Improved Differential Evolution Algorithms for Solving Multi-Level Location-Allocation Problem: A Case Study of Oil Palm Logistics in Narathiwat Province
Keywords:
Oil palm logistics, Differential evolution algorithm, , Improved differential evolution algorithms, Multi-level location-allocation problemAbstract
This research presented the multi-level location-allocation problem solution of oil palm logistics in Narathiwat province with lowest cost by considering the pollution release in transportation and risk from civil unrest situations. Three methods of mathematical modelling and metaheuristic solution were used to find solutions as follows: (1) Differential evolution algorithm (DE), 2) Improved differential evolution algorithm by shifting algorithm (IDE-S) and 3) Improved differential evolution algorithm by insertion move algorithm (IDE-IM). This research was conducted with small, medium, and large sampling groups and studied from a case study. It was found that the IDE-IM gave the best solution, and its finding was applied with a real case study of 77 farmers and 2 palm oil plants. The lowest cost of logistics was 24,015,294 baht.
References
Akkararungruangkul, R. & Kaewman, S. (2018). Modified differential evolution algorithm solving the special case of location routing problem. Mathematical and Computational Applications, 23(34).
Bargos, F.F., Wendell, Q.L., Bargos, D.C., Morun, B.N. & Paula, C.P.M.P. (2016). Location problem method applied to sugar and ethanol mills location optimization. Renewable and Sustainable Energy Reviews, 65, 274-282.
Boon, E.T., Ponnambalam, S.G. & Kanagaraj, G. (2013). Differential evolution algorithm with local search for capacitated vehicle routing problem. International Journal of Bio-Inspired Computations, 7, 321-342.
Chomchalao, C., Kaewman, S., Pitakaso, R. & Sethanan, K. (2018). An algorithm to manage transportation logistics that considers sabotage risk. Adm. Sci, 8, 39.
Deepsouthwatch. (2019). Cumulative Incidents in Deep Southern Thailand January 2004-June 2019. Retrieved July 26, from https://dswdatabase.info/?page_id=984.
Gao, L., Zhou, Y., Li, X., Pan, Q. & Yi, W. (2015). Multi-objective optimization based reverse strategy with differential evolution algorithm for constrained optimization problems. Expert Systems with Applications, 42(14), 5976-5987.
Jia, D., Zheng, G. & Khurram, K.M. (2011). An effective memetic differential evolution algorithm based on chaotic local search. Inf. Sci, 181, 3175-3187.
Kaewman, S., Srivarapongse, T., Theeraviriya, T. & Jirasirilerd, G. (2018). Differential evolution algorithm for multilevel assignment problem: a case study in chicken transportation. Math. Comp. Appl, 23, 55.
Ketsripongsa, U., Pitakaso, R., Sethanan, K. & Srivarapongse, T. (2018). An improved differential evolution algorithm for crop planning in the northeastern region of Thailand. Math. Comput. Appl, 23.
Krahomwong, M. (2016). Problem of logistics management of oil palm producers for commercial in three southern border provinces. Academic Services Journal, Prince of Songkla University, 27, 14-28.
Lai, M. & Er-bao, C. (2010). An improved differential evolution algorithm for vehicle routing problem with simultaneous pickups and deliveries and time windows. Engineering Application of Artificial Intelligent, 23, 188-195.
Lampinen, J. & Zelinka, I. (1999). Mechanical engineering design optimization by differential evolution. In New Ideas in Optimization. Edited by David Corne, Marco Dorigo, Fred Glover, Dipankar Dasgupta, Pablo Moscato, Riccardo Poli and Kenneth V. Price. London: McGraw-Hill, pp. 127-146.
Liao, T.W., Pius, J.E. & Pei-chann, C. (2012). Two hybrid differential evolution algorithms for optimal inbound and outbound truck sequencing in cross docking operation. Applied Soft Computing, 12, 3683-3697.
Mayachearw, P. (2012). Solving a multi-stages multi objectives location problem in supply chain: a case study in oil palm industry in specific developed area in deep south of Thailand. Ph.D. thesis, Department of Industrial Engineering, Ubon Ratchathani University, Ubon Ratchathai, Thailand. (in thai).
Office of Agricultural Economics. (2018). Agricultural Economic Information. Retrieved July 30, from http://www.oae.go.th/view/1/Information/EN-US.
Pijitbanjong, P., Akararungruangkul, R., Pitakaso, R. & Sethanan, K. (2018). Improved differential evolution algorithms for solving multi-stage crop planning model in southern region of Thailand. Songklanakarin J. Sci. Technol, 41(5), 1116-1123.
Pijitbanjong, P., Chomchalao, C. & Pitakaso, R. (2019). Solving location-allocation problem of oil palm under sabotage risk in Narathiwat province using differential evolution algorithm. The proceeding of the operations research network of Thailand. Chiang Mai, Thailand, 252-257. (in thai).
Pitakaso, R. (2015). Differential evolution algorithm for simple assembly line balancing type 1 (SALBP-1). Journal of Industrial and Production Engineering, 32, 104-114.
Pitakaso, R. & Sethanan, K. (2016). Modified differential evolution algorithm for simple assembly line balancing with a limit on the number of machine types. Engineering Optimization, 48, 253-271.
Qin, A., Vicky, L.H. & Ponnuthurai N.S. (2009). Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation, 13, 398-417.
Sahin, C. & Yusuf, K. (2016). Differential evolution based meta-hueristic algorithm for dynamic continuous berth allocation problem. Applield Mathematical Modelling, 40, 10679-10688.
Sethanan, K. & Pitakaso, R. (2016a). Differential evolution algorithms for scheduling raw milk transportation. Computers and Electronic in Agriculture, 121, 245-259.
Sethanan, K. & Pitakaso, R. (2016b). Improved differential evolution algorithms for solving generalized assignment problem. Expert Systems with Applications, 45, 450-459.
Srivarapongse, T. & Pijitbanjong, P. (2019) Solving a special case of the generalized assignment problem using the modified differential evolution algorithms: a case study in sugarcane harvesting. J. Open Innov.Technol, Mark, Complex, 5, 5.
Storn, R. & Price, K. (1995). Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, New Delhi, ICSI.
Storn, R. & Price, K. (1997). Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341-359.
Tasgetiren, M.F., Suganthan, P.N., Chua, T.J. & Al-Hajri, A. (2009). Differential evolution algorithms for the generalized assignment problem. The proceedings of the 2009 IEEE Congress on Evolutionary Computation, Trondheim, Norway.
Thongdee, T. & Pitakaso, R. (2015). Differential evolution algorithms solving a multi-objective, source and stage location-allocation problem. Industrial Engineering and Management System, 14, 11-21.
Wang, Y., Zhi-Zhong, L., Jian-bin, L., Han-Xiong, L. & Gary, G.Y. (2016). Utilizing cumulative population distribution information in differential evolution. Applied Soft Computing, 48, 329-346.
Xu, H. & Wen, J. (2012). Differential evolution algorithm for the optimization of the vehicle routing problem in logistics. Paper present at of the Eighth International Conference on Computational Intelligence and Security, Guangzhou, China, 48-51.
Zio, E. & Viadana, G. (2011). Optimization of the inspection intervals of a safety system in a nuclear power plant by multi-objective differential evolution (MODE). Reliability Engineering & System Safety, 1552-1563.
