Ant Colony Optimization Algorithm for the Capacitated Plant Location Problem

Levanova, Tatyana; Loresh, Maksim A.

doi:10.3182/20060517-3-fr-2903.00222

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…• Location: the p-median problem [39], traffic sensors locations problem on highways [40], inspection stations locations in transport networks [41], anticovering location problem [8], p-center problem [3] location of distributed generation resources [33], and capacitated plant location problem [16].…”

Section: Bco Applicationsmentioning

confidence: 99%

“…The BCO approach to the well-known capacitated plant location problem was developed in [16]. The considered problem can be formulated in the following way: Given are a set of potential locations for plants with fixed costs and capacities, and a set of customers with demands for goods supplied from these plants.…”

Section: The Bco Approach To the Capacitated Plant Location Problemmentioning

confidence: 99%

“…In [16] the authors enriched constructive BCO with a local search routine to enable obtaining good results. Differences from the basic BCO algorithm are in the construction of new solutions.…”

Section: The Bco Approach To the Capacitated Plant Location Problemmentioning

confidence: 99%

“…BCO has been successfully applied to various problems by Teodorović and co-authors [3,6,7,8,18,19,20,21,22,28,29,40,41,36,37,39,38]. Recently, some other researchers also used BCO meta-heuristic as a tool to solve numerous, complex problems [15,16,26,27,31,32,33,42,44,45].…”

Section: Introductionmentioning

confidence: 99%

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Bee Colony Optimization - part II: The application survey

Teodorović

Šelmić

Davidović

2015

YUGOSLAV J OPERATION

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Bee Colony Optimization (BCO) is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications.

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Section: Bco Applicationsmentioning

confidence: 99%

Section: The Bco Approach To the Capacitated Plant Location Problemmentioning

confidence: 99%

Section: The Bco Approach To the Capacitated Plant Location Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bee Colony Optimization - part II: The application survey

Teodorović

Šelmić

Davidović

2015

YUGOSLAV J OPERATION

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“…Another type of network problems that have been solved using the ACO metaheuristic is the facility location problem. The objective function for these problems is set to find a subset of alternative facility locations (vertices) that minimize the combined fixed cost of facilities and the variable cost of delivering products from the selected facilities to the customer locations (Levanova 2005;McKendall and Shang 2006). Unlike the above transportation problems, the objective of FTPP is to find a subset of edges in a given network that connect timber sale locations to destinations at a minimum fixed and variable cost.…”

Section: Introductionmentioning

confidence: 99%

Applying ant colony optimization metaheuristic to solve forest transportation planning problems with side constraints

2008

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Forest transportation planning problems (FTPP) have evolved from considering only the financial aspects of timber management to more holistic problems that also consider the environmental impacts of roads. These additional requirements have introduced side constraints, making FTPP larger and more complex. Mixed-integer programming (MIP) has been used to solve FTPP, but its application has been limited by the difficulty of solving large, real-world problems within a reasonable time. To overcome this limitation of MIP, we applied the ant colony optimization (ACO) metaheuristic to develop an ACO-based heuristic algorithm that efficiently solves large and complex forest transportation problems with side constraints. Three hypothetical FTPP were created to test the performance of the ACO algorithm. The environmental impact of forest roads represented by sediment yields was incorporated into the economic analysis of roads as a side constraint. Four different levels of sediment constraints were analyzed for each problem. The solutions from the ACO algorithm were compared with those obtained from a commercially available MIP solver. The ACO solutions were equal to or slightly worse than the MIP solution, but the ACO algorithm took only a fraction of the computation time that was required by the MIP solver.

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Ant Based Heuristics for the Capacitated Fixed Charge Location Problem

Venables

Moscardini

Ant Colony Optimization and Swarm Intelligence

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Ant Colony Optimization Algorithm for the Capacitated Plant Location Problem

Cited by 3 publications

References 9 publications

Bee Colony Optimization - part II: The application survey

Bee Colony Optimization - part II: The application survey

Applying ant colony optimization metaheuristic to solve forest transportation planning problems with side constraints

Ant Based Heuristics for the Capacitated Fixed Charge Location Problem

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