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.
As the world is moving into a sustainable era, achieving zero hunger has become one of the top three Sustainable Development Goals, applying a considerable amount of pressure on the agri-food systems to make decisions contemplating the sustainability dimensions. Accordingly, making effective supply chain decisions holistically while achieving sustainability goals has become a major challenge faced by the present agri-food systems. Thus, to address the challenge, a novel supply chain configuration addressing multiple supply chain decisions to reduce global warming potential (GWP) and post-harvest losses have been presented by taking the banana supply chain in Sri Lanka as a case study. In the proposed approach, farmers have been clustered based on their geo positions using K-Means clustering followed by route planning within clusters using a heuristics approach. Retailer points are catered by assigning to wholesalers optimally modeling as an assignment model and then route planning executed using a heuristic approach. The solution generated from the above approaches has been implemented on a simulation platform to calculate the overall supply chain performance including the transportation component, in terms of the net GWP, post-harvest losses, and lead time including routing operations. Simulated supply chain performance has been compared with the existing system and verified the performance of the proposed supply chain configuration. The suggested configuration has reduced the net GWP by 15.3%, post-harvest loss by 2.1%, lead time by 28.2%, and travel distance by 20.47%. The proposed configuration can be further improved by adding dynamic characteristics to the model.
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