To read this content please select one of the options below:

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

Punsara Hettiarachchi (Department of Manufacturing and Industrial Engineering, University of Peradeniya, Kandy, Sri Lanka)
Subodha Dharmapriya (Department of Manufacturing and Industrial Engineering, University of Peradeniya, Kandy, Sri Lanka)
Asela Kumudu Kulatunga (Department of Manufacturing and Industrial Engineering, University of Peradeniya, Kandy, Sri Lanka)

Journal of Global Operations and Strategic Sourcing

ISSN: 2398-5364

Article publication date: 6 March 2023

Issue publication date: 16 April 2024

46

Abstract

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.

Keywords

Acknowledgements

The authors would like to acknowledge the brewery product distribution company in Sri Lanka for providing us with data to conduct this study.

Citation

Hettiarachchi, P., Dharmapriya, S. and Kulatunga, A.K. (2024), "Planning of a distribution network utilizing a heterogeneous fixed fleet with a balanced workload", Journal of Global Operations and Strategic Sourcing, Vol. 17 No. 2, pp. 351-367. https://doi.org/10.1108/JGOSS-05-2022-0045

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

Related articles