Preference aggregation with multiple criteria of ordinal significance

In this paper we address the problem of aggregating outranking situations in the presence of multiple preference criteria of ordinal significance. The concept of ordinal concordance of the global outranking relation is defined and an operational test for its presence is developed. Finally, we propose a new kind of robustness analysis for global outranking relations taking into account classical dominance, ordinal and classical majority oncordance in a same bipolar-valued logical framewor

Electre Methods: Main Features and Recent Developments

We present main characteristics of Electre family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation in the set of actions - it is constructed in result of concordance and non-discordance tests involving a specific input preference information. After a brief description of the constructivist conception in which the Electre methods are inserted, we present the main features of these methods. We discuss such characteristic features as: the possibility of taking into account positive and negative reasons in the modeling of preferences, without any need for recoding the data; using of thresholds for taking into account the imperfect knowledge of data; the absence of systematic compensation between "gains" and "losses". The main weaknesses are also presented. Then, some aspects related to new developments are outlined. These are related to some new methodological developments, new procedures, axiomatic analysis, software tools, and several other aspects. The paper ends with conclusions

Algorithmic Decision Theory for solving complex decision problems

Today's decision makers in fields ranging from engineering to psychology, from medicine to economics and/or homeland security are faced with remarkable new technologies, huge amounts of information to help them in reaching good decisions, and the ability to share information at unprecedented speeds and quantities. These tools and resources should lead to better decisions. Yet, the tools bring with them daunting new problems: the massive amounts of data available are often incomplete, unreliable and/or distributed and there is great uncertainty in them; interoperating/distributed decision makers and decision making devices need to be coordinated; many sources of data need to be fused into a good decision; information sharing under new cooperation/competition arrangements raises security problems. When faced with such issues, there are few highly efficient algorithms available to support decision makers. The objective of Algorithmic Decision Theory (ADT) is to improve the ability of decision makers to perform well when facing these new challenges and problems through the use of methods from theoretical computer science, in particular algorithmic methods. The primary goal of ADT is hence to explore and develop algorithmic approaches for solving decision problems arising in a variety of applications areas. Examples include, but are not limited to: - Computational tractability/intractability of social consensus and multiple criteria compromise functions; - Improvement of decision support and recommender systems; - Development of automatic decision devices including on-line decision procedures; - Robust decision making; - Learning for multi-agent systems and other on-line decision devices. This presentation will focus more specifically on multiple criteria decision aiding methodology, the actual research field of the author

Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters

Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP into multiple single-optimization sub-problems, achieving better diversity and a better approximation of the Pareto front, and dealing with some of the challenges of MaOPs. However, these approaches still require one to solve a multi-criteria selection problem that will allow a Decision-Maker (DM) to choose the final solution. Incorporating preferences may provide results that are closer to the region of interest of a DM. Most of the proposals to integrate preferences in decomposition-based MOEAs prefer progressive articulation over the “a priori” incorporation of preferences. Progressive articulation methods can hardly work without comparable and transitive preferences, and they can significantly increase the cognitive effort required of a DM. On the other hand, the “a priori” strategies do not demand transitive judgements from the DM but require a direct parameter elicitation that usually is subject to imprecision. Outranking approaches have properties that allow them to suitably handle non-transitive preferences, veto conditions, and incomparability, which are typical characteristics of many real DMs. This paper explores how to incorporate DM preferences into MOEA/D using the “a priori” incorporation of preferences, based on interval outranking relations, to handle imprecision when preference parameters are elicited. Several experiments make it possible to analyze the proposal's performance on benchmark problems and to compare the results with the classic MOEA/D without preference incorporation and with a recent, state-of-the-art preference-based decomposition algorithm. In many instances, our results are closer to the Region of Interest, particularly when the number of objectives increases

R UBIS: A bipolar-valued outranking method for the choice problem

International audienceThe main concern of this article is to present the RUBIS method for tackling the choice problem in the context of multiple criteria decision aiding. Its genuine purpose is to help a decision maker to determine a single best decision alternative. Methodologically we focus on pairwise comparisons of these alternatives which lead to the concept of bipolar-valued outranking digraph. The work is centred around a set of five pragmatic principles which are required in the context of a progressive decision aiding methodology. Their thorough study and implementation in the outranking digraph lead us to define a choice recommendation as an extension of the classical digraph kernel concept

A comparison between TOPSIS and SAW methods

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) are among the most employed approaches for aggregating performances in Multi-Criteria Decision-Making (MCDM). TOPSIS and SAW are two MCDM methods based on the value function approach and are often used in combination with other MCDM methods in order to produce rankings of alternatives. In this paper, first, we analyse some common features of these two MCDM methods with a specific reference to the additive properties of the value function and to the sensitivity of the value function to trade-off weights. Based on such methodological insights, an experimental comparison of the results provided by these two aggregation methods across a computational test is performed. Specifically, similarities in rankings of alternatives produced by TOPSIS and SAW are evaluated under three different Minkowski distances (namely, the Euclidean, Manhattan and Tchebichev ones). Similarities are measured trough a set of statistical indices. Results show that TOPSIS, when used in combination with a Manhattan distance, produces rankings which are extremely similar to the ones resulting from SAW. Similarities are also Experimental results confirm that rankings produced by TOPSIS methods are closer to SAW ones when similar formal properties are satisfied

공급네트워크 복원력에 대한 통합 모델: 역량, 교환 관계 및 네트워크 속성

학위논문 (박사)-- 서울대학교 대학원 : 경영대학 경영학과, 2018. 2. 박상욱.The supply chain management (SCM) activities and its performance become vulnerable due to sudden disruptive events in the business process. Specifically, among three phases (sense, respond, recover) supply chain (SC) experience under disruption, we are interested in post-event recovery activities. For example, after the supply disruption, firms must transfer equipment and switch production to alternative or new suppliers utilizing network capability and flexibility. Such recovery activities are termed as resilience activities or a term, SC resilience. The primary objective of this thesis is to thoroughly investigate all the important attributes related to SC resilience, and to propose a comprehensive scheme to show the level of resilience among multiple firms from a network perspective. This thesis considers three problems in a sequential manner so that the critical issues fostering SC resilience can be practically resolved: (1) to determine the critical attributes for SC resilience(2) to present a network-based structure for managing the levels of resilienceand (3) to propose comprehensive network resilience model for both deterministic and probabilistic situations. This thesis first elicits important resilience attributes, among which a number of determinant attributes are critical for supply chain sustainability. The resilience capabilities introduced in the existing literature are systematically investigated and classified, based on a value hierarchy. A survey study is then conducted in order to validate the important exchange relationship attributes and supply chain capabilities. Second, a graphical representation is proposed to visualize the resilience relationship in a network formation. A node here represents a partner firms resilience capability in the supply network and the network value consists of the positional value of the firm. We then adopt an outranking methodology, concordance discordance approach, to provide a process to identify the improvement priority order. Finally, a total network resilience model is proposed to handle resilience levels and interrelationships of the firms simultaneously. The proposed model is also extended to serve as a probabilistic model, along with a number of sensitivity studies, to improve its applicability. The study may contribute theoretically to the literature as follows: First, this thesis isolated four key determinant attributes of supply chain resilience through a comprehensive analysis of existing capabilities. The impact of the four attributes on resilience has been verified with a survey study. Second, the interrelationships of the firms have been expressed using leader-member exchange theory. Through the survey analysis, it was found that leader member exchange affects supply chain resilience significantly. Third, a bicriterion network resilience model using resilience and network value has been proposed, along with an ordering approach. The network representation visualizes not only all the levels of resilience of the firms but also their influences within the network structure. Fourth, a total network resilience (TNR) model is developed, through which one can handle both resilience and interrelations among the firms. The model is applicable to both deterministic and probabilistic cases. Investigating the impact of supply chain capabilities, exchange relationship, and network attributes on supply network resilience offers a fertile avenue for future research. From supply chain perspective, it is recommended that future studies explore the causal relationships among SC capabilities and SC resilience based on different phases of a disruption (i.e., pre-, during-, and post-disruption). One can also investigate the relational behavior based on divergence or crossvergence contexts for more comprehensive analysis. Another possible research direction is to utilize our proposed TNR model in considering triadic relationship and diverse network structural properties. With a further effort on elaboration, we believe that the research results may prove to be a solid basis for network based research in the area of supply chain management.1 INTRODUCTION 1 1.1 General background 1 1.2 Research objectives 3 2 PROBLEM STATEMENTS AND LITERATURE REVIEW 6 2.1 Problem statements 6 2.2 Literature review 7 2.2.1 SC capabilities driven SC resilience management 7 2.2.2 Network perspective integrated SC resilience management 8 2.2.3 Exchange relationship based comprehensive network resilience view 10 2.3 Research assumptions, terminologies, and notations 11 2.3.1 Assumptions 11 2.3.2 Terminologies 12 2.3.3 Mathematical notations 14 3 EXCHANGE RELATIONSHIP, SC CAPABILITIES AND RESILIENCE 15 3.1 Theoretical background and conceptual model 15 3.1.1 SC resilience and competitive advantage 15 3.1.2 SC capabilities related to SC resilience 17 3.1.3 Leader-Member exchange theory based SC management 21 3.2 Research design and methodologies 22 3.2.1 Study 1 – Interpretive structural modeling 22 3.2.2 Study 2 – Hypothesis development 30 3.3 Results and analyses 32 3.3.1 Survey design and data characteristics 32 3.3.2 Model reliability and validity 33 3.3.3 Structural effects 34 3.4 Discussion 35 3.4.1 Five partition levels of SC capabilities 35 3.4.2 Insignificant role of flexibility and agility 35 3.4.3 Significance role of LMX on SC capabilities 36 3.5 Conclusions, implications, and limitations 37 4 BICRITERION NETWORK RESILIENCE MODEL 39 4.1 Literature review 39 4.1.1 SC resilience from the perspective of networks 39 4.1.2 SC resilience studies by disruption phases 43 4.1.3 Social network theory based studies on network typologies 44 4.2 Methodology 45 4.2.1 SC resilience capabilities 46 4.2.2 Operationalization of resilience attributes 48 4.2.3 Operationalization of network attributes 49 4.3 Bicriterion network resilience (BNR) representation 50 4.3.1 Network representation (illustration) 50 4.3.2 Prioritization method: Concordance-discordance approach 52 4.4 A case example 56 4.4.1 Prioritization assessment 58 4.4.2 Interpretation 62 4.5 Conclusions, implications, and limitations 64 5 TOTAL NETWORK RESILIENCE MODEL 66 5.1 Literature review 66 5.1.1 Leader-member exchange theory and exchange relation theory 66 5.1.2 Relational studies in SN context 69 5.2 Development of total network resilience (TNR) model 72 5.2.1 Incorporation of SLMX into a network perspective 72 5.2.2 The Structure of Total Network Resilience Model 74 5.3 The TNR model – A probabilistic model 77 5.3.1 Conceptual framework 77 5.3.2 A TNR probabilistic model - An illustration case 78 5.3.3 Sensitivity analysis - SLMX 82 5.3.4 Sensitivity analysis - Network 88 5.4 Discussion 91 5.4.1 Bayesian modeling based approach 91 5.4.2 Critical path based approach 94 5.5 Conclusion, implication and limitations 98 6 CONLCLUSION 101 6.1 Theoretical implications 101 6.2 Managerial implications 102 6.3 Research limitation and future research 103 REFERENCES 107 APPENDIX 120 ABSTRACT IN KOREAN 122Docto

Hierarchical outranking methods for multi-criteria decision aiding

Els mètodes d’Ajut a la Decisió Multi-Criteri assisteixen en la pressa de decisions implicant múltiples criteris conflictius. Existeixen dos enfocaments principals per resoldre aquest tipus de problemes: els mètodes basats en utilitat i d’outranking, cadascun amb les seves fortaleses i debilitats. Els mètodes outranking estan basats en models d’elecció social combinats amb tècniques d’intel·ligència artificial (com gestió de dades categòriques o d’incertesa). Son eines per una avaluació i comparació realista d’alternatives, basant-se en les necessitats i coneixements del prenedor de la decisió. Una de les debilitats dels mètodes outranking és la no consideració de jerarquies de criteris, que permeten una organització natural del problema, distingint diferents nivells de generalitat que modelen les relacions taxonòmiques implícites entre criteris. En aquesta tesi ens enfoquem en el desenvolupament d’eines d’outranking jeràrquiques i la seva aplicació en casos d’estudi reals per problemes de classificació i rànquing.Los métodos de Ayuda a la Decisión Multi-Criterio asisten en la toma de decisiones involucrando múltiples criterios conflictivos. Existen dos enfoques principales para resolver éste tipo de problemas: los métodos basados en utilidad y de outranking, cada uno con sus fortalezas y debilidades. Los métodos outranking están basados en modelos de elección social combinados con técnicas de Inteligencia Artificial (como gestión de datos categóricos o de incertidumbre). Son herramientas para una evaluación y comparación realista de alternativas, basándose en las necesidades y conocimientos del tomador de decisión. Una de las debilidades de los métodos outranking es la no consideración de jerarquías de criterios, que permiten una organización natural del problema, distinguiendo distintos niveles de generalidad que modelan las relaciones taxonómicas implícitas entre criterios. En ésta tesis nos enfocamos en el desarrollo de herramientas de outranking jerárquicas y su aplicación en casos de estudio reales para problemas de clasificación y ranking.Multi-Criteria Decision Aiding (MCDA) methods support complex decision making involving multiple and conflictive criteria. MCDA distinguishes two main approaches to deal with this type of problems: utility-based and outranking methods, each with its own strengths and weaknesses. Outranking methods are based on social choice models combined with Artificial Intelligence techniques (such as the management of categorical data or uncertainty). They are recognized as providing tools for a realistic assessment and comparison of a set of alternatives, based on the decision maker’s knowledge and needs. One of the main weaknesses of the outranking methods is the lack of consideration of hierarchies of criteria, which enables the decision maker to naturally organize the problem, distinguishing different levels of generality that model the implicit taxonomical relations between the criteria. In this thesis we focus on developing hierarchical outranking tools and their application to real-world case studies for ranking and sorting problems

Decision support system for project monitoring portfolio

Vallejo Antich, RA. (2010). Decision support system for project monitoring portfolio. http://hdl.handle.net/10251/8632.Archivo delegad