The Vehicle Routing Problem with Time Constraints

Ikegami, Atsuko; Niwa, Akira

doi:10.15807/jorsj.38.107

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…As a method for solving distribution problems with delivery time constraints, a method based on IP (Integer Programming) [1] has been proposed. Although this method can solve these problems optimally, it cannot satisfy the above-mentioned real-time responsiveness.…”

Section: Introductionmentioning

confidence: 99%

Selfish-gene Tolerant Generic Algorithms to solve large-scale constraint TSPs

Sakurai

Onoyama

Kubota

et al. 2006

2006 IEEE International Conference on Information Reuse &Amp;amp; Integration

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Large-scale distribution network simulation applicable to supply-chain management requires to solve hundreds of time-constraint large-scale (max 2000 cities) Traveling Salesman Problems (TSP) within interactive response time, with practicable optimality. To meet this requirement, a selfish-gene tolerant type GA is proposed. Here, each gene of an individual satisfies only its constraints selfishly, disregarding the constraints of other genes in the same individual. Further, to some extent, even individuals that violate constraints can survive over generations and are given the chance of improvement. Our experiment proves that this method provides expert-level solutions for time constraint largescale TSPs within a few seconds.

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Section: Introductionmentioning

confidence: 99%

Selfish-gene Tolerant Generic Algorithms to solve large-scale constraint TSPs

Sakurai

Onoyama

Kubota

et al. 2006

2006 IEEE International Conference on Information Reuse &Amp;amp; Integration

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show abstract

“…The solving method integrating E' with heuristics [3] marked high optimality of below 3% error for SO cities' TSPs. However, since this method needs 30 seconds for such scale TSPs, it cannot be applicable to the case that requires interactive response time.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…2) Then, through NI method, insert the removed nodes having no time constraints into the remaining nodes (genes) of the tour (chromosome). 3) Lastly, through the selfish NI method, remove nodes having time constraint thmugh selfish NI method.…”

Section: (3) Mutationmentioning

confidence: 99%

“…And as the creation of each route is equivalent to a TSP (Traveling Salesman Problem) of tens of cities, an approximate solving method that enables both interactive responsiveness and practical optimality is required [2]. As a method for solving distribution problems with delivery time constraints, a method based on IP (Integer Programming) [3] has been proposed. Although this method can solve these problems optimally, it cannot satisfy the above-mentioned responsiveness.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent evolutional algorithm for distribution network optimization

Onoyama

Maekawa

Kubota

et al.

Proceedings of the International Conference on Control Applications

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Based on experimental comparison, this paper discusses GA applied solving methods of medium-scale (100 cities) time constraint Traveling Salesman Problem (TSP) that suit repetitive use in interactive simulation for optimizing a large-scale distribution network. To solve both energy problems and environmental problems simultaneously, it is important to optimize a large-scale distribution network shared by multiple enterprises.Recently, in addition to the distribution efficiency, transportation specified time-constraints are increasingly required to improve productivity through supply chain management. Moreover, the network optimality should be considered from various aspects by human experts. Thus, both practical optimality and interactive response time are required to this simulation. To satisfy these requirements, a "selfish-gene with limited allowance" type GA is proposed. Here, each gene of an individual satisfies only its constraints selfishly, disregarding the constraints of other genes in the same individual. Further, to some extent, even individuals that violate constraints can survive over generations. And, even inferior individuals have the chance of their reproductions over generations. Thus, these individuals get the chance of improvement. As a result of our experimental comparison, the proposed solving method could solve one hundred time-constraint TSPs within 10% errors, less than a few minntes.

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Computer-aided distribution network designing system

Taniguchi

Onoyama²,

Tsuruta³

Proceedings of the International Conference on Control Applications

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To enhance distribution efficiency, optimizing a large-scale distribution network is necessary. However, in such a network, there are hundreds of distribution I collection locations, and dozens of distribution centers each having tens or hundreds of trucks. Furthermore, the networks optimality should be considered from various aspects such as social, political, and environmental factors, which needs human decision. Thus, the design to optimize such a distribution network is a large-scale combinatorial and man-in-the-loop problem.To solve this problem, a computer-aided distribution network designing system is proposed, which has intelligent Genetic Algorithm (GA) and goal-oriented strategic Knowledge Based (KB) control framework. The former guarantees interactive responsiveness and realizes experts' level accuracy. The latter supports to design optimal networks through almost automatically designing distribution networks that satisfy human input goals. Experimental results proved that the proposed method was useful for supporting the design of large-scale distribution networks totally optimal from various aspects such as social or political factors.

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The Vehicle Routing Problem with Time Constraints

Cited by 8 publications

References 9 publications

Selfish-gene Tolerant Generic Algorithms to solve large-scale constraint TSPs

Selfish-gene Tolerant Generic Algorithms to solve large-scale constraint TSPs

Intelligent evolutional algorithm for distribution network optimization

Computer-aided distribution network designing system

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