Performance of an add-drop-interchange heuristic for the capacitated facility location problem

Sinha, Pritibhushan

doi:10.1504/ijams.2009.026199

Cited by 5 publications

(6 citation statements)

References 15 publications

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“…In the local search-based Add [33] and Drop [19] heuristics, facilities are successively placed in or deleted from locations, dependent on some measures of suitability. In this way, different combinations of selected locations are checked in the search procedure [49].…”

Section: Modified Add and Drop Heuristicsmentioning

confidence: 99%

“…The idea of lost sales has been rarely considered in the capacitated median problems. While respecting the capacity restrictions, the majority of related studies have assumed that there would be the possibility of covering all the demands using the available resources [28,34,49]. Harris et al [25] developed a bi-objective capacitated inventory-location model to minimize the environmental effects of established facilities and the financial costs simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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A congested capacitated location problem with continuous network demand

2022

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This paper presents a multi-objective mixed-integer non-linear programming model for a congested multiple-server discrete facility location problem with uniformly distributed demands along the network edges. Regarding the capacity of each facility and the maximum waiting time threshold, the developed model aims to determine the number and locations of established facilities along with their corresponding number of assigned servers such that the traveling distance, the waiting time, the total cost, and the number of lost sales (uncovered customers) are minimized simultaneously. Also, this paper proposes modified versions of some of the existing heuristics and metaheuristic algorithms currently used to solve NP-hard location problems. Here, the memetic algorithm along with its modified version called the stochastic memetic algorithm, as well as the modified add and modified drop heuristics are used as the solution methods. Computational results and comparisons demonstrate that although the results obtained from the developed stochastic memetic algorithm are slightly better, the applied memetic algorithm could be considered as the most efficient approach in finding reasonable solutions with less required CPU times.

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Section: Modified Add and Drop Heuristicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A congested capacitated location problem with continuous network demand

2022

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“…Researchers focus on repairing strategy and penalty function strategy which are the most frequently used for their efficiency. Actually, EABOP repairs its infeasible solutions by a specific repair operator which called DROP (Pritibhushan, 2009). It is a simple greedy algorithm (Kon & Tian, 2006;Chu & Beasley, 1998;Fox & Scudder, 1985) aims of obtaining a feasible solution x repaired by changing each bit x i infeasible [ ] (where i n = ...1 ) set to 1 to 0 as long as any constraint is violated.…”

Section: Repair Operatormentioning

confidence: 99%

“…It is a greedy algorithm (called ADD) aims at improving the fitness of feasible solutions. ADD method (Pritibhushan, 2009)…”

Section: The Improved Eabopmentioning

confidence: 99%

A Novel Evolutionary Algorithm for Multidimensional Knapsack Problem

Sabba

Chikhi

2015

International Journal of Operations Research and Information Systems

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Binary optimization problems are in the most case the NP-hard problems that call to satisfy an objective function with or without constraints. Various optimization problems can be formulated in binary expression whither they can be resolved in easier way. Optimization literature supplies a large number of approaches to find solutions to binary hard problems. However, most population-based algorithms have a great tendency to be trapped in local optima particularly when solving complex optimization problems. In this paper, the authors introduce a new efficient population-based technique for binary optimization problems (that we called EABOP). The proposed algorithm can provide an effective search through a new proposed binary mutation operator. The performance of our approach was tested on hard instances of the multidimensional knapsack problem. The obtained results show that the new algorithm is able of quickly obtaining high-quality solutions for most hard instances of the problem.

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“…For feasible solution recovery methods, a restricted neighborhood search and a repeated-matching algorithm were studied (Hindi and Pienkosz, 1999;Holmberg et al, 1999) and a subgradient-based heuristic algorithm in conjunction with the volume algorithm and randomized rounding was developed (Barahona and Chudak, 2005). Also, Chen and Ting (2008) developed a hybrid algorithm which combined the Lagrangian heuristic and a multiple ant colony system in order to solve the single source capacitated facility location problem, and an add-drop-interchange heuristic presented by Sinha (2009) was used to improve a lower bound based on the LP-relaxation. Furthermore, Du et al (2010) proposed an approximation algorithm for the k-level CFLP that unified and extended several existing facility location problems and Sun (2012) developed an effective tabu search heuristic procedure for CFLP, as compared to the Lagrangian heuristic method with improved subgradient scheme.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Mean Value Cross Decomposition Algorithm for Capacitated Facility Location Problems

Kim¹,

Choi²,

Ko³

2013

Informatica

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In this article, we propose a practical algorithm for capacitated facility location problems (CFLP). There are some approaches which can obtain primal solutions while simultaneously exploiting the primal structure and the dual structure. One of these approaches is the mean value cross decomposition (MVCD) method that ensures convergence without solving master problems. However, MVCD has been previously applied only to uncapacitated facility location problems (UFLP), due to the fact that the performance is highly dependent on the structure of the problem. The proposed algorithm, named the dynamic mean value cross decomposition algorithm (DMVCD), is effectively integrated with MVCD and cutting plane methods in order to tighten the bounds by reducing the duality gap. Computational results of various instances are also reported to verify the effectiveness and efficiency of DMVCD.

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Performance of an add-drop-interchange heuristic for the capacitated facility location problem

Cited by 5 publications

References 15 publications

A congested capacitated location problem with continuous network demand

A congested capacitated location problem with continuous network demand

A Novel Evolutionary Algorithm for Multidimensional Knapsack Problem

Dynamic Mean Value Cross Decomposition Algorithm for Capacitated Facility Location Problems

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