SOLUTIONS FOR TRANSPORTATION AND MAINTENANCE SCHEDULING NEEDS: A CASE STUDY OF A FUEL DISTRIBUTION COMPANY IN UTTARADIT PROVINCE
Optimized Transportation and Maintenance Scheduling for Fuel Firms
DOI:
https://doi.org/10.55766/sujst-2024-06-e05932Keywords:
Demand Problem, Fuel Transportation Costs, Optimal Transportation Scheduling, Program DevelopmentAbstract
This research focused on the issue of fuel demand management. It proposed scheduling transportation and maintenance of a fuel distribution company and developing a mathematical scheduling model for planning the fuel needs of the substation and automatic gas station for scheduling transportation and maintenance. The researcher developed the program using economical algorithmic heuristics and Ant Colony Optimization (ACO) to solve this problem. The experiment revealed that any problem can find its optimal solution using the developed mathematical scheduling model. They could also schedule transportation and maintenance at the lowest cost. The research compared the results obtained before and after the process. The researcher revealed that the efficiency percentage values were 49 for July 2022 and January 2023, 39 for August 2022 and February 2023, 42 for September 2022 and March 2023, 38 for October 2022 and April 2023, 35 for November 2022 and May 2023, and 31 for December 2022 and June 2023, respectively. Therefore, this research achieved its objectives.
References
Ahmed, Z.H. and Yousefikhoshbakht, M. (2022). An improved tabu search algorithm for solving heterogeneous fixed fleet open vehicle routing problems with time windows. Alexandria Engineering Journal, 64:349-363. https://doi.org/10.1016/j.aej.2022.09.008
Banerjee, A., Sufian, A., Srivastava, A., Gupta, S.K., Kumari, S., and Kumar, S. (2023). An energy and task scheduling algorithm for UAV-IoT collaborative system. Microprocessors and Microsystems, 101:104875. https://doi.org/10.1016/j.micpro.2023.104875
Chaouch, R., Ghorbel, H., and Khalfallah, S. (2021). Model for the classification of scheduling problems based on ontology. Procedia Computer Science, 181:890-896. https://doi.org/10.1016/j.procs.2021.01.244
Clarke, G. and Wright, J.R. (1964). Scheduling of vehicle routing problem from a central depot to a number of delivery points. Operations Research, 12:568-581. https://doi.org/10.1287/opre.12.4.568
Cordone, R. and Hosteins, P. (2019). A bi-objective model for the single-machine scheduling problem with rejection cost and total tardiness minimization. Computers & Operations Research, 102:130-140. https://doi.org/10.1016/j.cor.2018.10.006
Davatgari, A., Cokyasar, T., Verbas, O., and Mohammadian, A. (2024). Heuristic solutions to the single depot electric vehicle scheduling problem with next-day operability constraints. Transportation Research Part C: Emerging Technologies, 163:104656. https://doi.org/10.1016/j.trc.2024.104656
Deb, I. and Gupta, R.K. (2023). A genetic algorithm-based heuristic optimization technique for solving balanced allocation problem involving overall shipping cost minimization with restriction on the number of serving units as well as customer hubs. Results in Control and Optimization, 11:100227. https://doi.org/10.1016/j.rico.2023.100227
Dorigo, M. and Stützle, T. (1964). Ant colony optimization. MIT Press.
Elatar, S., Abouelmehdi, K., and Riffi, M.E. (2023). The vehicle routing problem in the last decade: Variants, taxonomy, and metaheuristics. Procedia Computer Science, 220:398-404. https://doi.org/10.1016/j.procs.2023.03.051
Fedele, M. and Formisano, V. (2022). Waste from criticality to resource through an innovative circular business model: A case study in the manufacturing industry. Journal of Cleaner Production, 407:137143. https://doi.org/10.1016/j.jclepro.2023.137143
Finkelstein, M., Hwan Cha, J., and Bedford, T. (2023). Optimal preventive maintenance strategy for populations of systems that generate outputs. Reliability Engineering & System Safety, 237:109334. https://doi.org/10.1016/j.ress.2023.109334
Frisch, S., Hungerlander, P., Jellen, A., Primas, B., Steininger, S., and Weinberger, D. (2021). Solving a real-world locomotive scheduling problem with maintenance constraints. Transportation Research Part B: Methodological, 150:386-409. https://doi.org/10.1016/j.trb.2021.06.017
Grishin, E., Pravdivets, N., Morozov, N., Lazarev, A., Korovkin, D., and Tyulenev, I. (2022). Comparison of mathematical programming models for optimization of transshipment point seaport - railway. IFAC-Papers Online, 55(10):2,557-2,562. https://doi.org/10.1016/j.ifacol.2022.10.094
Kuyffer, E.D., Shen, K.L., Martens, J.W., and De Pessemier, T. (2023). Offshore windmill and substation maintenance planning with distance, fuel consumption, and tardiness optimization. Operations Research Perspectives, 10:100267. https://doi.org/10.1016/j.orp.2023.100267
Lingling, L., Zhiyun, D., Chenyang, S., and Weiming, S. (2023). A variable neighborhood search algorithm for airport ferry vehicle scheduling problem. Transportation Research Part C: Emerging Technologies, 154:104262. https://doi.org/10.1016/j.trc.2023.104262
Phuk-in, A. (2022a). Maintenance management of Sri Sa-Nga Phatthana Company Limited. In The 8th Thai-Nichi Institute of Technology Academic Conference (TNIAC 2022) (pp. 237-243). Thai, May 19-20.
Phuk-in, A. (2022b). Production-scheduling problem: A case of SD Tractors Company, Limited. In The Conference of Industrial Engineering Network (IE NETWORK 2022) (pp. 469-474). Thai, May 11-12.
Phuk-in, A. (2024a). Production planning and machine maintenance schedule of Dragon Green Energy Company, Limited. The Journal of Industrial Technology, 20(1):62-80.
Phuk-in, A. (2024b). Solving the scheduling problem of stainless steel and alloy factory: A case study of stainless steel and alloy factory in Uttaradit Province. Engineering and Technology Horizons, 41(4):1-9. https://doi.org/10.55003/ETH.410408
Rezaei, B., Guimaraes, F.G., Enayatifar, R., and Haddow, P.C. (2023). Combining genetic local search into a multi-population imperialist competitive algorithm for the capacitated vehicle routing problem. Applied Soft Computing, 142:110309. https://doi.org/10.1016/j.asoc.2023.110309
Siping, X. (2023). An adaptive ant colony algorithm for crowdsourcing multi-depot vehicle routing problems with time windows. Sustainable Operations and Computers, 4:62-75. https://doi.org/10.1016/j.susoc.2023.02.002
Soares, R., Marques, A., Amorim, P., and Parragh, S.N. (2023). Synchronization in vehicle routing: Classification schema, modeling framework, and literature review. European Journal of Operational Research, 313(3):817-840. https://doi.org/10.1016/j.ejor.2023.04.007
Song, M., Cheng, L., and Lu, B. (2024). Solving the multi-compartment vehicle routing problem by an augmented Lagrangian relaxation method. Expert Systems with Applications, 237(A):121511. https://doi.org/10.1016/j.eswa.2023.121511
Sriburum, A., Wichapa, N., and Khantirat, W. (2022). Multi-compartment vehicle routing problem using exact method: A case study of fuel delivery in Khon Kaen Province. In The Conference of Industrial Engineering Network (IE NETWORK 2022) (pp. 110-115). Thai, May 120.
Sui, F., Tang, X., Dong, Z., Gan, X., Luo, P., and Sun, J. (2023). ACO+PSO+A*: A bi-layer hybrid algorithm for multi-task path planning of an AUV. Computers and Industrial Engineering, 175:108905. https://doi.org/10.1016/j.cie.2022.108905
Tayfun, O. and Aysegul, T. (2022). A hybrid metaheuristic algorithm based on iterated local search for vehicle routing problems with simultaneous pickup and delivery. Expert Systems with Applications, 202:117401. https://doi.org/10.1016/j.eswa.2022.117401
Tiwari, K.V. and Sharma, S.K. (2023). An optimization model for vehicle routing problem in last-mile delivery. Expert Systems with Applications, 222:119789. https://doi.org/10.1016/j.eswa.2023.119789
Umam, M.S., Mustafid, M., and Suryono, U. (2022). A hybrid genetic algorithm and tabu search for minimizing makespan in flow shop scheduling problem. Journal of King Saud University - Computer and Information Sciences, 34(9):7459-7467. https://doi.org/10.1016/j.jksuci.2021.08.025
Vanderschueren, T., Boute, R., Verdonck, T., Baesens, B., and Verbeka, W. (2023). Optimizing the preventive maintenance frequency with causal machine learning. International Journal of Production Economics, 258:108798. https://doi.org/10.1016/j.ijpe.2023.108798
Veile, J.W., Schmidt, M.C., and Voigt, K.I. (2022). Toward a new era of cooperation: How industrial digital platforms transform business models in Industry 4.0. Journal of Business Research, 143:387-405. https://doi.org/10.1016/j.jbusres.2021.11.062
Vieira, B.S., Ribeiro, G.M., and Bahiense, L. (2023). Metaheuristics with variable diversity control and neighborhood search for the heterogeneous site-dependent multi-depot multi-trip periodic vehicle routing problem. Computers and Operations Research, 153:106189. https://doi.org/10.1016/j.cor.2023.106189
Zhao, J., Mao, H., Mao, P., and Hao, J. (2024). Learning path planning methods based on learning path variability and ant colony optimization. Systems and Soft Computing, 6:200091. https://doi.org/10.1016/j.sasc.2024.200091
Zheng, R., Liu, M., Zhang, Y., Wang, Y., and Zhong, T. (2024). An optimization method based on an improved ant colony algorithm for complex product change propagation path. Intelligent Systems with Applications, 23:200412. https://doi.org/10.1016/j.iswa.2024.200412








