Abstract
Scheduling problems are NP-hard in nature. Flowshop scheduling problems, are consist of sets of machines with number of resources. It matins the continuous flow of task with minimum time. There are various traditional algorithms to maintain the order of resources. Here, in this paper a new stochastic Ant Colony optimization technique based on Pareto optimal (PA-ACO) is implemented for solving the permutation flowshop scheduling problem (PFSP) sets. The proposed technique is employed with a novel local path search technique for initializing and pheromone trails. Pareto optimal mechanism is used to select the best optimal path solution form generated solution sets. A comparative study of the results obtained from simulations shows that the proposed PA-ACO provides minimum makespan and computational time for the Taillard dataset. This work will applied on large scale manufacturing production problem for efficient energy utilization.
Highlights
A permutation flowshop scheduling problem (PFSP) consists of a constant sequence set of the non-permutable real-world problem
The work presented in this paper is to reduce the computational time (CTPJ max) and makespan of the Taillard dataset
The results show that this approach has high efficiency and effectiveness
Summary
A PFSP consists of a constant sequence set of the non-permutable real-world problem. It states that jobs ‘i’. The processing time of the machine is assumed to be ‘0’ if the job doesn’t take part in the execution. In the year 1992, Dorigo introduced the population-based search technique based on the behaviors of ant’s hive [13].Ants are the natural food seeker and they use pheromone trail to create the shortest routes for their fellow ants. Ants do not have any visual power instead of they use pheromones to find the shortest route between foods to the nest. It has been experimentally proved that the ants will find the shortest path by using the pheromone trail. The first example of ant pheromone trail search is proposed by Dorigo for traveling salesman problem. The colony of artificial ants helps users to finding the optimal solution from the given problem set. There are various versions of ACO algorithms are developed by different researchers to find the optimal results for various datasets
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