Increased productivity and lower cost in manufacturing processes can be achieved through growing reduction of idle time so this justifies and requires a great deal to obtain an optimal scheduling. Newly, powerful methods for running optimization problems have been inspired by research on social insects’ behaviour and in particular on ants’ way. The cooperative behaviour that emerges from their organization has been dubbed as Swarm Intelligence. Ant System (AS) is a meta-heuristic approach with multi agent systems inspired by the foraging behaviour of real ant. Ants’ colony is able to find the shortest path among a lot of possible ones by stigmergic cooperation. This suitable framework joined with representation of a scheduling instance as graph, within each node represents an operation, makes the Ant Colony Optimization (ACO) algorithms well suited for scheduling duty. This paper deals with the application of an improved model based on ants’ way, with several specific rules incorporate, to schedule job shops associated with manufacturing operations with the aim to minimize the makespan. A comparison of solutions yielded by the proposed ant-colony algorithm with several priority dispatching rules and previous heuristic algorithms for several benchmark problems evinces that our model exhibits high performances.

From Ant Colony to Artificial Ants: A Nature Inspired Algorithm to Solve Job Scheduling Problems

FRUGGIERO, FABIO;
2005

Abstract

Increased productivity and lower cost in manufacturing processes can be achieved through growing reduction of idle time so this justifies and requires a great deal to obtain an optimal scheduling. Newly, powerful methods for running optimization problems have been inspired by research on social insects’ behaviour and in particular on ants’ way. The cooperative behaviour that emerges from their organization has been dubbed as Swarm Intelligence. Ant System (AS) is a meta-heuristic approach with multi agent systems inspired by the foraging behaviour of real ant. Ants’ colony is able to find the shortest path among a lot of possible ones by stigmergic cooperation. This suitable framework joined with representation of a scheduling instance as graph, within each node represents an operation, makes the Ant Colony Optimization (ACO) algorithms well suited for scheduling duty. This paper deals with the application of an improved model based on ants’ way, with several specific rules incorporate, to schedule job shops associated with manufacturing operations with the aim to minimize the makespan. A comparison of solutions yielded by the proposed ant-colony algorithm with several priority dispatching rules and previous heuristic algorithms for several benchmark problems evinces that our model exhibits high performances.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/13950
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