Abstract
Multi objective Job Shop scheduling is a difficult task in both theoretical and practical solving issues. Problem statement: In the present scenario the modern Engineering and Industrial manufacturing units are facing lot of problems in many aspects such as machining time, raw material movement, man power requirement, electricity demand and customer satisfaction. Approach: A triangular fuzzy membership function is used to represent customer priority and due date. Results: A fuzzy rule-based system is developed which determines the study to be allocated to N number of machines with M number of Jobs in the following premise variables: size of the job, workload on the shop floor and the priority of the job. Multi objective fuzzy job shop scheduling problems are formulated as three-objective ones which not only maximize the minimum agreement index but also maximize the average agreement index and minimize the maximum fuzzy completion time. Conclusion/Recommendations: The study is analyzed on real-world data obtained from a printing company and the results are found satisfactory.
Highlights
Fuzzy set theory has been utilized to develop hybrid scheduling approaches and it can be useful in modeling and solving job shop scheduling problems with uncertain processing times, constraints and setup times
Real-world scheduling problems are usually very different from the mathematical models studied by researchers in academia
Existing approach: Modern Fuzzy logic control for job-shop scheduling allows the modeling of scheduling knowledge with linguistic variables defined by membership functions showing the degree of strictness ai = di - ri of the data and the reasoning about the imprecise data by using fuzzy rules
Summary
Fuzzy set theory has been utilized to develop hybrid scheduling approaches and it can be useful in modeling and solving job shop scheduling problems with uncertain processing times, constraints and setup times. Existing approach: Modern Fuzzy logic control for job-shop scheduling allows the modeling of scheduling knowledge with linguistic variables defined by membership functions showing the degree of strictness ai = di - ri of the data and the reasoning about the imprecise data by using fuzzy rules. If the antecedent of a given rule has more than one part, the fuzzy operator is Implementation using Fuzzy MAT LAB simulink: The fuzzy logic toolbox allows doing several things, but the most important thing it is create and edit fuzzy inference systems We can create these systems using graphical tools or command-line functions, or you can generate them automatically using either clustering or adaptive neuro-fuzzy techniques.
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