Fuzzy Efficient Interactive Goal Programming Approach for Multi-objective Transportation Problems

Singh, Pitam; Kumari, Saru; Singh, Pratibha

doi:10.1007/s40819-016-0155-x

Cited by 21 publications

(7 citation statements)

References 37 publications

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“…Overachievement of the DM goals subject to constraints is attained if d + t > 0, while underachievement means d − t > 0. When the deviations are driven to zero, the goals of the model are achieved [7,19]. A typical GP model for a min-type cost function is formulated as follows:…”

Section: The Goal Programming Methodsmentioning

confidence: 99%

“…The light robustness approach is structured in two stages to ensure a less conservative robust solution. The first stage considers a solution for the nominal problems (7) and (8). Subsequently, the constraints are relaxed for local violations that are absorbed by slack variables γ i , acting as measures of infeasibility for each constraint i ∈ M of the nominal problem.…”

Section: Light Robustnessmentioning

confidence: 99%

“…The RO has gained a great interest among academics and practitioners as an important concept in uncertain linear programming since the seminal paper of [5]. Other approaches in the literature include the stochastic approach and the fuzzy method (e.g., [6,7]). One of the drawbacks of this RO approach is that the solutions provided can be suboptimal if compared with the solution of the so-called nominal problem, i.e., a problem without uncertainty in which the parameter values are fixed (for instance, to some point estimation).…”

Section: Introductionmentioning

confidence: 99%

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Light Robust Goal Programming

Mensah

Rocca

2019

MCA

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Robust goal programming (RGP) is an emerging field of research in decision-making problems with multiple conflicting objectives and uncertain parameters. RGP combines robust optimization (RO) with variants of goal programming techniques to achieve stable and reliable goals for previously unspecified aspiration levels of the decision-maker. The RGP model proposed in Kuchta (2004) and recently advanced in Hanks, Weir, and Lunday (2017) uses classical robust methods. The drawback of these methods is that they can produce optimal values far from the optimal value of the “nominal” problem. As a proposal for overcoming the aforementioned drawback, we propose light RGP models generalized for the budget of uncertainty and ellipsoidal uncertainty sets in the framework discussed in Schöbel (2014) and compare them with the previous RGP models. Conclusions regarding the use of different uncertainty sets for the light RGP are made. Most importantly, we discuss that the total goal deviations of the decision-maker are very much dependent on the threshold set rather than the type of uncertainty set used.

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Section: The Goal Programming Methodsmentioning

confidence: 99%

Section: Light Robustnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Light Robust Goal Programming

Mensah

Rocca

2019

MCA

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“…The basic approach for GP is to set up a specific numeric goal, under-achievement and over-achievement, respectively, wherein achievement implies that the goal has been achieved. 50 A pre-emptive GP model of the PFGP problem (3) is formulated as follows:…”

Section: The Pre-emptive Goal Programming Approach To the Pfgp-moscn mentioning

confidence: 99%

A Fuzzy Goal Programming Approach for Solving Multi-Objective Supply Chain Network Problems with Pareto-Distributed Random Variables

Charles

Gupta

Ali

2019

Int. J. Unc. Fuzz. Knowl. Based Syst.

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Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model.

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“…Therefore, a multiobjective transportation problem (MOTP) became an important optimization technique. And several researches have been carried out on MOTP such as [ 12 , 22 – 27 ]. An IFS is expressed by a membership function and a non-membership function, and therefore, it a better tool than FS to deal the problem involving hesitation and uncertainty both.…”

Section: Introductionmentioning

confidence: 99%

Transportation problem with interval-valued intuitionistic fuzzy sets: impact of a new ranking

Bharati

2021

Prog Artif Intell

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Fuzzy Efficient Interactive Goal Programming Approach for Multi-objective Transportation Problems

Cited by 21 publications

References 37 publications

Light Robust Goal Programming

Light Robust Goal Programming

A Fuzzy Goal Programming Approach for Solving Multi-Objective Supply Chain Network Problems with Pareto-Distributed Random Variables

Transportation problem with interval-valued intuitionistic fuzzy sets: impact of a new ranking

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