Robust Scheduling for Berth Allocation and Quay Crane Assignment Problem

Abstract

Decision makers must face the dynamism and uncertainty of real-world environments when they need to solve the scheduling problems. Different incidences or breakdowns, for example, initial data could change or some resources could become unavailable, may eventually cause the infeasibility of the obtained schedule. To overcome this issue, a robust model and a proactive approach are presented for scheduling problems without any previous knowledge about incidences. This paper is based on proportionally distributing operational buffers among the tasks. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems. The dynamism and uncertainty are managed by assessing the robustness of the schedules. The robustness is introduced by means of operational buffer times to absorb those unknown incidences or breakdowns. Therefore, this problem becomes a multiobjective combinatorial optimization problem that aims to minimize the total service time, to maximize the buffer times, and to minimize the standard deviation of the buffer times. To this end, a mathematical model and a new hybrid multiobjective metaheuristic is presented and compared with two well-known multiobjective genetic algorithms: NSGAII and SPEA2+.

Combined Configuration of Container Terminal Berth and Quay Crane considering Carbon Cost

“…Zhang Haiyong and Yan Wei [13] studied the joint optimization of berths and quay cranes under discrete berths, and established a dynamic model based on rolling plan, taking into account the transfer rules of berths and quay cranes. Rodriguez et al [14] considered the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems. e problem became a multiobjective combinatorial optimization problem that aims to minimize the total service time, to maximize the buffer times, and to minimize the standard deviation of the buffer times.…”

Integrated proactive-reactive approach and a hybrid adaptive large neighborhood search algorithm for berth and quay crane scheduling under uncertain combination

“…The reactive strategy produces a reactive schedule which is used to adjust the original baseline schedule in real time once an uncertainty occurs, improve its scheduling recoverability accordingly [37,31]. Most of existing researches in dealing with uncertainties have adopted only one of the two strategies (Zeng et al [41]; Rodriguez-Molins et al [24]; Shang et al [25]; Liu et al [19]; Xiang et al [37]; Xiang et al [36]). The main reason is that most scholars focus on one type of uncertainty in BACAP research.…”

“…Integrated proactive-reactive approach is most suitable for coping with uncertain combination problems, because proactive strategy tends to solve frequent and predictable uncertainties, while sudden and unpredictable uncertainties need to be solved by reactive strategy. Based on the actual case of automated container terminal in reality, the uncertain vessels' arrival time is treated as predictable uncertainty in numerous studies [24,39], and the breakdown of QC is deemed to be unpredictable uncertainty [19]. The uncertain combination of vessel arrival time and breakdown of QC is almost impossible to solve with only one of the proactive and reactive strategies.…”

“…Some others studied robustness enhancement of schedule considering time buffers in a proactive strategy (e.g. Xu et al [39]; Rodriguez-Molins et al [24]). The other major area of research is the reactive strategy studies which likewise cover disruption recovery approaches.…”

“…In contrast with the above two studies, which only considered a discrete berth property. Rodriguez-Molins et al [24] focused on a time-invariant and continuous BACAP in container terminals under uncertain operation times. To solve this problem and improve the robustness of schedule, the objectives proposed by the authors are to maximize the vessel buffer time.…”