A Two-Phase Approach for Single Container Loading with Weakly Heterogeneous Boxes

Abstract

We propose in this paper a two-phase approach that decomposes the process of solving the three-dimensional single Container Loading Problem (CLP) into subsequent tasks: (i) the generation of blocks of boxes and (ii) the loading of blocks into the container. The first phase is deterministic, and it is performed by means of constructive algorithms from the literature. The second phase is non-deterministic, and it is performed with the use of Generate-and-Solve (GS), a problem-independent hybrid optimization framework based on problem instance reduction that combines a metaheuristic with an exact solver. Computational experiments performed on benchmark instances indicate that our approach presents competitive results compared to those found by state-of-the-art algorithms, particularly for problem instances consisting of a few types of boxes. In fact, we present new best solutions for classical instances from groups BR1 and BR2.

A Multi-Heuristic Algorithm for Multi-Container 3-D Bin Packing Problem Optimization Using Real World Constraints

“…In order to generate loading patterns for the items a placement strategy needs to be developed. In the literature, placement strategies are classified into the following major types: horizontal layer building [11]- [13], [22], [27], [30]- [32], [36], [37], [40], [52]- [54], block building [7], [19], [21], [24], [33], [35], [54], [55], stack building [23], guillotine cutting [8], wall building [34], [56], [57] and cuboid building [15]. Different loading patterns will manage the usable packing volume in slightly different ways (prioritizing a certain packing orientation and direction over others, restricting the placement to specific areas, enforcing certain contact points between the packed items, etc.).…”

Multi-strategy hybrid heuristic algorithm for single container weakly heterogeneous loading problem

“…In contrast, meta-heuristic algorithms introduce stochastic process in their search strategy for better convergence (Dereli and Das, 2011;Bayraktar et al, 2021). Recently, there are some hybrid algorithms introduced to further improve the loading performance (Saraiva et al, 2019;Ozcan and Evren, 2021). Despite high container volume utilization rate reported by many advanced algorithms, it is still a challenging task to achieve satisfactory loading levels within a limited time frame.…”

A comparative analysis of two matheuristics by means of merged local optima networks

“…Another example concerns the so-called generate-and-solve (GS) framework that was originally presented in [36]. The latest application of this framework can be found in [17]. Another prominent example of related work concerns kernel search [2], which is a heuristic framework based on the identification of a restricted set of promising solution components (called the kernel) and on the exact solution of sub-instances by ILP solvers.…”