A Multi-World Genetic Algorithm to Optimize Delivery Problem with Interactive-Time

Sakurai, Yoshitaka; Onoyama, Takashi; Kubota, Sen; Tsuruta, Setsuo

doi:10.5772/12872

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…The issue of distribution logistics must also be addressed in the area of maintenance and service processes. In particular, the following factors affect the organizational forms of maintenance and repair activities [1]:…”

Section: Logistics In Providing Maintenance and Servicementioning

confidence: 99%

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Optimizing Logistics Routes in the Field of Maintenance

Frischer¹,

Pollak²,

Jančíková³

2019

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In the event that there is a requirement to provide maintenance with a service intervention somewhere in the area, it is necessary to select a service team which is optimal for the given task for all the assessed aspects. In this article, tests and methods based on TSP principles have been developed that are able to optimize route selection between key points. Due to the complexity of algorithm design, two approaches have been validated. The "Brute Force" method, which can provide information on the choice of the optimal route according to the specified parameters, and the "Nearest Neighbour" method, which is able to quickly calculate a large group of intermediate points, but only provides sufficient results. The aim was to create a methodology, guidance, and direction in evaluating route selection. That is why two basically opposed methods of TSP solution have been chosen, modified by the authors into a form suitable for implementation on virtually any platform. Although the design and program implementation was based on the MATLAB platform, no proprietary functions and libraries are used and the entire software design is implemented without the need for their use.

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Section: Logistics In Providing Maintenance and Servicementioning

confidence: 99%

“…where dij is the distance between the city i and the city j, the decision variable xij = 1 is the route made by the business traveller (including the route from the city i and the city j), xij = 0 is the route not made by the passenger. | S | means the number of elements contained in the S set (1).…”

Section: The Mathematical Description Of Problems Tspmentioning

confidence: 99%

Optimizing Logistics Routes in the Field of Maintenance

Frischer¹,

Pollak²,

Jančíková³

2019

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Case based human oriented delivery route optimization

Kawabe

Kobayashi

Tsuruta

et al. 2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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Delivery route optimization is a well-known NPcomplete problem based on the Traveling Salesman Problem (TSP) involving 20-2000 cities though human oriented factors make the problem more complex. Despite of NP-completeness, the scheduling should be solved every time within interactive response time and below expert level error or local optimality, considering human oriented factors including personal, social, and cultural factors. To cope with this, Cases and NI (Nearest Insertion) are introduced into a Genetic Algorithm (GA), based on the insight that real problems are similar to previous ones. A solution can be derived from former solutions, considering human oriented factors as follows: (1) retrieving the most similar cases, (2) modifying them by removing and adding locations by NI, and (3) further optimizing them by a GA using only NI operations. This cannot only diminish the costs to compute new solutions from scratch but also inherit many parts of previous routes to respect human factors. Experimental evaluation revealed remarkable results. Though the most effective TSP solving method LKH needed more than 3 seconds, the proposed method yielded results within 3% of the worst error rate and in less than 3 seconds. Furthermore, the proposed method is able to inherit most of the delivery routes, while LKH leads to significant changes.

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Distributed GAs with case-based initial populations for real-time solution of combinatorial problems

Kawabe

Suzuki

Matsumaru

et al. 2014

2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

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Combinatorial problems are NP-complete, which means even infinite number of CPUs take polynomial time to search an optimal solution. Therefore approximate search algorithms such as Genetic Algorithms are used. However, such an approximate search algorithm easily falls into local optimum and just distributed / parallel processing seems inefficient. In this paper, we introduce distributed GAs, which compute their initial population in a case-based manner and compose their upcoming generations by the particular GAs, which exchange their solutions and make their individual decisions, when composing a next generation based on the fitness of the candidates and diversity issues.

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A Multi-World Genetic Algorithm to Optimize Delivery Problem with Interactive-Time

Cited by 11 publications

References 29 publications

Optimizing Logistics Routes in the Field of Maintenance

Optimizing Logistics Routes in the Field of Maintenance

Case based human oriented delivery route optimization

Distributed GAs with case-based initial populations for real-time solution of combinatorial problems

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