Decision making using modified s-curve membership function in fuzzy linear programming problem

In order to develop approaches to solve a fuzzy linear programming problem, it is necessary to study first the formulation of membership functions and then the methodology for applying the solution to real life problems. A S-curve membership function is proposed in this paper. It is important to note that the S-curve membership function has to be flexible to describe the fuzziness in the problem. Fuzziness may occur in several levels of an industrial production management such as manpower requirements, resource availability such as software and the demand to be met. In order to show that the S-curve membership function works well for fuzzy problems, a numerical example is demonstrated. A thorough study on how the non linear membership function used in dealing with fuzzy parameters and fuzzy constraints is also presented. Only one case where all three coefficients (such as objective coefficients, technical coefficients and resource variables) that normally occur in production planning problem, are considered and fuzzified. However, there are several other cases. The result obtained from this paper is to provide confidence in using the proposed S-curve membership function in a real life production planning industrial problem

Conventional and intelligent models for detection and prediction of fluid loss events during drilling operations: a comprehensive review

Fluid loss to subsurface formations is a challenging aspect during drilling operations in petroleum industry. Several other drilling issues such as fluid influx and pipe sticking can be triggered in such scenarios, posturing a significant risk to rig personnel, environment, and economical drilling. Therefore, prediction and early detection of lost circulation events are required for safe and economic drilling operation. Several theoretical studies have been performed to detect and predict fluid loss event during hydrocarbon extraction. This paper reviews the existing conventional and intelligent models developed for early detection and prediction of lost circulation events. These predictive and detecting models comprise of Artificial Intelligence (AI) algorithms that require improvements for data reduction, universal prediction and compatibility. The review also covers several sensor-based techniques, different geostatistical-based models and Pressure-While-Drilling (PWD) tools for their applications in early loss circulation detection. In addition, loss circulation zones types, severity level, scenario and common preventive measures are also included in this review. This study aims to provide a systematic review of the published literature from the last forty years on the developed conventional and intelligent models for detection and prediction of fluid loss events and emphasizes on increasing AI involvement for precise results

Hybrid Optimization Techniques for Industrial Production Planning : Ph. D. Thesis, Dec 2008

In this Ph. D thesis, the main significant contributions are: formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in the industrial production planning problems. This non-linear membership function is flexible and convenience to the decision makers in their decision making process. Secondly, a nonlinear objective function in the form of cubic function for fuzzy optimization problems is successfully solved by 15 hybrid and non-hybrid optimization techniques from the area of soft computing and classical approaches. Among the 15 techniques, three outstanding techniques are selected based on the percentage of quality solution. An intelligent performance analysis table is tabulated to the convenience of decision makers and implementers to select the niche optimization techniques to apply in real word problem solving approach particularly related to industrial engineering problems.Facultad de Informátic

Hybrid Optimization Techniques for Industrial Production Planning : Ph. D. Thesis, Dec 2008

In this Ph. D thesis, the main significant contributions are: formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints in the industrial production planning problems. This non-linear membership function is flexible and convenience to the decision makers in their decision making process. Secondly, a nonlinear objective function in the form of cubic function for fuzzy optimization problems is successfully solved by 15 hybrid and non-hybrid optimization techniques from the area of soft computing and classical approaches. Among the 15 techniques, three outstanding techniques are selected based on the percentage of quality solution. An intelligent performance analysis table is tabulated to the convenience of decision makers and implementers to select the niche optimization techniques to apply in real word problem solving approach particularly related to industrial engineering problems.Facultad de Informátic

FUZZY LINEAR PROGRAMMING : A MODERN TOOL FOR DECISION MAKING

The modem trend in industrial application problem deserves modeling of all relevant vaque or fuzzy information involved in a real decision making problem. In the first part of the paper, some explanations on tripartite fuzzy linear programming approach and its importance have been given. In the second part, the usefulness of the proposed S curve membership function is established using a real life industrial production planning of a chocolate manufacturing unit The unit produces 8 product using 8 raw materials, mixed in various proportions by 9 different processes under 29 constraint. A solution to this problem establishes the usefulness of the suggested membership function for decision making in industrial production planning

FUZZY LINEAR PROGRAMMING FOR DECISION MAKING AND PLANNING UNDER UNCERTAINTY

In this paper, the S-curve membership function methodology is used in a real life industrial problem of mix product selection. This problem occurs in the chocolate manufacturing industry whereby a decision maker, analyst and implementer play important roles in making decisions in an uncertain environment. As analysts, we try to find a solution with a higher level of satisfaction for the decision maker to make a final decision. This problem of mix product selection is considered because all the coefficients such as objective, technical and resource variables are fuzzy. This is considered as one of the sufficiently large problem involving 29 constraints and 8 variables. A decision maker can identify which vagueness (α) is suitable for achieving satisfactory optimal revenue. The decision maker can also suggest to the analyst some possible and practicable changes in fuzzy intervals for improving the satisfactory revenue. This interactive process has to go on among the analyst, the decision maker and the implementer until an optimum satisfactory solution is achieved and implemented.S-curve membership function, vagueness, degree of satisfaction, decision-making

Possibilistic optimization in planning decision of construction industry

This paper proposes a new method to obtain optimal solution using satisfactory approach in uncertain environment. The optimal solution is obtained by using possibilistic linear programming approach and MATLAB® computation. The possibilistic linear programming approach uses modified S-curve membership function (MF). The developed model is applied to a construction industry's problem adopted from Lazarevic and Abraham [2003. Hybrid fuzzy-linear programming approach for multicriteria decision making problems. International Journal of Neural, Parallel and Scientific Computations, 11, 53–68]. Profit function, index of work quality and worker satisfaction index of the construction industry are considered for the optimal solution. Decision maker (DM) and implementer visualize final possibilistic and realistic outcome for objective functions under disparate level-of-satisfaction of the DM and vagueness hidden in the decision made for such planning decision

Detection of level of satisfaction and fuzziness patterns for MCDM model with modified flexible S-curve MF

The present research work deals with a logistic membership function (MF), within non-linear MFs, in finding out fuzziness patterns in disparate level of satisfaction for Multiple Criteria Decision-Making (MCDM) problem. This MF is a modified form of general set of S-curve MF. Flexibility of this MF in applying to real world problem has also been validated through a detailed analysis. An example illustrating an MCDM model applied in an industrial engineering problem has been considered to demonstrate the veracity of the proposed technique. The approach presented here provides feedback to the decision maker, implementer and analyst and gives a clear indication about the appropriate application and usefulness of the MCDM model. The key objective of this paper is to guide decision makers in finding out the best candidate-alternative with higher degree of satisfaction with lesser degree of vagueness under tripartite fuzzy environment