Multi-commodity Location Problem Research Articles

A Tabu Search with Slope Scaling for the Multicommodity Capacitated Location Problem with Balancing Requirements

In this paper, a tabu search heuristic is combined with slope scaling to solve a discrete depot location problem, known as the multicommodity location problem with balancing requirements. Although the uncapacitated version of this problem has already been addressed in the literature, this is not the case for the more challenging capacitated version, where each depot has a fixed and finite capacity. The slope scaling approach is used during the initialization phase to provide the tabu search with good starting solutions. Numerical results are reported on various types of large-scale randomly generated instances. The quality of the heuristic is assessed by comparing the solutions obtained with those of a commercial mixed-integer programming code.

Tabu Search With Exact Neighbor Evaluation For Multicommodity Location With Balancing Requirements

In this paper, we present a tabu search heuristic for solving the multicommodity location problem with balancing requirements. This heuristic improves upon a previous implementation by performing an exact, rather than approximate, evaluation of neighboring solutions. It also includes a number of refinements, in particular, a new initialization procedure and enhanced neighborhood reduction techniques. The heuristic is shown to be very effective, as it identifies the optimal solution on every instance taken from a set of randomly generated problems. It also finds optimal or near-optimal solutions on a set of large-size instances derived from an actual application, with computation times that show little variance as compared with the currently best known exact method.

A parallel branch-and-bound algorithm for multicommodity location with balancing requirements

This article presents a parallel branch-and-bound algorithm for solving the multicommodity location problem with balancing requirements, that is based on the best known sequential method for solving the problem. The algorithm aims to exploit parallelism by dividing the search tree among processes and by performing operations on several subproblems simultaneously. The algorithm is divided into two phases: synchronous initialization and asynchronous exploration. Experimental results on a distributed network of workstations are reported and analyzed.

A branch-and-bound algorithm for depot location and container fleet management

Abstract The multicommodity location problem with balancing requirements is related to one of the major logistics issues faced by distribution and transportation firms: the management of a fleet of vehicles over a medium to long term planning horizon. To solve this problem, we present a branch-and-bound algorithm in which bounds are computed by a dual-ascent procedure. We particularly emphasize the design of efficient branching, fathoming and preprocessing rules. The algorithm was tested on a wide variety of randomly generated problems, and on a large-scale application to the planning of the land operations of a heterogenous container fleet. Results show that the algorithm is highly efficient, and outperforms other existing methods.

Parallel Implementations Of A Branch-And-Bound Algorithm For Multicommodity Location With Balancing Requirements

AbstractWe present two parallel branch-and-bound algorithms for the multicommodity location problem with balancing requirements. The first algorithm implements a master-slave approach in order to perform a parallel exploration of the search tree. The second algorithm decomposes the problem in order to compute the lower bounds in parallel. We realized implementations of these algorithms on a PC-based network of Transputers. Experimental results are reported and analyzed.

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.

Probabilistic Demands and Costs in Facility Location Problems

When deterministic assumptions in ‘classical’ location problems are relaxed some new theoretical and algorithmic problems arise. In this paper, the definition of medians is generalized to the cases: (1) when generated demands and travel costs are random, and (2) when the demand induced at the facilities is random because of competition. Under a particular set of assumptions, the well-known theorems on ‘optimality of node locations' hold for the underlying ‘probabilistic networks’. For case (1) the model is shown to be a generalization of the probabilistic and deterministic median location problems on networks. The relationship of the model to a multicommodity location problem is also pointed out. Additionally, within the framework of the problem formulation, certain parallels with multiobjective location problems are drawn. For case (2), the competitive median location problem is formulated, and some results presented. For the special structure of probabilistic tree networks some interesting localization properties for the 1-median and 2-median are discussed.

The Multi-Commodity Facilities Location Problem

The Multi-commodity Location Problem was one of the ‘multi-level’ location problems introduced by Warszawski and Peer in their paper on building sites. Methods of solution are developed for this problem via a dual-based approach and via a Lagrangean dual-based approach with hill-climbing. Numerical results are presented.

An Improved Algorithm for the Multi-Commodity Location Problem

An Improved Algorithm for the Multi-Commodity Location Problem

An Improved Algorithm for the Multi-commodity Location Problem

Distribution systems designs commonly require the optimal location decisions of regional ware-houses or distribution centers which function as intermediate facilities between plants and customers. This paper deals with such a location problem in which the facilities can handle one of several commodities. We term this problem the multi-commodity facility location problem. A branch and bound algorithm is proposed for solving this problem. Improved bounds are developed for increasing the efficiency of the algorithm. Computational results are provided.

A Note on Warszawski's Multi-Commodity Location Problem

This note shows two easily incorporated improvements in computing the lower bounds for Warszawski's branch and bound algorithm for the multi-commodity location problem. The lower bounds become relatively stronger, thereby making the branch and bound algorithm more efficient.