Fuzziness in analytic network process under interval numbers for criteria and alternatives

Because of the lack of data or knowledge or limited time, decision makers could not express their experiences exactly, perhaps they prefer interval numbers for such situations. Whenever uncertainty is involved in the decision making process, fuzzy and stochastic models would be arisen. Recently, fuzzy theory for Multiple Attribute Decision Making (MADM) under interval numbers has attracted a lot of researchers. This paper deals with a fuzzy MADM approach under interval numbers. We propose to apply the approach for Analytic Network Process (ANP) as a new class of decision making methods. The interval numbers are formed for criteria weights and values that have effect on alternatives’ values. The process of this method is clarified by an example

A genetic algorithm for capital budgeting problem with fuzzy parameters

When an organization utilizes modern technology in its manufacturing process, it needs to update and upgrade its facilities repetitively by efficient ways to stay with great productivity along with efficiency so. Capital Budgeting (CB) problem is one of the most important issues in decision makings about capital in the manufacturing management. Sometimes all variables and parameters are not necessarily deterministic and enough experiments are not available. Current study develops a chance constrained integer programming in the fuzzy environment for capital budgeting. Considering the complexity theory, a good answer could not be found in reasonable time, so that an intelligent Genetic Algorithm (GA) as a metaheuristic approach is provided to trace this problem with satisfying solutions. Thereupon, a fuzzy simulation-based genetic algorithm is provided for solving chance constrained integer programming model with fuzzy parameters

Improving equipment effectiveness by designing optimal production process

Reliable manufacturing equipment is an indispensable factor to the performance and profitability of manufacturing systems. Total productive maintenance (TPM) has been recognized as a comprehensive manufacturing strategy to maximize equipment reliability and effectiveness through rooting out all manufacturing losses. Availability of equipment is a focus area in TPM to improve effectiveness throughout the lifetime of the equipment. This study develops a mixed integer linear programming model to increase equipment availability considering maintenance cost of each machine in the system. The main objective is minimizing total cost while designing optimal material flows between different operational levels of manufacturing process. A hypothetical problem is presented and solved by the developed model

Production scheduling of reconfigurable manufacturing systems using fuzzy logic techniques

Evolution of manufacturing systems passed through different environment to full fill a need for optimal operating system. This led dynamic environment of manufacturing to Reconfigurable Manufacturing Systems (RMSs), which characterized by shorter product life cycle and changes in demand. To implement a successful RMS, effective process plan and production schedule is essential. However, little is known about applicability and effectiveness of intelligent techniques in reconfigurable manufacturing. On the other hand, investigation on the influence of intelligent production scheduling techniques on RMS performance is not negligible. This research proposed fuzzy model is subject to evaluate thedefined performance measures, such as; Machine utilization, Scalability, Product completion time and Lateness. To verify the methodology, the obtained result brought to comparison with those results acquired in conventional manufacturing systems. In addition a recursive mathematical model is developed to enhance the research validation. In this study, fuzzy logic model usedfour suitable fuzzy input variables, namely machine allocated processing time, machine priority, due date priority and machine structure, to solve production scheduling problem in selecting machine for each job operation and determining the processing sequence for each machine,simultaneously. The proposed fuzzy model used a fuzzy rule based inference system to determine job priority as a fuzzy output variable for the production scheduling purpose. The production schedule showed that is able to improve performance criteria in machine utilization, machine completion time, and also in product completion time as well as scalability. Experimental and comparative test indicates superiority of RMS environment over Flexible Manufacturing System environment (FMS)on using the same fuzzy based production scheduling model in terms of machine utilization (increased by 6.6%), machine completion time (increased by 63.3%), lateness (decreaseddeistically) and product completion time (increased by 22.13), as well as throughput (increased by 20%). Employing mathematical programming showed that the fuzzy scheduling is the most successful approach to solve RMS scheduling problem. However, investigating on the effect of changing manufacturing environment and fuzzy production scheduling model on a conventional production scheduling demonstrated slight increment in machine utilization, machine completion time and product completion time increased by 0.82%, 9.1%, and 2.4% respectively while lateness decreased by 35%, however the through put reduced by -2.5%.Although the obtained results are discussable based on assumptions and input data, however the positive impact of fuzzy technique in RMS environment is easily interpretable. The performance in fuzzy based production scheduling of RMS is superior to that of conventional manufacturing systems.The results would motivates researches to continue with evaluating different performance measures and assumptions and evaluating the effect of fuzzy logic techniques in order to come up with utilized production scheduling model for future manufacturing environment

Development of World Class Manufacturing Framework by Using Six-Sigma, Total Productive Maintenance and Lean

The purpose of this study is to develop a new framework to provide guidance and support for those companies who aim to reach world-class standards both in maintenance and manufacturing processes through continual improvement. Based on this study, a strategic framework was developed through conceptual integration of three popular process improvement strategies, which are six-sigma, total productive maintenance and lean. An attempt was made to analyze and address some major limitations of existing frameworks to pave the way of achieving manufacturing excellence. The result of this study showed that achieving world-class level is a moving target and the quest for reaching to such status is not a destination, but an ongoing journey that creates more and more improvement opportunities over time