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This paper develops a fuzzy goal programming methodology for solving chance constrained programming problem involving fuzzy numbers and fuzzy random variables which follow standard normal distribution. In the model formulation process, the problem is converted into an equivalent fuzzy programming problem by applying chance constrained programming technique. Then the problem is divided into equivalent sub problems by considering the tolerance limit of the fuzzy numbers relating to the system constraint having different forms of membership functions in the decision making context. Afterwards the objective and the constraints resulting from chance constraints are converted into fuzzy goals by assigning some imprecise aspiration levels. In the decision process, a fuzzy goal programming methodology is introduced to find the most satisfactory solution in the decision making environment. To demonstrate the potentiality of the proposed approach, an illustrative example, studied previously, is solved and is compared with the existing methodologies. [ABSTRACT FROM AUTHOR]

This paper describes how the fuzzy goal programming (FGP) can be efficiently used for modelling and solving land allocation problems having chance constraints for optimal production of seasonal crops in agricultural system. In the proposed model, utilization of total cultivable land, different farming resources, achievement of the aspiration levels of production of seasonal crops are fuzzily described. The water supply as a productive resource and certain socio-economic constraints are described probabilistically in the decision making environment. In the solution process, achievement of the highest membership value (unity) of the membership goals defined for the fuzzy goals of the problem to the extent possible on the basis of the needs and desires of the decision maker (DM) is taken into account in the decision making horizon. The potential use of the approach is demonstrated by a case example of the Nadia District, West Bengal (W. B.), INDIA. [ABSTRACT FROM AUTHOR]

This article presents a fuzzy goal programming (FGP) procedure for modeling and solving multilevel programming (MLP) problems by using genetic algorithm (GA) in a large hierarchical decision making system. In the proposed approach, an GA scheme is introduced first for searching of solutions at different stages and thereby solving the problem and making decision in the order of hierarchy of execution of decision powers of the decision makers (DMs) located at different hierarchical levels. In the proposed GA scheme, Roulette-wheel selection scheme, single point crossover and random mutation are adopted to search a satisfactory solution in the hierarchical decision system. To illustrate the potential use of the approach, a numerical example is solved. [ABSTRACT FROM AUTHOR]

In the hi-tech industries the emerging bottleneck(s) will restrict the whole production line and influence the factories' capability to make money, especially in Taiwan where OEM is the prevail way to earn low margin. In order to solve the problem efficiently, TOC can provide on-going improvement to the work sites. This paper shows how to use goal programming to identify the bottlenecks and the sensitivity analysis is presented. The analysis is illustrated with an empirical case. [ABSTRACT FROM AUTHOR]

Aggregate Production Planning (APP) is a medium term Planning that links between the long term planning and the short range planning. Although there are many models developed by the researchers in this area, few of them are being implemented. This reason was the motive to develop a model in a different way than the traditional models used to be developed. The Integrated Definition level 0 (IDEF0) is used in this paper to represent the development of a typical APP model. The IDEF0 shows the model formulation using goal programming (GP) including identification of the model goals and model constraints, preparation of the model format, and finally solution of the model using a Genetic Algorithm (GA) approach. The IDEF0 as a structured methodology enhances the model development process, and make the relationships between the objective function and constraints crystal pure, which in consequences enhances the problem representation and solution procedure. [ABSTRACT FROM AUTHOR]

The benefits of Collaborative Manufacturing (CM) are widely recognized both by the industry and the academic world. However, the engagement of CM for Small and Medium Manufacturing Enterprises (SMMEs) has proven to be a challenging task due to reoccurring complex decision-making processes toward the attainment of strategic objectives for CM. The focus of this paper is thus to propose an Interactive Meta-Goal Programming (IMGP) based decision-support framework to facilitate the decision-making processes for CM. The proposed framework will have a critical positive impact on the operation of SMMEs engaging in CM, as the accuracy and efficiency of their collaborative decision-making processes will be significantly improved. [ABSTRACT FROM AUTHOR]