TY - JOUR
T1 - A simulation–optimization framework for enhancing robustness in bulk berth scheduling
AU - Dávila de León, Alan
AU - Lalla-Ruiz, Eduardo
AU - Melián-Batista, Belén
AU - Moreno-Vega, J. Marcos
N1 - Funding Information:
This work has been partially supported by the Ministerio deEconomía y Competitividad, Spain with FEDER funds (project TIN2015-70226-R ) and by the Agencia Estatal de Investigación (Spain) (project PID2019-104410RB-I00/AEI/10.13039/501100011033 ).
Publisher Copyright:
© 2021 The Author(s)
PY - 2021/8
Y1 - 2021/8
N2 - The service time of the vessels is one of the main indicators of ports’ competitiveness. This, together with the increasing volume of bulk transportation, make the efficient management of scarce resources such as berths a crucial option for enhancing the productivity of the overall terminal. In real scenarios, the information available to port operators may vary once the planning has been elaborated. Unforeseen events, errors, or modifications in the available information can lead to inefficient terminal management and the initial scheduling might become unfeasible. This implies that the use of deterministic approaches may not be enough to maximize productivity. Therefore, in this work, proactive simulation–optimization approaches that utilize the information collected during the simulation for guiding the optimization search to provide robust solutions are proposed. Moreover, a multi-objective approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for jointly tackling the problem objective as well as the deviations because of stochastic changes is developed. Finally, we also investigate the contribution of time management strategies such as buffers to absorb stochastic modifications and hence increase solutions’ robustness. The computational results indicate, on the one hand, the benefit of integrating both types of objectives (i.e., deterministic and stochastic) to guide the simulation–optimization process, and on the other hand, the benefit of using the multi-objective approaches like NSGA-II. Finally, the incorporation of buffers leads to better performance in terms of reducing penalty costs due to disruptions, shortening the planning risks related to only considering deterministic planning.
AB - The service time of the vessels is one of the main indicators of ports’ competitiveness. This, together with the increasing volume of bulk transportation, make the efficient management of scarce resources such as berths a crucial option for enhancing the productivity of the overall terminal. In real scenarios, the information available to port operators may vary once the planning has been elaborated. Unforeseen events, errors, or modifications in the available information can lead to inefficient terminal management and the initial scheduling might become unfeasible. This implies that the use of deterministic approaches may not be enough to maximize productivity. Therefore, in this work, proactive simulation–optimization approaches that utilize the information collected during the simulation for guiding the optimization search to provide robust solutions are proposed. Moreover, a multi-objective approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for jointly tackling the problem objective as well as the deviations because of stochastic changes is developed. Finally, we also investigate the contribution of time management strategies such as buffers to absorb stochastic modifications and hence increase solutions’ robustness. The computational results indicate, on the one hand, the benefit of integrating both types of objectives (i.e., deterministic and stochastic) to guide the simulation–optimization process, and on the other hand, the benefit of using the multi-objective approaches like NSGA-II. Finally, the incorporation of buffers leads to better performance in terms of reducing penalty costs due to disruptions, shortening the planning risks related to only considering deterministic planning.
KW - Berth Allocation Problem
KW - Buffer management
KW - Maritime logistics
KW - Metaheuristics
KW - Simheuristics
KW - Simulation–optimization
KW - operations research
KW - Logistics
KW - optimization
KW - UT-Hybrid-D
UR - http://www.scopus.com/inward/record.url?scp=85106225645&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2021.104276
DO - 10.1016/j.engappai.2021.104276
M3 - Article
AN - SCOPUS:85106225645
SN - 0952-1976
VL - 103
JO - Engineering applications of artificial intelligence
JF - Engineering applications of artificial intelligence
M1 - 104276
ER -