João Amaro Oliveira Ferreira

Solving the Team Orienteering Problem: Developing a Solution Tool Using a Genetic Algorithm Approach

Nowadays, the collection of separated solid waste for recycling is still an expensive process, specially when performed in large-scale. One main problem resides in fleet-management, since the currently applied strategies usually have low efficiency. The waste collection process can be modelled as a vehicle routing problem, in particular as a Team Orienteering Problem (TOP). In the TOP, a vehicle fleet is assigned to visit a set of customers, while executing optimized routes that maximize total profit and minimize resources needed. The objective of this work is to optimize the waste collection process while addressing the specific issues around fleet-management. This should be achieved by developing a software tool that implements a genetic algorithm to solve the TOP. We were able to accomplish the proposed task, as our computational tests have produced some challenging results in comparison to previous work around this subject of study. Specifically, our results attained 60% of the best known scores in a selection of 24 TOP benchmark instances, with an average error of 18.7 in the remaining instances. The usage of a genetic algorithm to solve the TOP proved to be an efficient method by outputting good results in an acceptable time.

Forecasting Household Packaging Waste Generation: A Case Study

Nowadays, house packaging waste (HPW) materials acquired a great deal of importance, due to environmental and economic reasons, and therefore waste collection companies place thousands of collection points (ecopontos) for people to deposit their HPW.

Non Emergency Patients Transport - A Mixed Integer Linear Programming

Fundacao para a Ciencia e Tecnologia; project GATOP - Genetic Algorithms for Team Orienteering Problem (Ref PTDC/EME- GIN/120761/2010), financed by national funds by FCT / MCTES, and co-funded by the European Social Development Fund (FEDER) through the COMPETE - Programa Operacional Fatores de Competitividade (POFC) Ref FCOMP-01-0124- FEDER-020609. This work has been partially supported by FCT – Fundacao para a Ciencia e Tecnologia within the Project Scope: PEst-OE/EEI/UI0319/2014 The authors would like to thank the NEOS Server at the University of Wisconsin in Madison for providing support to this study

A Multi-Criteria Decision Support System for a Routing Problem in Waste Collection

This work presents a decision support system for route planning of vehicles performing waste collection for recycling. We propose a prototype system that includes three modules: route optimization, waste generation prediction, and multiple-criteria decision analysis (MCDA). In this work we focus on the application of MCDA in route optimization. The structure and functioning of the DSS is also presented.

Household packaging waste management

Household packaging waste (HPW) has an important environmental impact and economic relevance. Thus there are networks of collection points (named “ecopontos” in Portugal) where HPW may be deposited for collection by waste management companies.

The Non-Emergency Patient Transport Modelled as a Team Orienteering Problem

This work presents an improved model to solve the non-emergency patients transport (NEPT) service issues given the new rules recently established in Portugal. The model follows the same principle of the Team Orienteering Problem by selecting the patients to be included in the routes attending the maximum reduction in costs when compared with individual transportation. This model establishes the best sets of patients to be transported together. The model was implemented in AMPL and a compact formulation was solved using NEOS Server. A heuristic procedure based on iteratively solving Orienteering Problems is presented, and this heuristic provides good results in terms of accuracy and computation time. Euclidean instances as well as asymmetric real data gathered from Google maps were used, and the model has a promising performance mainly with asymmetric cost matrices.

Genetic algorithms for the non emergency patients transport service

This study presents three genetic algorithms developed to solve a routing problem related with a transportation service for nonemergency patients in Portugal. A model based on an extension of the Team Orienteering Problem was developed to carry out several legal constraints. The results of computational experiments made to validate the adopted methodology, using both public TOP instances and real data, are presented.

An Improved Evolutionary Algorithm to Sequence Operations on an ASRS Warehouse

This paper describes the hybridization of an evolutionary algorithm with a greedy algorithm to solve a job-shop problem with recirculation. We model a real problem that arises within the domain of loads’ dispatch inside an automatic warehouse. The evolutionary algorithm is based on random key representation. It is very easy to implement and allows the use of conventional genetic operators for combinatorial optimization problems. A greedy algorithm is used to generate active schedules. This constructive algorithm reads the chromosome and decides which operation is scheduled next. This option increases the efficiency of the evolutionary algorithm. The algorithm was tested using some instances of the real problem and computational results are presented.

Multi-technique approach for the assessment of historical masonry constructions

In this paper the NDTs research project is briefly described. This project aims at developing and improving several nondestructive techniques typically used in masonry diagnosis. So far, a new acquisition system based on sonic measurements, a geoelectric prototype to perform tomographic measurements of the cross-section of masonry elements and a new type of flat-jack test, the tube-jack, were developed. Finally, the project aims at merging the data of these improved techniques with existing ones in order to produce a better diagnosis of these structures. The initial results of the case study of S. Torcatos church is illustrated here to show the potential of these methods and the quality of the information that can be derived from the merging of the data obtained through different methodologies.