An Algorithm to Manage Transportation Logistics That Considers Sabotage Risk

Chaiya Chomchalao, Sasitorn Kaewman, Rapeepan Pitakaso and Kanchana Sethanan
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Chaiya Chomchalao: Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
Sasitorn Kaewman: Department of Computer Science, Faculty of Informatic, Mahasarakham University, Mahasarakham 44150, Thailand
Rapeepan Pitakaso: Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand
Kanchana Sethanan: Research Unit on System Modeling for Industry, Department of Industrial Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand

Administrative Sciences, 2018, vol. 8, issue 3, 1-17

Abstract: This paper presents an algorithm to solve the multilevel location–allocation problem when sabotage risk is considered (MLLAP-SB). Sabotage risk is the risk that a deliberate act of sabotage will happen in a living area or during the transportation of a vehicle. This can change the way decisions are made about the transportation problem when it is considered. The mathematical model of the MLLAP-SB is first presented and solved to optimality by using Lingo v. 11 optimization software, but it can solve only small numbers of test instances. Second, two heuristics are presented to solve large numbers of test instances that Lingo cannot solve to optimality within a reasonable time. The original differential evolution (DE) algorithm and the extended version of DE—the modified differential evolution (MDE) algorithm—are presented to solve the MLLAP-SB. From the computational result, when solving small numbers of test instances in which Lingo is able to find the optimality, DE and MDE are able to find a 100% optimal solution while requiring much lower computational time. Lingo uses an average 96,156.67 s to solve the problem, while DE and MDE use only 104 and 90 s, respectively. Solving large numbers of test instances where Lingo cannot solve the problem, MDE outperformed DE, as it found a 100% better solution than DE. MDE has an average 0.404% lower cost than DE when using a computational time of 90 min. The difference in cost between MDE and DE changes from 0.08% when using 10 min to 0.54% when using 100 min computational time. The computational result also explicitly shows that when sabotage risk is integrated into the method of solving the problem, it can reduce the average total cost from 32,772,361 baht to 30,652,360 baht, corresponding to a 9.61% reduction.

Keywords: location–allocation problem; renewable energy crops; differential evolution algorithm; modified differential evolution algorithm; risk conditions (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2018
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