دانلود مقاله ISI انگلیسی شماره 8185
عنوان فارسی مقاله

دسته بندی فازی مبتنی بر رویکرد الگوریتم ژنتیک برای مسائل زمان هزینه وکیفیت تجارت : مطالعه موردی پروژه ساخت بزرگراه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
8185 2013 14 صفحه PDF سفارش دهید 10200 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A fuzzy clustering-based genetic algorithm approach for time–cost–quality trade-off problems: A case study of highway construction project
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Engineering Applications of Artificial Intelligence, Available online 5 June 2013

کلمات کلیدی
بهینه سازی چند هدفه - دسته بندی فازی - مدیریت پروژه
پیش نمایش مقاله
پیش نمایش مقاله دسته بندی فازی مبتنی بر رویکرد الگوریتم ژنتیک برای مسائل  زمان هزینه وکیفیت تجارت : مطالعه موردی پروژه ساخت بزرگراه

چکیده انگلیسی

Recently government agencies have started to utilize innovative contracting methods that provide incentives for improving construction quality. These emerging contracting methods place an enormous pressure on the contractors to improve construction quality. For a general contractor, which subcontracts most tasks of a project and invites a number of bids, choosing an appropriate bid which satisfies the time, cost and quality of construction project is complex and challenging. To solve this problem involving conflicting objectives, a fuzzy clustering-based genetic algorithm (FCGA) approach is proposed in this paper. A case study of highway construction is used to demonstrate the applicability of the proposed approach. A comparative study is conducted over three test cases involving varying dimensions and complexities to test performance of the proposed FCGA against existing approaches. Results reveal that the FCGA is capable of generating better Pareto front than other existing approaches.

مقدمه انگلیسی

Transportation agencies have been implementing various contracting methods for highway construction, such as (1) bidding on cost/time (i.e., A+B method) that encourages contractors to minimize project duration (Herbsman, 1995); (2) multi-parameter contract that provides incentives to the contractors for improving quality performance (Anderson and Russell, 2001); (3) incentive/disincentive contract that provides contractors with financial support to reduce a project completion time (Jaraiedi et al., 1995); and (4) warranties contract that attempts to improve construction quality by making contractors liable for the performance of the facility after project completion (Anderson and Russell, 2001). These contracting methods place an increasing pressure on the main contractors to improve their project performance. While many contractors invited bids for subcontracting activities based on time–cost trade-off only previously, they need to incorporate quality together with time–cost trade-off nowadays when subcontracting the activities due to the immense pressure on improving the performance quality by government agencies such as Department of Transportation. It is quite tedious and complex to decide which bid/subcontracting alternative to be accepted in planning and scheduling of the entire project, and to simultaneously satisfy the project requirements such as project time, cost and quality due to its conflicting nature.

نتیجه گیری انگلیسی

In this paper, we formulated the multi-objective mathematical model to mimic the time–cost–quality trade-off problem in construction industry. The problem has been formulated with conflicting project time, cost and quality objectives and is subjected to a number of constraints. We presented the detailed quality measurement approach using AHP technique to evaluate the quality of construction activity. A novel approach called fuzzy clustering-based genetic algorithm (FCGA) has been successful implemented to solve time–cost–quality trade-off problem. Furthermore, a case study of the highway construction project is considered to demonstrate the effectiveness of the proposed approach. The proposed approach agglomerated the computational procedure to determine the Pareto front and the decision making strategy, in order to provide a better compromise solution. In order to strengthen the proposed approach, various established techniques like NSGA-II, external repository of elite solutions, fuzzy-based clustering, and fuzzy decision making have been integrated with suggested improvement. A comparative study has also been conducted on the three test cases to demonstrate the superiority of the proposed approach with regard to the converged Pareto front. Study shows the capability of the FCGA to generate a better Pareto front than existing approaches. Furthermore, the performance analysis has been conducted to investigate the performance of the proposed approach on three test cases against those of other multi-objective optimization approaches, namely CSMOPSO, MOGA and SPEA-II. Performance analysis indicates that the FCGA outperforms CSMOPSO, MOGA and SPEA-II with regard to convergence degree, diversity and speed of convergence for all the three test cases considered. This research works provides an efficient multi-objective optimization methodology to solve construction time–cost–quality trade-off problem in limited time frame. Particularly, the proposed methodology helps to construction contractor or decision maker in determining the optimal set of subcontracting plans for all the activities that correspond to the minimum project time and cost, and maximum quality. The proposed FCGA is expected to assist researchers or industry practitioners in analyzing and planning the construction project effectively. As further directions of this study, it is planned to conduct a comprehensive study of the proposed approach on a more test instances of the time–cost trade-off problem. This is because the time–cost–quality trade-off problem is an extension of the time–cost-trade-off. However, literature is not rich with the test instances of time–cost trade-off problem. Therefore, in future, random network generator can be used to generate the test instance of time–cost trade-off problem. Moreover, it is important to consider the integration of an automatic parameter tuning methodology with the proposed FCGA to increase the applicability of the proposed approach on time–cost–quality trade-off problem. Such integration can reduce the time consumed by the proposed approach to identify the appropriate values of initial algorithmic parameters.

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