A Fuzzy Goal Programming Formulation with Multiple Target Levels

Mouslim, Hocine; Belmokaddem, Mustapha; Benbouziane, Mohamed; Melloul, Sakina

doi:10.1002/mcda.1511

Cited by 7 publications

(2 citation statements)

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“…Iskander (2006) extended these works and proposed four formulations of the chance constrained programming method, to include exponential membership functions and preemptive priority structures. In addition, Mouslim et al (2013) presented a generalized multiple target level fuzzy goal programming formulation to allow for the introduction of all function forms. For instances where chance‐constrained goals are dependent, that is, the goals cannot be considered in isolation or converted to their deterministic equivalents, Baoding (1996) developed a general formulation of dependent‐chance goal programming and applied it to an example of water allocations and supply, a system in which there are multiple inputs and outputs with their own reliability levels.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic preemptive goal programming to balance goal achievements under uncertainty

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Hufstetler

Gallagher

2021

Multi Criteria Decision Anal

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Structured decision‐making in the presence of conflicting goals is difficult, and even more so when accounting for uncertainties in the goals or constraints. In this article, we propose a new approach to multi‐criteria decision‐making that extends the deterministic preemptive goal programming approach to account for such uncertainties. The uncertainties may be characterized in various ways including a Bayesian network or extensive Monte Carlo multi‐variate output. We contend the proposed stochastic preemptive goal programming approach is particularly applicable when better goal achievement increases uncertainty or induces risk to other goals. Resulting solutions tend to be a balance of the goals' achievements and robust to minor changes to the goals or constraints. We demonstrate the approach using three illustrative examples: a univariate example, a bivariate example, and a stock portfolio optimization example along with an application to determine military requests for absence. Our preliminary results suggest that a stochastic preemptive goal programming approach represents an effective means of analysing multi‐criteria decision‐making problems under uncertainty.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic preemptive goal programming to balance goal achievements under uncertainty

Ledwith

Hufstetler

Gallagher

2021

Multi Criteria Decision Anal

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“…Moreover, the FGP models have frequently been developed by many authors (e.g. [Rubin and Narasimhan, 1984, Yang et al, 1991, Gen et al, 1993, Wang and Fu, 1997, Liu and Iwamura, 1998, Kim and Whang, 1998, Mohammed, 2000, Ramik, 2000, Parra et al, 2001, Chen and Tsai, 2001, El-Wahed and Abo-Sinna, 2001, Rasmy et al, 2002, Lin, 2004, Xu, 2004, Saad, 2005, Akz and Petrovic, 2007, Chang, 2007, Hu et al, 2007, Pramanik and Roy, 2007, Yaghoobi and Tamiz, 2007, Yaghoobi et al, 2008, Arora and Gupta, 2009, Baky, 2009, Jana and Sharma, 2010, Kara et al, 2009, Khalili-Damghani et al, 2013, Mouslim et al, 2014, Chen, 1994). Aouni et al [2009] classified the different FGP formulations into four categories: (i) Lexicographic FGP, (ii) Weighted FGP, (iii) Fuzzy MINMAX Goal Programming and (iv) Interactive FGP.…”

Section: Fuzzy Goal Programming (Fgp)mentioning

confidence: 99%

Multi-objective optimisation under deep uncertainty

Shavazipour

Stewart

2019

Oper Res Int J

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Most of the decisions in real-life problems need to be made in the absence of complete knowledge about the consequences of the decision. Furthermore, in some of these problems, the probability and/or the number of different outcomes are also unknown (named deep uncertainty). Therefore, all the probability-based approaches (such as stochastic programming) are unable to address these problems. On the other hand, involving various stakeholders with different (possibly conflicting) criteria in the problems brings additional complexity.The main aim and primary motivation for writing this thesis have been to deal with deep uncertainty in Multi-Criteria Decision-Making (MCDM) problems, especially with long-term decision-making processes such as strategic planning problems.To achieve these aims, we first introduced a two-stage scenario-based structure for dealing with deep uncertainty in Multi-Objective Optimisation (MOO)/MCDM problems. The proposed method extends the concept of two-stage stochastic programming with recourse to address the capability of dealing with deep uncertainty through the use of scenario planning rather than statistical expectation. In this research, scenarios are used as a dimension of preference (a component of what we term the meta-criteria) to avoid problems relating to the assessment and use of probabilities under deep uncertainty. Such scenario-based thinking involved a multi-objective representation of performance under different future conditions as an alternative to expectation, which fitted naturally into the broader multi-objective problem context. To aggregate these objectives of the problem, the Generalised Goal Programming (GGP) approach is used. Due to the capability of this approach to handle large numbers of objective functions/criteria, the GGP is significantly useful in the proposed framework. Identifying the goals for each criterion is the only action that the Decision Maker (DM) needs to take without needing to investigate the trade-offs between different criteria.Moreover, the proposed two-stage framework has been expanded to a three-stage structure and a moving horizon concept to handle the existing deep uncertainty in more complex problems, such as strategic planning. As strategic planning problems will deal with more than two stages and real processes are continuous, it follows that more scenarios will continuously be unfolded that may or may not be periodic. "Stages", in this study, are artificial constructs to structure thinking of an indefinite future. A suitable length of the planning window and stages in the proposed methodology are also investigated.Philosophically, the proposed two-stage structure always plans and looks one step ahead while the three-stage structure considers the conditions and consequences of two upcoming steps in advance, which fits well with our primary objective. Ignoring longterm consequences of decisions as well as likely conditions could not be a robust strategic approach. Therefore, generally, by utilising the three-stage structure, we may e...

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