ABSTRACT
In traditional approaches, process planning and scheduling are usually carried out separately and sequentially. Since these manufacturing functions are complementary and interrelated, the development of effective models for integrated process planning and scheduling (IPPS) is essential for improving the performance of the whole manufacturing system. This paper presents a methodology based on the nature-inspired Ant Lion Optimization (ALO) algorithm for solving this NP-hard combinatorial optimization problem effectively. As the ALO algorithm mimics the intelligent behaviour of antlions during the hunting process, this paper provides a mathematical modelling of the main steps of hunting prey as well as the optimization procedure for the integration of process planning and scheduling functions. Optimal scheduling plans are obtained using three objective functions: (i) makespan, (ii) balanced level of machine utilization, and (iii) mean flow time. The ALO algorithm is implemented in the MATLAB® software package, experimentally tested on 25 benchmark problems through two experimental studies, and its results are compared with the results obtained by GA and PSO algorithms. The experimental results demonstrate the applicability of the proposed approach in solving IPPS problem.
