The Pallet Loading Problem: Three-dimensional bin packing with practical constraints

2020 ◽  
Vol 287 (3) ◽  
pp. 1062-1074 ◽  
Author(s):  
Fatma Gzara ◽  
Samir Elhedhli ◽  
Burak C. Yildiz
Keyword(s):  
Bin Packing ◽  
Three Dimensional ◽  
Pallet Loading ◽  
Loading Problem ◽  
Practical Constraints

3D-Multi Constrained Heterogeneous Packing Using Genetic Approach

2012 ◽  
Vol 479-481 ◽  
pp. 1825-1830 ◽  
Author(s):  
Rajesh Kanna ◽  
P. Malliga ◽  
K. Sarukesi
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Three Dimensional ◽  
Empty Space ◽  
Computational Time ◽  
Orientation Tuning ◽  
Optimal Position ◽  
Different Shapes ◽  
Practical Constraints ◽  
Better Than

This paper presents a combination of Genetic Algorithm (GA) and Tuning algorithm, for optimizing Three Dimensional (3D) arbitrary sized heterogeneous box packing into a container, by considering practical constraints facing in the logistics industries. Objective of this research is to pack four different shapes of boxes of varying sizes into a container of standard dimension, without violating various practical constraints. Inorder to obtain a real time feasible packing pattern, Genetic Algorithm is developed to maximize the container volume utilization and inturn profit [9]. It significantly improves the search efficiency with less computational time and loads most of the heterogeneous boxes into the container by considering its optimal position and orientation. Tuning algorithm is used to decode the genetic output into user understandable sequential packing pattern and to fill the left-out empty space inside the container. In general, GA in conjunction with the tuning algorithm is substantially better than those obtained by applying heuristics to the bin packing directly.

scholarly journals Complexity Constraint in the Distributor’s Pallet Loading Problem

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1742
Author(s):  
Hugo Barros ◽  
Teresa Pereira ◽  
António G. Ramos ◽  
Fernanda A. Ferreira
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Statistical Methods ◽  
Principal Component ◽  
Pallet Loading ◽  
Main Variable ◽  
Loading Problem ◽  
Using Data ◽  
Practical Constraints

This paper presents a study on the complexity of cargo arrangements in the pallet loading problem. Due to the diversity of perspectives that have been presented in the literature, complexity is one of the least studied practical constraints. In this work, we aim to refine and propose a new set of metrics to measure the complexity of an arrangement of cargo in a pallet. The parameters are validated using statistical methods, such as principal component analysis and multiple linear regression, using data retrieved from the company logistics. Our tests show that the number of boxes was the main variable responsible for explaining complexity in the pallet loading problem.

scholarly journals Necessity and complexity of order picking routing optimisation based on pallet loading features

2017 ◽  
Vol 9 (2) ◽  
pp. 162-194 ◽  
Author(s):  
Tamás Bódis ◽  
János Botzheim ◽  
Péter Földesi
Keyword(s):  
Best Practices ◽  
Lead Time ◽  
Bin Packing ◽  
Packing Problem ◽  
Order Picking ◽  
Policy Routing ◽  
Pallet Loading ◽  
Storage Assignment ◽  
Unique Customer ◽  
Loading Problem

AbstractOrder picking is the most labour-intensive and costly activity of warehouses. The main challenges of its improvement are the synchronisation of warehouse layout, storage assignment policy, routing, zoning, and batching. Furthermore, the competitiveness of the warehouse depends on how it adapts to the unique customer demands and product parameters and the changes. The operators usually have to manage the picking sequence based on best practices taking into consideration the product stacking factors and minimising the lead time. It is usually necessary to support the operators by making e ective decisions. Researchers of the pallet loading problem, bin packing problem, and order picking optimisation provide a wide horizon of solutions but their results are rarely synchronised.

scholarly journals Solving a Real-Life Distributor’s Pallet Loading Problem

2021 ◽  
Vol 26 (3) ◽  
pp. 53
Author(s):  
Mauro Dell’Amico ◽  
Matteo Magnani
Keyword(s):  
Bin Packing ◽  
Real Life ◽  
Second Phase ◽  
Pallet Loading ◽  
Minimum Number ◽  
Loading Problem ◽  
The Difference ◽  
Two Phases ◽  
The Stability ◽  
Selection Of

We consider the distributor’s pallet loading problem where a set of different boxes are packed on the smallest number of pallets by satisfying a given set of constraints. In particular, we refer to a real-life environment where each pallet is loaded with a set of layers made of boxes, and both a stability constraint and a compression constraint must be respected. The stability requirement imposes the following: (a) to load at level k+1 a layer with total area (i.e., the sum of the bottom faces’ area of the boxes present in the layer) not exceeding α times the area of the layer of level k (where α≥1), and (b) to limit with a given threshold the difference between the highest and the lowest box of a layer. The compression constraint defines the maximum weight that each layer k can sustain; hence, the total weight of the layers loaded over k must not exceed that value. Some stability and compression constraints are considered in other works, but to our knowledge, none are defined as faced in a real-life problem. We present a matheuristic approach which works in two phases. In the first, a number of layers are defined using classical 2D bin packing algorithms, applied to a smart selection of boxes. In the second phase, the layers are packed on the minimum number of pallets by means of a specialized MILP model solved with Gurobi. Computational experiments on real-life instances are used to assess the effectiveness of the algorithm.

The Improvement of a Hybrid Genetic Algorithm for Three Dimensional Bin-Packing Problems

2012 ◽  
Vol 200 ◽  
pp. 470-473
Author(s):  
Zhen Zhai ◽  
Li Chen ◽  
Xiao Min Han
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Three Dimensional ◽  
Packing Problem ◽  
Flow Process ◽  
Packing Problems ◽  
Loading Problem ◽  
The Cost ◽  
Process Diagrams

The multi-constrained bi-objective bin packing problem has many extensive applications. In the loading section of logistics it has mainly been transported by truck. The cost of transportation is not only determined by the bin space utilization, but also by the number of vehicles in transporta¬tion utilization. The type of items and bins is introduced in the mathematical model, as well as the volume of the items. In this paper, the hybrid genetic algorithm which tabu and simulated annealed rules are added for complex container-loading problem is studied. The effective coding and decod-ing method together with flow process diagrams are given.

A three-dimensional pallet loading method for single-size boxes

1997 ◽  
Vol 48 (7) ◽  
pp. 726-735 ◽  
Author(s):  
Fuh‐Hwa F Liu ◽  
C‐J Hsiao
Keyword(s):  
Three Dimensional ◽  
Pallet Loading ◽  
Loading Method ◽  
Single Size

An efficient approach for the multi-pallet loading problem

2000 ◽  
Vol 123 (2) ◽  
pp. 372-381 ◽  
Author(s):  
Johannes Terno ◽  
Guntram Scheithauer ◽  
Uta Sommerweiß ◽  
Jan Riehme
Keyword(s):  
Efficient Approach ◽  
Pallet Loading ◽  
Loading Problem

AN EFFICIENT APPROACH FOR THE THREE-DIMENSIONAL CONTAINER PACKING PROBLEM WITH PRACTICAL CONSTRAINTS

2004 ◽  
Vol 21 (03) ◽  
pp. 279-295 ◽  
Author(s):  
ZHIHONG JIN ◽  
KATSUHISA OHNO ◽  
JIALI DU
Keyword(s):  
Three Dimensional ◽  
Real Life ◽  
Packing Problem ◽  
Utilization Rate ◽  
Benchmark Problems ◽  
Rectangular Container ◽  
Efficient Packing ◽  
High Degree ◽  
Practical Constraints ◽  
Inherent Stability

This paper deals with the three-dimensional container packing problem (3DCPP), which is to pack a number of items orthogonally onto a rectangular container so that the utilization rate of the container space or the total value of loaded items is maximized. Besides the above objectives, some other practical constraints, such as loading stability, the rotation of items around the height axis, and the fixed loading (unloading) orders, must be considered for the real-life 3DCPP. In this paper, a sub-volume based simulated annealing meta-heuristic algorithm is proposed, which aims at generating flexible and efficient packing patterns and providing a high degree of inherent stability at the same time. Computational experiments on benchmark problems show its efficiency.

Sign in / Sign up

Export Citation Format

Share Document