An ant colony approach to operation sequencing optimization in process planning

Hu, Quanwei; Qiao, Lihong; Peng, Guanwei

doi:10.1177/0954405415616786

Cited by 35 publications

(22 citation statements)

References 32 publications

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“…In recent years, novel evolutionary or meta-heuristic algorithms have received wide applications. Hu et al [4] developed a hybrid ant colony algorithm to facilitate a feasible operation sequencing in process planning optimization with the objective of total weighted production cost reduction. More meta-heuristic algorithm based process planning optimization methods can be found in other existing publications.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, lots of investigations on process planning optimizations have been performed and some fruitful results are observed. For instance, Hu et al [4] suggested an ant colony algorithm in process planning to ensure feasible operation permutations. In general, existing publications regarding process planning optimization put more emphasis on obtaining an optimal process plan of a part with the minimum machining cost or the shortest machining time [5].…”

Section: Introductionmentioning

confidence: 99%

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Process Planning Optimization With Energy Consumption Reduction From a Novel Perspective: Mathematical Modeling and a Dynamic Programming-Like Heuristic Algorithm

Jin

Zhang

2019

IEEE Access

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Process planning can be deemed as an important component in manufacturing systems. It bridges the gap between designing and manufacturing by specifying the manufacturing requirements and details to convert a part from raw materials to the finished form. For the purpose of low carbon emission, this paper pays attention to both technical performance measures and environmental impact criteria. In this problem, a part may have more than one process plans and only one process plan can finally be adopted. Due to the non-deterministic polynomial-hardness, it is rather difficult to conduct operation selection, machine determination, operation sequencing, and energy consumption reduction simultaneously with various constraints from technical requirements or the shop floor status. A novel position-based mixed integer linear programming model is developed to reduce total production time and the total energy consumption. The energy consumption coefficient matrix is proposed to evaluate the energy consumption in process planning. Because of the complexity in solving the model, this research proposes dynamic programminglike heuristic algorithm to tackle this problem. The weighted sum method is applied in multi-objective optimization and three typical instances with operation flexibility, sequencing flexibility, and processing flexibility are used to test the proposed algorithm. According to the results, both the total production time and the energy consumption criteria are optimized; in the best case, the energy consumption after optimization takes only 21.2% of the one before optimization. On average, about 40.9% of the total energy consumption can be reduced after optimization. Computational results also show that the proposed algorithm is generally better than the genetic algorithm. This research gives a novel perspective to reduce energy consumption in the process planning stage. INDEX TERMS Energy consumption reduction, dynamic programming, green process planning, heuristic algorithms, operations selection & sequencing, production time minimization.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Process Planning Optimization With Energy Consumption Reduction From a Novel Perspective: Mathematical Modeling and a Dynamic Programming-Like Heuristic Algorithm

Jin

Zhang

2019

IEEE Access

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“…where a j j 2 + b j j 2 = 1. a j j 2 gives the probability that the Q-bit will be found in the ''0'' state, and b j j 2 gives the probability that the Q-bit will be found in the ''1'' state.…”

Section: Qieamentioning

confidence: 99%

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Liu

Wen

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantuminspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

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“…Liu et al 19 considered one operation with several MRs. Then, the AC algorithm was used for OMS determination. Hu et al 20 also employed the AC algorithm for OMS optimization. They considered the machining rules and moved the ants by the OPG.…”

Section: Introductionmentioning

confidence: 99%

Reducing search space of optimization algorithms for determination of machining sequences by consolidating decisive agents

Manafi

Nategh

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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One of the main objectives of computer-aided process planning is to determine the optimum machining sequences and setups. Among the different methods to implement this task, it can be named the constrained optimization algorithms. The immediate drawback of these algorithms is usually a large space needed to be searched for the solution. This can easily hinder the convergence of the solution and increase the possibility of getting trapped in the local minima. A novel approach has been developed in this work with the objective of reducing the search space. It is based on consolidating the decisive factors influencing the consecutive features. This helps prevent creation of sequences which need the change of setup, machine tool, and cutting tool. The proposed method has been applied to three different optimization methods, including genetic, particle swarm, and simulated annealing algorithms. It is shown that these algorithms with reduced search spaces outperform those reported in the literature, with respect to the convergence rate. The best results are found in the genetic algorithm from the viewpoint of the obtained solution and the convergence rate. The worst results belong to the particle swarm algorithm in connection with the strategy of generating new solutions.

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An ant colony approach to operation sequencing optimization in process planning

Cited by 35 publications

References 32 publications

Process Planning Optimization With Energy Consumption Reduction From a Novel Perspective: Mathematical Modeling and a Dynamic Programming-Like Heuristic Algorithm

Process Planning Optimization With Energy Consumption Reduction From a Novel Perspective: Mathematical Modeling and a Dynamic Programming-Like Heuristic Algorithm

Quantum-inspired hybrid algorithm for integrated process planning and scheduling

Reducing search space of optimization algorithms for determination of machining sequences by consolidating decisive agents

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