Using genetic algorithms in process planning for job shop machining

Zhang, F.; Zhang, Y.F.; Nee, A.Y.C.

doi:10.1109/4235.687888

Cited by 146 publications

(82 citation statements)

References 17 publications

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“…In order to reduce the dissimilarity among the process plans selection they first generated alternative optimal process plan for each part type and later merged the plans. A genetic algorithm approach to solve the process planning problem for a job shop was attempted by Zhang et al (1997). Kolisch and Hess (2000) solved these types of problems using three approaches; a biased random sampling method and rest of the two approaches are Tabu-search based large-step optimization techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling model

Chan

Kumar

Tiwari³

2008

International Journal of Production Research

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Over the past few years the grown global competition has enforced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result of which, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies

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Section: Literature Reviewmentioning

confidence: 99%

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling model

Chan

Kumar

Tiwari³

2008

International Journal of Production Research

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“…Research studies on process planning include object-oriented approaches [105], [132], GA-based approaches [70], [131], neural-network-based approaches [21], [69], Petri net-based approaches [53], feature recognition or feature-driven approaches [114], [119], and knowledge-based approaches [108], [118]. These approaches and their combinations have been applied to some specific problem domains, such as tool selection [24], [56], tool path planning [7], [45], machining parameters selection [3], [37], process sequencing [129], and setup planning [75], [125].…”

Section: A Traditional Approachesmentioning

confidence: 99%

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Shen

Wang

Hao

2006

IEEE Trans. Syst., Man, Cybern. C

352

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Abstract-Manufacturing process planning is the process of selecting and sequencing manufacturing processes such that they achieve one or more goals and satisfy a set of domain constraints. Manufacturing scheduling is the process of selecting a process plan and assigning manufacturing resources for specific time periods to the set of manufacturing processes in the plan. It is, in fact, an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. Manufacturing process planning and scheduling are usually considered to be two separate and distinct phases. Traditional optimization approaches to these problems do not consider the constraints of both domains simultaneously and result in suboptimal solutions. Without considering real-time machine workloads and shop floor dynamics, process plans may become suboptimal or even invalid at the time of execution. Therefore, there is a need for the integration of manufacturing process-planning and scheduling systems for generating more realistic and effective plans. After describing the complexity of the manufacturing process-planning and scheduling problems, this paper reviews the research literature on manufacturing process planning, scheduling as well as their integration, particularly on agent-based approaches to these difficult problems. Major issues in these research areas are discussed, and research opportunities and challenges are identified.

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“…[13] produced an integrated process planning and scheduling model for a manufacturing unit by applying Simulated Annealing (SA) based random search optimization technique. [14] attempted the genetic algorithm approach to solve the process planning problem in a job shop. [15] utilized the advantages of Ant Colony Optimization (ACO) approach to resolve the issues related to job shop scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing the Performance of an Integrated Process Planning and Scheduling Problem: An AIS-FLC based Approach

Chan¹,

Kumar²,

Tiwari³

2006

2006 IEEE Conference on Cybernetics and Intelligent Systems

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Abstract-The present market scenario demands an integration of process planning and scheduling to stay competitive with others. In the present work, an integrated process planning and scheduling model encapsulating the salient features of outsourcing strategy has been proposed. The paper emphasizes on the role of outsourcing strategy in optimizing the performance of enterprises in rapidly changing environment. In the present work authors have proposed an Artificial Immune System based AIS-FLC algorithm embedded with the fuzzy logic controller to solve the complex problem prevailing under such scenario, while simultaneously optimizing the performance. The authors have proven the efficacy of the proposed algorithm by comparing the results with other random search methods.

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Using genetic algorithms in process planning for job shop machining

Cited by 146 publications

References 17 publications

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling model

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling model

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

Optimizing the Performance of an Integrated Process Planning and Scheduling Problem: An AIS-FLC based Approach

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