A tree search algorithm for the multi-commodity location problem

The multi-commodity location problem is an extension of the simple plant location problem. The problem is to decide on locations of facilities to meet customer demands for several commodities in such a way that total fixed plus variable costs are minimized. Only one commodity may be supplied from any location.In this paper a primal and a dual heuristic for producing good bounds are presented. A method of improving these bounds by using a new Lagrangean relaxation for the problem is also presented. Computational results with problems taken from the literature are provided.     

The maximum return-on-investment plant location problem with market share

This paper examines the plant location problem under the objective of maximizing return-on-investment. However, in place of the standard assumption that all demands must be satisfied, we impose a minimum acceptable level on market share. The model presented takes the form of a linear fractional mixed integer program. Based on properties of the model, a local search procedure is developed to solve the problem heuristically. Variable neighbourhood search and tabu search heuristics are also developed and tested. Thus, a useful extension of the simple plant location problem is examined, and heuristics are developed for the first time to solve realistic instances of this problem.     

Exact solution methods for uncapacitated location problems with convex transportation costs

In this paper we study exact solution methods for uncapacitated facility location problems where the transportation costs are nonlinear and convex. An exact linearization of the costs is made, enabling the formulation of the problem as an extended, linear pure zero–one location model. A branch-and-bound method based on a dual ascent and adjustment procedure is developed, and compared to application of a modified Benders decomposition method. The specific application studied is the simple plant location problem (SPLP) with spatial interaction, which is a model suitable for location of public facilities. Previously approximate solution methods have been used for this problem, while we in this paper investigate exact solution methods. Computational results are presented.     

Discrete stochastic location models

In this paper, we study how the two classical location models, the simple plant location problem and thep-median problem, are transformed in a two-stage stochastic program with recourse when uncertainty on demands, variable production and transportation costs, and selling prices is introduced. We also discuss the relation between the stochastic version of the SPLP and the stochastic version of thep-median.     

Location problems

Recent developments for several location problems are surveyed. These include: graph theoretic and combinatorial formulations of the simple plant location problem, the NP-hardness of some p-center problems, worst-case bounds for several polynomial-time heuristics for some p-center problems, and a general solution to a class of one facility network problems with convex cost functions.     

Some facets of the simple plant location polytope

We relate the simple plant location problem to the vertex packing problem and derive several classes of facets of their associated integer polytopes.This work was supported by NSF Grant ENG 79-02506.     

On the resolution of the single product capacitated machine siting problem

The single product capacitated machine siting problem (SPCMSP) is an extension of the simple plant location problem, in which plant production depends on installing capacitated machines. In this paper we compare, both theoretically and computationally, three heuristic algorithms for the SPCMSP based upon Lagrangean relaxation and reduction tests of a mixed-integer formulation of the problem, which is NP-hard. We test the performance of the algorithms with examples involving up to 100 potential plants, 1000 customers and six potential machines per plant, which we obtain encouraging results.     

A method for solving to optimality uncapacitated location problems

In this paper we develop a method for solving to optimality a general 0–1 formulation for uncapacitated location problems. This is a 3-stage method that solves large problems in reasonable computing times.The 3-stage method is composed of a primal-dual algorithm, a subgradient optimization to solve a Lagrangean dual and a branch-and-bound algorithm. It has a hierarchical structure, with a given stage being activated only if the optimal solution could not be identified in the preceding stage.The proposed method was used in the solution of three well-known uncapacitated location problems: the simple plant location problem, thep-median problem and the fixed-chargep-median problem. Computational results are given for problems of up to the size 200 customers ×200 potential facility sites.     

Efficient solution of large scale,single-source,capacitated plant location problems

The single-source, capacitated plant location problem is considered. This problem differs from the capacitated plant location problem by the additional requirement that each customer must be supplied with all its demand from a single plant. An efficient heuristic solution, capable of solving large problem instances, is presented. The heuristic combines Lagrangian relaxation with restricted neighbourhood search. Computational experiments on two sets of problem instances are presented.     

A simple plant-location model for quantity-setting firms subject to price uncertainty

The uncapacitated plant location problem under uncertainty is formulated in a mean-variance framework with prices in various markets correlated via their response to a common random factor. This formulation results in a mixed-integer quadratic programming problem. However, for a given integer solution, the resulting quadratic programming problem is amenable to a very simple solution procedure. The simplicity of this algorithm means that reasonably large problems should be solvable using existing branch-and-bound techniques.     

Solving the Simple Plant Location Problem using a Data Correcting Approach

The Data Correcting Algorithm is a branch and bound type algorithm in which the data of a given problem instance is `corrected' at each branching in such a way that the new instance will be as close as possible to a polynomially solvable instance and the result satisfies an acceptable accuracy (the difference between optimal and current solution). In this paper the data correcting algorithm is applied to determining exact and approximate optimal solutions to the simple plant location problem. Implementations of the algorithm are based on a pseudo-Boolean representation of the goal function of this problem, and a new reduction rule. We study the efficiency of the data correcting approach using two different bounds, the Khachaturov-Minoux bound and the Erlenkotter bound. We present computational results on several benchmark instances, which confirm the efficiency of the data-correcting approach.     

A note on duality gap in the simple plant location problem

This paper studies the duality gap in the simple plant location problem, and presents general formulas for the gap when certain complementary slackness conditions are satisfied. We show that the duality gap derived by Erlenkotter [A dual-based procedure for uncapacitated facility location, Operations Research 26 (1978) 992–1009], and which has been widely used in the literature, is a special case of the formulas presented here. A counterexample demonstrates that an underlying assumption in Erlenkotter may be violated. The results may be used to obtain improved lower bounds for branch-and-bound algorithms.     

Solving the plant location problem on a line by linear programming

This paper investigates the simple uncapacitated plant location problem on a line. We show that under general conditions the special structure of the problem allows the optimal solution to be obtained directly from a linear programming relaxation. This result may be extended to the related p-median problem on a line. Thus, the practitioner is now able to use readily available LP codes in place of specialized algorithms to solve these one-dimensional models. The findings also shed some light on the “integer friendliness” of the general problem.     

Mixed integer programming model formulations for dynamic multi-item multi-level capacitated lotsizing

Recently there have been two new proposals to model the dynamic multi-item multi-level capacitated lotsizing problem by variable redefinitions based on either a shortest route or a simple plant location representation of the underlying decision problem. Here we will introduce an extended version of the well-known inventory and lotsize model followed by a new model formulation based on modeling the changes of end-of-period inventory levels explicitly. Secondly, we will compare the suitability of different model formulations when solving problem instances with up to 40 items and 16 periods utilizing state-of-the-art standard MIP software on a personal computer.     

一类带单源约束的选址运输问题算法研究

带单源约束的选址运输问题是在经典的选址运输问题基础上考虑每个顾客需求的产品仅由一家工厂供应的情况。所建立的模型是整数规划，是NP难的。本文先考虑了开办费用为零的带单源约束的选址运输问题，即带单源约束的运输问题。松弛其中一种变量约束，借鉴求解运输问题的表上作业法，给出了一种修正的表上作业法，然后将算法推广。最后给出了将算法应用在Excel随机生成的测试问题上所得到的结果，与LINDO求得的最优解相比，差距很小。由此得出结论：对规模较小的带单源约束的选址运输问题，本文提出的算法是简便且行之有效的。     

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.     

Adaptation of the plant location model for regional environmental facilities and cost allocation strategy

This paper is divided into two parts. In the first part of the paper, the plant location model is adapted for the special case of siting wastewater treatment facilities when the wastewater sources and treatment facilities are arranged in a chain or linear configuration. In this problem, flows or shipments may be merged in common pipes that provide economies of scale in transport. In order to apply the plant location model, an appropriate definition of the additional cost incurred when a waste source joins a regional facility is required. In addition, sequential priority constraints are developed in the siting model in order to make possible proper accounting of transport costs. The new siting model can be conveniently solved by linear programming.In the second part of the paper, a dual of the plant location model is explored as a cost allocation method for the fixed charge facility siting problem. The constraint sets of the dual model can be shown to imply the core conditions of the related cost game; hence, a set of the dual variables from the dual problem can be regarded as rational cost allocations. The analysis places both facility siting and cost allocation in a common framework.     

混凝土搅拌站的选址问题研究

混凝土搅拌站的选址,在施工中占有十分重要的地位.针对混凝土需求随时间不规则变化的情况以及混凝土有效期短等特点,提出了混凝土需求不规则变化的选址模型.该模型把选址与各个时间段的资源配置结合起来确定混凝土搅拌站的位置,在保证需求最大限度得到满足的同时,使选址能够兼顾到尽可能多的需求点,最大化搅拌站的利润.应用该模型和算法成功地解决了一个实际问题,算例验证了模型和算法的有效性.     

Minisum location of a travelling salesman on simple networks

In this paper, we study the travelling salesman location problem on simple networks. The problem is to find the optimal home location of the salesman (e.g., a repair unit) that in each working day, must visit all the customers that require service. The number of customers as well as their location can change from day to day. In simple networks, each link belongs to at most one cycle. The paper includes O(n) algorithms for several types of simple networks and thus, avoids the calculation of 2ⁿ − 1 probabilities for each possible tour that may occur (customers are located at n nodesof the network).     

Case-Based Reasoning for Repetitive Combinatorial Optimization Problems, Part II: Numerical Results

This paper presents numerical results from the application of a case-based reasoning approach to several repetitive operations research problems. These experiments are applications of the ideas presented in the previous framework paper, Part I. The three combinatorial optimization problems explored in this paper are the knapsack problem, the travelling salesman problem and the uncapacitated plant location problem. These numerical experiments permit a comparison of the performance of this technique across these three problem classes as well as with the traditional solution algorithms.