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This book presents a novel approach to the formulation and solution of three classes of problems: the fully fuzzy transportation problem, the fully fuzzy transshipment problem, and fully fuzzy solid transportation problem. It points out some limitations of the existing formulations and approaches, indicating some possible, conceptually and algorithmically attractive solutions to alleviate them. In particular, the book describes new conceptual and algorithmic solutions for finding the fuzzy optimal solutions of the single-objective fully fuzzy transportation problems, the fully fuzzy transshipment problems and the fully fuzzy solid transportation problems. Moreover, based on the novel concepts and solutions proposed by combining the concept of a fully fuzzy solid transportation problem and a fully fuzzy transshipment problem, it describes a new class of problems, i.e. the fully fuzzy solid trans-shipment problem, together with its fuzzy linear programming formulation and some methods to find its fuzzy optimal solution. The book offers the readers a timely piece of literature in the field of fuzzy linear programming, and is expected to act as a source of inspiration for future research and applications.
This book describes a set of methods for finding more-for-less solutions of various kind of fuzzy transportation problems. Inspired by more-for-less approaches to the basic transportation problem initiated by Abraham Charnes and his collaborators during 1960s and 1970s, this book describes new methods developed by the authors to solve different types of problems, including symmetric balanced fuzzy transportation problems, symmetric intuitionistic fuzzy transportation problems with mixed constraints, and symmetric intuitionistic fuzzy linear fractional transportation problems with mixed constraints. It offers extensive details on their applications to some representative problems, and discusses some future research directions
Transportation Problem is widely studied in Operations Research field and mainly used to simulate different real-life problems. In real-world transportation planning, decision problems, input data and related parameters, such as available supply and forecast demand, are often imprecise /fuzzy because some information is incomplete or unavailable. Also, the decision maker must simultaneously handle conflicting goals that govern the use of constrained resources within organizations.
George Dantzig is widely regarded as the founder of this subject with his invention of the simplex algorithm in the 1940's. In this second volume, the theory of the items discussed in the first volume is expanded to include such additional advanced topics as variants of the simplex method; interior point methods, GUB, decomposition, integer programming, and game theory. Graduate students in the fields of operations research, industrial engineering and applied mathematics will thus find this volume of particular interest.
The aim of this paper is to propose a method for finding an optimal solution for Neutrosophic Fuzzy Transportation Problem (TP). Here, a new ranking approach to solve fuzzy Neutrosophic transportation problems under the approach of zero suffix method together with the scalar division of fuzzy numbers is introduced. The solution of the given problem which satisfies the conditions of optimality and feasibility by proposing the method together with scalar division and triangular ranking technique.
In this contribution, for the first time, an effcient model of multi-objective product blending fixed-charge transportation problem with truck load constraints through transfer station is formulated.
It is frequently observed that most decision-making problems involve several objectives, and the aim of the decision makers is to find the best decision by fulfilling the aspiration levels of all the objectives. Multi-objective decision making is especially suitable for the design and planning steps and allows a decision maker to achieve the optimal or aspired goals by considering the various interactions of the given constraints. Multi-Objective Stochastic Programming in Fuzzy Environments discusses optimization problems with fuzzy random variables following several types of probability distributions and different types of fuzzy numbers with different defuzzification processes in probabilistic situations. The content within this publication examines such topics as waste management, agricultural systems, and fuzzy set theory. It is designed for academicians, researchers, and students.
This book presents the necessary and essential backgrounds of fuzzy set theory and linear programming, particularly a broad range of common Fuzzy Linear Programming (FLP) models and related, convenient solution techniques. These models and methods belong to three common classes of fuzzy linear programming, namely: (i) FLP problems in which all coefficients are fuzzy numbers, (ii) FLP problems in which the right-hand-side vectors and the decision variables are fuzzy numbers, and (iii) FLP problems in which the cost coefficients, the right-hand-side vectors and the decision variables are fuzzy numbers. The book essentially generalizes the well-known solution algorithms used in linear programming to the fuzzy environment. Accordingly, it can be used not only as a textbook, teaching material or reference book for undergraduate and graduate students in courses on applied mathematics, computer science, management science, industrial engineering, artificial intelligence, fuzzy information processes, and operations research, but can also serve as a reference book for researchers in these fields, especially those engaged in optimization and soft computing. For textbook purposes, it also includes simple and illustrative examples to help readers who are new to the field.
Monte Carlo Methods in Fuzzy Optimization is a clear and didactic book about Monte Carlo methods using random fuzzy numbers to obtain approximate solutions to fuzzy optimization problems. The book includes various solved problems such as fuzzy linear programming, fuzzy regression, fuzzy inventory control, fuzzy game theory, and fuzzy queuing theory. The book will appeal to engineers, researchers, and students in Fuzziness and applied mathematics.
From driverless cars to vehicular networks, recent technological advances are being employed to increase road safety and improve driver satisfaction. As with any newly developed technology, researchers must take care to address all concerns, limitations, and dangers before widespread public adoption. Intelligent Transportation and Planning: Breakthroughs in Research and Practice is an innovative reference source for the latest academic material on the applications, management, and planning of intelligent transportation systems. Highlighting a range of topics, such as automatic control, infrastructure systems, and system architecture, this publication is ideally designed for engineers, academics, professionals, and practitioners actively involved in the transportation planning sector.