Abstract
Scheduling problems are generally treated as NP – complete combinatorial optimization problems which is a multi-objective and multi constraint one. Repair shop Job sequencing and operator allocation is one such NP – complete problem. For such problems, an efficient technique is required that explores a wide range of solution space. This paper deals with Simulated Annealing Technique, a Meta - heuristic to solve the complex Car Sequencing and Operator Allocation problem in a car repair shop. The algorithm is tested with several constraint settings and the solution quality exceeds the results reported in the literature with high convergence speed and accuracy. This algorithm could be considered as quite effective while other heuristic routine fails.
Highlights
The objective of the scheduling problems is to deal with allocating resources with a single or set of objectives and constraints to perform a set of activities [1]
The best possible allocation of operation which satisfies all the above criteria is presented in Table – 06
The proposed methodology demonstrated a high rate of success, being able to solve problems for which other heuristic optimization routines fail
Summary
The objective of the scheduling problems is to deal with allocating resources with a single or set of objectives and constraints to perform a set of activities [1]. The complexity involved in solving these scheduling problems renders attraction for the academia and researchers in developing new optimization algorithms. The solution of these scheduling problems yields benefits of lowering cost, matching workload to available capacity, minimizing the completion time, smoothen the flow of customers and improving productivity in organizations. Parameshwaran R. et al [2528] utilized the tools such as Brown Gibson Model, fuzzy Analytical Hierarchical process (AHP), Integrated fuzzy logic, Data Envelopment Analysis (DEA), Fuzzy failure mode and effect analysis (FMEA) for improving service quality in automobile repair shops.
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