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

Exploring the resilience of uncertain nonlinear handling chain systems in container ports with a novel sliding mode control

Uncertain handling chain system (HCS) of container ports brings steady-state error to the original control decisions, and even worse, dramatically degrades the system performance. The steady-state error will cause unsatisfied freight requirement to be much higher than the expected value for a long time, resulting in the decrease of system robustness and resilience. In this work, a novel sliding mode control with power integral reaching law (SMC-P) is presented for nonlinear HCS of container ports under uncertainty. Specifically, the integral of system state variable, the exponential reaching law and the power of the switching function are integrated to the traditional reaching law. And it is proven that the eliminated steady-state error, the accelerated approach speed, and the reduced chattering can be effectively obtained by SMC-P. A nonlinear HCS in container ports with uncertain freight requirement and handling ability is considered. SMC-P is compared with traditional method, genetic algorithm, quasi-sliding mode control and integral sliding mode control. Simulation results show that SMC-P does not only balance both steady-state error reduction and chattering avoidance caused by uncertainty, but also optimize the performance, robustness, and resilience of the uncertain nonlinear HCS. This study also brings economic and sustainability contributions for port authorities.info:eu-repo/semantics/publishedVersio

Cooperative Double-Layer Genetic Programming Hyper-Heuristic for Online Container Terminal Truck Dispatching

In a marine container terminal, truck dispatching is a crucial problem that impacts on the operation efficiency of the whole port. Traditionally, this problem is formulated as an offline optimisation problem, whose solutions are, however, impractical for most real-world scenarios primarily because of the uncertainties of dynamic events in both yard operations and seaside loading–unloading operations. These solutions are either unattractive or infeasible to execute. Herein, for more intelligent handling of these uncertainties and dynamics, a novel cooperative double-layer genetic programming hyper-heuristic (CD-GPHH) is proposed to tackle this challenging online optimisation problem. In this new CD-GPHH, a novel scenario genetic programming (GP) approach is added on top of a traditional GP method that chooses among different GP heuristics for different scenarios to facilitate optimised truck dispatching. In contrast to traditional arithmetic GP (AGP) and GP with logic operators (LGP) which only evolve on one population, our CD-GPHH method separates the scenario and the calculation into two populations, which improved the quality of solutions in multi-scenario problems while reducing the search space. Experimental results show that our CD-GPHH dominates AGP and LGP in solving a multi-scenario function fitting problem as well as a truck dispatching problem in container terminal

Ocean container transport in global supply chains: Overview and research opportunities

This paper surveys the extant research in the field of ocean container transport. A wide range of issues is discussed including strategic planning, tactical planning and operations management issues, which are categorized into six research areas. The relationships be- tween these research areas are discussed and the relevant literature is reviewed. Representative models are selected or modified to provide a flavour of their functions and application context, and used to explain current shipping practices. Future research opportunities bearing in mind the emerging phenomena in the field are discussed. The main purpose is to raise awareness and encourage more research into and application of operations management techniques and tools in container transport chains

The one container drayage problem with soft time windows

Intermodal freight transport consists of using different modes of transport without changing the load unit. This results in a significant reduction in the time that goods spend at intermodal terminals, where transshipment takes place. Drayage refers to the transport of freight on trucks among intermodal terminals, depots, customers and suppliers. In spite of the fact that drayage only represents between 5 and 10 percent of total distance, it may amount up to more than 30 percent of the total costs. The aim of this work is to study drayage operations. First, an extensive literature review is undertaken. Since the intermodal transport chain can become more efficient by means of a proper organisation of the drayage movements, the optimization of the daily drayage problem has been identified as one of the main ways of reducing the drayage cost and improving intermodal operations. On this problem, the lack of a common benchmark has hindered reaching further conclusions from all the research carried out. Therefore, this paper proposes a common framework and presents a generalized formulation of the problem, which allows modeling most drayage policies, with the limitation of only considering one-container problems. Results show that flexible tasks in the repositioning of empty containers as well as soft time windows can reduce the operating costs and facilitate the management of drayage companies. This work may help consider adequate policies regarding drayage operations in intermodal terminals