A multi-dimensional tabu search algorithm for the optimization of process planning

Abstract

Computer-aided process planning (CAPP) is an essential component of computer integrated manufacturing (CIM) system. A good process plan can be obtained by optimizing two elements, namely, operation sequence and the machining parameters of machine, tool and tool access direction (TAD) for each operation. This paper proposes a novel optimization strategy for process planning that considers different dimensions of the problem in parallel. A multi-dimensional tabu search (MDTS) algorithm based on this strategy is developed to optimize the four dimensions of a process plan, namely, operation sequence (OperSeq), machine sequence (MacSeq), tool sequence (ToolSeq) and tool approach direction sequence (TADSeq), sequentially and iteratively. In order to improve its efficiency and stability, tabu search, which is incorporated into the proposed MDTS algorithm, is used to optimize each component of a process plan, and some neighbourhood strategies for different components are presented for this tabu search algorithm. The proposed MDTS algorithm is employed to test four parts with different numbers of operations taken from the literature and compared with the existing algorithms like genetic algorithm (GA), simulated annealing (SA), tabu search (TS) and particle swarm optimization (PSO). Experimental results show that the developed algorithm outperforms these algorithms in terms of solution quality and efficiency. process planning, cooperative tabu search, genetic algorithm, simulated annealing, particle swarm optimization Citation:Lian K L, Zhang C Y, Shao X Y, et al. A multi-dimensional tabu search algorithm for the optimization of process planning.

Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance

“…In addition, according to the slow speed and low-frequency signals in the paper, the initial search range of the system parameters a and b can be set into [0. 1,10]. Then, the current method is applied to the fault diagnosis of the gearbox in combined machining center.…”

“…Under operation, the components' faults degrade the machining accuracy [6][7][8][9][10][11]. It is difficult to detect fault feature, especially weak fault feature, which is usually submerged in the noise because the structures of combined machining centers are complex.…”

Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem

“…As one of main features in the TS heuristic, the tabu list recording the recent search history is used to avoid searching recently explored solutions. The TS heuristic has successfully solved various combinatorial optimization problems, such as job shop scheduling [42], computer-aided process planning [43], and multiple yard scheduling [44]. It has solved some of VRP variants [45][46][47].…”

A Graph-Based Ant Colony Optimization Approach for Process Planning

“…In the past three decades, many optimization approaches have been developed and widely applied for solving process planning problem, such as knowledge-based reasoning approach [ 5 , 6 ], graph manipulation [ 7 , 8 ], the genetic algorithm (GA) [ 9 – 11 ], tabu search approach (TS) [ 4 , 12 ], simulated annealing (SA) algorithm [ 3 , 13 ], particle swarm optimization (PSO) [ 14 , 15 ], artificial neural networks [ 16 ], ant colony optimization (ACO) [ 17 , 18 ], and artificial immune system (AIS) [ 19 ].…”