Hybrid population-based metaheuristic approaches for the space allocation problem

The berth allocation problem is one of the main concerns of port operators at a container terminal. In this paper, the authors study the berth allocation problem at the strategic level commonly known as the strategic berth template problem (SBTP). This problem aims to find the best berth template for a set of calling ships accepted to be served at the port. At strategic level, port operator can reject some ships to be served for avoid congestion. Since the computational complexity of the mathematical formulation proposed for SBTP, solution approaches presented so far for the problem are limited especially at level of large-scale instances. In order to find high quality solutions with a short computational time, this work proposes a population based memetic algorithm which combine a first-come-first-served (FCFS) technique, two genetics operators, and a simulating annealing algorithm. Different computational experiences and comparisons against the best known solutions so far have been presented to show the performance and effectiveness of the proposed method.

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Population-based Metaheuristics for the Dynamic Minimum Cost Hybrid Berth Allocation Problem

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021500172 ◽

2021 ◽

Vol 30 (04) ◽

pp. 2150017

Author(s):

Nataša Kovač ◽

Tatjana Davidović ◽

Zorica Stanimirović

Keyword(s):

Container Terminal ◽

Mathematical Formulation ◽

Minimum Cost ◽

Population Based ◽

Allocation Problem ◽

Maritime Transportation ◽

Mixed Integer ◽

Berth Allocation ◽

Berth Allocation Problem ◽

Metaheuristic Methods

This study considers the Dynamic Minimum Cost Hybrid Berth Allocation Problem (DMCHBAP) with fixed handling times of vessels. The objective function to be minimized consists of three components: costs of positioning, waiting, and tardiness of completion for all vessels. A mathematical formulation of DMCHBAP, based on Mixed Integer Linear Programming (MILP), is proposed and used within the framework of commercial CPLEX 12.3 solver. As the speed of finding high-quality solutions is of crucial importance for an efficient and reliable decision support system in container terminal, two population-based metaheuristic approaches to DMCHBAP are proposed: combined Genetic Algorithm (cGA) and improvement-based Bee Colony Optimization (BCOi). Both cGA and BCOi are evaluated and compared against each other and against state-of-the-art solution methods for DMCHBAP on five sets of problem instances. The conducted computational experiments and statistical analysis indicate that population-based metaheuristic methods represent promising approaches for DMCHBAP and similar problems in maritime transportation.

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Optimizing Solution for Storage Space Allocation Problem in Container Terminal Using Genetic Algorithm

Handbook of Research on Natural Computing for Optimization Problems - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-0058-2.ch007 ◽

2016 ◽

pp. 159-184

Author(s):

Gamal Abd El-Nasser A. Said ◽

El-Sayed M. El-Horbaty

Keyword(s):

Genetic Algorithm ◽

Service Time ◽

Container Terminal ◽

Study Data ◽

Allocation Problem ◽

Container Terminals ◽

Storage Space ◽

Space Allocation ◽

Storage Space Allocation ◽

Np Hard Problem

Seaport container terminals are essential nodes in sea cargo transportation networks. In container terminal, one of the most important performance measures in container terminals is the service time. Storage space allocation operations contribute to minimizing the vessel service time. Storage space allocation problem at container terminals is a combinatorial optimization NP-hard problem. This chapter proposes a methodology based on Genetic Algorithm (GA) to optimize the solution for storage space allocation problem. A new mathematical model that reflects reality and takes into account the workload balance among different types of storage blocks to avoid bottlenecks in container yard operations is proposed. Also the travelling distance between vessels berthing positions and storage blocks at container yard is considered in this research. The proposed methodology is applied on a real case study data of container terminal in Egypt. The computational results show the effectiveness of the proposed methodology.

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Population Based Techniques for Solving the Student Project Allocation Problem

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2020040110 ◽

2020 ◽

Vol 11 (2) ◽

pp. 192-207 ◽

Cited By ~ 1

Author(s):

Patrick Kenekayoro ◽

Promise Mebine ◽

Bodouowei Godswill Zipamone

Keyword(s):

Ant Colony Optimization ◽

Constraint Satisfaction ◽

Search Algorithm ◽

Gravitational Search Algorithm ◽

Population Based ◽

Constraint Satisfaction Problems ◽

Allocation Problem ◽

Ant Colony ◽

Optimal Solutions ◽

Gravitational Search

The student project allocation problem is a well-known constraint satisfaction problem that involves assigning students to projects or supervisors based on a number of criteria. This study investigates the use of population-based strategies inspired from physical phenomena (gravitational search algorithm), evolutionary strategies (genetic algorithm), and swarm intelligence (ant colony optimization) to solve the Student Project Allocation problem for a case study from a real university. A population of solutions to the Student Project Allocation problem is represented as lists of integers, and the individuals in the population share information through population-based heuristics to find more optimal solutions. All three techniques produced satisfactory results and the adapted gravitational search algorithm for discrete variables will be useful for other constraint satisfaction problems. However, the ant colony optimization algorithm outperformed the genetic and gravitational search algorithms for finding optimal solutions to the student project allocation problem in this study.

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