David L. Olson

Comparison of weights in TOPSIS models

TOPSIS is a multiple criteria method to identify solutions from a finite set of alternativesbased upon simultaneous minimization of distance from an ideal point and maximization of distance from a nadir point. TOPSIS can incorporate relative weights of criterion importance. This paper reviews several applications of TOPSIS using different weighting schemes and different distance metrics, and compares results of different sets of weights applied to a previously used set of multiple criteria data. Comparison is also made against SMART and centroid weighting schemes. TOPSIS was not found to be more accurate, but was quite close in accuracy. Using first-order and second-order metrics were found to be quite good, but the infinite order (Tchebycheff norm, L-~) was found to decline in accuracy.

Co‐innovation: convergenomics, collaboration, and co‐creation for organizational values

Purpose – The aim of this paper is to present a macro view of the evolution of innovation for value creation, from the closed to collaborative, open, and now co‐innovation. It reviews several mega trends that have dramatically changed the dynamic nature of the global market place and also several new forces that have made innovation imperative for organizational value creation.Design/methodology/approach – The paper provides a conceptual overview of co‐innovation through some of its basic elements such as convergence revolution, collaboration, and co‐creation with stakeholders.Findings – Co‐innovation is a new innovation paradigm where new ideas and approaches from various internal and external sources are integrated in a platform to generate new organizational and shared values. The core of co‐innovation includes engagement, co‐creation, and compelling experience for value creation. Thus, the practices of co‐innovative organizations are difficult to imitate by competition.Practical implications – Innovat...

Decision aids for selection problems

1 Introduction.- Multiple Criteria Decision Making.- Value.- Software Sources.- References.- 2 Developing Criteria Hierarchies.- Hierarchies.- Attributes.- Hierarchy Development Process.- Suggestions for Cases where Preferential Independence is Absent.- Energy Hierarchies.- Conclusions.- References.- 3 Multiattribute Utility Theory.- Case: Nuclear Depository Siting.- Multiattribute Utility Theory.- Tradeoff Analysis.- Nuclear Dump Site Selection.- House Selection.- Conclusions.- References.- 4 Smart.- The SMART Technique.- Independence.- Nuclear Dump Site Example.- House Selection Example.- SMARTS.- Nuclear Site Selection with SMARTS.- SMARTER.- House Selection Example Using SMARTER.- Summary.- References.- 5 The Analytic Hierarchy Process.- Description of AHP.- Hierarchy Development.- Subjective Pairwise Comparisons.- Calculation of Implied Weights.- Consistency Measure.- Synthesis.- Calculations for the Nuclear Waste Disposal Siting Problem.- Calculations for the Housing Selection Decision.- Real-World Applications of AHP.- Computer Support for AHP.- Summary.- Appendix: Calculation of Maximum Eigen Value and Eigenvector.- References.- 6 Geometric Mean Technique.- Geometric Mean Solution.- REMBRANDT.- Nuclear Disposal Site Selection Problem.- House Selection Calculations.- References.- 7 Preference Cones.- Procedure.- Basing Objective on Current Best Choice.- Basing Objective on Worst Choice to Date.- Adjacency Formulation.- Cellular Manufacturing Example.- Basing Objective on Current Best Choice.- Basing Objective on Worst Choice to Date.- Automation Example.- Nuclear Dump Site Selection.- Conclusions.- References.- 8 Outranking Methods.- ELECTRE.- Cellular Manufacturing Example.- Automation Example.- Nuclear Dump Site Selection - ELECTRE II.- PROMETHEE.- GAIA Output.- Nuclear Dump Site Selection-PROMETHEE.- Applications of Outranking Methods.- Conclusions.- References.- 9 Zapros.- Demonstration Model.- Nuclear Dump Site Selection.- House Selection.- Conclusions.- References.- Appendix: Input Files.- 10 Aspiration-Level Interactive Model.- Methodology.- Nuclear Dump Site Selection.- Cellular Manufacturing Example.- Summary.- References.- 11 Visual Interactive Method.- VIMDA Overview.- VIMDA Procedure.- Nuclear Dump Site Selection.- Cellular Manufacturing Example.- Summary.- References.- 12 Models with Uncertain Estimates.- ARIADNE.- Parking Example.- Nuclear Waste Disposal Siting Problem.- Uncertain Value Contributions.- House Buying Example.- HIPRE 3+.- Parking Example.- Nuclear Dump Site Example.- Summary.- References.- 13 Comparisons.- Task Type.- Task Dimensionality.- Task Uniqueness.- Decision Maker Cognitive Effort.- Human Subject Responses.- Aid to Decision Maker Learning.- Synthesis.- Conclusion.- References.- Author Index.- Topic Index.

The method of grey related analysis to multiple attribute decision making problems with interval numbers

Multiple attribute decision making is important in many decision making contexts where tradeoffs are involved. The use of qualitative input has proven especially attractive, allowing subjective inputs to be used. However, such systems inherently involve uncertainty with respect to parameter inputs, especially when multiple decision makers are involved. This paper presents the method of grey related analysis to this problem, using interval fuzzy numbers. The method standardizes inputs through norms of interval number vectors. Interval valued indexes are used to apply multiplicative operations over interval numbers. The method is demonstrated on a practical problem.

Similarity measures between intuitionistic fuzzy (vague) sets: A comparative analysis

Existing similarity measures between intuitionistic fuzzy sets/vague sets are analyzed, compared and summarized by their counter-intuitive examples in pattern recognition. The positive aspects of each similarity measure are demonstrated, along with counter cases and discussion of the conditions under which each may not work as desired. The research presented here could benefit selection and applications of similarity measures for intuitionistic fuzzy sets and vague sets in practice.

Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach

Selection of supply chain partners is an important decision involving multiple criteria and risk factors. This paper proposes a fuzzy multi-objective programming model to decide on supplier selection taking risk factors into consideration. We model a supply chain consisting of three levels and use simulated historical quantitative and qualitative data. We propose a possibility approach to solve the fuzzy multi-objective programming model. Possibility multi-objective programming models are obtained by applying possibility measures of fuzzy events into fuzzy multi-objective programming models. Results indicate when qualitative criteria are considered in supplier selection, the probability of a certain supplier being selected is affected.

Enterprise risk management: a DEA VaR approach in vendor selection

Enterprise risk management (ERM) has become an important topic in todays more complex, interrelated global business environment, replete with threats from natural, political, economic, and technical sources. The development and current status of ERM is presented, with a demonstration of how risk modelling can be applied in supply chain management. Within supply chain management, a major managerial decision is vendor selection. We start with discussion of the advanced ERM technology, i.e. value-at-risk (VaR) and develop DEA VaR model as a new tool to conduct risk management in enterprises. A vendor selection set of data is used to demonstrate how this model can be used to assess supply risks in ERM. Such models provide means to quantitatively improve decision making with respect to risk.

Management of multicriteria inventory classification

The most common method that materials managers use for classifying inventory items for planning and control purposes is the annual-dollar-usage ranking method (ABC classification). Recently, it has been suggested that multiple criteria ABC classification can provide a more comprehensive managerial approach, allowing consideration of other criteria such as lead time and criticality. This paper proposes the use of the Analytical Hierarchy Process (AHP) to reduce these multiple criteria to a univariate and consistent measure to consider multiple inventory management objectives.

Enterprise risk management: coping with model risk in a large bank

Enterprise risk management (ERM) has become an important topic in todays more complex, interrelated global business environment, replete with threats from natural, political, economic, and technical sources. Banks especially face financial risks, as the news makes ever more apparent in 2008. This paper demonstrates support to risk management through validation of predictive scorecards for a large bank. The bank developed a model to assess account creditworthiness. The model is validated and compared to credit bureau scores. Alternative methods of risk measurement are compared.

Comparative analysis of data mining methods for bankruptcy prediction

A great deal of research has been devoted to prediction of bankruptcy, to include application of data mining. Neural networks, support vector machines, and other algorithms often fit data well, but because of lack of comprehensibility, they are considered black box technologies. Conversely, decision trees are more comprehensible by human users. However, sometimes far too many rules result in another form of incomprehensibility. The number of rules obtained from decision tree algorithms can be controlled to some degree through setting different minimum support levels. This study applies a variety of data mining tools to bankruptcy data, with the purpose of comparing accuracy and number of rules. For this data, decision trees were found to be relatively more accurate compared to neural networks and support vector machines, but there were more rule nodes than desired. Adjustment of minimum support yielded more tractable rule sets.