Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment
The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focus...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9511413/ |
| _version_ | 1819084962477899776 |
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| author | Iqra Sadaf Khan Usman Ghafoor Taiba Zahid |
| author_facet | Iqra Sadaf Khan Usman Ghafoor Taiba Zahid |
| author_sort | Iqra Sadaf Khan |
| collection | DOAJ |
| description | The need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems. |
| first_indexed | 2024-12-21T20:56:48Z |
| format | Article |
| id | doaj.art-257abeea14274b74909cdb71c5d185ff |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T20:56:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-257abeea14274b74909cdb71c5d185ff2022-12-21T18:50:34ZengIEEEIEEE Access2169-35362021-01-01911350811352010.1109/ACCESS.2021.31041169511413Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production EnvironmentIqra Sadaf Khan0https://orcid.org/0000-0002-5459-4794Usman Ghafoor1https://orcid.org/0000-0001-5464-4002Taiba Zahid2Industrial Engineering and Management, Faculty of Technology, University of Oulu, Oulu, FinlandDepartment of Mechanical Engineering, Institute of Space Technology, Islamabad, PakistanNational University of Science and Technology, Islamabad, PakistanThe need for automated production plans has evolved over the years due to internal and external drivers like developed products, new enhanced processes and machinery. Reconfigurable manufacturing systems focus on such needs at both production and process planning level. The age of Industry 4.0 focused on mass customization requires computer aided planning techniques that are able to cope with custom changes in products and explores intelligent algorithms for efficient scheduling solutions to reduce lead time. This problem has been categorized as NP-Hard in literature and is addressed by providing intelligent heuristics that focus on reducing machining time of the products at hand. However, as 70% of the lead time is consumed in non-value added tasks, it is fundamental to provide modular solutions that can reduce this time and handle part variety. To address the subject, this paper focuses on the generation of automated process plans for a single machine problem while focusing on reducing time lead time. Two evolutionary algorithms (EAs) have been proposed and compared to answer complex problem of process planning. A modified genetic algorithm (GA) has been proposed in addition to cuckoo search (CS) heuristic for this discrete problem. On testing with selected benchmark part ANC101, significant improvement was seen in terms of convergence with proposed EAs. Moreover, a novel Precedence Group Algorithm (PGA) is proposed to generate quality input for heuristics. The algorithm produces a set of initial population which significantly effects the performance of proposed heuristics. For the discrete constrained process planning problem, GA outperforms CS providing 10% more feasible scheduling options and three times lesser run time as compared to CS. The proposed technique is flexible and responsive in order to accommodate part variety, a necessary requirement for reconfigurable systems.https://ieeexplore.ieee.org/document/9511413/Process planningreconfigurable systemsheuristicsevolutionary algorithmsgenetic algorithmcuckoo search |
| spellingShingle | Iqra Sadaf Khan Usman Ghafoor Taiba Zahid Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment IEEE Access Process planning reconfigurable systems heuristics evolutionary algorithms genetic algorithm cuckoo search |
| title | Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment |
| title_full | Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment |
| title_fullStr | Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment |
| title_full_unstemmed | Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment |
| title_short | Meta-Heuristic Approach for the Development of Alternative Process Plans in a Reconfigurable Production Environment |
| title_sort | meta heuristic approach for the development of alternative process plans in a reconfigurable production environment |
| topic | Process planning reconfigurable systems heuristics evolutionary algorithms genetic algorithm cuckoo search |
| url | https://ieeexplore.ieee.org/document/9511413/ |
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