Thirty years of heterogeneous vehicle routing

It has been around thirty years since the heterogeneous vehicle routing problem was introduced, and significant progress has since been made on this problem and its variants. The aim of this survey paper is to classify and review the literature on heterogeneous vehicle routing problems. The paper also presents a comparative analysis of the metaheuristic algorithms that have been proposed for these problems

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

Internet of Things in urban waste collection

Nowadays, the waste collection management has an important role in urban areas. This paper faces this issue and proposes the application of a metaheuristic for the optimization of a weekly schedule and routing of the waste collection activities in an urban area. Differently to several contributions in literature, fixed periodic routes are not imposed. The results significantly improve the performance of the company involved, both in terms of resources used and costs saving

A hybrid solution approach for the 3L-VRP with simultaneous delivery and pickups

This paper deals with a special vehicle routing problem with backhauls where each customer receives items from a depot and, at the same time, returns items back to the depot. Moreover, time windows are assumed and three-dimensional loading constraints are to be observed, i.e. the items are three-dimensional boxes and packing constraints, e.g. regarding load stability, are to be met. The resulting problem is the vehicle routing problem with simultaneous delivery and pickup (VRPSDP), time windows, and three-dimensional loading constraints (3L-VRPSDPTW). This problem occurs, for example, if retail stores are supplied by a central warehouse and wish to return packaging material.A particular challenge of the problem is to transport delivery and pickup items simultaneously on the same vehicle. In order to avoid any reloading effort during a tour, we consider two different loading approaches of vehicles: (i) loading from the back side with separation of the loading space into a delivery section and a pickup section and (ii) loading at the long side. A hybrid algorithm is proposed for the 3L-VRPSDPTW consisting of an adaptive large neighbourhood search for the routing and different packing heuristics for the loading part of the problem. Extensive numerical experiments are conducted with VRPSDP instances from the literature and newly generated instances for the 3LVRPSDPTW

Real-polarized genetic algorithm for the three-dimensional bin packing problem

This article presents a non-deterministic approach to the Three-Dimensional Bin Packing Problem, using a genetic algorithm. To perform the packing, an algorithm was developed considering rotations, size constraints of objects and better utilization of previous free spaces (flexible width). Genetic operators have been implemented based on existing operators, but the highlight is the Real-Polarized crossover operator that produces new solutions with a certain disturbance near the best parent. The proposal presented here has been tested on instances already known in the literature and real instances. A visual comparison using boxplot was done and, in some situations, it was possible to say that the obtained results are statistically superior than the ones presented in the literature. In a given instance class, the presented Genetic Algorithm found solutions reaching up to 70% less bins

Stowage Planning System for Ferry Ro-Ro Ships Using Particle Swarm Optimization Method

Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo.Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo