Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts

Li, Weidong; Ong, S. K.; Nee, A.Y.C.

doi:10.1080/00207540110119991

Cited by 174 publications

(109 citation statements)

References 14 publications

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“…Thus, the MODASA has higher stability and shorter running time than others. [18,19], [20,22], [17,18] 3 1,3 [18,19], [20,22] Figure 6: Gantt chart of PCD tasks in the context of without emergencies and considering emergencies. …”

Section: Computational Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

Yu¹,

Yang²,

Zhao³

et al. 2014

Int. j. simul. model.

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Disturbance is inevitable in product collaborative design (PCDSP), which has always posed a great challenge for enterprises making quick response. Thus, in the paper, a multi-objective rescheduling model and its solution algorithm are presented. Disturbance cases are analysed and dynamic scheduling procedure based on event-driven and lifecycle-driven is developed firstly. Then a multiobjective rescheduling model aiming to minimum the makespan and tardiness penalty is developed. As a solution, multi-objective dynamic adaptive scheduling algorithm (MODASA), based on bi-layer coding strategy, self-adaptive double point crossover and self-adaptive mutation, is proposed following closely. Finally, analytic results from a case of a wind turbine are used to illustrate the model and method proposed in this paper. Simulation results shows that the model and algorithm have full advantages in computing speed and precision. With the analysis, it can provide insight into ways of improving the strategic and operational decision making for enterprises.

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Section: Computational Resultsmentioning

confidence: 99%

“…Accordingly, traditional optimization methods are hardly to achieve an optimal solution [22]. Many heuristic solutions, such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and others, have been developed for optimization.…”

Section: Algorithmmentioning

confidence: 99%

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

Yu¹,

Yang²,

Zhao³

et al. 2014

Int. j. simul. model.

View full text Add to dashboard Cite

show abstract

“…Welch 2 used branch-and-bound search to find D-optimal designs, Haines 3 used simulated annealing to find exact optimal designs for linear regression models, and numerous authors [4][5][6][7] have used the genetic algorithm to find optimal or near-optimal designs. Researchers in other fields have recognized that hybrid heuristics-combinations of simple heuristics into more complex algorithms-can solve some problems more effectively than individual heuristic algorithms (Li et al 8 and Kragelund 9 , for example), but applications to date in design of experiments have not taken full advantage of this research. The discussion following is intended to demonstrate the importance of hybrid heuristics in design of experiments, and also to give some guidance on their use.…”

Section: Introductionmentioning

confidence: 98%

A Genetic Algorithm Hybrid for Constructing Optimal Response Surface Designs

Drain

Carlyle

Montgomery

et al. 2004

Quality & Reliability Eng

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Hybrid heuristic optimization methods can discover efficient experiment designs in situations where traditional designs cannot be applied, exchange methods are ineffective, and simple heuristics like simulated annealing fail to find good solutions. One such heuristic hybrid is GASA (genetic algorithm-simulated annealing), developed to take advantage of the exploratory power of the genetic algorithm, while utilizing the local optimum exploitive properties of simulated annealing. The successful application of this method is demonstrated in a difficult design problem with multiple optimization criteria in an irregularly shaped design region.

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“…Operation sequencing determines in what order to perform a set of operations so that the resulting operation sequence could satisfy the precedence constraints established by geometric, tolerance, technological requirements and manufacturing knowledge (Yun and Moon 2011;Huang et al 2012;Yun et al 2013). In summary, it is essential to generate a good operation sequence to accomplish the manufacturing process since the sequence of operations will affect the cost, efficiency and flexibility of a production system (Li et al 2002a;Liang and Xiaohang 2011;Costa et al 2015).…”

Section: Introductionmentioning

confidence: 98%

A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy

Chu

Chen

et al. 2015

J Intell Manuf

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Operation sequencing in CAPP aims at determining the optimal order of machining operations with minimal machining cost and satisfying all the precedence constraints. The genetic algorithm (GA) is widely used to solve precedence constrained operation sequencing problem (PCOSP) due to its efficiency and parallel processing capability. How to guarantee the precedence constraints is always a hot research topic and there are mainly two classes of methods. The first ones use additional adjustment approaches to repair the infeasible solutions that break precedence constraints. It is unreliable and low efficient. The second ones avoid infeasible solutions in initialization through some encoding approaches such as topological storing based encoding approach, but the premature convergence problem may occur facing some complicated PCOSPs. To solve these problems, an edge selection strategy based GA is proposed. The edge selection based strategy could produce feasible solutions in initialization, and assures that every feasible solution will be generated with acceptable probability so as to improve GA's converging efficiency. Then the precedence constraints are kept by order crossover. Modified mutation operator is designed to opti-B Yuliang Su mize the selection of machine tool, tool access direction and cutting tool for each operation. The experiments illustrate that the proposed algorithm is effective and efficient.

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Hybrid genetic algorithm and simulated annealing approach for the optimization of process plans for prismatic parts

Cited by 174 publications

References 14 publications

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

Multi-Objective Rescheduling Model for Product Collaborative Design Considering Disturbance

A Genetic Algorithm Hybrid for Constructing Optimal Response Surface Designs

A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy

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