Abstract
A B S T R A C T Scheduling 'n' jobs on 'm' machines in a flow shop is NP- hard problem and places itself at prominent place in the area of production scheduling. The essence of any scheduling algorithm is to minimize the makespan in a flowshop environment. In this paper an attempt has been made to develop a heuristic algorithm, based on the reduced weightage of machines at each stage to generate different combination of 'm-1' sequences. The proposed heuristic has been tested on several benchmark problems of Taillard (1993) (Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64, 278-285.). The performance of the proposed heuristic is compared with three well-known heuristics, namely Palmer's heuristic, Campbell's CDS heuristic, and Dannenbring's rapid access heuristic. Results are evaluated with the best-known upper-bound solutions and found better than the above three.
Highlights
Scheduling is a decision making practice used on a regular basis in most of the manufacturing industries
We show the best-known upper bounds and percentage gap from the best-known upper bound for each problem
We have presented a heuristic for the general flow shop scheduling to minimize the makespan
Summary
Scheduling is a decision making practice used on a regular basis in most of the manufacturing industries. Rajendran (1994) introduced a new heuristic for flow shop, in which heuristic preference relation is developed He considered the problem of scheduling in flow shop and flow-line based manufacturing cell with bi-criteria of minimizing makespan and total flow time of jobs. Bhongade and Khodke (2012) proposed two heuristics NEH-BB (Branch & Bound) and Disjunctive to solve assembly flow shop scheduling problem where every part may not be processed on each machine By computational experiments these methods are found to be applicable to large size problems. Behnamian and Ghomi (2014) considered bi-objective hybrid flow shop scheduling problems with bell-shaped fuzzy processing and sequence-dependent setup times To solve these problem a bi-level algorithm with a combination of genetic algorithm and particle swarm optimization algorithm is used.
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