Abstract
Sectorization problems refer to dividing a large set, area or network into smaller parts concerning one or more objectives. A decision support system (DSS) is a relevant tool for solving these problems, improving optimisation procedures, and finding feasible solutions more efficiently. This paper presents a new web-based Decision Support System for Sectorization (D3S). D3S is designed to solve sectorization problems in various areas, such as school and health districting,planning sales territories and maintenance operations zones, or political districting. Due to its generic design, D3S bridges the gap between sectorization problems and a state-of-the-art decision support tool. The paper aims to present the generic and technical attributes of D3S by providing detailed information regarding the problem-solution approach (based on Evolutionary Algorithms), objectives (most common in sectorization), constraints, structure and performance.

Abstract
Multi -objective optimization (MOO) considers several objectives to find a feasible set of solutions. Selecting a solution from Pareto frontier (PF) solutions requires further effort. This work proposes a new classification procedure that fits into the analytic hierarchy Process (AHP) to pick the best solution. The method classifies PF solutions using pairwise comparison matrices for each objective. Sectorization is the problem of splitting a region into smaller sectors based on multiple objectives. The efficacy of the proposed method is tested in such problems using our instances and real data from a Portuguese delivery company. A non -dominated sorting genetic algorithm (NSGA-II) is used to obtain PF solutions based on three objectives. The proposed method rapidly selects an appropriate solution. The method was assessed by comparing it with a method based on a weighted composite single -objective function.

Abstract
The efficient last-mile delivery of goods involves complex challenges in optimizing driver sectors and routes. This problem tends to be large-scale and involves several criteria to meet simultaneously, such as creating compact sectors, balancing the workload among drivers, minimizing the number of undelivered packages and reducing the dissimilarity of sectors on different days. This work proposes a Decision Support System (DSS) that allows decision-makers to select improved allocation strategies to define sectors. The main contribution is an interactive DSS tool that addresses a many-objective (more than 3 objectives) sectorization problem with integrated routing. It establishes a global allocation strategy and uses it as a benchmark for the created daily allocations and routes. A Preference-Inspired Co-Evolutionary Algorithm with Goal vectors using Mating Restriction (PICEA-g-mr) is employed to solve the many-objective optimization problem. The DSS also includes a visualization tool to aid decision-makers in selecting the most suitable allocation strategy. The approach was tested in a medium-sized Metropolitan Area and evaluated using resource evaluation metrics and visualization methods. The proposed DSS deals effectively and efficiently with the sectorization problem in the context of last-mile delivery by producing a set of viable and good-quality allocations, empowering decision-makers in selecting better allocation strategies. Focused on enhancing service efficiency and driver satisfaction, the DSS serves as a valuable tool to improve overall service quality.

Abstract
Sectorization consists of grouping the basic units of a large territory to deal with a complex problem involving different criteria. Resectorization rearranges a current sectorization avoiding substantial changes, given a set of conditions. The paper considers the case of the distribution of geographic areas of fire brigades in the north of Portugal so that they can protect and rescue the population surrounding the fire stations. Starting from a current sectorization, assuming the geographic and population characteristics of the areas and the fire brigades’ response capacity, we provide an optimized resectorization considering two objectives: to reduce the rescue time by maximizing the compactness criterion, and to avoid overload situations by maximizing the equilibrium criterion. The solution method is based on the Non-dominated Sorting Genetic Algorithm (NSGA-II). Finally, computational results are presented and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
This paper explores the problem of sectorization of a parcel delivery service that wants to assign an action region to each of its teams, regarding the number of deliveries scheduled for each zone, so that there is a balanced service amongst sectors, covering contiguous zones, and considering limited capacities for the teams. Besides being relatively easy to model, the available optimization tools and software provide poor results when dimension increases in these types of problems, with computational capacity exceeding. In this paper an integer programming model, combined with an heuristic to return a faster solution, was implemented to solve a sectorization problem in two different situations. The main advantage of the strategy proposed, compared to previous ones, is its simplicity and easy implementation while still returning an optimal solution. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.