Abstract
Whereas automated guided vehicles (AGVs) have traditionally been used for material handling, the utilisation of autonomous mobile robots (AMRs) is growing quickly owing to their scalability, versatility, and lower costs. In this paper, we address the pickup and delivery problem with consideration of the characteristics of AMRs in manufacturing environments. To solve the problem, we first propose a new mathematical formulation with consideration of both partial and full recharging strategies for minimisation of the total tardiness of transportation requests. We then propose two constructive heuristic algorithms with high computation speed, which are called the Transportation-Request-Initiated Grouping Algorithm (TRIGA) and the Vehicle-Initiated Grouping Algorithm (VIGA). Additionally, we develop a memetic algorithm (MA) that incorporates a genetic algorithm into local-search techniques for finding near-optimal solutions within a reasonable time. We evaluate the performance of the proposed algorithms in comparison with two dispatching rules, genetic algorithm, and neighbourhood search through simulation experiments with three sets of problem instances under different battery levels. The simulation results indicate that the proposed algorithms outperform the others with regard to the average total tardiness and the relative deviation index.
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