Julio Brito

GVNS for a real-world Rich Vehicle Routing Problem with Time Windows

Rich Vehicle Routing Problems are vehicle routing problems (VRPs) that deal with additional constraints, which aim to better take into account the particularities of real-world applications. They combine multiple attributes, which constitute a complement to the traditional models. This work proposes an adaptive solution method based on metaheuristics for solving a Rich Vehicle Routing Problem with Time Windows. This software has been embedded into the fleet management system of a company in the Canary Islands. The attributes considered by the company are a fixed heterogeneous fleet of vehicles, soft and multiple time windows, customer priorities and vehicle-customer constraints. Furthermore, the company requires the consideration of several objective functions that include travelled distance and time/distance balance. Exact algorithms are not applicable when solving real-life large VRP instances. This work presents a General Variable Neighbourhood Search metaheuristic, which obtains high quality solutions. The computational experiments are presented in four sections, which comprise the parameter setting, the analysis of the effect of the considered attributes, the comparative with the literature for the standard VRP with Time Windows, and the study of the solutions provided by the algorithm when compared with the solutions implemented by the company.

Fuzzy optimization for distribution of frozen food with imprecise times

Problems concerning the distribution routes for frozen products need to incorporate constraints that avoid breaks in the cold chain. The decision making process under uncertain environments is a common one in real logistics problems. The purpose of this study is to apply a fuzzy approach which will provide an optimal solution to the distribution of frozen food with uncertainty in its time values. A soft computing approach is used where fuzzy constraints are included in the modeling and the solution of the problem.

Fuzzy approach for Vehicle Routing Problems with fuzzy travel time

The activities of route planning in logistics can be modeled according to the standard forms seen in vehicle route problems. This paper is devoted to the development of comprehensive fuzzy approach on variants of Vehicle Routing Problems when travel time is imprecise or incomplete. The problem of vehicle routing with fuzzy costs in the objective function and vehicle routing with time windows and fuzzy constraints are formulated. Hybrid heuristic algorithms for solving this problem are presented and analysed.

ACO-GRASP-VNS metaheuristic for VRP with fuzzy windows time constraints

In this article we propose a methodological approach based on Soft Computing to obtain solutions to the vehicle routing problem when time windows constraints are imprecise and flexible. A fuzzy model and method is obtained by applying a Fuzzy Optimization approach. A new hybrid algorithm is presented and applied to real problem instances of a distribution company that combines Ant Colony Optimization, Variable Neighbourhood Search and Greedy Randomize Adaptive Search Procedure for the corresponding fuzzy optimization problem.

A GRASP---VNS Hybrid for the Fuzzy Vehicle Routing Problem with Time Windows

We consider the Vehicle Routing Problem with time windows where travel times are triangular fuzzy numbers. The weighted possibility and necessity measure of fuzzy relations is used to specify a confidence level at which it is desired that the travel times to reach each customer fall into their time windows. In this paper we propose and analyze a solution procedure consisting in hybridizing a Variable Neighborhood Search (VNS) and a Greedy Randomize Adaptive Search Procedure (GRASP) for the corresponding optimization problem.

Solving the Team Orienteering Problem with fuzzy scores and constraints

In many routing planning problems of the real world the available information about the problem is imprecise, vague or uncertain. In addition, decision makers work with decisions with some subjectivity. This work formulates the Team Orienteering Problem (TOP) where the scores and travel time constraints are fuzzy. The soft computing methodology proposed to solve the problem integrates fuzzy optimization methods with a constructive metaheuristic. Computational results on instances from the team orienteering problem benchmarks are analyzed.

Soft Computing Methods in Transport and Logistics

The current economic context generates in supply chain management greater demands for flexibility and dynamism. In addition, there is an increase in uncertainty that adds more complexity to the problems associated with planning and management. Soft Computing offers a set of methodologies capable of responding to these challenges. This work provides an overview of transport and logistics problems, as well as the most representative combinatorial optimization models. Specifically, it focuses on the treatment of uncertainty through fuzzy optimization and metaheuristics methodologies. Promising results from the use of this approach suggest emerging areas of application, which are presented and described.

Bi-objective Discrete PSO for Service-Oriented VRPTW

In this paper we deal with a variant of the VRPTW that is oriented to the quality of service to customers. In this model, we incorporate a measure of quality associated with the time the vehicles reach customers within their time window as an objective. We apply a bi-objective discrete PSO to deal with the problem. The procedure performance is analyzed on classical and real data based instances.

A fuzzy GRASP algorithm for solving a Tourist Trip Design Problem

This paper considers a route-planning problem in the tourism sector, the Tourist Trip Design Problem which aims to design the routes that maximize the satisfaction of the visited points of interest. A model for the multi-day planning problem for sightseeing is proposed. In order to solve this optimization problem, a new fuzzy Greedy Randomized Adaptive Search Procedure (GRASP) is developed in order to obtain high-quality solutions. A fuzzy approach is used to evaluate the points of interest to be included in the solution considering the ambiguity and imprecision of promising points of interest to visit. The computational experiments confirm that fuzzy GRASP is able to report competitive solutions in reasonable computational times.

GRASP-VNS for a Periodic VRP with Time Windows to Deal with Milk Collection

This paper considers the planning of the collection of fresh milk from local farms with a fleet of refrigerated vehicles. The problem is formulated as a version of the Periodic Vehicle Routing Problem with Time Windows. The objective function is oriented to the quality of service by minimizing the service times to the customers within their time windows. We developed a hybrid metaheuristic that combines GRASP and VNS to find solutions. In order to help the hybrid GRASP-VNS find high-quality and feasible solutions, we consider infeasible solutions during the search using different penalty functions.