A robust optimization approach to the integrated berth allocation and quay crane assignment problem

2016 ◽  
Vol 94 ◽  
pp. 44-65 ◽  
Author(s):  
Xiao Ting Shang ◽  
Jin Xin Cao ◽  
Jie Ren
Keyword(s):  
Robust Optimization ◽  
Assignment Problem ◽  
Optimization Approach ◽  
Berth Allocation ◽  
Quay Crane Assignment

The improved AFSA algorithm for the berth allocation and quay crane assignment problem

2018 ◽  
Vol 22 (S2) ◽  
pp. 3665-3672
Author(s):  
Yi Liu ◽  
Jian Wang ◽  
Sabina Shahbazzade
Keyword(s):  
Assignment Problem ◽  
Berth Allocation ◽  
Quay Crane Assignment

scholarly journals Robust Scheduling for Berth Allocation and Quay Crane Assignment Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
M. Rodriguez-Molins ◽  
M. A. Salido ◽  
F. Barber
Keyword(s):  
Assignment Problem ◽  
Optimization Problem ◽  
Decision Makers ◽  
Berth Allocation ◽  
Scheduling Problems ◽  
Berth Allocation Problem ◽  
Proactive Approach ◽  
Multiobjective Genetic Algorithms ◽  
Robust Model ◽  
Quay Crane Assignment

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+.

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

2019 ◽  
Vol 11 (7) ◽  
pp. 2018 ◽  
Author(s):  
Hsien-Pin Hsu ◽  
Tai-Lin Chiang ◽  
Chia-Nan Wang ◽  
Hsin-Pin Fu ◽  
Chien-Chang Chou
Keyword(s):  
Assignment Problem ◽  
Container Terminal ◽  
Allocation Problem ◽  
Container Terminals ◽  
Mutation Operator ◽  
Berth Allocation ◽  
Berth Allocation Problem ◽  
Fitness Value ◽  
Quay Crane Assignment ◽  
Time Invariant

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.

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