Application of tolerance approach to fuzzy goal programming to aggregate production planning

Mékidiche, Mohammed; Mouslim, Hocine; Sahed, Abdelkader

doi:10.1504/ijmor.2013.052460

Cited by 9 publications

(3 citation statements)

References 52 publications

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“…The FGP also has a wide variety of applications in the literature such as supply chain management [Kumar et al, 2004, Torabi1 and Hassini, 2009, Tsai and Hung, 2009, waste management systems [Chang and Wang, 1997], water quality management [Lee and Wen, 1997], project networking [Arkan and Gngr, 2001], agricultural planning [Sinha et al, 1988, Biswas and Pal, 2005, forestry [Pickens and Hof, 1991], energy planning [Jinturkar and Deshmukh, 2011], engineering design in Quality Function Deployment (QFD) processes [Chen and Weng, 2006], portfolio selection [Watada, 1997, Parra et al, 2001, Wang and Zhu, 2002, Bilbao et al, 2007, Mansour et al, 2007, Azmi and Tamiz, 2010, Bilbao-Terol et al, 2012 and management , investment management Bhattacharjee, 2010, Gupta andBhattacharya, 2010], transportation problems [Chalam, 1994, El-Wahed and Lee, 2006, Pramanik and Roy, 2008, Giri et al, 2014, machine tool selection [Rai et al, 2002, Chan et al, 2005, Mishra et al, 2006, production planning [Chan and Swarnkar, 2006, Javadi et al, 2008, Belmokaddem et al, 2009, Jamalnia and Soukhakian, 2009, Kara et al, 2009,Özcan and Toklu, 2009, Mkidiche et al, 2013, nurse scheduling [Tamiz and Yaghoobi, 2010], demand coverage…”

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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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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“…Esto, con el objetivo de pergeñar estrategias direccionadas a satisfacer las demandas futuras, asumiendo el costo mínimo posible. (Mékidiche, Mouslim & Sahed, 2013;Noegraheni & Nuradli, 2016). De esta forma, se enlazan los objetivos estratégicos y sus directrices generales con los procesos internos, propiciando simultáneamente la vinculación sinérgica de las áreas funcionales que constituyen la empresa.…”

Section: Introductionunclassified

Análisis comparativo de modelos de planificación agregada. El caso de las empresas manufactureras colombianas

Rahmer

Saénz

Garzón

2022

RMCE

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La intención primaria del presente artículo es realizar un análisis comparativo entre estrategias de planeación de la capacidad de firmas pertenecientes a la industria manufacturera colombiana. Los planes erigidos son determinados a partir la aplicación de sofisticadas técnicas de programación matemática restringida. Se concluye, a partir de los resultados obtenidos, que la alternativa más fiable para resolver el problema de planificación agregada corresponde a aquella basada en el método de transporte, dado que permite satisfacer los requerimientos de la demanda sin transgredir las limitantes del sistema productivo y, por supuesto, las restricciones establecidas por la alta dirección. La implementación del plan permite anticipar decisiones que respectan a la programación de existencias en stock, la determinación del tamaño de fuerza laboral, así como también, en lo concerniente al dimensionamiento de las capas de inversión. La conjunción de todos estos factores será un determinantes clave para ajustar la tasa de respuesta de la empresa frente a un entorno mercantil naturalmente variante y especializado. El plan agregado, en última instancia, permite coordinar las operaciones en el orden táctico y tiene como objetivo ulterior, la proyección y suministro de los recursos necesarios para establecer un óptimo equilibrio entre la oferta y la demanda.

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“…An objective in conjunction with an aspiration level is termed a goal. An aspiration level is a specific value associated with an acceptable level of achievement of an objective (Mékidiche et al, 2013). Goal programming is a is a goal-oriented optimization method used to achieve several goals according to related constraints.…”

Section: Introductionmentioning

confidence: 99%

Application of fuzzy goal programming model to assess optimal multi crop cultivation planning

2022

ANRES

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Importance of the work: Planning for the optimal use of resources in agricultural systems considering uncertainty, with the objective of maximizing profit and production, will improve the social and economic conditions of farmers. Objectives: A rural farming area in Sri Lanka was used as a study site to apply the fuzzy goal programming (FGP) approach to identify the optimal cultivation plan and land resource allocation under uncertainty to optimize profit, production, labor, water use, fertilizer costs and land allocation. Materials & Methods:A tolerance-based FGP technique was used to quantify the fuzziness of different goals for the model. This study was carried out using 24 crops on a total land area under cultivation of 47.4 ha. These crops were categorized into three varieties: vegetable, fruit and other. Furthermore, the crops were classified into seven groups according to the required period of cultivation. Results: The proposed model suggested statistically significant increments of 11% and 10.6% for the net return and harvest amount, respectively, for the 24 crops compared to existing cultivation techniques. Main finding: The FGP multi-crop cultivation planning approach is a new application for the Sri Lankan rural farming community and it should be useful for agricultural planners, by allowing them to make more informed recommendations to the farming community. Crops that provide higher levels of production and profit than those currently being cultivated should be developed to extend cultivation under the supervision of agricultural experts or officers to obtain sustainable development of cultivation.

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Application of tolerance approach to fuzzy goal programming to aggregate production planning

Cited by 9 publications

References 52 publications

Multi-objective optimisation under deep uncertainty

Multi-objective optimisation under deep uncertainty

Análisis comparativo de modelos de planificación agregada. El caso de las empresas manufactureras colombianas

Application of fuzzy goal programming model to assess optimal multi crop cultivation planning

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