Advances in Production Engineering & Management
Volume 13 | Number 3 | September 2018 | pp 279–296
https://doi.org/10.14743/apem2018.3.290
Dynamic integration of process planning and scheduling using a discrete particle swarm optimization algorithm
Yu, M.R.; Yang, B.; Chen, Y.
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A B S T R A C T
Because of the inherent relationship between process planning and scheduling, integration of process planning and scheduling (IPPS) provides a new path for further improvements of these two activities. Therefore, a novel two-phase IPPS approach is put forward in this paper. In the new method, the preplanning phase generates a process network for each job with consideration of the static shop floor status. After that, the final planning phase simultaneously creates the process plan of each job and the scheduling plan according to the current shop floor status. Based on the modified definition of IPPS and the proposed mathematical model, the IPPS problem and the dynamic IPPS problem can be solved together. Furthermore, a discrete particle swarm optimization (DPSO) algorithm is proposed to solve the IPPS optimization problem. In the DPSO algorithm, the particles update their positions by crossing with their own historical best positions (pbests) and the global best position of the population (gbest). In order to avoid local convergence, an external archive is introduced to keep more than one elite, and the gbest of each particle is randomly selected from the external archive. Furthermore, mutation operation is introduced to enhance the local search ability of DPSO algorithm. Finally, some comparative results are given to verify the efficiency and effectiveness of the proposed IPPS method and the DPSO algorithm as well as the dynamic IPPS method.
A R T I C L E I N F O
Keywords • Process planning; Scheduling; Dynamic integration; Mathematical model; Optimization; Discrete particle swarm optimiza-tion (DPSO)
Corresponding author • Yu, M.R.
Article history • Received 4 December 2017, Revised 21 August 2018, Accepted 24 August 2018
Published on-line • 21 September 2018
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