Collaborative distribution - application to the city of Yogyakarta, Indonesia

Asih, Anna Maria Sri; Jatiningrum, Wandhansari Sekar; Sopha, Bertha Maya

doi:10.1109/ieem.2016.7798056

Cited by 4 publications

(3 citation statements)

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“…The geographical map for those two operators was taken from Asih et al and shown in Figure 2. 37 The four approaches were used to evaluate three scenarios:…”

Section: Case Studymentioning

confidence: 99%

“…Scenarios with collaborative strategy has clustering process to assign retailers to closer DC prior to distance optimization for each cluster (Asih et al, 2016).…”

Section: Case Studymentioning

confidence: 99%

“…Asih et al has demonstrated that transport consolidation and warehouse sharing in Yogyakarta city has resulted less distance traveled and cost. 37 The study was carried out by using ACO metaheuristic to find the shortest path. Although the collaborative strategy appeared to be promising, the method to search for optimized routing has not been explored.…”

Section: Introductionmentioning

confidence: 99%

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Comparison study of metaheuristics

Asih¹,

Sopha²,

Kriptaniadewa

2017

International Journal of Engineering Business Management

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Many existing studies have used hypothetical data to evaluate the performance of various metaheuristics in solving delivery route optimization. As empirical data impose characteristics of a particular problem, it is necessary to evaluate whether the problem characteristics may influence to the performance of metaheuristics. This article therefore attempts to compare the performance of metaheuristics, that is, genetic algorithm, ant colony optimization (ACO), particle swarm optimization, and simulated annealing (SA), to solve an empirical delivery problem in Yogyakarta, Indonesia. Two cases are developed to capture different characteristics of empirical data. The first case introduces delivery problem of one logistics operator and 58 retailers; the second case presents collaborative strategy in delivery problem, involving two logistics operators and 142 retailers. Results indicate that ACO and SA perform better with respect to less distance traveled for both cases and higher truck utility and lower number of routes for the second case.

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“…The geographical map for those two operators was taken from Asih et al and shown in Figure 2. 37 The four approaches were used to evaluate three scenarios:…”

Section: Case Studymentioning

confidence: 99%

“…Scenarios with collaborative strategy has clustering process to assign retailers to closer DC prior to distance optimization for each cluster (Asih et al, 2016).…”

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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