An Algorithm to Manage Transportation Logistics That Considers Sabotage Risk

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.

Methodology to Solve a Special Case of the Vehicle Routing Problem: A Case Study in the Raw Milk Transportation System

“…This article added these two steps to increase the performance of the original DE. The DE can be improved by adding more steps into the original steps and other techniques such as use various mutation and recombination formula [21,22,28], add effective local search techniques [27], add the best vector reproduction process [29], develop new decoding method which is combined with ant colony optimization [30]. Therefore, in this article, the DE has been improved in following ways so that the effectiveness of DE is increased: (1) provided an effective decoding method; (2) presented new rules of the selection process to let DE escape from the local optimal; and (3) used 3 different local searches so that the DE has intensive search efficiency.…”

Location Decision Making and Transportation Route Planning Considering Fuel Consumption

“…There are many palm oil plantations in the case study area; thus, it made the problem more complicated for the plan to find a collection point and transport to the palm oil processing plant. Inappropriate location of palm oil collection points, as well as improper transportation routes can be resulted in the higher cost of logistics system [3]. Consequently, the management and decision making of logistics systems of palm oil in the South of Thailand is therefore necessary to be considered.…”

Improved Differential Evolution Algorithm to Solve the Advertising Method Selection Problem

“…DE was first proposed by Storn and Price [32]. Since then, many researchers have used DE to solve various problems, such as the traveling salesman problem (Akararungruangkul, Chokanat, Pitakaso, Supakdee, and Sethanan [33]; Sethanan and Pitakaso [34]), the generalized assignment problem (Srivarapongse, and Pijitbanjong [35]; Sethanan and Pitakaso [36]), the assembly line balancing problem (Pitakaso [37]; Pitakaso and Sethanan [38]), the location problem (Chomchalao, Kaewman, Pitakaso, and Sethanan [39]; Thongdee, and Pitakaso [40]), and the crop planning problem (Ketsripongsa, Pitakaso, Sethanan, and Srivarapongse [41]; Pijitbanjong, Akararungruangkul, Pitakaso, and Sethanan [42]).…”

“…Random walk behavior has been frequently added to the general DE mechanism in order to improve the algorithm's efficiency. Some studies have generated new random numbers in the mature stage while searching with DE (Sethanan and Pitakaso [34]; Sethanan and Pitakaso [36]), and some have also guided the search into different problem areas (Chomchalao et al [39]). In the present study, in order to enhance DE, the authors improved the exploitation and exploration search behavior by combining DE with two other metaheuristics: Adaptive large neighborhood search (ALNS) and iterated local search (ILS).…”