Near optimal process plan selection for multiple jobs in networked based manufacturing using multi-objective evolutionary algorithms

• We developed a mathematical model to address a multi-objective problem in the context of networked based manufacturing environment.
• Multi-objective evolutionary algorithms (MOEA’s) have been proposed.
• We tested and compared the performance of the three algorithms with an illustrative example.

The networked manufacturing offers several advantages in current competitive atmosphere by way of reducing the manufacturing cycle time and maintenance of the production flexibility, thereby achieving several feasible process plans. In this paper, we have addressed a Multi Objective Problem (MOP) which covers-minimize the makespan and to maximize the machine utilization while generating the feasible process plans for multiple jobs in the context of network based manufacturing system. A new multi-objective based Territory Defining Evolutionary Algorithm (TDEA) to resolve the above computationally challenge problem have been developed. In particular, with two powerful Multi-Objective Evolutionary Algorithms (MOEAs), viz. Non-dominated Sorting Genetic Algorithm (NSGA-II) and Controlled Elitist-NSGA-II (CE-NSGA-II) the performance of the proposed TDEA has been compared. An illustrative example along with three complex scenarios is presented to demonstrate the feasibility of the approach. The proposed algorithm is validated and the results are analyzed and compared.