Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/35769
Title: | Genetic algorithm for a delivery problem with mixed time windows | Authors: | Ongcunaruk, Wisute Ongkunaruk, Pornthipa JANSSENS, Gerrit K. |
Issue Date: | 2021 | Publisher: | PERGAMON-ELSEVIER SCIENCE LTD | Source: | Computers & industrial engineering (Print), 159 (Art N° 107478) | Abstract: | This research aims to improve transportation planning decisions for a production company, which produces seasoning powder in Thailand and the logistics provider. Due to restrictions in Bangkok and its metropolitan area, the routing problem becomes one with two types of time windows. A mixed integer programming model is formulated, which aims to minimize a cost function which consists of fixed vehicle costs, variable vehicle costs and fuel costs. This approach has its limits in terms of problem size. Therefore a genetic algorithm (GA) has been developed to approximate the optimal solution. The proposed GA has a specific initialization algorithm which generates feasible random solutions. A partial factorial design of GA parameters is implemented to determine the suitable parameter values, which guide the genetic algorithm. The solution of the GA and the mixed integer programming model of the current problems were compared. The maximum optimal gap was between 0 and 0.21%, while the computational time was reduced between 67.78 and 99.45%. The results show that the planning time by a dispatcher is reduced significantly and the cost is strongly reduced, due to the fact that less vehicles are used. | Notes: | Ongkunaruk, P (corresponding author), Kasetsart Univ, Fac Agro Industry, Dept Agro Industrial Technol, 50 Ngam Wong Wan Rd,Ladyao, Bangkok, Thailand. pornthipa.o@ku.ac.th |
Keywords: | Vehicle routing problem with time window;Mixed integer programming;Genetic algorithm;Construction heuristic | Document URI: | http://hdl.handle.net/1942/35769 | ISSN: | 0360-8352 | e-ISSN: | 1879-0550 | DOI: | 10.1016/j.cie.2021.107478 | ISI #: | WOS:000698643000016 | Rights: | 2021 Elsevier Ltd. All rights reserved. | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2022 |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
1-s2.0-S036083522100382X-main.pdf Restricted Access | Published version | 896.39 kB | Adobe PDF | View/Open Request a copy |
WEB OF SCIENCETM
Citations
14
checked on Sep 12, 2024
Page view(s)
32
checked on Sep 7, 2022
Download(s)
4
checked on Sep 7, 2022
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.