Selecting Appropriate Type of Package with Machine Learning Models in Logistic Companies

There are many factors for Logistics Companies to be successful and financially profitable. These factors can be grouped under two main headings, namely, the efficiency values of the processes and the costs. One of the most important costs is the cost of packaging and shifting. When the packaged orders are delivered to the cargo companies, the packaging and shifting costs are incurred in a way that is directly proportional to the volume value of the order. In logistics companies, these costs increase as a result of placing the order in a package with a larger volume value instead of the appropriate package type. Solving the pallet loading or container loading problems with mathematical models, the packaging personnel's output of the mathematical model for each order, and the employee's placing these products in the package according to the results of the model significantly reduce the efficiency value of the processes. For this reason, in this article, it is aimed to examine and learn the historical packaging data with different machine learning models and to inform the packaging personnel about which package type should be used for the current order

A Multi-Objective Genetic Algorithm for the Vehicle Routing with Time Windows and Loading Problem

This work presents the Vehicle Routing with Time Windows and Loading Problem (VRTWLP) as a multi-objective optimization problem, implemented within a Genetic Algorithm. Specifically, the three dimensions of the problem to be optimized – the number of vehicles, the total travel distance and volume utilization – are considered to be separated dimensions of a multi-objective space. The quality of the solution obtained using this approach is evaluated and compared with results of other heuristic approaches previously developed by the author. The most significant contribution of this work is our interpretation of VRTWLP as a Multi-objective Optimization Problem

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

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

Hybrid quantum-classical heuristic for the bin packing problem

Optimization problems is one of the most challenging applications of quantum computers, as well as one of the most relevants. As a consequence, it has attracted huge efforts to obtain a speedup over classical algorithms using quantum resources. Up to now, many problems of different nature have been addressed through the perspective of this revolutionary computation paradigm, but there are still many open questions. In this work, a hybrid classical-quantum approach is presented for dealing with the one-dimensional Bin Packing Problem (1dBPP). The algorithm comprises two modules, each one designed for being executed in different computational ecosystems. First, a quantum subroutine seeks a set of feasible bin configurations of the problem at hand. Secondly, a classical computation subroutine builds complete solutions to the problem from the subsets given by the quantum subroutine. Being a hybrid solver, we have called our method H-BPP. To test our algorithm, we have built 18 different 1dBPP instances as a benchmarking set, in which we analyse the fitness, the number of solutions and the performance of the QC subroutine. Based on these figures of merit we verify that H-BPP is a valid technique to address the 1dBPP.QUANTEK project (ELKARTEK program from the Basque Government, expedient no. KK-2021/00070) Spanish Ramón y Cajal Grant RYC-2020-030503- I QMiCS (820505) and OpenSuperQ (820363) of the EU Flagship on Quantum Technologies EU FET Open project Quromorphic (828826) and EPIQUS (899368

Learning Gradient Fields for Scalable and Generalizable Irregular Packing

The packing problem, also known as cutting or nesting, has diverse applications in logistics, manufacturing, layout design, and atlas generation. It involves arranging irregularly shaped pieces to minimize waste while avoiding overlap. Recent advances in machine learning, particularly reinforcement learning, have shown promise in addressing the packing problem. In this work, we delve deeper into a novel machine learning-based approach that formulates the packing problem as conditional generative modeling. To tackle the challenges of irregular packing, including object validity constraints and collision avoidance, our method employs the score-based diffusion model to learn a series of gradient fields. These gradient fields encode the correlations between constraint satisfaction and the spatial relationships of polygons, learned from teacher examples. During the testing phase, packing solutions are generated using a coarse-to-fine refinement mechanism guided by the learned gradient fields. To enhance packing feasibility and optimality, we introduce two key architectural designs: multi-scale feature extraction and coarse-to-fine relation extraction. We conduct experiments on two typical industrial packing domains, considering translations only. Empirically, our approach demonstrates spatial utilization rates comparable to, or even surpassing, those achieved by the teacher algorithm responsible for training data generation. Additionally, it exhibits some level of generalization to shape variations. We are hopeful that this method could pave the way for new possibilities in solving the packing problem

Heuristics approaches for three-dimensional strip packing and multiple carrier transportation plans

In transport logistic operations, an efficient delivery plan and better utilisation of vehicles will result in fuel cost savings, reduced working hours and even reduction of carbon dioxide emissions. This thesis proposes various algorithmic approaches to generate improved performance in automated vehicle load packing and route planning. First, modifications to best-fit heuristic methodologies are proposed and then incorporated into a simple but effective “look-ahead” heuristic procedure. The results obtained are very competitive and in some cases best-known results are found for different sets of constraints on three-dimensional strip packing problems. Secondly, a review and comparison of different clustering techniques in transport route planning is presented. This study shows that the algorithmic approach performs according to the specific type of real-world transport route planning scenario under consideration. This study helps to achieve a better understanding of how to conduct the automated generation of vehicle routes that meet the specific conditions required in the operations of a transport logistics company. Finally, a new approach to measuring the quality of transportation route plans is presented showing how this procedure has a positive effect on the quality of the generated route plans. In summary, this thesis proposes new tailored and effective heuristic methodologies that have been tested and incorporated into the real-world operations of a transport logistics company. The research work presented here is a modest yet significant advance to better understanding and solving the difficult problems of vehicle loading and routing in real-world scenarios

The location-routing problem with multi-compartment and multi-trip: formulation and heuristic approaches

The location-routing problem with multi-compartment and multi-trip is an extension to the standard location-routing problem. In this problem, depots are used to deliver different products using heterogeneous vehicles with several compartments. Each compartment has a limited capacity and is dedicated to a single type of product. The problem is formulated as a mixed integer program. A constructive heuristic and a hybrid genetic algorithm (HGA) are proposed. Numerical experiments show that both heuristics can efficiently determine the optimal solutions on small size instances. For larger ones, the HGA outperforms the constructive heuristic with relatively more computational time. Managerial insights have been obtained from sensitivity analyses which would be helpful to improve the performance of the supply network

La logistique collaborative

L'objectif d’une logistique collaborative et coopérative est de générer conjointement un profit en mettant en commun les ressources, en partageant et en tirant parti des forces et des capacités spécifiques des entreprises participantes