A Hybrid Multi-Criteria Analysis Model for Solving the Facility Location–Allocation Problem: a Case Study of Infectious Waste Disposal

Choosing locations for infectious waste disposal (IWD) is one of the most significant issues in hazardous waste management due to the risk imposed on the environment and human life. This risk can be the result of an undesirable location of IWD facilities. In this study a hybrid multi-criteria analysis (Hybrid MCA) model for solving the facility location–allocation (FLA) problem for IWD was developed by combining two objectives: total cost minimization and weight maximization. Based on an actual case of forty-seven hospitals and three candidate municipalities in the northeastern region of Thailand, first, the Fuzzy AHP and Fuzzy TOPSIS techniques were integrated to determine the closeness of the coefficient weights of each candidate municipality. After that, these weights were converted to weighting factors and then these factors were taken into the objective function of the FLA model. The results showed that the Hybrid MCA model can help decision makers to locate disposal centers, hospitals and incinerator size simultaneously. Besides that the model can be extended by incorporating additional selection criteria/objectives. Therefore, it is believed that it can also be useful for addressing other complex problems

Increasing discrimination in multi-criteria analysis

Multi criteria methods for supporting decision-making procedures are widely used in sustainability assessment. One of the most important steps in decision-making procedures is the evaluation of policy options or alternatives in order to find a hierarchy of option choices. Utility function value distributions are often constructed for the range of indicators for the options to be assessed. This distribution can be presented as an impact matrix stacked with indicator weights to reflect the relative importance of different indicators for the decision-maker. Solving rules are then introduced to integrate all individual indicator evaluations into a single integral utility estimation. These are often based on the averaging procedures, one of the simplest being arithmetic averaging. Whilst averaging rules are very attractive to decision makers due to their simplicity and logical transparency, using averaging as the first step of the decision-making procedures can significantly reduce the discrimination of the options, especially if there are counteractive individual indicator estimations.This paper proposes a method to evaluate and overcome this loss of discrimination. The paper explains the basis of a discriminatory analysis approach to sustainability assessment demonstrates its application through the use of illustrative data and describes its application to an existing case study where researchers had applied a number of multi criteria analysis tools. It was concluded that the discriminatory analysis provided a useful addition to the decision-makers toolbox as it provided a means of assessment of the validity of the application of the simple arithmetic averaging technique

Multi-criteria Anomaly Detection using Pareto Depth Analysis

We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist a single dissimilarity measure that captures all possible anomalous patterns. In such a case, multiple criteria can be defined, and one can test for anomalies by scalarizing the multiple criteria using a linear combination of them. If the importance of the different criteria are not known in advance, the algorithm may need to be executed multiple times with different choices of weights in the linear combination. In this paper, we introduce a novel non-parametric multi-criteria anomaly detection method using Pareto depth analysis (PDA). PDA uses the concept of Pareto optimality to detect anomalies under multiple criteria without having to run an algorithm multiple times with different choices of weights. The proposed PDA approach scales linearly in the number of criteria and is provably better than linear combinations of the criteria.Comment: Removed an unnecessary line from Algorithm

Taxonomical analysis of regional development by outranking relations on multiple principal components

This paper proposes a method of ranking the elements of a set of objects by multi-criteria analysis on multiple principal components derived from a multiple variable dataset pertaining to the objects. Concordance-discordance method of outranking and derivation of preference relations has been suggested as a technique of multi-criteria decision-making. The method has been illustrated by conducting the taxonomical analysis of regions according to their level of development.Taxonomical analysis; levels of regional development; principal components analysis; multi-criteria decision-making; concordance analysis; outranking relations; Bihar

Multi-criteria analysis applied to multi-objective optimal pump scheduling in water systems

This work presents a multi-criteria-based approach to automatically select specific non-dominated solutions from a Pareto front previously obtained using multi-objective optimization to find optimal solutions for pump control in a water supply system. Optimal operation of pumps in these utilities is paramount to enable water companies to achieve energy efficiency in their systems. The Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the Pareto solutions found by the Non-Dominated Sorting Genetic Algorithm (NSGA-II) employed to solve the multi-objective problem. Various scenarios are evaluated under leakage uncertainty conditions, resulting in fuzzy solutions for the Pareto front. This paper shows the suitability of the approach for quasi real-world problems. In our case-study, the obtained solutions for scenarios including leakage represent the best trade-off among the optimal solutions, under some considered criteria, namely, operational cost, operational lack of service, pressure uniformity and network resilience. Potential future developments could include the use of clustering alternatives to evaluate the goodness of each solution under the considered evaluation criteria