Tradeoff time cost quality in repetitive construction project using fuzzy logic approach and symbiotic organism search algorithm
TL;DR: F fuzzy logic is utilized to model the uncertainty embedding α-cut approach and it is shown that the proposed model is powerful to explore the solution for the shortest project duration with minimum incurred cost and high overall quality in the construction project.
Abstract: Time cost and quality are three important factors in planning and controlling construction projects. Getting these factors in balance, which minimizes the project duration, the total project cost, and maximizes the total quality could define the success of a construction project. Aside from planning and estimating the project properly, a consideration of uncertainty while implementing the project is needed to represent a more realistic outcome for time, cost, and quality trade-off (TCQT) problem in construction projects. In this paper, fuzzy logic is utilized to model the uncertainty embedding α-cut approach to see the effect of the uncertainty on the time, cost, and quality of the project. Then multi-objective Symbiotic Organism Search (SOS) algorithm is applied to find a set of the optimal solution in different uncertainty levels and provide the project manager several possible actions to implement the project. Two numerical case studies of a repetitive construction project were analyzed to see the effectiveness of the model and its capability to solve the TCQT problem in the construction project. The results showed that the proposed model is powerful to explore the solution for the shortest project duration with minimum incurred cost and high overall quality in the construction project. In comparison to the other widely used methods and other algorithms, the proposed model is proven to be effective and competitive in solving the TCQT problem.
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TL;DR: The fuzzy multi-criteria decision-making (MCDM) methods are exploited to choose the best mode for performing each activity for construction project scheduling so that the projects can be completed in fewer durations and costs along with higher quality.
Abstract: The increasing number of construction projects together with the limited resources of organizations led to tough competition for achieving project goals. Time, cost, and quality have been known as the project iron triangle. Project managers attempt to allocate the appropriate resources and make the best decisions for accomplishing projects with the shortest durations, lowest costs, and the highest quality. No study has examined the time–cost–quality trade-off problem with decision-making approaches. In this study, the fuzzy multi-criteria decision-making (MCDM) methods are exploited to choose the best mode for performing each activity. For this purpose, the SWARA method is applied to determine the importance weights of time, cost, and quality. In addition, the TOPSIS (Technique for the Order Preference by Similarity to Ideal Solution) technique is used to rank and select the best activity execution modes. The proposed model is implemented on two medium- and large-size construction projects to evaluate its efficiency. Several execution modes with fuzzy duration, cost, and quality are considered for each project activity. Finally, sensitivity analysis is conducted taking three different conditions into account: the shortest duration of the execution modes, the lowest cost of the execution modes, and the highest quality of execution modes for each activity. The solution of each trade-off is compared with the solution obtained from the fuzzy SWARA–TOPSIS method. The schedule is developed according to the best execution mode for each project activity. The obtained results in two different construction projects show significant improvements in the overall project objectives so that the projects can be completed in fewer durations and costs along with higher quality. Because of the higher importance of cost, the cost of each activity is closer to the lowest cost. The activity duration is also closer to the most likely duration, and quality is closer to the high-quality level. The application of this approach can create new opportunities for research and knowledge development in the field of construction project scheduling.
8 citations
1 citations
TL;DR: In this paper , a structural model of four cooperative development elements, including functional cooperative, operational cooperative, information cooperative, and operation cooperative, is constructed with the guidance of system coordination and a cooperative efficiency evaluation system is established based on it.
Abstract: In order to effectively evaluate the cooperative efficiency of intelligent transportation facilities, a structural model of four cooperative development elements, including functional cooperative, operational cooperative, information cooperative, and operation cooperative, is constructed with the guidance of system coordination and a cooperative efficiency evaluation system is established based on it. Then, a dynamic efficiency evaluation model based on variable weight and matter-element extension method was constructed to describe the cooperative efficiency of intelligent transportation facilities and analyze the cooperative efficiency of key road sections in the Jinan area as an example. The results show that of the ten sections, two are in poor performance status, three are in good performance status, and five are in excellent performance status. The four indexes of vertical cooperative construction, functional scheduling level, information element completeness, and multi-departmental information integration level have the most significant impact on facility cooperative efficiency and are the most sensitive; the three indexes of plan executability, functional ease of upgrading, and space–time alignment rate have the most negligible impact on facility cooperative efficiency and are the least sensitive.
1 citations
TL;DR: In this article , two approaches are presented to handle proposed fuzzy multi-objective mathematical model under fuzzy uncertainty to deal with the project cost-risk-quality tradeoff problem (CRQT) under time constraints.
Abstract: To successfully finalize projects and attain their determined purposes, it is indispensable to control all success criteria of a project. The time–cost trade-off (TCT) is known as a prevalent and efficient approach applied when the planned finish date of a project is not admitted by stakeholders, and consequently, the project duration must be decreased. This paper proposes a new mathematical model under fuzzy uncertainty to deal with the project cost–risk–quality trade-off problem (CRQT) under time constraints. Because of the unique nature of projects and their uncertain circumstances, applying crisp values for some project parameters does not seem appropriate. Hence, this paper employs fuzzy sets to resolve these weaknesses. In this study, two approaches are presented to handle proposed fuzzy multi-objective mathematical model. First, fuzzy credibility theory and then goal attainment method are used. Secondly, the model is solved by a fuzzy method based on expected interval and value and augmented ɛ-constraint method. A project from the literature review is adopted and solved by the presented methodology. The results demonstrate the accuracy and efficiency of the two proposed approaches for the introduced practical problem.
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01 Aug 1996
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Abstract: A fuzzy set is a class of objects with a continuum of grades of membership. Such a set is characterized by a membership (characteristic) function which assigns to each object a grade of membership ranging between zero and one. The notions of inclusion, union, intersection, complement, relation, convexity, etc., are extended to such sets, and various properties of these notions in the context of fuzzy sets are established. In particular, a separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
52,705 citations
TL;DR: The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface.
Abstract: Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.
7,512 citations
TL;DR: This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques.
Abstract: An important issue in multiobjective optimization is the quantitative comparison of the performance of different algorithms. In the case of multiobjective evolutionary algorithms, the outcome is usually an approximation of the Pareto-optimal set, which is denoted as an approximation set, and therefore the question arises of how to evaluate the quality of approximation sets. Most popular are methods that assign each approximation set a vector of real numbers that reflect different aspects of the quality. Sometimes, pairs of approximation sets are also considered. In this study, we provide a rigorous analysis of the limitations underlying this type of quality assessment. To this end, a mathematical framework is developed which allows one to classify and discuss existing techniques.
3,702 citations
TL;DR: Results confirm the excellent performance of the SOS method in solving various complex numerical problems and compared with well-known optimization methods.
1,152 citations
TL;DR: Experimental results indicate that the proposed approach to solving the constrained optimization problems may yield better solutions to engineering problems than those obtained by using current algorithms.
514 citations