A Hybrid GA with Variable Quay Crane Assignment for Solving Berth Allocation Problem and Quay Crane Assignment Problem Simultaneously

Abstract

Container terminals help countries to sustain their economic development. Improving the operational efficiency in a container terminal is important. In past research, genetic algorithms (GAs) have been widely used to cope with seaside operational problems, including the berth allocation problem (BAP) and quay crane assignment problem (QCAP) individually or simultaneously. However, most GA approaches in past studies were dedicated to generate time-invariant QC assignment that does not adjust QCs assigned to a ship. This may underutilize available QC capacity. In this research, three hybrid GAs (HGAs) have been proposed to deal with the dynamic and discrete BAP (DDBAP) and the dynamic QCAP (DQCAP) simultaneously. The three HGAs supports variable QC assignment in which QCs assigned to a ship can be further adjusted. The three HGAs employ the same crossover operator but a different mutation operator and a two-stage procedure is used. In the first stage, these HGAs can generate a BAP solution and a QCAP solution that is time-invariant. The time-invariant QC assignment solution is then further transformed into a variable one in the second stage. Experiments have been conducted to investigate the effects of the three HGA and the results showed that these HGAs outperformed traditional GAs in terms of fitness value. In particular, the HGA3 with Thoros mutation operator had the best performance.

A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station

“…In Equations (20)- (23) and Algorithm 1, the parameters of the CPIO proposed herein are: N = 120, R = 0.2. The values of these parameters are referred to [43] and have a slight change.…”

“…In Table 4, it is easy to see that in the same computing situation, CPIO uses less memory than PIO. For example, during an iteration, CPIO uses an iteration Equations (16)- (23); the formula for PIO update iteration is Equations (9)-(13). Table 4.…”

“…Metaheuristic algorithms perform well on wireless sensor networks [7,8]. Since 2000, many scholars have designed many ant colony optimization algorithms, particle swarm optimization algorithms (PSO) [9], gray wolf optimization algorithms (GWO) [10,11], bat inspired algorithms (BA) [12,13], flower pollination algorithms (FPA) [14,15], cat swarm optimization (CSO) [16,17], differential evolution algorithm (DE) [18,19], quasi-affine transformation evolution algorithms (QUATRE) [20,21], genetic algorithms (GA) [22,23], etc. Based on the simulation of the above-mentioned functional mechanisms through an in-depth study, it is easy to observe that the adaptive phenomenon can widely exist in nature.…”

Propagation of Uncertain Events in Multilevel Handlings at Container Terminals from the Perspective of Hypernetwork

“…However, these studies did not consider the container terminal as a whole. Most studies focused on the scheduling of two or three handlings and did not consider the uncertain event propagation among the multilevel handlings at the container terminals [6][7][8][9][10][11].…”

Resources Planning for Container Terminal in a Maritime Supply Chain Using Multiple Particle Swarms Optimization (MPSO)

“…In terms of advantage, metaheuristics can address the simplicity problem of simple heuristics while avoiding the computationally intractable problem of exact approaches. To deal with seaside operational problems, metaheuristics such as ant colony optimization [10], genetic algorithms (GAs) [8,[11][12][13][14][15][16][17], particle swarm optimization (PSO) [18,19] have been used, in which GAs have been the mainstream approach [20]. However, particle swarm optimization has never been used to deal with the DCBAP and variable-in-time QCAP (number) simultaneously.…”