Solving large immobile location–allocation by affinity propagation and simulated annealing. Application to select which sporting event to watch

Torrent-Fontbona, Ferran; Muñoz, Víctor; López, Beatriz

doi:10.1016/j.eswa.2013.01.065

Cited by 12 publications

(8 citation statements)

References 9 publications

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“…The Simulated Annealing method is simple in terms of execution and yields results comparable with the Genetic algorithm. Torrent et al applied the Simulated Annealing algorithm to a location-allocation problem, thereby reducing the solving time [21]. The results of this research show that Simulated Annealing algorithm is a simple and good algorithm in solving location-allocation problems.…”

Section: Literature Reviewmentioning

confidence: 80%

The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam)

et al. 2020

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The Vector Assignment Ordered Median Problem (VAOMP) is a new unified approach for location-allocation problems, which are one of the most important forms of applied analysis in GIS (Geospatial Information System). Solving location-allocation problems with exact methods is difficult and time-consuming, especially when the number of objectives and criteria increases. One of the most important criteria in location-allocation problems is the capacity of facilities. Firstly, this study develops a new VAOMP approach by including capacity as a criterion, resulting in a new model known as VAOCMP (Vector Assignment Ordered Capacitated Median Problem). Then secondly, the results of applying VAOMP, in scenario 1, and VAOCMP, in scenario 2, for the location-allocation of fire stations in Tehran, with the objective of minimizing the arrival time of fire engines to an incident site to no more than 5 min, are examined using both the Tabu Search and Simulated Annealing algorithms in GIS. The results of scenario 1 show that 52,840 demands were unable to be served with 10 existing stations. In scenario 2, given that each facility could not accept demand above its capacity, the number of demands without service increased to 59,080, revealing that the number of stations in the study area is insufficient. Adding 35 candidate stations and performing relocation-reallocation revealed that at least three other stations are needed for optimal service. Thirdly, and finally, the VAOMP and VAOCMP were implemented in a modest size problem. The implementation results for both algorithms showed that the Tabu Search algorithm performed more effectively.

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Section: Literature Reviewmentioning

confidence: 80%

The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam)

et al. 2020

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“…In addition, the major limitation of Integer Linear Programming is that the computational time increases rapidly when the size of the problem increases. Torrent-Fontbona et al [43] proposed a method that first uses clustering technique to reduce the complexity and then applied heuristic methods to solve the problem. It reduces the computation time as well as the quality of the solutions.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem

Tan

Mei

et al. 2021

IEEE Trans. Serv. Comput.

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With the ever increasing number of functionally similar web services being available on the Internet, the market competition is becoming intense. Web service providers (WSPs) realize that good Quality of Service (QoS) is a key of business success and low network latency is a critical measurement of good QoS. Because network latency is related to location, a straightforward way to reduce network latency is to allocate services to proper locations. However, Web Service Location Allocation Problem (WSLAP) is a challenging task since there are multiple objectives potentially conflicting with each other and the solution search space has a combinatorial nature. In this paper, we consider minimizing the network latency and total cost simultaneously and model the WSLAP as a multi-objective optimization problem. We develop a new PSO-based algorithm to provide a set of trade-off solutions. The results show that the new algorithm can provide a more diverse range of solutions than the compared three well known multi-objective optimization algorithms. Moreover, the new algorithm performs better especially on large problems.

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“…Based on the essence of servers, FLPs could be categorized as mobile or immobile server FLPs. In the case of mobile servers, servers travel to the location of customers (Baptista and Oliveira, 2012), while in immobile servers, customers are supposed to visit the servers (Torrent-Fontbona et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

A capacitated biobjective location problem with uniformly distributed demands in the UAV‐supported delivery operation

Shavarani

Golabi

İzbirak

2019

Int Trans Operational Res

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In recent years, small unmanned aerial vehicles have been used to deliver medicine and goods as a solution to severe traffic jams and to serve the purpose of fast and effective delivery, especially for medical and emergency applications where time is vital. On the other hand, in the competitive market of today, retailers are considering the use of drones to minimize the customers' waiting times and as a way to lower their transportation costs. This study aims to develop a biobjective mathematical model to account for the optimum number and spatial location of facilities among a set of candidate locations such that the total travel distance, costs, and lost demand are minimized simultaneously. It is assumed that the demand occurrence follows a Poisson distribution and is uniformly distributed along the network edges. The proposed biobjective capacitated facility location model is NP-hard, thus nondominated sorting genetic algorithm II and reference-point based nondominated sorting genetic algorithm are applied to solve the problem. The performance of the algorithms, quality of solutions, and the results are investigated and discussed.

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Solving large immobile location–allocation by affinity propagation and simulated annealing. Application to select which sporting event to watch

Cited by 12 publications

References 9 publications

The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam)

The Capacitated Location-Allocation Problem Using the VAOMP (Vector Assignment Ordered Median Problem) Unified Approach in GIS (Geospatial Information Systam)

Evolutionary Multi-Objective Optimization for Web Service Location Allocation Problem

A capacitated biobjective location problem with uniformly distributed demands in the UAV‐supported delivery operation

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