏离距离描述消费者的选择行为,并确定截流设施的需求量;然后,采用基于聚集度的启发式算法对门店进行分类,借助双层规划法,建立门店选址与车辆路径安排的多目标整数规划模型;最后,采用改进的蚁群算法进行求解。通过分析对比实验结果,验证了模型的有效性和可行性。     

A leader-follower game in competitive facility location

We address the problem of locating new facilities of a firm or franchise that enters a market where a competitor operates existing facilities. The goal of the new entrant firm is to decide the location and attractiveness of its new facilities that maximize its profit. The competitor can react by opening new facilities, closing existing ones, and adjusting the attractiveness levels of its existing facilities, with the aim of maximizing its own profit. The demand is assumed to be aggregated at certain points in the plane and the new facilities of both the firm and the competitor can be located at predetermined candidate sites. We employ the gravity-based rule in modeling the behavior of the customers where the probability that a customer visits a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. We formulate a bilevel mixed-integer nonlinear programming model where the firm entering the market is the leader and the competitor is the follower. We propose heuristics that combine tabu search with exact solution methods.     

A planar single-facility competitive location and design problem under the multi-deterministic choice rule

A new customer choice rule, which may model in some cases the actual patronising behaviour of customers towards the facilities closer to reality than other existing rules, is proposed. According to the new rule, customers split their demand among the firms in the market by patronising only one facility from each firm, the one with the highest utility, and the demand is split among those facilities proportionally to their attraction. The influence of the choice rule in the location of facilities is investigated. In particular, a new continuous competitive single-facility location and design problem using this new rule is proposed. Both exact and heuristic methods are proposed to solve it. A comparison with the classical proportional (or Huff) choice rule when solving the location model reveals that both the location and the quality of the new facility to be located may be quite different depending on the patronising behaviour of customers. Most importantly, the profit that the locating chain may lose if a wrong choice is made can be quite high in some instances.