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A bi-objective multi-population biased random key genetic algorithm for joint scheduling quay cranes and speed adjustable vehicles in container terminals

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Abstract

This work formulates a mixed-integer linear programming (MILP) model and proposes a bi-objective multi-population biased random key genetic algorithm (mp-BRKGA) for the joint scheduling of quay cranes and speed adjustable vehicles in container terminals considering the dual-cycling strategy. Under such a strategy, a combination of loading and unloading containers are handled by a set of cranes (moved between ships and vehicles) and transported by a set of vehicles (transported between the quayside and the storage area). The problem consists of four components: crane scheduling, vehicle assignment, vehicle scheduling, and speed assignment both for empty and loaded journey legs. The results show that an approximated true Pareto front can be found by solving the proposed MILP model and that the mp-BRKGA finds uniformly distributed Pareto fronts, close to the true ones. Additionally, the results clearly demonstrate the advantages of considering speed adjustable vehicles since both the makespan and the energy consumption can be considerably reduced.

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Notes

  1. In this section, for the sake of simplicity and readability, the horizontal transport vehicles are all referred to as AGVs. Although some of the works reviewed consider other types of vehicles, e.g., yard trucks, the optimization problems being addressed are the same.

  2. Note that \({GD}^+\) and \(\Delta\) values were not calculated for instances DS21, DS22, DS25, and DS26 for which the MILP model was not capable of finding a \({PF}^*\).

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Authors and Affiliations

  1. LIAAD, INESC TEC, Porto, Portugal

    Dalila B. M. M. Fontes & S. Mahdi Homayouni

  2. Faculdade de Economia, Universidade do Porto, Porto, Portugal

    Dalila B. M. M. Fontes

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  1. Dalila B. M. M. Fontes
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  2. S. Mahdi Homayouni
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Correspondence to S. Mahdi Homayouni.

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Supported by ERDF - European Regional Development Fund, COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation, and by National Fundsthrough FCT - Fundação para a Ciência e a Tecnologia within projects POCI-01-0145-FEDER-031821-PTDC/EGE-OGE/31821/2017 and POCI-01-0145-FEDER-031447-PTDC/EEI-AUT/31447/2017.

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Fontes, D.B.M.M., Homayouni, S.M. A bi-objective multi-population biased random key genetic algorithm for joint scheduling quay cranes and speed adjustable vehicles in container terminals. Flex Serv Manuf J 35, 241–268 (2023). https://doi.org/10.1007/s10696-022-09467-6

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