Abstract
This paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime. We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment. The GA functions as an add-in in the spreadsheet. It is demonstrated that with proposed approach any criteria can be optimised without modifying the GA routine or spreadsheet model. Furthermore, the proposed method for solving this class of problem is general purpose, as it can be easily customised by adding or removing jobs and machines. Several benchmark problems already published in the literature are used to demonstrate the problem-solving capability of the proposed approach. Benchmark problems set ranges from small (7-jobs, 7 machines) to large (100-jobs, 10-machines). The performance of the GA is compared with different meta-heuristic techniques used in earlier literature. Experimental analysis demonstrate that solutions obtained in this research offer equal quality as compared to algorithms already developed for NWFS problems.
Highlights
Scheduling is an important aspect of any manufacturing concern
This paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime
We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment
Summary
Scheduling is an important aspect of any manufacturing concern. The importance of efficient scheduling function cannot be denied as it ensures timely dispatch of products to the market before the competitors, yielding higher profits. The primary objective in any scheduling problem is to efficiently allocate jobs to the available machines and to determine the start and ending time of each operation, such that certain objective function is minimised or maximised. The schedule developed should satisfy various production constraints. In order to achieve high-efficiency production, efficient scheduling algorithms/schemes are considered to be a key factor. Flow shop scheduling is one of the widely studied models of the manufacturing environment.
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