Multi-objective optimization of the integrated problem of location assignment and straddle carrier scheduling in maritime container terminal at import

International audienc

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Optimisation des systèmes de stockage de conteneurs dans les terminaux maritimes automatisés

AIn our study, we consider two optimization problems in automated container terminals at import; the first is the vehicle scheduling problem; and the second is the integrated problem of location assignment and vehicle scheduling. In the first part of our study, we propose different traffic layout adapted to the two studied problems and to every kind of automated container terminal. We also introduce relevant reviews of literature treating the optimization of container handling systems at maritime terminal, the optimization of general automated guided vehicle system and the multi-objective optimization in general, and in particular context of maritime container terminals. In the second part, we resolve the planning of QC-AV-ASC (Quay Cranes-Automated Vehicles - Automated Stacking Cranes). We present an effective model for every kind of traffic layout. Moreover, we propose an efficient bi-objective model which is important to determine the optimal storage time and the minimal number of required AVs. CPLEX resolutions are used to prove the efficiency of our modelling approach. In the third part of this thesis, we explore a problem which has not been sufficiently studied: the integrated problem of location assignment and vehicle scheduling (IPLAVS), in Maritime Automated Container Terminal (MACT) at import. This part represents a new and realistic approach of MACT optimization considering mono-objective and multi-objective aspect.Notre travail s’intéresse à un cas très particulier des terminaux à conteneurs, il s’agit des terminaux à conteneurs automatisés, qui en plus des véhicules autoguidés, sont équipés de grues de quai et de grues de stockage automatiques (grues de cour), ce qui pousse souvent les scientifiques à considérer les problèmes d’ordonnancement intégré dans les terminaux automatisés ou semi-automatisés. Nous traitons dans ce travail l’optimisation de plusieurs objectifs pour stocker les conteneurs d'une manière efficace et réaliste. Nous traitons le problème d’ordonnancement intégré considérant les trois équipements d’un terminal à conteneurs automatisé soient: les véhicules autoguidés, les grues de quai et les grues de baie (éventuellement). L’objectif principal de cette étude est la minimisation du coût opérationnel de stockage de conteneurs dans un terminal maritime automatis

The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research