A simple optimization method based on Backtrack and GA for delivery schedule

Sakurai, Yoshitaka; Takada, Kouhei; Tsukamoto, Natsuki; Onoyama, Takashi; Knauf, Rainer; Tsuruta, Setsuo

doi:10.1109/cec.2011.5949968

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…If the delivery area has new locations, the delivery routes are created offline to store in the case base for all locations and for all typical locations such as public facilities, shops, factories, offices etc. Due to the offline nature and hence no need for a real-time response, either a non case-based GA [21] or other methods such as LKH [21] can be used for this purpose, even though time-consuming manual adjustment might also be necessary. Less important sites are removed, adding some key sites affecting the delivery route (e.g.…”

Section: The Proposed Techniquementioning

confidence: 99%

“…In our earlier work, some types of Genetic Algorithms (GAs) were proposed including a Multi-outer-world GA (Mow-GA) [19], a Multi-inner-world Genetic Algorithm (Miw-GA) [20], and a Backtrack/Restart GA (BR-GA) [21]. These GAs incorporated simple heuristics such as NI type heuristics aiming at interactive real-time responses as well as avoiding significant errors for any kinds of delivery location patterns.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: The Proposed Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Case based human oriented delivery route optimization

Kawabe

Kobayashi

Tsuruta

et al. 2015

2015 IEEE Congress on Evolutionary Computation (CEC)

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“…However, when the scale of TSP became more than 200 cities, for example, Miw-GA, the error rate exceeded 4%. To overcome this drawback, we proposed Backtrack and Restart GA (BR-GA) [22], which maintains the diversity of the population by conducting random restart. It could achieve below 3% level error rate for less than 1000 cities TSPs within 3 seconds.…”

Section: B Genetic Algorithmsmentioning

confidence: 99%

A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

Yamamoto

Kawabe

Kobayashi

et al. 2015

2015 11th International Conference on Signal-Image Technology &Amp; Internet-Based Systems (SITIS)

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This paper introduces a refined knowledge based approach to make Genetic Algorithms (GA) more effective. A concept called Case Based Reasoning (CBR) is combined with GA. This technology uses former solutions to the problem for deriving new solutions. The contribution of this CBR combination is a Case Base Maintenance technology that cares for both diversity and fitness in population. Former solutions namely, former problem's GA's solutions whose human / culture related problems are modified by human (expert) support, is maintained, selected, and used as GA's initial population for new but usually more than 80% similar problems. This technology weakens the factor "accidence" in favor of fitness and diversity balanced search especially towards human / culture related optimality. Instead of hoping for "accidentally good solutions" such technologies create "intentionally good solutions". Also, the Case Base (CB) combined with a new NI (Nearest Insertion) is used as a way to include / maintain human / cultural related knowledge into GA, which is called CBHOGA (Case Based Human Oriented GA). Human related knowledge is hard to formally express and mostly maintained by human support. This knowledge is kept in the Case Base and inherited as the initial population. Further, NI operations maintain this knowledge or preserve important parts of the solution in the initial population particularly by specifying insertion locations in the above mentioned new NI. The effects are shown by experiments.

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“…Therefore, we proposed a so called Backtrack and Restart GA (BR-GA) [12], which maintains a population's diversity by conducting random restart and fostering new children. It achieves below 3% error rate for less than 1000 cities TSPs within 3 seconds.…”

Section: Introductionmentioning

confidence: 99%

A case based approach for an intelligent route optimization technology

Suzuki

Matsumaru

Tsuruta

et al. 2014

Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation

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The paper introduces a Case Based Approximation method to solve large scale Traveling Salesman Problems in a short time with a low error rate. It is useful for domains with most solutions being similar to solutions that have been created. Thus, a solution can be derived by (1) selecting a most similar TSP from a library of former TSP solutions, (2) removing the locations that are not part of the current TSP and (3) adding the missing locations of the current TSP by mutation, namely Nearest Insertion (NI). This way of creating solutions by Case Based Reasoning (CBR) avoids the computational costs to create new solutions from scratch.

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A simple optimization method based on Backtrack and GA for delivery schedule

Cited by 14 publications

References 15 publications

Case based human oriented delivery route optimization

Case based human oriented delivery route optimization

A Refined Case Based Genetic Algorithm for Intelligent Route Optimization

A case based approach for an intelligent route optimization technology

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