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The analytic hierarchy process is combined with multi-objective mixed integer programming to determine the optimal allocation of a limited number of aircraft among a group of airlift users with varying levels of priority and length of usage. Canadian Forces airlift planners typically encounter such a capacity planning problem. The solution to this problem requires the constrained assignment of n variable length missions (tasks) integrating hundreds of airlift requests from several users with many priorities to m airframes (parallel machines).

We introduce an agent-based, evolutionary model of decisions inspired by the increasing return theory. Agents have to choose between options taking into account their own preferences and externalities from their neighbors. The aim is to analyze the distribution of decisions in a square lattice domain and its dependence on the initial conditions. Numerical results show that an undesirable option may be adopted by the majority and may lock in markets by means of clever or lucky movements done at the beginning.

This paper outlines a decision framework that incorporates state-of-the-art earthquake engineering information and decision maker preferences into multicriteria decision models to support earthquake risk mitigation decisions. Seismic risk analysis of a structure is utilized for probabilistic estimation of the anticipated seismic losses, which in turn is used as inputs to the decision analysis for seismic rehabilitation of the structure. Three decision models are used to provide insight into the value of system interventions to reduce earthquake risks: (1) an equivalent cost model, (2) multi-attribute utility theory, and (3) joint probability decision making. Guidelines for selecting and applying multicriteria decision models for seismic rehabilitation of building structures are derived based on preferences for including risk attitudes and for measuring values. The detailed procedures for the selection and application of the decision models for seismic rehabilitation of building structures are demonstrated through a case study, where a collection of hospitals in a metropolitan area is examined.

In this paper, we present a decision model for evaluating and selecting Cloud Computing Services. A strong focus is put on the economic, environmental and social perspectives which are subsumed under the term 'sustainable information systems management'. The model supports decision makers in comprehensively evaluating relevant cost types. We seek to formulate a realistic model by applying a combination of deductive and inductive steps to reveal the core characteristics of Cloud Computing Services and their economic, environmental and social impacts. For the construction of the pricing model and the determination of carbon emission costs, we drew on several theoretical and practical sources. The quality of the model is confirmed on the one hand by expert interviews and on the other hand by the outcomes of a simulation study including two scenarios and a statistical evaluation. The presented research results clarify the need to consider more than the economic dimension — including factors and attributes of sustainable information systems management — in evaluating and selecting Cloud Computing Services.

The purpose of this paper is to analyze the results obtained by the information technology (IT) governance committee (ITGC) of a company undergoing a strategic realignment in the sorting and prioritizing of its portfolio of IT investment projects (CAPEX). The establishment of committees is one of the best practices in corporate governance, and it is often associated with the sorting and prioritizing of project alternatives, a problem typical of multicriteria decision-making (MCDM) and multicriteria decision analysis (MCDA). One of the aids to resolve this problem was the development of a methodology in steps, and a new hybrid multicriteria method consists of the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution-2 normalization (TOPSIS-2N) techniques. The application of the hybrid AHP–TOPSIS-2N model proved to be consistent and robust, generating two priority sorting possibilities aligned with the strategic situation of the organization and a range of improvements in terms of governance and processes for the ITGC of the company.

Predictive maintenance represents a promising application of Artificial Intelligence in the industrial context. The evaluation and selection of predictive maintenance algorithms primarily rely on statistical measures such as absolute and relative prediction errors. However, a purely statistical approach to algorithm selection may not necessarily lead to the optimal economic outcome, as the two types of prediction errors are negatively correlated, thus, cannot be jointly optimized, and are associated with different costs. As the current literature lacks corresponding guidance, we developed a decision model for industrial full-service providers, applying an economic perspective to selecting predictive maintenance algorithms. The decision model was instantiated and evaluated in a real-world setting with a European machinery company providing full-service solutions in the field of car wash systems. Building on sensor data from 4.9 million car wash cycles, the instantiation demonstrates the applicability and effectiveness of the decision model with fidelity to a real-world phenomenon. In sum, the decision model provides economic insights into the trade-off between the algorithms’ error types and enables users to focus on economic concerns in algorithm selection. Our work contributes to the prescriptive knowledge of algorithm selection and predictive maintenance in line with the consideration of different types of cost.

Make-or-buy decision is an important factor affecting the profitability of the firms in all sectors. The goal of this study is to propose a model for firms in engineering design services sector for make-or-buy decisions. A survey was conducted to determine the importance percentages given in an engineering company in make-or-buy decisions and a model was developed. The results of the case study show intriguing clusters of company personnel.

As the lack of consensus among company managers and personnel may inhibit the successful implementation of the developed strategy, we use K-Means Clustering to determine the different perspectives of different groups of employees (managers, senior engineers, junior engineers, technical and administrative support personnel) which may contribute to the understanding of social dynamics of decision making within the company. 4-cluster and 5-cluster analysis results indicate the need for further study on the dynamics of cluster membership.