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Dr. Sou-Sen Leu
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology

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Journal article
Published: 20 April 2021 in Sustainability
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The classical dependency structure matrix (DSM) can effectively deal with iterative schedules that are highly coupled and interdependent, such as the design process and the concurrent process. Classical DSM generally follows the assumption that the least iteration occurs to achieve the shortest completion time. Nevertheless, the assumption may not hold because tasks ought to be re-visited several times if the design qualities do not meet the requirements. This research proposed a novel iterative scheduling model that combines the classical DSM concept with quality equations. The quality equations were used to determine the number of tasks that ought to be re-visited for fulfilling quality requirements during the iterative design process. Moreover, resources for concurrent activities are generally limited in the real world. Resource allocation should be incorporated in scheduling to avoid the waste and shortage of resources on a design project. This research proposed a new iterative scheduling model based on the classical DSM to optimize the iterative activities’ structure in terms of minimizing completion time with the consideration of design quality under resource constraints. A practical design schedule was introduced to demonstrate the applicability of the proposed DSM algorithm.

ACS Style

Sou-Sen Leu; Theresia Suparman; Cathy Hung. An Exploratory Study on Optimal Iterative Design Schedules with the Consideration of Design Quality and Resource Constraints. Sustainability 2021, 13, 4584 .

AMA Style

Sou-Sen Leu, Theresia Suparman, Cathy Hung. An Exploratory Study on Optimal Iterative Design Schedules with the Consideration of Design Quality and Resource Constraints. Sustainability. 2021; 13 (8):4584.

Chicago/Turabian Style

Sou-Sen Leu; Theresia Suparman; Cathy Hung. 2021. "An Exploratory Study on Optimal Iterative Design Schedules with the Consideration of Design Quality and Resource Constraints." Sustainability 13, no. 8: 4584.

Journal article
Published: 15 May 2020 in Energies
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After the long-term operation of reservoir facilities, they will become nonoperational due to the material deterioration and the performance degradation. One of crucial decisions is to determine the maintenance or replacement of the facilities in a cost-effective manner. Conventional replacement models seldom consider the maintenance effect. The facilities after maintenance are generally not as good as new, but are relatively restored. The target of this study is to establish a replacement decision model of the reservoir facilities under imperfect maintenance. By combining the theories of reliability analysis, imperfect maintenance, and engineering economics, the best timing of replacement that achieves cost-effectiveness is analyzed and proposed. Lastly, based on the design of experiments (DOE) and simulation, the regression curve chart for the economical replacement decision is established. Once the failure rate, the age of recovery after maintenance, and the ratio of maintenance cost to replacement cost are estimated based on historical data, the cost-effective replacement time of hydraulic machinery facilities will be efficiently determined.

ACS Style

Sou-Sen Leu; Tao-Ming Ying. Replacement and Maintenance Decision Analysis for Hydraulic Machinery Facilities at Reservoirs under Imperfect Maintenance. Energies 2020, 13, 2507 .

AMA Style

Sou-Sen Leu, Tao-Ming Ying. Replacement and Maintenance Decision Analysis for Hydraulic Machinery Facilities at Reservoirs under Imperfect Maintenance. Energies. 2020; 13 (10):2507.

Chicago/Turabian Style

Sou-Sen Leu; Tao-Ming Ying. 2020. "Replacement and Maintenance Decision Analysis for Hydraulic Machinery Facilities at Reservoirs under Imperfect Maintenance." Energies 13, no. 10: 2507.

Journal article
Published: 19 December 2014 in KSCE Journal of Civil Engineering
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The challenge of construction procurement negotiation arises partly because each negotiation side has private information on their payoff function but is uninformed of the values and strategies of the opposite side. The uncertain and limited contractor information as well as complex correlations among various factors affect contractor behaviors, making learning a supplier’s negotiation strategy and deciding the appropriate offer price difficult for contractors. Therefore, the purpose of this study was to apply the forecasting ability of a novel Bayesian Fuzzy Game Model (BFGM) in providing negotiation support and recommendations for contractors determining an appropriate current bid price. The validation analysis revealed that the contractor can foresee the supplier’s future bidding strategy to increase the success rate and profit, reduce the time spent in unnecessary negotiation, and improve negotiation efficiency in the construction material procurement process. Based upon the survey data from Taiwan and Vietnam, the preliminary validation of the model shows that both contractor and supplier can obtain the profitable agreement by applying the BFGM within a reasonable negotiation time.

ACS Style

Sou-Sen Leu; Pham Vu Hong Son; Pham Thi Hong Nhung. Optimize negotiation price in construction procurement using Bayesian Fuzzy Game Model. KSCE Journal of Civil Engineering 2014, 19, 1566 -1572.

AMA Style

Sou-Sen Leu, Pham Vu Hong Son, Pham Thi Hong Nhung. Optimize negotiation price in construction procurement using Bayesian Fuzzy Game Model. KSCE Journal of Civil Engineering. 2014; 19 (6):1566-1572.

Chicago/Turabian Style

Sou-Sen Leu; Pham Vu Hong Son; Pham Thi Hong Nhung. 2014. "Optimize negotiation price in construction procurement using Bayesian Fuzzy Game Model." KSCE Journal of Civil Engineering 19, no. 6: 1566-1572.

Journal article
Published: 30 June 2002 in Automation in Construction
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Precast production tasks mainly consist of reinforcement cage prefabrication, form fabrication, installment of reinforcement cages, mosaic or tiling fitting, placing concrete, steam curing, cleaning and finishing operations. These tasks are generally arranged as a flow shop process, in which all the precast elements are processed in the same processing order through all the tasks. The precast production needs to take into account the impact of limited resources, including equipment and skilled labor, on production makespan. Moreover, to economically and effectively utilize these valuable resources, mixed production gains a potential advantage over the conventional separate production under resource constraints. A resource-constrained mixed-production flow-shop scheduling system is proposed in this paper. A genetic algorithm-based searching technique is adopted in the system to provide the optimal or near-optimal combination of production sequences, resource utilization, and minimum makespan in consideration of resource constraints and mixed production. Sensitivity analysis is further conducted to identify the impact of mixed production strategies on the overall precast production makespan.

ACS Style

Sou-Sen Leu; Shao-Ting Hwang. GA-based resource-constrained flow-shop scheduling model for mixed precast production. Automation in Construction 2002, 11, 439 -452.

AMA Style

Sou-Sen Leu, Shao-Ting Hwang. GA-based resource-constrained flow-shop scheduling model for mixed precast production. Automation in Construction. 2002; 11 (4):439-452.

Chicago/Turabian Style

Sou-Sen Leu; Shao-Ting Hwang. 2002. "GA-based resource-constrained flow-shop scheduling model for mixed precast production." Automation in Construction 11, no. 4: 439-452.