An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem
This research paper introduces an adaptive differential evolution algorithm (ADE algorithm) designed to address the multi-compartment vehicle routing problem (MCVRP) for cold chain transportation of a case study of twentyeight customers in northeastern Thailand. The ADE algorithm aims to minimize th...
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| Format: | Article |
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Universitat Politècnica de València
2024-01-01
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| Series: | International Journal of Production Management and Engineering |
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| Online Access: | https://polipapers.upv.es/index.php/IJPME/article/view/19928 |
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| author | Supaporn Sankul Naratip Supattananon Raknoi Akararungruangkul Narong Wichapa |
| author_facet | Supaporn Sankul Naratip Supattananon Raknoi Akararungruangkul Narong Wichapa |
| author_sort | Supaporn Sankul |
| collection | DOAJ |
| description | This research paper introduces an adaptive differential evolution algorithm (ADE algorithm) designed to address the multi-compartment vehicle routing problem (MCVRP) for cold chain transportation of a case study of twentyeight customers in northeastern Thailand. The ADE algorithm aims to minimize the total cost, which includes both the expenses for traveling and using the vehicles. In general, this algorithm consists of four steps: (1) The first step is to generate the initial solution. (2) The second step is the mutation process. (3) The third step is the recombination process, and the final step is the selection process. To improve the original DE algorithm, the proposed algorithm increases the number of mutation equations from one to four. Comparing the outcomes of the proposed ADE algorithm with those of LINGO software and the original DE based on the numerical examples In the case of small-sized problems, both the proposed ADE algorithm and other methods produce identical results that align with the global optimal solution. Conversely, for larger-sized problems, it is demonstrated that the proposed ADE algorithm effectively solves the MCVRP in this case. The proposed ADE algorithm is more efficient than Lingo software and the original DE, respectively, in terms of total cost. The proposed ADE algorithm, adapted from the original, proves advantageous for solving MCVRPs with large datasets due to its simplicity and effectiveness. This research contributes to advancing cold chain logistics with a practical solution for optimizing routing in multi-compartment vehicles. |
| format | Article |
| id | doaj-art-00ea4a94a1b049d190653f7c8debecb5 |
| institution | Kabale University |
| issn | 2340-4876 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Universitat Politècnica de València |
| record_format | Article |
| series | International Journal of Production Management and Engineering |
| spelling | doaj-art-00ea4a94a1b049d190653f7c8debecb52025-01-03T00:00:32ZengUniversitat Politècnica de ValènciaInternational Journal of Production Management and Engineering2340-48762024-01-011219110410.4995/ijpme.2024.1992819121An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problemSupaporn Sankul0https://orcid.org/0009-0003-8880-0757Naratip Supattananon1https://orcid.org/0000-0001-6107-882XRaknoi Akararungruangkul2https://orcid.org/0000-0003-2744-7999Narong Wichapa3https://orcid.org/0000-0002-7292-8647Khonkaen UniversityRajamangala University of Technology IsanKhonkaen UniversityKalasin University This research paper introduces an adaptive differential evolution algorithm (ADE algorithm) designed to address the multi-compartment vehicle routing problem (MCVRP) for cold chain transportation of a case study of twentyeight customers in northeastern Thailand. The ADE algorithm aims to minimize the total cost, which includes both the expenses for traveling and using the vehicles. In general, this algorithm consists of four steps: (1) The first step is to generate the initial solution. (2) The second step is the mutation process. (3) The third step is the recombination process, and the final step is the selection process. To improve the original DE algorithm, the proposed algorithm increases the number of mutation equations from one to four. Comparing the outcomes of the proposed ADE algorithm with those of LINGO software and the original DE based on the numerical examples In the case of small-sized problems, both the proposed ADE algorithm and other methods produce identical results that align with the global optimal solution. Conversely, for larger-sized problems, it is demonstrated that the proposed ADE algorithm effectively solves the MCVRP in this case. The proposed ADE algorithm is more efficient than Lingo software and the original DE, respectively, in terms of total cost. The proposed ADE algorithm, adapted from the original, proves advantageous for solving MCVRPs with large datasets due to its simplicity and effectiveness. This research contributes to advancing cold chain logistics with a practical solution for optimizing routing in multi-compartment vehicles.https://polipapers.upv.es/index.php/IJPME/article/view/19928adaptive differential evolution algorithmcold chain transportation networkmetaheuristicsmulti-compartment vehicle routing problem |
| spellingShingle | Supaporn Sankul Naratip Supattananon Raknoi Akararungruangkul Narong Wichapa An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem International Journal of Production Management and Engineering adaptive differential evolution algorithm cold chain transportation network metaheuristics multi-compartment vehicle routing problem |
| title | An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem |
| title_full | An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem |
| title_fullStr | An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem |
| title_full_unstemmed | An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem |
| title_short | An adaptive differential evolution algorithm to solve the multi-compartment vehicle routing problem: A case of cold chain transportation problem |
| title_sort | adaptive differential evolution algorithm to solve the multi compartment vehicle routing problem a case of cold chain transportation problem |
| topic | adaptive differential evolution algorithm cold chain transportation network metaheuristics multi-compartment vehicle routing problem |
| url | https://polipapers.upv.es/index.php/IJPME/article/view/19928 |
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