Vessel Scheduling Optimization Model Based on Variable Speed in a Seaport with One-Way Navigation Channel

Abstract

To improve the efficiency of in-wharf vessels and out-wharf vessels in seaports, taking into account the characteristics of vessel speeds that are not fixed, a vessel scheduling method with whole voyage constraints is proposed. Based on multi-time constraints, the concept of a minimum safety time interval (MSTI) is clarified to make the mathematical formula more compact and easier to understand. Combining the time window concept, a calculation method for the navigable time window constrained by tidal height and drafts for vessels is proposed. In addition, the nonlinear global constraint problem is converted into a linear problem discretely. With the minimum average waiting time as the goal, the genetic algorithm (GA) is designed to optimize the reformulated vessel scheduling problem (VSP). The scheduling methods under different priorities, such as the first-in-first-out principle, the largest-draft-vessel-first-service principle, and the random service principle are compared and analyzed experimentally with the simulation data. The results indicate that the reformulated and simplified VSP model has a smaller relative error compared with the general priority scheduling rules and is versatile, can effectively improve the efficiency of vessel optimization scheduling, and can ensure traffic safety.

Research on safety and efficiency warranted vessel scheduling in unidirectional multi-junction waterways of port waters

“…The WSSP was extended by introducing a triangular fuzzy number and a method for determining the feasible navigable time window of a vessel subject to the tide height constraint. Liu et al, (2021b) considered the characteristics of variable vessel speeds and proposed a speed-constrained scheduling method based on time window.…”

An adaptive heuristic algorithm based on reinforcement learning for ship scheduling optimization problem

“…However, the precise algorithms take long computational time, and it is difficult to obtain a global optimal solution even for small-scale calculation examples. Therefore, heuristic methods are proposed to solve the problem, including a simulated annealing multi-population genetic algorithm [6], a simulated annealing algorithm [7,8], and a hybrid algorithm combining heuristic rules and simulated annealing algorithm [9], a meta-heuristic algorithm [10,11,12], a non-dominated sorting genetic algorithm [13,14], a genetic algorithm [16,17,18], a tabu search algorithm [19], a large neighborhood search algorithm [15]. Although showing much attractiveness in ship scheduling optimization, previous studies still revealed some concerns of which the theoretical implications have yet been well addressed in the existing literature.…”