Integration of Production Scheduling Inventory Control and Maintenance Planning in Multi Machine Manufacturing Systems

Abstract

The research work in this dissertation has been carried out explicitly in three phases. In the first newlinephase, an integrated model of inventory control and production scheduling is proposed for newlinepermutation flow shop scheduling problem. The objective is to obtain the optimum production newlineschedule, which minimize the sum of work-in-process (WIP) inventory holding cost and penalty newlinecost due to batch delay. As a well-known fact of scheduling problems being NP-hard, four metaheuristics viz. teaching learning based optimization (TLBO) algorithm, Jaya algorithm, newlinesimulated annealing (SA), particle swarm optimization (PSO) are proposed to solve it. The newlinepeculiarity of TLBO and Jaya algorithms as compared to other evolutionary and swarm newlineintelligence based heuristics is that they are parameter less algorithms. Thus, they do not require newlinetuning of algorithm-specific parameters, which makes the optimization procedure easy to newlineunderstand and implement. The problem is solved for several instances ranging from 8 jobs and newline5 machines to 500 jobs and 20 machines. Computational results show that for small instances, all newlinealgorithms performed equally good when compared with the exact solution (total enumeration newlinemethod). However, for medium and large size problems, enumeration method was unable to give newlinethe results in a reasonable computation time. Therefore, the computational results of all four newlinealgorithms are compared amongst themselves. An exhaustive comparative study along with newlinestatistical analysis is also provided in order to present the effectiveness of algorithms for solving newlinethe proposed problem. Computational results reveal that Jaya algorithm outperforms all other newlinealgorithms. TLBO and SA yielded comparable results while PSO is least productive. The overall newlineperformance of all algorithms reveals that the new meta-heuristics viz. TLBO and Jaya are quite newlineefficient to solve discrete combinatorial problems such as permutation flow-shop scheduling newlineproblems.