Abstract
Sectorization refers to partitioning a large territory, network, or area into smaller parts or sectors considering one or more objectives. Sectorization problems appear in diverse realities and applications. For instance, political districting, waste collection, maintenance operations, forest planning, health or school districting are only some of the application fields. Commonly, sectorization problems respect a set of features necessary to be preserved to evaluate the solutions. These features change for different sectorization applications. Thus, it is important to conceive the needs and the preferences of the decision-makers about the solutions. In the current paper, we solve sectorization problems using the Genetic Algorithm by considering three objectives: equilibrium, compactness, and contiguity. These objectives are collected within a single composite objective function to evaluate the solutions over generations. Moreover, the Analytical Hierarchy Process, a powerful method to perceive the relative importance of several objectives regarding decision makers' preferences, is used to construct the weights. We observe the changes in the solutions by considering different sectorization problems that prioritize various objectives. The results show that the solutions' progress changed accurately to the given importance of each objective over generations.

Abstract
Sectorization problems have significant challenges arising from the many objectives that must be optimised simultaneously. Several methods exist to deal with these many-objective optimisation problems, but each has its limitations. This paper analyses an application of Preference Inspired Co-Evolutionary Algorithms, with goal vectors (PICEA-g) to sectorization problems. The method is tested on instances of different size difficulty levels and various configurations for mutation rate and population number. The main purpose is to find the best configuration for PICEA-g to solve sectorization problems. Performance metrics are used to evaluate these configurations regarding the solutions’ spread, convergence, and diversity in the solution space. Several test trials showed that big and medium-sized instances perform better with low mutation rates and large population sizes. The opposite is valid for the small size instances. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
Este trabalho dá a conhecer um novo problema, Problema Capacitado de Rotas em Arcos Misto, com Múltiplos Aterros Limitados. Baseado na situação de recolha/transporte de Resíduos Sólidos Urbanos no concelho de Monção, são apresentadas características que, não sendo únicas em Portugal, nunca foram mencionadas na literatura. Diferencia-se pela existência de diversos pontos de deposição que, especialmente devido às reduzidas dimensões, apresentam restrições relacionadas com o número de visitas recebidas por dia. Um novo modelo de otimização, baseado na formulação do Mixed Ca- pacitated Arc Routing Problem é apresentado. São incluídos resultados computacionais provenientes de instâncias adaptadas da literatura e do problema real descrito.

Abstract
For efficient planning of waste collection routing, large municipalities may be partitioned into convenient sectors. The real case under consideration is the municipality of Monção, in Portugal. Waste collection involves more than 1600 containers over an area of 220 km2 and a population of around 20,000 inhabitants. This is mostly a rural area where the population is distributed in small villages around the 33 boroughs centres (freguesia) that constitute the municipality. In most freguesias, waste collection is usually conducted 3 times a week. However, there are situations in which the same collection is done every day. The case reveals some general and specific characteristics which are not rare, but are not widely addressed in the literature. Furthermore, new methods and models to deal with sectorization and routing are introduced, which can be extended to other applications. Sectorization and routing are tackled following a three-phase approach. The first phase, which is the main concern of the presentation, introduces a new method for sectorization inspired by Electromagnetism and Coulomb’s Law. The matter is not only about territorial division, but also the frequency of waste collection, which is a critical issue in these types of applications. Special characteristics related to the number and type of deposition points were also a motivation for this work. The second phase addresses the routing problems in each sector: new Mixed Capacitated Arc Routing with Limited Multi-Landfills models will be presented. The last phase integrates Sectoring and Routing. Computational results confirm the effectiveness of the entire novel approach.

Abstract
A Setorização entendida como a divisão de um território em regiões mais pequenas pode apresentar vantagens em problemas de rotas com capacidades. Decidir, a priori, que região atribuir a que veículos, reduz substancialmente a dimensão das instâncias e conduz a resultados que, para além de serem obtidos de forma mais rápida podem expressar o interesse do agente decisor. A introdução de sectores em rotas de veículos permite igualmente lidar com situações de planeamento/calendário e outras restrições. Uma situação real será apresentada onde uma abordagem integrada sectores-rotas foi aplicada.