Evacuation planning is an important component of emergency preparedness in urban areas. The number and location of rescue facilities is an important aspect of this planning, as is the identification of primary and secondary evacuation routes for residents to take. This article introduces a multiobjective approach to identify these aspects of evacuation planning. The approach incorporates a multiobjective model into a geographical information systems-based decision support system that planners can access via the Internet. The proposed approach is demonstrated with a case study for the City of Coimbra, Portugal, for evacuation during major fires. Although presented in this context, this approach is applicable to other emergency situations such as earthquakes, floods, and acts of terrorism.

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The location of hazardous material incineration facilities is an important problem due to the environmental, social, and economic impacts that they impose. The costs associated with the facilities and the risks placed on nearby populations are important concerns as are the distributions of these costs and risks. This paper introduces a mixed-integer, multiobjective programming approach to identify the locations and capacities of such facilities. The approach incorporates a Gaussian dispersion model and a multiobjective optimization model in a GIS based inter-active decision support system that planners can access via the Internet. The proposed approach is demonstrated via a case study in central Portugal where the national government has decided to locate a large facility for the incineration of hazardous industrial waste. Due to intense local and national opposition, construction of the facility has been delayed. The system has been designed so that it can be used by decision makers with no special training in dispersion modeling, multiobjective programming, or GIS.

This work addresses the problem of determining the most suitable sites for locating biogas plants using dairy manure as feedstock, specifically in the Entre-Douro-e-Minho Region in Portugal. A Multicriteria Spatial Decision Support System is developed to tackle this complex multicriteria decision-making problem, involving constraints and many environmental, economic, safety, and social factors. The approach followed combines the use of a Geographic Information System (GIS) to manage and process spatial information with the flexibility of Multicriteria Decision Aid (MCDA) to assess factual information (e.g., soil type, slope, infrastructures) with more subjective information (e.g., expert opinion). The MCDA method used is ELECTRE TRI, an outranking-type method that yields a classification of the possible alternatives. The results of the performed analysis show that the use of ELECTRE TRI is suitable to address real-world problems of land suitability, leading towards a flexible and integrated assessment.

This paper introduces a mixed-integer, bi-objective programming approach to identify the locations and capacities of semi-desirable (or semi-obnoxious) facilities. The first objective minimizes the total investment cost; the second one minimizes the dissatisfaction by incorporating together in the same function "pull" and "push" characteristics of the decision problem (individuals do not want to live too close, but they do not want to be too far, from facilities). The model determines the number of facilities to be opened, the respective capacities, their locations, their respective shares of the total demand, and the population that is assigned to each candidate site opened. The proposed approach was tested with a case study for a particular urban planning problem: the location of sorted waste containers. The complete set of (supported or unsupported) non-inferior solutions, consisting of combinations of multi-compartment containers for the disposal of four types of sorted waste in nineteen candidate sites, and population assignments, was generated. The results obtained for part of the historical center of an old European city (Coimbra, Portugal) show that this approach can be applied to a real-world planning scenario.

The assessment of the environmental sustainability of agricultural infrastructures involves the use of multiple evaluation criteria and the analysis of geographical information. A Geographic Information System (GIS) is a computer system capable of assembling, storing, analyzing, and displaying geographically referenced information. However, the GIS technology still suffers from several shortcomings due in large part to a lack of capable analytical capacity of supporting spatial decision problems. The most 2 common solution for GIS to evolve into an effective tool for decision support is to couple them with operational research tools and in particular Multicriteria Decision Aid (MCDA). Due to the technological advances in the field of information systems, there is a great need to research how to integrate GIS, MCDA, the Internet, modeling and databases aiming at creating Web Multicriteria Spatial Decision Support Systems (Web MC-SDSS). A Web MC-SDSS methodological framework is proposed for a fully integrated system of GIS and a specific MCDA method-ELECTRE TRI, through the construction of a Macro written in Visual Basic for Applications (VBA) in ArcGIS software. This macro interacts with a Web Algorithm Server for computing MCDA results. The developed Web MC-SDSS, named ELECTRE TRI in ArcGIS, is applied on a case study analysing the environmental sustainability of dairy farms in the Entre-Douro-e-Minho (EDM) Region.

A bi‐objective decision aid model for planning long‐term maintenance of infrastructure systems is presented, oriented to interventions on their constituent elements, with two upgrade levels possible for each element (partial/full repairs). The model aims at maximizing benefits and minimizing costs, and its novelty is taking into consideration, and combining, the system/element structure, volume discounts, and socioeconomic factors. The model is tested with field data from 229 sidewalks (systems) and compared to two simpler repair policies, of allowing only partial or full repairs. Results show that the efficiency gains are greater in the lower mid‐range budget region. The proposed modeling approach is an innovative tool to optimize cost/benefits for the various repair options and analyze the respective trade‐offs.