A dispatching method for automated lifting vehicles in automated port container terminals
Computers and Industrial Engineering
Accumulative sampling for noisy evolutionary multi-objective optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal of Intelligent Manufacturing
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We propose a multi-criteria dispatching strategy for efficiently operating automated guided vehicles (AGVs) in an automated container terminal. The criteria employed by our strategy are carefully devised so that the enforcement of the strategy can lead to achieving two objectives, which are minimization of the delay of quay cranes and minimization of empty travel by AGVs. The first objective is for the productivity of the terminal and the second the reduction of CO2 emission. Optimization of the strategy is done by using a multi-objective evolutionary algorithm (MOEA) to obtain a set of Pareto optimal strategies. When dispatching AGVs, we can apply any strategy from this set depending on which objective is more important than the other at that moment. Since the evaluation of a strategy during the search requires a computationally expensive simulation, it becomes inaccurate unless done thoroughly. We use a noise-tolerant MOEA that takes multiple evaluations of the candidate strategies for a more accurate estimation. Yet, it saves the computation by tactically allocating more chances of evaluations to better candidates.