Industrial vehicle routing problem: a case study

Abstract

This study is motivated by a real-world application of ABC (Pvt) Ltd., a well-known FMCG (fast-moving consumer goods) industry. The industry have nine agents (in two operating regions) from which it can serve its 5483 clients. We focus on this industry’s outbound logistics in its two operating regions, namely Colombo and Gampaha, while taking into account its distribution and current decentralized redistribution processes, since additional routing costs have been identified in the existing setup. The goal of this study is to implement a better route plan that optimizes the truck allocation system at the lowest possible costs of transportation, warehouse, and administration. First to determine a best location for the new facility, the gravity model is used to pinpoint the exact location of the central warehouse. Then a centralized delivery strategy is applied to establish a better path between the sub-clusters. The performance of this centralized policy is evaluated on a real-world case study data. The relative cost benefit/saving (34%) of the centralized distribution system is then brought into focus and verified with solid statistics by comparing the overall cost of the centralized distribution system to the total cost of the existing decentralized distribution system. Finally, we highlight how decision-makers and policymakers in the logistics area might use our centralized delivery strategy to reduce extra-costs, particularly during the transportation of commodities.

Using Routing Heuristics to Improve Cost Interoperability: Strategy, Modelling Annotations, and Dynamism

“…In some cases, the inclusion of one or more vehicles may not be suitable for the problem, depending on demand levels and cost availability. Therefore, the inclusion of strategic path traversal approaches needs to take into consideration several key delimiters for successful integration, among these include dimensionality of the purpose model adhering to the routing variable exclusivity, location-allocation improvisation under certain routing model's aspiration criteria and nature of optimization heuristics in terms of exploration versus exploitation (Jayarathna et al, 2022;Ouhader & El kyal, 2017b;Saeidian, Mesgari, Pradhan, & Ghodousi, 2018).…”

“…The distribution of resources must be flexible to account for operational constraints, various forecasting stages, and the rate of task distribution replenishments. The ideal location for the central warehouses among the designated areas, the capacity of the central warehouses, and the best order strategy for replenishing and restoring perishable goods must all be taken into account in the modeling schematic (Jayarathna et al, 2022;Tavana, Abtahi, Di Caprio, Hashemi, & Yousefi-Zenouz, 2018;Vahdani, Veysmoradi, Noori, & Mansour, 2018). Transportation of goods and cross-products between regional distribution hubs also has a chance of lowering the amount of unmet demand.…”

Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

“…Manufacturing company products require good distribution in order to reach customers, directly from manufacturers to customers or indirectly through distributors [6]. The form of distribution of goods can also occur from distributors to customers.…”