Artificial Bee Colony Algorithm with Pareto-Based Approach for Multi-Objective Three-Dimensional Single Container Loading Problems

Abstract

The ongoing container shortage crisis has presented significant challenges for the freight forwarding industry, requiring companies to implement adaptive measures in order to maintain peak operational efficiency. This article presents a novel mathematical model and artificial bee colony algorithm (ABC) with a Pareto-based approach to solve single-container-loading problems. The goal is to fit a set of boxes with strongly heterogeneous boxes into a container with a specific dimension to minimize the broken space and maximize profits. Furthermore, the proposed algorithm incorporates the bottom-left fill method, which is a heuristic strategy for packing containers. We conducted numerical testing to identify optimal parameters using the C~ metric method. Subsequently, we evaluated the performance of our proposed algorithm by comparing it to other heuristics and meta-heuristic approaches using the relative improvement (RI) value. Our analysis showed that our algorithm outperformed the other approaches and achieved the best results. These results demonstrate the effectiveness of the proposed algorithm in solving real-world single-container-loading problems for freight forwarding companies.

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

“…These constraints encompass container-related factors such as weight limits and weight distribution, item-specific considerations like loading priorities, orientations, and stacking limits, cargo-related constraints regarding parcel completeness and item allocation, and positioning constraints for hazardous items, relative positioning, and multidrop scenarios, as well as load-related constraints, focusing on cargo stability and operational complexity [12]. Different algorithmic, heuristic, and metaheuristic approaches are commonly used to find near-optimal or optimal solutions to this problem, depending on the specific objectives and constraints of the problem instance [13,14].…”

“…Conversely, Phongmoo et al [13] introduce the Artificial Bee Colony (ABC) algorithm for solving the multiobjective three-dimensional single-container loading problem. The ABC algorithm, inspired by the foraging behaviour of honeybees, is integrated with a Pareto-based approach.…”

Query Optimization in Distributed Database Based on Improved Artificial Bee Colony Algorithm

“…The bees in the artificial bee colony (ABC) algorithm are categorized into three groups [33][34][35] including leader bees, follower bees, and scout bees. Half of the colony consists of leader bees, and the other half consists of follower bees.…”

A Novel Bat Algorithm with Asymmetrical Weighed Variational Method in the Path Planning of UAVs

“…In recent years, researchers worked extensively on this topic. Many algorithms, such as particle swarm algorithms [21][22][23][24][25][26], ant colony algorithms [27][28][29][30][31][32], genetic algorithms [33][34][35][36][37][38], and bat algorithms [39][40][41][42][43][44], have made great developments and attracted more and more attention, especially in the field of solving path planning problems in obstacle environments. UAVs perform firefighting tasks in forest fire areas, and the actual trajectory of UAVs in forest firefighting must be processed based on the appropriate trajectory generation algorithms in conjunction with the characteristics of the UAV itself and the environmental characteristics to ensure that the final trajectory matches the dynamics of the UAV [45][46][47][48].…”