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The aim of this paper is to introduce a newly developed multi-criteria analysis for the comparison of two grid expansion alternatives, conventional and voltage-regulated distribution transformer. The case study comprises environmental, economic, technical and social aspects.

The newly developed method decision condition Preference Ranking Organization METHod for Enrichment Evaluation (DC-PROMETHEE) combines scenario planning with the multi-attribute decision-making method PROMETHEE. DC-PROMETHEE supports the decision-maker to evaluate the total potential of an alternative considering a large number of decision conditions. The calculated performance indicator supports the decision-maker to select the best alternative.

The voltage-regulated distribution transformer shows a high overall potential in the present case study. This leads to the recommendation to the investigated distribution system operator to include the voltage-regulated distribution transformers as a grid expansion measure.

The DC-PROMETHEE can be applied to other distribution system operators by considering their individual grid topology and preferences. Other fields of application are infrastructure investments in the service area, in which expansion alternatives are evaluated in a large number of decision conditions. Examples include telecommunication, gas supply, water supply, sewage and rail networks.

This paper develops the DC-PROMETHEE approach. The DC-PROMETHEE enables the multi-criteria evaluation of a few alternatives in a large number of decision conditions.

The purpose of this paper is to develop a model that permits more rational planning for preventive maintenance, by controlling failures in the specific context of equipment breakdown. Thus not only the cost and reliability parameters are dealt with, but also the peculiarities of different contexts in which maintenance activities occur. Furthermore, it aims to include Bayesian methodology in the procedure to overcome main difficulties in failure data.

A multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion, and the ability to provide a solution in uncertainties situations, has been developed from adaptation of classical models. This model also uses Bayesian elements to address uncertainties during equipment failures.

The paper finds that in a preventive maintenance planning, as a multi‐criteria decision problem, different types of uncertainties may be identified, which may be categorized as external uncertainties and internal uncertainties. In the proposed model a division of procedures has been established, dealing with external uncertainties first, then the internal uncertainties related to the structure of the problem and analysis of the decision are addressed using the multi‐criteria decision‐making method PROMETHEE III that allows the amplification of the notion of indifference. In this way, a suitable structure to connect two types of uncertainties was structured.

The model will assist the decision‐maker in preventive maintenance planning to take uncertainties into account, seeing the alternatives that are closest, through an amplification of the notion of indifference provided by PROMETHEE III. Furthermore, in practice the proposed model have an impact on maintenance cost and reliability of production plant.

This paper proposes a multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion; and the ability to provide a solution even when failure data are unavailable or incomplete.

Presents the findings concerning a search for an objective way of assessing the quality of commercially‐available, multi‐criteria decision support software. As hardware prices continue to decline, consumers are spending relatively large sums of money on software. There is a powerful need for decision makers to justify these cash outlays based on the impact on the quality of the decisions made using multi‐criteria tools. After a thorough literary search for such an assessment uncovered the lack of field‐tested assessment batteries, develops an evaluation tool and undertakes an experiment to gain insight on the performance of this evaluation tool.

For the purpose of decision-making in real estate portfolio management, alternatives are to be collected, assessed and prioritized. Due to the complexity of real estate markets, investment products and management processes, the respective decision situations are subject to several parameters. Multi-criteria models must be used to exactly evaluate and prioritize alternatives. Such models can be found in other economic and social areas but must be transferred or re-developed for the purposes of real estate management. The paper aims to discuss these issues.

The paper includes the transfer of the basic outranking methodology, the PROMETHEE (Preference Ranking Organization METHod für Enrichment Evaluations) method in particular, to real estate issues. Methods used outside the real estate industry are analyzed, selected and adapted by using real estate parameters (transfer approach).

Structured multi-criteria processes such as PROMETHEE are suitable for the solution of complex real estate selection decisions. The methodology with regard to the respective issues is much more consistent and efficient. PROMETHEE avoids the restriction of criteria in mathematical calculations and the restriction of quality in simple scorings.

The target system and decision criteria of investors were used exemplarily. The individual parameters and criteria can lead to new model solutions.

Multi-criteria models such as PROMETHEE stringently and transparently solve complex decision problems and alternative evaluations in real estate portfolio management. They can be developed for strategic, tactical and operative decision situations. The decision quality and verification for compliance requirements improve.

Multi-criteria models such as PROMETHEE can also be developed for social, societal and political decision situations.

First adaption of the outranking procedure PROMETHEE to a real estate decision situation in market analysis and portfolio management.

The discretionary expense budget required to maintain public infrastructure has declined in recent years, even as public expectations and accountability for performance have increased. The purpose of this paper is to leverage previously reported research to create a decision support tool (DST) for prioritizing institutional facility maintenance.

A structured literature review was developed to identify critical aspects of facility maintenance shown to have a positive relationship with academic performance in K-12 schools within the USA. Analytical hierarchy process (AHP) serves as a framework for a multi-criteria DST based on the findings of the literature review. Finally, a targeted focus group of industry professionals was used to validate the usability of the resulting DST.

The framework for the DST developed for this study effectively represents the scale and scope of an institutional facility. Results of the study suggest that when evaluating multi-criteria work orders, the proposed visual AHP methodology can be used to generate usable DSTs to assist with the prioritization of work.

This study provides a methodology for building a multi-criterion DST leveraging precedent research, using a visual AHP to assist facility management (FM) decision-makers in the prioritization of routine work orders.

The developed process indicates a practical approach to incorporating disparate research findings into a concise and useable manner to guide FM decision-makers, who have traditionally not been able to explicitly leverage this information to make evidence-based spending decisions.

Emergency situations may differ in many ways but they share some common characteristics, such as the sudden onset and the need to transparently evaluate various usually conflicting objectives. In nuclear power generation, however, emergency situations constitute a special challenge. The focus of this paper is to highlight the role of multi‐criteria decision analysis (MCDA) in nuclear emergency and recovery management on the basis of a hypothetical case study.

Multi‐attribute value theory as one field of research within MCDA is introduced. Special emphasis is placed on the modelling of the decision makers' preferences which is a crucial part in any multi‐criteria analysis. A central aim is to facilitate the preference elicitation in group decision processes.

The management of emergency situations in nuclear power generation necessitates the consideration of technical, economic, environmental, socio‐psychological and political aspects. Furthermore, various stakeholder and expert groups with diverse background knowledge and different views, responsibilities and interests are involved in such a decision‐making process. MCDA can help to take into account various incommensurable aspects and the subjective preferences of the decision makers and thus contribute to transparency and traceability of decision‐making processes. Since the preference parameters are inherently afflicted with uncertainties, thorough sensitivity analyses are important to visualise the impact of the uncertainties in an understandable way.

A new approach to sensitivity analysis is proposed, allowing one to comprehensibly visualise and communicate the impact of the uncertainties associated with the subjective preference parameters on the results of the decision analysis.

Due to the increasing complexity of decision environments, suitable multi-criteria methods are gaining importance for the decision support function in management accounting. The analytic hierarchy process (AHP), a well-known and established OR method for solving complex decision settings, is accompanied by the ongoing development of suitable software solutions. Especially for practical issues, software support can reduce barriers to applying AHP and can enhance acceptance by managers. For this reason, five heterogeneous software products are evaluated from a management accounting perspective. The paper aims to discuss these issues.

Based on the increasing relevance of AHP and the major changes in the field of AHP software solutions, the study of Ossadnik and Lange was replicated, with modifications. Five leading software products that use AHP were selected and evaluated with regard to their quality for solving decision problems. Pairwise comparisons were generated and integrated into an AHP-based decision model. The relevant criteria contained in this model were developed from the international standard norm for software evaluation.

In addition to revealing the necessity for further research on the development of appropriate software for multi-criteria decision problems, the result also shows that, under certain assumptions, “ Make It Rational” is the preferred software product.

Originating from different demands, the evaluation reveals the strengths and weaknesses of various software solutions for practical purposes.

This study shows that characteristics of software products using AHP vary, enabling users to select an appropriate software solution.

The purpose of this paper is the application of a tool to assist the multi-criteria decision-making process for selecting an asset for a company in the metallurgical industry which manufactures metal parts for diverse industries.

This investment, complex due to the commitment of resources it requires, has been made with the assistance of decision-making methodologies, specifically versions I and IV of the ELECTRE method.

This model of multi-criteria decision making has been chosen over other models because it offers the possibility of including technical and economic decisions so they can be analyzed simultaneously, therefore the decision is not based solely on financial aspects. Many companies base their decisions exclusively on financial returns, however in this case it is also appropriate to include the technical parameters, since the asset being replaced is the most important asset of the company.

Applying version I of the methodology, the optimal technical configuration of the asset will be analyzed based on the features requirements, all of which are among options available in the market. Once a subset of technically and economically viable alternatives has been defined, version IV will be applied and a ranking of the alternatives from the best to the worst will be obtained and, based on this ranking, the final decision will be made.

The regular occurrence of natural disasters elevates the need for an effective method to measure organizational preparedness in responding to the adverse impact of disasters. In this context, this paper presents a new decision support model to assess organizational disaster preparedness using both subjective and objective disaster preparedness criteria in a multi-criteria decision-making context.

The statistical variance method is integrated with the proximity value index (PVI) technique to determine priority scores in order to rank organizational disaster readiness.

The results of applying the integrated model developed herein enable decision-makers to make informed decisions for assigning priority ranking of organizational disaster preparedness in a simpler and more efficient way.

Human resource is the most impacting criterion affecting hospital preparedness in undertaking action to cure disaster victims.

This paper offers an exemplar of a simple and efficient decision-making process considering the subjectivity associated with decision-making as well as the objectivity of data used for determining the priority ranking of organizational disaster preparedness.

Integrating statistical variance method with the PVI technique is novel and it has not been presented in previous studies. In fact, this study is the first to integrate both methods for selecting the priority ranking of organizational disaster preparedness.

This chapter presents application of multi-criteria mathematical programming models by integer and mixed-integer programming for optimal allocation of workers among supporting services in a hospital. The services include logistics, inventory management, financial management, operations management, medical analysis, etc. The optimality criteria of the problem are minimization of operational costs of supporting services subject to some specific constraints. The constraints represent specific conditions for resource allocation in a hospital. The overall problems are formulated as assignment models, where the decision variables represent the assignment of people to various jobs. Numerical examples are presented. Some computational results modeled on a real data from a hospital in Poland are reported.