A new approach to find optimal solution of fuzzy assignment problem using penalty method for hendecagonal fuzzy number

This article addresses the 3-dimensional mixed intuitionistic fuzzy assignment problems (3D-MIFAPs). In this article, firstly, the author formulates an assignment problem (AP) and assumes the parameters are in uncertainty with hesitation. Secondly, based on the nature of the parameter the author defines various types of solid assignment problem (SAP) in uncertain environment. Thirdly, to solve 3D-MIFAP the PSK method for finding an optimal solution of fully intuitionistic fuzzy assignment problem (FIFAP) is extended by the author. Fourthly, the author presents the proofs of the proposed theorems and corollary. Fifthly, the proposed approach is illustrated with three numerical examples and the optimal objective value of 3D-MIFAP is obtained in the form of intuitionistic fuzzy number and the solution is checked with MATLAB and their coding are also given by the author. Sixthly, the author presents the comparison results and their graphical representation, merits and demerits of the proposed and existing methods and finally the author presents conclusion and future research directions.

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Optimal Robust Fixture Configuration Design Using Computer Experiment

Manufacturing ◽

10.1115/imece2003-42628 ◽

2003 ◽

Author(s):

L. Shelley Xie ◽

Agus Sudjianto

Keyword(s):

Evaluation Method ◽

Robustness Analysis ◽

Optimal Solution ◽

Computer Experiment ◽

System Response ◽

New Approach ◽

New Information ◽

Reliability Method ◽

Fixture Configuration

A new FEA based design approach of optimal robust fixture configuration is proposed in this paper, which employs a surrogate model through computer experiment to significantly reduce the intensive computing effort involving numerous FEA system response evaluations. The effects of the fixture variability to the workpiece performance variability are assessed through an efficient robustness evaluation method, First Order Reliability Method (FORM), based on the surrogate computer model. Not restricted to primary datum surface, this new approach enables simultaneous determination of robust locator/clamp locations and clamping forces for a deformable workpiece and thus captures interaction between locating and clamping. The effectiveness of this approach is illustrated though an application example. The results of robustness analysis reveal new information and suggest that the optimal solution resulted from deterministic optimization may not be the best solution when the design is subjected to variability.

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An optimal solution to the "Philadelphia" channel assignment problem

IEEE Transactions on Vehicular Technology ◽

10.1109/25.765037 ◽

1999 ◽

Vol 48 (3) ◽

pp. 1012-1014 ◽

Cited By ~ 11

Author(s):

J.C.M. Janssen ◽

K. Kilakos

Keyword(s):

Channel Assignment ◽

Assignment Problem ◽

Optimal Solution ◽

Channel Assignment Problem

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Solving Solid Transportation Problems With Multi-Choice Cost and Stochastic Supply and Demand

Intelligent Transportation and Planning ◽

10.4018/978-1-5225-5210-9.ch007 ◽

2018 ◽

pp. 137-170 ◽

Cited By ~ 1

Author(s):

Sankar Kumar Roy ◽

Deshabrata Roy Mahapatra

Keyword(s):

Stochastic Programming ◽

Objective Function ◽

Transportation Problem ◽

Supply And Demand ◽

Optimal Solution ◽

Programming Approach ◽

Solid Transportation Problem ◽

New Approach ◽

Non Linear Programming ◽

The Cost

In this chapter, the authors propose a new approach to analyze the Solid Transportation Problem (STP). This new approach considers the multi-choice programming into the cost coefficients of objective function and stochastic programming, which is incorporated in three constraints, namely sources, destinations, and capacities constraints, followed by Cauchy's distribution for solid transportation problem. The multi-choice programming and stochastic programming are combined into a solid transportation problem, and this new problem is called Multi-Choice Stochastic Solid Transportation Problem (MCSSTP). The solution concepts behind the MCSSTP are based on a new transformation technique that will select an appropriate choice from a set of multi-choice, which optimize the objective function. The stochastic constraints of STP converts into deterministic constraints by stochastic programming approach. Finally, the authors construct a non-linear programming problem for MCSSTP, and by solving it, they derive an optimal solution of the specified problem. A realistic example on STP is considered to illustrate the methodology.

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