Bird mating optimizer for discrete berth allocation problem

Arram, Anas; Nazri, Mohd Zakree Ahmad; Ayob, Masri; Abunadi, Ahmad

doi:10.1109/iceei.2015.7352543

Cited by 9 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another DD-BAP is discussed in [59], where a novel EA, namely bird mating optimizer (BMO) algorithm is developed. The problem is modelled as a vehicle routing problem with time windows and solved by the BMO algorithm.…”

Section: Discrete and Dynamic Bapmentioning

confidence: 99%

See 1 more Smart Citation

A survey on computational intelligence approaches for intelligent marine terminal operations

Aslam,

Michaelides,

Herodotou

2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Marine container terminals (MCTs) play a crucial role in intelligent maritime transportation (IMT) systems. Since the number of containers handled by MCTs has been increasing over the years, there is a need for developing effective and efficient approaches to enhance the productivity of IMT systems. The berth allocation problem (BAP) and the quay crane allocation problem (QCAP) are two well‐known optimization problems in seaside operations of MCTs. The primary aim is to minimize the vessel service cost and maximize the performance of MCTs by optimally allocating berths and quay cranes to arriving vessels subject to practical constraints. This study presents an in‐depth review of computational intelligence (CI) approaches developed to enhance the performance of MCTs. First, an introduction to MCTs and their key operations is presented, primarily focusing on seaside operations. A detailed overview of recent CI methods and solutions developed for the BAP is presented, considering various berthing layouts. Subsequently, a review of solutions related to the QCAP is presented. The datasets used in the current literature are also discussed, enabling future researchers to identify appropriate datasets to use in their work. Eventually, a detailed discussion is presented to highlight key opportunities along with foreseeable future challenges in the area.

show abstract

Section: Discrete and Dynamic Bapmentioning

confidence: 99%

“…The problem is modelled as a vehicle routing problem with time windows and solved by the BMO algorithm. The authors of the study [59] also implement PSO and exact approaches for the sake of comparison. Results demonstrate the effectiveness of the BMO method in terms of minimum turnaround time.…”

Section: Current Literature On Stand‐alone Bapmentioning

confidence: 99%

A survey on computational intelligence approaches for intelligent marine terminal operations

Aslam,

Michaelides,

Herodotou

2024

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…Constraints ( 9) and (10) specify the lower and upper bounds of the vessel's arrival time and handling time, respectively. Constraint (11) indicates that each vessel can only select one berth. Constraint (12) indicates that the start time is later than the arrival time.…”

Section: A Bilevel Biobjective Modelmentioning

confidence: 99%

“…Sun et al [10] study the influence of tide on discrete berth allocation from two aspects of the water level change in tide and the variance of vessel arrival time. Arram et al [11] apply a bird mating optimizer algorithm, which can effectively explore and use the search space to find the global solution for solving the discrete berth allocation problem. On the other hand, according to temporal attributes, berth allocation problems can be divided into static BAP and dynamic BAP.…”

Section: Introductionmentioning

confidence: 99%

Robust Low-Carbon Discrete Berth Allocation under Uncertainty

Shan

Xiao

et al. 2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

A robust discrete berth allocation method under a low-carbon target is proposed in this study, considering the uncertainty of vessels’ arrival time and handling time. According to the actual situation of port operations, a bilevel, biobjective model is established to minimize both average carbon emission and the range of carbon emission during the berthing period. A set of alternative berth allocation schemes, namely, the set of Pareto solutions, are obtained by a heuristic algorithm based on a genetic algorithm. The effectiveness of the proposed method is verified by simulation.

show abstract

“…We also investigate the effectiveness of each method to identify the most promising one. Two well-known combinatorial optimization problems are chosen to test the proposed methods: The Travelling Salesman problem (TSP) (Reinelt 1991 instances) [47] and the Berth Allocation Problem (BAP) (Cordeau et al 2005 instances) [48] Note this research study is different from [49], in which only RKBMO was applied and only a set of BAP instances were used and they are different from those the set of BAP used in this work. In addition, no analysis study was provided.…”

Section: Introductionmentioning

confidence: 99%

Bird Mating Optimizer for Combinatorial Optimization Problems

et al. 2020

Self Cite

View full text Add to dashboard Cite

The bird mating optimizer is a new metaheuristic algorithm that was originally proposed to solve continuous optimization problems with a very promising performance. However, the algorithm has not yet been applied for solving combinatorial optimization problems. Thus, the formulation may not be able to generate a discrete feasible solution. Many continuous algorithms used random-key representation to represent the discrete solution using real numbers or a discrete variant of the algorithm is used to deal with the discrete solution of the problem. However, there is no evidence which methodology is better for solving combinatorial optimization problems. Therefore, this work proposes two variants of bird mating optimizer (random-key bird mating optimizer and the discrete bird mating optimizer), to identify which one is more efficient in solving combinatorial optimization problems. In the first one, we use a random-key encoding scheme, whilst, in the later one, we use crossover (multi-parent) and mutation operators to combine the components of the selected parents to generate new broods. The performance of these algorithms is tested on the travelling salesman problem and berth allocation problem, and are compared with the results of two well-known optimization algorithms: Genetic Algorithm and Particle Swarm Optimization. Experimental results show that the discrete bird mating optimizer is more efficient than the others on all tested benchmark instances. Indeed, it is able to attain the best-known results in some of the BAP benchmark instances. These indicate the applicability and the effectiveness of the proposed discrete bird mating optimizer in solving combinatorial optimization problems. INDEX TERMS Heuristics, bird mating optimizer, berth allocation problem, travelling salesman problem, random-key, combinatorial optimization, metaheuristics.

show abstract

Bird mating optimizer for discrete berth allocation problem

Cited by 9 publications

References 13 publications

A survey on computational intelligence approaches for intelligent marine terminal operations

A survey on computational intelligence approaches for intelligent marine terminal operations

Robust Low-Carbon Discrete Berth Allocation under Uncertainty

Bird Mating Optimizer for Combinatorial Optimization Problems

Contact Info

Product

Resources

About