A Multi-Exchange Heuristic for the Single-Source Capacitated Facility Location Problem

Ahuja, Ravindra K.; Orlin, James B.; Pallottino, Stefano; Scaparra, Maria Paola; Scutellà, Maria Grazia

doi:10.1287/mnsc.1030.0193

Cited by 98 publications

(54 citation statements)

References 21 publications

(29 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the first set assignment costs dominate fixed opening costs for most of the instances, whereas in the second set we have the opposite situation. The first set of benchmark instances contains the 71 problems used in Holmberg et al (1999) and in Ahuja et al (2004). The instances of this set are divided in 4 classes, G 1 -G 4 , according to the dimension of the problems.…”

Section: Computational Experiencementioning

confidence: 99%

“…The contents of the columns are the following: Column Opt gives the values of the optimal solutions for each instance. The optimal solutions are reported in Ahuja et al (2004) (for the problems of the first set) and in Díaz and Fernández (2002) (for the problems of the second set). Column Best shows the best solution values found for each instance.…”

Section: Computational Experiencementioning

confidence: 99%

“…We also compare the obtained results with other heuristic approaches. In particular, for the first set of instances, we compare our approach to the VLNS algorithm proposed in Ahuja et al (2004), and for the second set of instances, to the Hybrid algorithm proposed by Delmaire et al (1999b). The obtained results are shown in Tables 12 and 13.…”

Section: Computational Experiencementioning

confidence: 99%

“…Upper bounds are obtained with a primal heuristic that uses the solution of the relaxed problem to provide a feasible solution that is then improved using tabu search. Recently, Ahuja et al (2004) propose a very large scale neighborhood search algorithm where the neighborhood structures are induced by customer multi-exchanges and by facility moves. The multi-exchanges are detected using improvement graphs that are dynamically built using a greedy scheme.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Scatter search for the single source capacitated facility location problem

Contreras

Díaz

2007

Ann Oper Res

View full text Add to dashboard Cite

This paper considers the Single Source Capacitated Facility Location Problem (SSCFLP). We propose a Scatter Search approach to provide upper bounds for the optimal solution of the problem. The proposed approach uses GRASP to initialize the Reference Set. Solutions of the Reference Set are combined using a procedure that consists of two phases:(1) the initialization phase and (2) the improvement phase. During the initialization phase each client is assigned to an open facility to obtain a solution that is then improved with the improvement phase. Also, a tabu search algorithm is applied. In order to evaluate the proposed approach we use different sets of test problems. According to the results obtained we observe that the method provides good quality solutions with reasonable computational effort.

show abstract

Section: Computational Experiencementioning

confidence: 99%

Section: Computational Experiencementioning

confidence: 99%

Section: Computational Experiencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Scatter search for the single source capacitated facility location problem

Contreras

Díaz

2007

Ann Oper Res

View full text Add to dashboard Cite

show abstract

“…Due to its wide applications, capacitated facility location problem (CFLP) has been studied by several authors including Holmberg et al [1], Melkote & Daskin [2], Ahuja et al [3], Cortinhal & Captivo [4], Contreras & Diaz [5], and Leitner, M., Raidl, G.R. [6].…”

Section: Introductionmentioning

confidence: 99%

Implementing Lagrangean Decomposition Technique to Acquire an Adequate Lower Boundon the Facility Location Problem Solution

Alenezy¹,

Khalaf²

2013

View full text Add to dashboard Cite

In this work, the Lagrangean Relaxation method has been discussed to solve different sizes of capacitated facility location problem (CFLP). A good lower bound has been achieved on the solution of the CFLP considered in this paper. This lower bound has been improved by using the Volume algorithm. The methods of setting two important parameters in heuristic have been given. The approaches used to gain the lower bound have been explained. The results of this work have been compared with the known results given by Beasley.

show abstract

Very Large‐Scale Neighborhood Search

Altner

Ahuja

Ergün

et al. 2011

Wiley Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

Many optimization problems that model the essential issues of important real‐world decision making are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic techniques that find nearly optimal solutions within a reasonable amount of computation time. Improvement algorithms generally start with a feasible solution and iteratively try to obtain a better solution. Neighborhood search algorithms, which are alternatively called local search algorithms , are a wide class of improvement algorithms where at each iteration an improving solution is found by searching a “neighborhood” of the current solution. A critical issue in the design of a neighborhood search algorithm is defining what solutions constitute the neighborhood of a solution. As a rule of thumb, the larger the neighborhood, the better is the quality of the locally optimal solutions, including the final solution selected upon termination. Similarly, the larger the neighborhood, the longer it takes to search the neighborhood. Thus, a larger neighborhood does not necessarily produce a more effective heuristic unless one can search the larger neighborhood efficiently. This article concentrates on neighborhood search algorithms where the size of the neighborhood is “very large” with respect to the size of the input data and the neighborhood can be searched efficiently. We survey three broad classes of very large‐scale neighborhood (VLSN) search algorithms: variable‐depth methods in which large neighborhoods are searched heuristically, large neighborhoods that are searched by solving a constrained minimum–cost flow problem, and other situations that give rise to efficiently searchable large neighborhoods.

show abstract

A Multi-Exchange Heuristic for the Single-Source Capacitated Facility Location Problem

Cited by 98 publications

References 21 publications

Scatter search for the single source capacitated facility location problem

Scatter search for the single source capacitated facility location problem

Implementing Lagrangean Decomposition Technique to Acquire an Adequate Lower Boundon the Facility Location Problem Solution

Very Large‐Scale Neighborhood Search

Contact Info

Product

Resources

About