Ingredients of Locational Analysis (J. Krarup & P. Pruzan). The p-Median Problem and Generalizations (P. Mirchandani). The Uncapacitated Facility Location Problem (G. Cornuejols, et al.). Multiperiod Capacitated Location Models (S. Jacobsen). Decomposition Methods for Facility Location Problems (T. Magnanti & R. Wong). Covering Problems (A. Kolen & A. Tamir). p-Center Problems (G. Handler). Duality: Covering and Constraining p-Center Problems on Trees (B. Tansel, et al.). Locations with Spatial Interactions: The Quadratic Assignment Problem (R. Burkard). Locations with Spatial Interactions: Competitive Locations and Games (S. Hakimi). Equilibrium Analysis for Voting and Competitive Location Problems (P. Hansen, et al.). Location of Mobile Units in a Stochastic Environment (O. Berman, et al.). Index.

https://link.springer.com/content/pdf/10.1057%2Fjors.1991.208.pdf

Discrete location theory

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Operations research: applications and algorithms / Wayne L. Winston

A new machine learning technique for an accurate diagnosis of coronary artery disease

The Maclaurin symmetric mean (MSM) operator is a classical mean type aggregation operator used in modern information fusion theory, which is suitable to aggregate numerical values. The prominent characteristic of the MSM operator is that it can capture the interrelationship among the multi‐input arguments. In this paper, we extend the MSM operator and dual MSM operator to q‐rung orthopair fuzzy sets to propose the q‐rung orthopair fuzzy MSM operator, q‐rung orthopair fuzzy dual MSM operator, q‐rung orthopair fuzzy weighted MSM operator, and q‐rung orthopair fuzzy weighted dual MSM operator. Then, some desirable properties and special cases of these operators are discussed in detail. Finally, a numerical example is provided to illustrate the feasibility of the proposed methods and deliver the sensitivity analysis and comparative analysis.

Some q-rung orthopair fuzzy maclaurin symmetric mean operators and their applications to potential evaluation of emerging technology commercialization

Introduction. Fundamentals of Fuzzy Set Theory. Fuzzy Linear Programming. Fuzzy Nonlinear Programming. Interactive Multiobjective Linear Programming with Fuzzy Parameters. Interactive Multiobjective Nonlinear Programming with Fuzzy Parameters. Interactive Computer Programs. Some Applications. Further Research Directions. Index.

Fuzzy sets and interactive multiobjective optimization

We introduce the concept of possibility fuzzy soft set and its operation and study some of its properties. We give applications of this theory in solving a decision-making problem. We also introduce a similarity measure of two possibility fuzzy soft sets and discuss their application in a medical diagnosis problem.

/pdf/possibility-fuzzy-soft-set-1hhxouvetp.pdf

Possibility Fuzzy Soft Set

In 1999 Molodtsov introduced the concept of soft set theory as a general mathematical tool for dealing with uncertainty. The solutions of such problems involve the use of mathematical principles based on uncertainty and imprecision. In this paper we recall the definition of a soft set, its properties and its operations. As a generalization of Molodtsov’s soft set we introduce the definitions of a soft multiset, its basic operations such as complement, union and intersection. We give examples for these concepts. Basic properties of the operations are also given.

/pdf/soft-multisets-theory-19xjohbwiw.pdf

Soft Multisets Theory

T-spherical fuzzy set (T-SPFS) is a generalization of several fuzzy concepts such as fuzzy set (FS), intuitionistic FS, picture FS, Pythagorean FS, and q-rung orthopair FS. T-SPFS is a more powerful mathematical tool to handle uncertain, inconsistent, and vague information than the above-defined sets. In this paper, some limitations in the operational laws for SPF numbers (SPFNs) are discussed and some novel operational laws for SPFNs are proposed. Furthermore, two new aggregation operators for aggregating SPF information are proposed and are applied to multiple-attribute group decision-making (MAGDM). To take the advantages of Muirhead mean (MM) operator and power average operator, the SPF power MM (SPFPMM) operator, weighted SPFPMM operator, SPF power dual MM (SPFPDMM) operator, weighted SPFPDMM operator are introduced and their anticipated properties are discussed. The main advantage of these developed aggregation operators is that they take the relationship among fused data and the interrelationship among aggregated values, thereby getting more information in the process of MAGDM. Moreover, a novel approach to MAGDM based on the developed aggregation operators is established. Finally, a numerical example is given to show the effectiveness of the developed approach and comparison with the existing approaches is also given.

/pdf/t-spherical-fuzzy-power-muirhead-mean-operator-based-on-1fgeid05i0.pdf

T-Spherical Fuzzy Power Muirhead Mean Operator Based on Novel Operational Laws and Their Application in Multi-Attribute Group Decision Making

Expressing the measure of uncertainty, in terms of an interval instead of a crisp number, provides improved results in fuzzy mathematics. Several such concepts are established, including the interval-valued fuzzy set, the interval-valued intuitionistic fuzzy set, and the interval-valued picture fuzzy set. The goal of this article is to enhance the T-spherical fuzzy set (TSFS) by introducing the interval-valued TSFS (IVTSFS), which describes the uncertainty measure in terms of the membership, abstinence, non-membership, and the refusal degree. The novelty of the IVTSFS over the pre-existing fuzzy structures is analyzed. The basic operations are proposed for IVTSFSs and their properties are investigated. Two aggregation operators for IVTSFSs are developed, including weighted averaging and weighted geometric operators, and their validity is examined using the induction method. Several consequences of new operators, along with their comparative studies, are elaborated. A multi-attribute decision-making method in the context of IVTSFSs is developed, followed by a brief numerical example where the selection of the best policy, among a list of investment policies of a multinational company, is to be evaluated. The advantages of using the framework of IVTSFSs are described theoretically and numerically, hence showing the limitations of pre-existing aggregation operators.

https://www.mdpi.com/2073-8994/11/3/357/pdf

Evaluation of Investment Policy Based on Multi-Attribute Decision-Making Using Interval Valued T-Spherical Fuzzy Aggregation Operators

In this work, we introduce the concept of fuzzy parameterized intervalvalued fuzzy soft set theory (fpivfss) and study their operations. We then define fpivfss-aggregation operator to form fpivfss-decision making method that allows constructing more efficient decision processes. Finally, some numerical examples are employed to substantiate the conceptual arguments.

/pdf/fuzzy-parameterized-interval-valued-fuzzy-soft-set-1bt3m8dihr.pdf

Nasruddin Hassan

Papers

Possibility Fuzzy Soft Set

Soft Multisets Theory

T-Spherical Fuzzy Power Muirhead Mean Operator Based on Novel Operational Laws and Their Application in Multi-Attribute Group Decision Making

Evaluation of Investment Policy Based on Multi-Attribute Decision-Making Using Interval Valued T-Spherical Fuzzy Aggregation Operators

Fuzzy Parameterized Interval-Valued Fuzzy Soft Set