Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

This study develops an enhanced ant colony optimization (e-aco) meta-heuristic to accomplish the integrated process planning and scheduling (ipps) problem in the job-shop environment. the ipps problem is represented by and/or graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. in accordance with the characteristics of the ipps problem, the mechanism of aco algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for aco parameter tuning for ipps problems provided. the results show that with the specific modifications made on aco algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.