Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Abstract

This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP), where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO) meta-heuristic hybridized with a Variable Neighborhood Descent (VND) local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals

“…In contrast, simulation optimization-based methods are only applied in 5 articles, although the method seems very suitable considering the nature of the container terminal operations. Most recently, Rouky et al (2019) are applied a simulation-optimization-based ant colony algorithm for the uncertain QC scheduling problem.…”

Optimization of Just-In-Sequence Supply: A Flower Pollination Algorithm-Based Approach

“…The heuristic optimization plays a very important role in the design of complex, interconnected systems. One of the most significant heuristic optimization algorithms are swarm intelligence algorithms, which includes the following swarming based solutions: ant colony optimization (ACO) [67], firefly algorithm (FFA) [68], black hole optimization (BHO) [69], bee colony algorithm (BCA) [70], bacteria algorithm (BA) [71], krill herd algorithm (KHA) [72], bat algorithm (BAT-A) [73], wasp swarm algorithm (WSA) [74], adaptive culture model (ACM) [75] and flower pollination algorithm (FPA) Figure 7. The model of a single source solution (one sequence is supplied by one supplier).…”

A study of a kanban based assembly line feeding system through integration of simulation and particle swarm optimization

“…To answer these research questions, a discrete event simulation model is created in order to strengthen the bond between the developed methodology and real life. Recently, there are few studies (Roukya et al, 2019) where simulation along with optimization is proved to be efficient than other methods. Therefore, in conjunction, a meta-heuristic algorithm is written to find the values of each variable that minimizes the total cost function.…”