An Effective Hybrid Genetic Simulated Annealing Algorithm for Process Planning Problem

Abstract

Process planning is an essential part for a Computer Aided Process Planning (CAPP) system in the dynamic workshop environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from practical workshop environment as well as the part to be processed. In this paper, a hybrid genetic simulated annealing algorithm has been developed, which combined the strengths of genetic algorithm (GA) and simulated Annealing (SA), to solve this problem. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima. A case study is employed to verify the performance and efficiency of the hybrid genetic simulated annealing algorithm (GASA) and the experiment results show that the developed hybrid GASA can generate satisfactory solutions.

A CBFSA approach to resolve the distributed manufacturing process planning problem in a supply chain environment

“…Tiwari et al (2004) applied a psychoclonal algorithm for the determination of an optimal assembly of a manufacturing process. Wang et al (2009b) proposed particle swarm optimisation whereas Lian et al (2009) proposed an effective hybrid genetic simulated annealing algorithm for the process planning problem. Shao et al (2009) also proposed a modified genetic algorithm-based approach to resolve the integrated process planning and scheduling problem.…”

Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission

“…the set of combinations of job i, n the set of jobs, M ij the set of available machines for O ij , R ih the set that contains the operations belonging to the hth combination of job i, T ij the set of available cutting tools of operation O ij , TAD ij the set of TADs of operation O ij , LO ih the number of operations of of the hth combination of job i, e.g., LO ih = |R ih |, PW k the power of machine k, t ijk the processing time of operation O ij on machine k, mc ijk the machine cost of operation O ij processed on machine k, mc ij f the cost of the f th tool to process operation O ij , MCCI the machine change cost index, TCCI the tool change cost index, SCCI the set-up change cost index, C EF,ele the electricity carbon emission factor; it takes the value 0.665 (kgCO 2 /kwh) according to the average of grids' emission factors in China [28], T c k the replacement cycle of cutting fluid; its value usually falls in the range [1,3] months [29], In this paper, this value is set to two months, C oil EF the carbon emission factor for the production of cutting fluid (kgCO 2 /L); according to [30], it takes the value 2.85 kgCO 2 …”

“…Existing research on process planning optimization mostly pays close attention to one or more conventional goals. For example, Lian et al [3] proposed a hybrid genetic simulated annealing algorithm in solving the process planning problem. In the algorithm, the genetic algorithm (GA) is treated as the main framework while SA is used as the local search method and machine cost, tool cost, machine change cost, set-up change cost as well as tool change cost were considered in a weighted sum.…”