应急联动区域下选址分配协同优化模型研究

针对我国现有应急储备库按照属地管理布局的不足,例如当受灾点发生重大灾害时,各应急储备库实行均匀配置,导致应急系统救援效率低以及资源的浪费,文章引入集合覆盖选址模型和"覆盖满意度"思想,将应急联动区域内的服务需求分为第一时间救援服务需求和后续救援服务需求,以应急服务成本最小和覆盖满意度最大为目标,建立应急联动下区域储备库选址分配协同优化模型.对四川省地震灾害下的应急储备库选址分配问题进行案例分析,考虑到各区县抗灾能力的不同,采用TOPSIS方法对模型中受灾点的脆弱性进行评价,并利用NSGA-Ⅱ算法对模型求解.研究表明,模型能降低应急联动区域内的应急服务成本,提升应急服务水平,同时模型可以为决策者提供多种优化方案.     

考虑横向供应的多阶段装备维修器材供应优化方法研究

装备维修器材的精确、定量和快速供应是部队遂行作战、训练等任务的重要物质保证。针对多阶段装备维修器材供应问题,考虑多种器材供应方式,构建装备维修器材多级供应模式。为提升器材供应方案的全局最优性,将野战仓库选址、器材库存控制及运输车辆路径规划等关键问题融合为一类选址-库存-路径组合优化问题,以总成本最小为目标,考虑部队级仓库间的横向供应方式,构建混合整数线性规划模型。设计一种基于逻辑的Benders分解算法,将原问题分解为主问题和子问题,通过生成Benders切割迭代求解。通过示例分析证明：(1)本文所提出的LBBD算法能够有效降低问题复杂度,提升求解质量,LBBD算法得出的供应方案总成本比CPLEX求解器得出方案成本低41.62%;(2)考虑横向供应能够有效降低保障费用,考虑横向供应能够使总成本降低6.24%,同时,考虑横向供应可提高装备维修器材供应系统的灵活性。     

时间满意逐渐覆盖电动汽车充电站选址及算法

作为电动汽车配套的基础设施,电动汽车充电站的选址对电动汽车的推广有着十分重要的意义.针对电动汽车充电站选址问题,别入逐渐覆盖思想和时间满意度函数,从需求点效用最大化的角度出发,提出了基于时间满意逐渐覆盖电动汽车充电站选址模型,并运用蝙蝠算法通过MATLAB实现.实例的求解比较验证了该模型及算法在电动汽车充电站选址决策中的有效性.     

基于Benders分解算法的反恐应急设施选址-定容-分配问题

反恐应急设施的合理布局和资源配置可缩短救援到达时间并提高应急效率。对已有反恐应急设施选址研究拓展，进一步考虑设施容量有限的情形，并将袭击前后关于应急设施的选址、定容和救援物资分配问题进行集成考虑。将该问题构造为三层规划模型，上中下各层规划分别对应袭击前的选址定容问题、袭击时的袭击点选择问题和袭击后的救援物资分配问题。利用下层规划的对偶变换转化为双层规划，并设计Benders分解算法求解。最后，结合南疆交通网络进行仿真分析，验证了模型和算法的有效性。     

线状需求下的长江航道危险品应急中心选址优化

本文分析了长江航道危险品船舶对应急中心救援服务的线状需求特征,基于点状需求的设施选址模型,构建了全覆盖下的最小成本选址模型,实现了线状需求特性与最小成本的最优结合。将点状需求的设施选址研究延伸到线状需求的设施选址研究领域。并且针对模型设计了确定性规划算法和遗传算法。最后以长江航道为具体算例,对比了确定性规划算法和遗传算法对模型进行求解的效率及精度。实例表明,本文建立的基于线状需求的应急中心选址模型是符合实际的,确定性规划算法相对于遗传算法可以在更短时间内求得较稳定的最优解;遗传算法运行时间相对较长,且结果稳定性较差。     

电动汽车充电站选址问题研究

研究了电动汽车作为通勤工具情况下的充电站选址问题.首先根据城市通勤道路长度、各条道路上的通勤人员拟使用电动汽车的数量,以及电动汽车的最大续航里程等信息,构建了包含两种边的赋权网络图,进一步将电动汽车充电站选址问题转化为赋权网络图的最大覆盖问题,并建立了以极大化满足用户需求为目标的充电站选址问题整数非线性规划模型,设计了求解模型的启发式算法.最后,通过一个具体算例对模型及算法进行了验证,结果显示,模型和算法是解决电动汽车充电站选址问题有效方法.     

带时效性约束的配送中心多源选址问题研究

应用启发式算法求解带时效性约束的多源选址问题.分析物流配送的时效性问题,建立带时效性约束的配送中心多源选址模型.构造两步启发式算法:1)借助传统迭代算法,求解物流服务分配矩阵,把多源选址问题转化为单源选址问题;2)基于M ATLAB函数,设计优化程序,计算带时效性约束的单源选址模型.并给出算例,验证模型和算法的可行性.研究表明两步启发式算法是求解带时效性约束的物流配送中心多源连续选址问题的有效算法.     

电子商务背景下的城市物流共享仓库动态选址研究

针对电商顾客需求波动较大、分布较广、时效性较强等特点,首次将顾客需求的波动性及仓库资源的共享性等因素引入动态选址问题,并综合考虑运输成本、租赁共享仓库成本、关闭成本及风险成本等因素,以总成本最小为目标,构建了电子商务背景下的物流共享仓库动态选址模型.针对模型的NP难特性,提出了一种改进的遗传算法来求解该模型.最后,通过算例验证了模型的可行性及算法的有效性.     

数学规划与约束规划整合下的多目标分组排序问题研究

分组排序问题属于NP-难题, 单纯的数学规划模型或约束规划模型都无法在有效时间内解决相当规模的此类问题. 控制成本、缩短工期和减少任务延迟是排序问题的三个基本目标, 在实际工作中决策者通常需要兼顾三者, 并在 三者之间进行权衡. 多目标分组排序问题 的研究增强了排序问题的实际应用价值, 有利于帮助决策者处理复杂的多目标环境. 然而, 多目标的引入也增加了问题求解难度, 针对数学规划擅长寻找最优, 约束规划擅长排序的特点, 将两类方法整合起来, 提出一个基于Benders分解算法, 极大提高了此类问题的求解 效率.     

高校物流公司联盟服务站选址的研究

以国内高校周边物流公司的现状入手,论述高校设立物流公司联盟服务站的特点和优势,提出高校物流公司联盟服务站设计程序.基于离散选址模型及其对高校物流联盟服务站选址模型的启示,给出高校物流公司联盟服务站选址的计算模型.最后,以某高校为例进行物流公司联盟服务站选址规划并与该校物流联盟服务站实际地址作比较,比较结果表明,选址计算法是一个有效的算法,可以很好地应用在高校物流公司联盟服务站选址的研究中.     

带惩罚的容错设施布局问题的近似算法

在带惩罚的容错设施布局问题中, 给定顾客集合、地址集合、以及每个顾客和各个地址之间的连接费用, 这里假设连接费用是可度量的. 每位顾客有各自的服务需求, 每个地址可以开设任意多个设施, 顾客可以被安排连接到某些地址的一些开设的设施上以满足其需求, 也可以被拒绝, 但这时要支付拒绝该顾客所带来的惩罚费用. 目标是确定哪些顾客的服务需求被拒绝并开设一些设施, 将未被拒绝的顾客连接到不同的开设设施上, 使得开设费用、连接费用和惩罚费用总和最小. 给出了带惩罚的容错设施布局问题的线性整数规划及其对偶规划, 进一步, 给出了基于其线性规划和对偶规划舍入的4-近似算法.     

Inventory sharing in integrated network design and inventory optimization with low-demand parts

Service Parts Logistics (SPL) problems induce strong interaction between network design and inventory stocking due to high costs and low demands of parts and response time based service requirements. These pressures motivate the inventory sharing practice among stocking facilities. We incorporate inventory sharing effects within a simplified version of the integrated SPL problem, capturing the sharing fill rates in 2-facility inventory sharing pools. The problem decides which facilities in which pools should be stocked and how the demand should be allocated to stocked facilities, given full inventory sharing between the facilities within each pool so as to minimize the total facility, inventory and transportation costs subject to a time-based service level constraint. Our analysis for the single pool problem leads us to model this otherwise non-linear integer optimization problem as a modified version of the binary knapsack problem. Our numerical results show that a greedy heuristic for a network of 100 facilities is on average within 0.12% of the optimal solution. Furthermore, we observe that a greater degree of sharing occurs when a large amount of customer demands are located in the area overlapping the time windows of both facilities in 2-facility pools.     

An approximation algorithm for a facility location problem with stochastic demands and inventories

We propose a 2-approximation algorithm for a facility location problem with stochastic demands. At open facilities, inventory is kept such that arriving requests find a zero inventory with (at most) some pre-specified probability. Costs incurred are expected transportation costs, facility operating costs and inventory costs.     

An integrated approach to dynamic plant location and capacity planning

We consider a dynamic capacitated plant location problem in which capacities of opened plants are determined by acquisition and/or disposal of multiple types of facilities. We determine the opening schedule of plants, allocations of customers' demands and plans for acquisition and/or disposal of plant capacities that minimise the sum of discounted fixed costs for opening plants, delivery costs of products, and acquisition and operation costs of facilities. The dynamic capacitated plant location problem is formulated as a mixed integer linear program and solved by a heuristic algorithm based on Lagrangian relaxation and a cut and branch algorithm which uses Gomory cuts. Several solution properties of the relaxed problem are found and used to develop efficient solution procedures for the relaxed problem. A subgradient optimisation method is employed to obtain better lower bounds. The heuristic algorithm is tested on randomly generated test problems and results show that the algorithm finds good solutions in a reasonable amount of computation time.     

Approximating connected facility location with buy-at-bulk edge costs via random sampling

In the connected facility location problem with buy-at-bulk edge costs we are given a set of clients with positive demands and a set of potential facilities with opening costs in an undirected graph with edge lengths obeying the triangle inequality. Moreover, we are given a set of access cable types, each with a cost per unit length and a capacity such that the cost per capacity decreases from small to large cables, and a core cable type of infinite capacity. The task is to open some facilities and to connect them by a Steiner tree using core cables, and to build a forest network using access cables such that the edge capacities suffice to simultaneously route all client demands unsplit to the open facilities. The objective is to minimize the total cost of opening facilities, building the core Steiner tree, and installing the access cables. In this paper, we devise a constant-factor approximation algorithm for this problem based on a random sampling technique.     

A new lot sizing and scheduling heuristic for multi-site biopharmaceutical production

Biopharmaceutical manufacturing requires high investments and long-term production planning. For large biopharmaceutical companies, planning typically involves multiple products and several production facilities. Production is usually done in batches with a substantial set-up cost and time for switching between products. The goal is to satisfy demand while minimising manufacturing, set-up and inventory costs. The resulting production planning problem is thus a variant of the capacitated lot-sizing and scheduling problem, and a complex combinatorial optimisation problem. Inspired by genetic algorithm approaches to job shop scheduling, this paper proposes a tailored construction heuristic that schedules demands of multiple products sequentially across several facilities to build a multi-year production plan (solution). The sequence in which the construction heuristic schedules the different demands is optimised by a genetic algorithm. We demonstrate the effectiveness of the approach on a biopharmaceutical lot sizing problem and compare it with a mathematical programming model from the literature. We show that the genetic algorithm can outperform the mathematical programming model for certain scenarios because the discretisation of time in mathematical programming artificially restricts the solution space.     

The general optimal market area model

Market area models determine the optimal size of market for a facility. These models are grounded in classical location theory, and express the fundamental tradeoff between economies-of-scale from larger facilities and the higher costs of transport to more distant markets. The simpler market area models have been discovered and rediscovered, and applied and reapplied, in a number of different settings. We review the development and use of market area models, and formulate a General Optimal Market Area model that accommodates both economies-of-scale in facilities costs and economies-of-distance in transport costs as well as different market shapes and distance norms. Simple expressions are derived for both optimal market size and optimal average cost, and also the sensitivity of average cost to a non-optimal choice of size. The market area model is used to explore the implications of some recently proposed distance measures and to approximate a large discrete location model, and an extension to price-sensitive demands is provided.The author thanks S. Jacobsen, M. O'Kelly, C. Tang, J.-F. Thisse, and S.C. Wirasinghe for their helpful comments.     

A decomposition approach to solve a bilevel capacitated facility location problem with equity constraints

In this paper, we study a capacitated facility location problem with two decision makers. One (say, the leader) decides on which subset of facilities to open and the capacity to be installed in each facility with the goal of minimizing the overall costs; the second decision maker (say, the follower), once the facilities have been designed, aims at maximizing the profit deriving from satisfying the demands of a given set of clients beyond a certain threshold imposed by the leader. The leader can foresee but cannot control the follower’s behavior. The resulting mathematical formulation is a discrete–continuous bilevel optimization problem. We propose a decomposition approach to cope with the bilevel structure of the problem and the integrality of a subset of variables under the control of the leader. Such a proposal has been tested on a set of benchmark instances available in the literature.     

The allocation of shared fixed costs

We consider the problem of sharing the fixed costs of facilities among a number of users. Typically the users have a benefit or revenue from the use of the facilities. Although the problem can be formulated and solved as an integer programme this provides limited accounting information. Such information is often needed in order to (i) decide on which facilities are viable and (ii) to charge the users. It is shown that it is impossible to meet both these needs in a satisfactory way. We examine different ways of partially meeting them. In addition, we consider the issue of fairness among different possible cost allocations and how such ‘fair’ costs may be derived.     

带有异常点的平方度量设施选址问题

传统的设施选址问题一般假设所有顾客都被服务,考虑到异常点的存在不仅会增加总费用(设施的开设费用与连接费用之和),也会影响到对其他顾客的服务质量.研究异常点在最终方案中允许不被服务的情况,称之为带有异常点的平方度量设施选址问题.该问题是无容量设施选址问题的推广.问题可描述如下:给定设施集合、顾客集,以及设施开设费用和顾客...