Serial correlation estimation through the imprecise Goal Programming model

Kharrat, Aïda; Chabchoub, Habib; Aouni, Belaı̈d; Smaoui, Soulef

doi:10.1016/j.ejor.2005.10.004

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…(e.g. [Ijiri, 1965, Lee and Clayton, 1972, Lee, 1973, Ignizio, 1976, Lee et al, 1981, Goicoechea et al, 1982, White, 1982, Lee and Olson, 1985, Sawaragi et al, 1985, Romero, 1991, Martel and Aouni, 1998, Lee and Olson, 1999, Aouni et al, 2005, El-Wahed and Lee, 2006, Kharrat et al, 2007, Aouni et al, 2014). More information about the history of GP and its applications can be found in [Jones and Tamiz, 2002, Caballero et al, 2009, Jones and Tamiz, 2010, Aouni et al, 2014, Colapinto et al, 2015.…”

Section: Goal Programming Under Uncertainty 241 General Formulationmentioning

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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Section: Goal Programming Under Uncertainty 241 General Formulationmentioning

confidence: 99%

Multi-objective optimisation under deep uncertainty

Shavazipour

Stewart

2019

Oper Res Int J

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“…Meanwhile, goal programming as a MODM method is used for minimizing the deviation between the achievements and targets, which has been proved effectively in many fields. 43–45…”

Section: Procedures For Solving the Mwpp Problemmentioning

confidence: 99%

A hybrid approach for multi-weapon production planning with large-dimensional multi-objective in defense manufacturing

Jiang

Yang

Yue-jin

2013

Proceedings of the Institution of Mechanical Engineers, Part B:

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Multi-weapon production planning contains multi-objective combinatorial optimization and decision-making problems with the NP-hard and large-dimensional natures, which are difficult to be attacked by one single technique successfully. A four-stage hybrid approach is proposed to solve this problem. In the first stage, the multi-weapon production planning problem is formulated with 2 N ( N > 5) objectives based on operational capability requirements and expected downside risk measure. In the second stage, the formulation addressed is converted into a bi-objective optimization model using goal programming. In the third stage, an algorithm DENS based on differential evolution and nondominated sorting genetic algorithm–II is developed to obtain the Pareto set. Finally, the multiple attribute decision-making method technique for order preference by similarity to ideal solution is employed to acquire the compromise solution from the Pareto set. A case study is given to demonstrate the effectiveness of the proposed approach. The concrete advantages of goal programming, DENS, and technique for order preference by similarity to ideal solution are also validated in this case. This approach can support the weapon production planning in defense manufacturing and is also applicable to solve the multi-level and multi-objective problem in other manufacturing fields.

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“…In recent years, gray correlation algorithm obtained a significant development, and many scholars have made great contributions [3][4][5][6][7][8]. From the relational degree itself, it experienced from the gray relational algorithm of no differential measurement information (such as Tang's correlation, the absolute correlation Ⅱ , the relative correlation, correlation interval Ⅰ, range correlation Ⅱ) to gray relational algorithm with first-order differential metrical information (such as absolute relational degree Ⅰ, slope correlation, and T-type correlation), and then turned to be the gray relational algorithm with secondorder differential metrical information (B-type correlation, C-type correlation).…”

Section: A Correlation Algorithm Reviewmentioning

confidence: 99%

Fractional Order Correlation Algorithm's Accuracy Analysis

Liu¹,

Hou²

2013

JSW

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In order to improve the correlation accuracy of correlation algorithm, the fractional order correlation algorithm of uncertain time sequences is proposed in this paper. By taking advantage of the memory property of fractional order, the algorithm introduces the measurement of fractional order differential for the local trend of time sequence into the correlation algorithm and also analyzes the influences of differential order and noise upon correlation accuracy, provides selection relations between noise level and order. It has been proven with examples that the correlation accuracy of fractional order correlation algorithm has increased by two orders of magnitude as compared with relative and C-type correlation, and increased by one orders of magnitude as compared with slope and T-type correlation.

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Serial correlation estimation through the imprecise Goal Programming model

Cited by 8 publications

References 10 publications

Multi-objective optimisation under deep uncertainty

Multi-objective optimisation under deep uncertainty

A hybrid approach for multi-weapon production planning with large-dimensional multi-objective in defense manufacturing

Fractional Order Correlation Algorithm's Accuracy Analysis

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