On fuzzy multiple objective linear programming problems

Chung, Cheng-Kung; Chen, Hsin Min; Chang, Ching-Ter; Huang, Hau-Lieng

doi:10.1016/j.eswa.2018.08.006

Cited by 11 publications

(7 citation statements)

References 30 publications

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“…In order to eliminate the fuzzy TOPSIS method based on FWA left and right score methods, drawback and achieve the accurate results, we are comparing two GP to verify result of the AGHSEVS LP model. Otherwise, If DMs use the new fuzzy TOPSIS method [37], the conjunction GP approach [38] can be a different result.…”

Section: Limitationsmentioning

confidence: 99%

Multi-Criteria Decision-Making Techniques for Solving the Airport Ground Handling Service Equipment Vendor Selection Problem

Shen

Peng

2019

Sustainability

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Appropriate airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. Previous research has seldom integrated multi-criteria decision-making techniques with goal programming to solve the AGHSEVS problem. This paper describes a new system evaluation model for AGHSEVS by considering both qualitative and quantitative methods. We compare the fuzzy TOPSIS method based on fuzzy weighted average left and right score methods with multi-choice and multi-aspiration goal programming approach of an AGHS company in Taiwan. These study results can help airport ground handling service company managers make optimal decisions for AGHSEVS problems. We hope the practicability of the comparable model with slight modifications of real situation data can be used in other AGHS companies.

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Section: Limitationsmentioning

confidence: 99%

Multi-Criteria Decision-Making Techniques for Solving the Airport Ground Handling Service Equipment Vendor Selection Problem

Shen

Peng

2019

Sustainability

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“…Taking into account the randomization of relevant parameters in the economic dispatch of a micro-grid, Shuai et al [8] offered a new approximate dynamic programming method to operate the micro-grid under these uncertainties, and showed its good performance in dealing with the historical forecast data to minimize the detrimental impacts of inexact prediction on the operating system with numerical analysis. In order to simplify a model's establishment in an expert system or a knowledge-based system, Chung et al [9] put forward a new fuzzy multiple choice goal programming model to resolve a kind of linear MOP problem, and verified its effectiveness in reducing computational complexity during the solutions and practicability in providing satisfactory solutions. Hamzehee et al [10] introduced a set of MOP problems under the rough environments and further categorized them into five types depending on the location of roughness in the decision set or the objectives.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Rough Programming Models: An Expected Value Perspective

et al. 2022

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Usually, the quasi-normal fluctuations in practical applications are described via symmetric uncertainty variables, which is a common phenomenon in the manufacturing industry. However, it is relatively scarce in the literature to discuss two-fold uncertainty due to the its complexity. To deal with roughness and ambiguity to accommodate inherent uncertainties, fuzzy rough programming approaches are put forward. In this paper, we pay attention to exploring two kinds of programming problems, namely fuzzy rough single-objective programming and fuzzy rough multi-objective programming, in which objective functions and/or constraints involve fuzzy rough variables (FRV). In accordance with the related existing research of FRVs, such as the chance measure and the expected value (EV) operator, this paper further discusses the EV model, convexity theory, and the crisp equivalent model of fuzzy rough programming. After that, combined with the latest published NIA-S fuzzy simulation technique, a new fuzzy rough simulation algorithm is developed to calculate the EVs of complicated functions for handling the presented fuzzy rough programming problems. In the end, the two types of numerical examples are provided for demonstration.

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“…Explicitly, the two most fundamental goals that need to be considered are maximization of finding the targets and minimizing the path length objective that can ensure minimum utilization of resources and implicitly ensure less operational time and energy consumption. It is challenging to find the ideal solution due to the conflicting nature of objective functions; therefore, researchers have proposed different approaches such as weighted sum [51], global criterion [52], goal programming [53], multi-choice goal programming [54], non-dominated sorting genetic algorithm II [55], fuzzy-two phase approach [56], etc., and the issue of a specific method largely depends on the decision-makers. Note that UAV path planning is itself an NPhard problem [57]; thus, we use a simple weighted sum approach in this study.…”

Section: Introductionmentioning

confidence: 99%

UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities

et al. 2021

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Unmanned aerial vehicles (UAVs) have been used extensively for search and rescue operations, surveillance, disaster monitoring, attacking terrorists, etc. due to their growing advantages of low-cost, high maneuverability, and easy deployability. This study proposes a mixed-integer programming model under a multi-objective optimization framework to design trajectories that enable a set of UAVs to execute surveillance tasks. The first objective maximizes the cumulative probability of target detection to aim for mission planning success. The second objective ensures minimization of cumulative path length to provide a higher resource utilization goal. A two-step variable neighborhood search (VNS) algorithm is offered, which addresses the combinatorial optimization issue for determining the near-optimal sequence for cell visiting to reach the target. Numerical experiments and simulation results are evaluated in numerous benchmark instances. Results demonstrate that the proposed approach can favorably support practical deployability purposes.

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On fuzzy multiple objective linear programming problems

Cited by 11 publications

References 30 publications

Multi-Criteria Decision-Making Techniques for Solving the Airport Ground Handling Service Equipment Vendor Selection Problem

Multi-Criteria Decision-Making Techniques for Solving the Airport Ground Handling Service Equipment Vendor Selection Problem

Fuzzy Rough Programming Models: An Expected Value Perspective

UAVs Path Planning under a Bi-Objective Optimization Framework for Smart Cities

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