A multi-objective evolutionary optimisation model for heterogeneous vehicles routing and relief items scheduling in humanitarian crises

Mishra, Bhupesh Kumar; Dahal, Keshav; Pervez, Zeeshan; Bhattarai, Suyesh

doi:10.1016/j.dajour.2022.100128

Cited by 4 publications

(1 citation statement)

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“…Furthermore, the stochastic nature of the industrial context has also been addressed (Baradaran and Shafaei, 2019). More interestingly, the application of HFVRP has been extended to other applications like disaster management and maritime logistics (De Cubber and Haelterman, 2019; Mishra et al , 2022). Both types of problems are solved using many approximation algorithms including SA, Tabu search, iterated local search based heuristic and a set partitioning (Abdel-Basset et al , 2018; Maini and Goel, 2017; Konstantakopoulos et al , 2020; Gogna and Tayal, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Planning of a distribution network utilizing a heterogeneous fixed fleet with a balanced workload

Hettiarachchi

Dharmapriya

Kulatunga

2023

JGOSS

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Purpose This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature. Design/methodology/approach This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario. Findings The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance. Research limitations/implications The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered. Originality/value The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

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