IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v36y2008i6p1072-1085.html
   My bibliography  Save this article

A hybrid approach to constrained evolutionary computing: Case of product synthesis

Author

Listed:
  • Liang, Wen-Yau
  • Huang, Chun-Che

Abstract

Evolutionary computing (EC) is comprised of techniques involving evolutionary programming, evolution strategies, genetic algorithms (GA), and genetic programming. It has been widely used to solve optimization problems for large scale and complex systems. However, when insufficient knowledge is incorporated, EC is less efficient in terms of searching for an optimal solution. In addition, the GA employed in previous literature is modeled to solve one problem exactly. The GA needs to be redesigned, at a cost, for it to be applied to another problem. Due to these two reasons, this paper develops a generic GA incorporating knowledge extracted from the rough set theory. The advantages of the proposed solution approach include: (i) solving problems that can be decomposed into functional requirements, and (ii) improving the performance of the GA by reducing the domain range of initial population and constraining crossover using the rough set theory. The solution approach is exemplified by solving the problem of product synthesis, where there is a conflict between performance and cost. Manufacturing or assembling a product of high performance and quality at a low cost is critical for a company to maximize its advantages. Based on our experimental results, this approach has shown great promise and has reduced costs when the GA is in processing.

Suggested Citation

  • Liang, Wen-Yau & Huang, Chun-Che, 2008. "A hybrid approach to constrained evolutionary computing: Case of product synthesis," Omega, Elsevier, vol. 36(6), pages 1072-1085, December.
  • Handle: RePEc:eee:jomega:v:36:y:2008:i:6:p:1072-1085
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305-0483(06)00048-X
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Beynon, Malcolm J. & Peel, Michael J., 2001. "Variable precision rough set theory and data discretisation: an application to corporate failure prediction," Omega, Elsevier, vol. 29(6), pages 561-576, December.
    2. Bautista, Joaquín & Pereira, Jordi, 2006. "Modeling the problem of locating collection areas for urban waste management. An application to the metropolitan area of Barcelona," Omega, Elsevier, vol. 34(6), pages 617-629, December.
    3. Bergey, Paul K. & Ragsdale, Cliff, 2005. "Modified differential evolution: a greedy random strategy for genetic recombination," Omega, Elsevier, vol. 33(3), pages 255-265, June.
    4. Min, Hokey & Jeung Ko, Hyun & Seong Ko, Chang, 2006. "A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns," Omega, Elsevier, vol. 34(1), pages 56-69, January.
    5. Ruiz, Rubén & Maroto, Concepciøn & Alcaraz, Javier, 2006. "Two new robust genetic algorithms for the flowshop scheduling problem," Omega, Elsevier, vol. 34(5), pages 461-476, October.
    6. Aldowaisan, Tariq & Allahverdi, Ali, 2004. "New heuristics for m-machine no-wait flowshop to minimize total completion time," Omega, Elsevier, vol. 32(5), pages 345-352, October.
    7. Carter, Arthur E. & Ragsdale, Cliff T., 2002. "Scheduling pre-printed newspaper advertising inserts using genetic algorithms," Omega, Elsevier, vol. 30(6), pages 415-421, December.
    8. Chan, Felix T. S. & Chung, S. H. & Wadhwa, Subhash, 2005. "A hybrid genetic algorithm for production and distribution," Omega, Elsevier, vol. 33(4), pages 345-355, August.
    9. Su, Chao-Ton & Hsu, Jyh-Hwa, 2006. "Precision parameter in the variable precision rough sets model: an application," Omega, Elsevier, vol. 34(2), pages 149-157, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Gang & Jiang, Hongxun & He, Tian, 2015. "A genetic algorithm-based decomposition approach to solve an integrated equipment-workforce-service planning problem," Omega, Elsevier, vol. 50(C), pages 1-17.
    2. Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
    3. Vidovic, Milorad & Dimitrijevic, Branka & Ratkovic, Branislava & Simic, Vladimir, 2011. "A novel covering approach to positioning ELV collection points," Resources, Conservation & Recycling, Elsevier, vol. 57(C), pages 1-9.
    4. Barker, Theresa J. & Zabinsky, Zelda B., 2011. "A multicriteria decision making model for reverse logistics using analytical hierarchy process," Omega, Elsevier, vol. 39(5), pages 558-573, October.
    5. Martin, Clarence H, 2009. "A hybrid genetic algorithm/mathematical programming approach to the multi-family flowshop scheduling problem with lot streaming," Omega, Elsevier, vol. 37(1), pages 126-137, February.
    6. Diabat, Ali & Kannan, Devika & Kaliyan, Mathiyazhagan & Svetinovic, Davor, 2013. "An optimization model for product returns using genetic algorithms and artificial immune system," Resources, Conservation & Recycling, Elsevier, vol. 74(C), pages 156-169.
    7. Shabtay, Dvir & Arviv, Kfir & Stern, Helman & Edan, Yael, 2014. "A combined robot selection and scheduling problem for flow-shops with no-wait restrictions," Omega, Elsevier, vol. 43(C), pages 96-107.
    8. Schweiger, Katharina & Sahamie, Ramin, 2013. "A hybrid Tabu Search approach for the design of a paper recycling network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 50(C), pages 98-119.
    9. Zhanwei Tian & Guoqing Zhang, 2021. "Multi-echelon fulfillment warehouse rent and production allocation for online direct selling," Annals of Operations Research, Springer, vol. 304(1), pages 427-451, September.
    10. Barry B. & Quim Castellà & Angel A. & Helena Ramalhinho Lourenco & Manuel Mateo, 2012. "ILS-ESP: An Efficient, Simple, and Parameter-Free Algorithm for Solving the Permutation Flow-Shop Problem," Working Papers 636, Barcelona School of Economics.
    11. Chiang, Wen-Chyuan & Russell, Robert & Xu, Xiaojing & Zepeda, David, 2009. "A simulation/metaheuristic approach to newspaper production and distribution supply chain problems," International Journal of Production Economics, Elsevier, vol. 121(2), pages 752-767, October.
    12. Ho, William, 2008. "Integrated analytic hierarchy process and its applications - A literature review," European Journal of Operational Research, Elsevier, vol. 186(1), pages 211-228, April.
    13. Pan, Quan-Ke & Ruiz, Rubén, 2012. "Local search methods for the flowshop scheduling problem with flowtime minimization," European Journal of Operational Research, Elsevier, vol. 222(1), pages 31-43.
    14. H. Ooghe & S. De Prijcker, 2006. "Failure process and causes of company bankruptcy: a typology," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/388, Ghent University, Faculty of Economics and Business Administration.
    15. Naderi, B. & Zandieh, M., 2014. "Modeling and scheduling no-wait open shop problems," International Journal of Production Economics, Elsevier, vol. 158(C), pages 256-266.
    16. Beynon, Malcolm J., 2005. "A novel technique of object ranking and classification under ignorance: An application to the corporate failure risk problem," European Journal of Operational Research, Elsevier, vol. 167(2), pages 493-517, December.
    17. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    18. Overholts II, Dale L. & Bell, John E. & Arostegui, Marvin A., 2009. "A location analysis approach for military maintenance scheduling with geographically dispersed service areas," Omega, Elsevier, vol. 37(4), pages 838-852, August.
    19. Bhattacharya, Sourabh & Govindan, Kannan & Ghosh Dastidar, Surajit & Sharma, Preeti, 2024. "Applications of artificial intelligence in closed-loop supply chains: Systematic literature review and future research agenda," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 184(C).
    20. Su, Chao-Ton & Hsu, Jyh-Hwa, 2006. "Precision parameter in the variable precision rough sets model: an application," Omega, Elsevier, vol. 34(2), pages 149-157, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:36:y:2008:i:6:p:1072-1085. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.