Prefabricated buildings have the advantages of energy-saving, high efficiency, and high quality, which is energetically promoted in China’s construction industry. But the prefabricated buildings’ high construction cost hinders their rapid development. Around the higher construction cost of prefabricated buildings, this paper starts with the full life cycle of prefabricated buildings and selects four stages: design, component production, transportation, and construction. And constructs the evaluation index system of influencing factors of the prefabricated buildings cost. Based on this, this paper proposes an evaluation framework based on the game theory-cloud model to evaluate the influencing factors of prefabricated buildings costs. Taking a prefabricated building in Tianjin as the research object, the game theory combinatorial empowerment method and cloud model are used to evaluate the impact effect of prefabricated buildings cost. Game theory is used to optimize the subjective and objective weights determined by the attribute hierarchy model and the entropy weight method and to confirm the comprehensive weight of the evaluation index. The index’s weight is scientific and accurate, and the subjective influence of a single process is avoided or does not conform to the actual situation and so on. The cloud model is used to evaluate the cost impact of prefabricated buildings comprehensively. The experimental results show that the model is feasible; the scientific and accurate evaluation results are improved; and the model is simple to operate and has some reference value.
With the globalization of the supply chain and the change of demand environment, designing an effective logistic system in the sustainable development of the supply chain becomes more critical. This study proposes a location-routing problem to determine an efficient integration of single factory and multidistribution centers and multi-customers in uncertain demands. This problem can be regarded as an optimization integrating location, distribution decision, and routing management. The objective function is to minimize the total cost and satisfy all the requirements, which is a highly complex NP-hard problem, so a hybrid algorithm of genetic algorithm (GA) and tabu search (TS) algorithm is proposed. A fuzzy c-means clustering algorithm is used to produce an initial solution. Fuzzy triangular number and confidence interval transformation are used to deal with fuzzy customer demand. The research findings concludes that (i) determine the numbers of facilities with locations that should be opened and (ii) minimize the total cost in supply chain. The experiments prove that the proposed hybrid algorithm of GA and TS algorithm overcomes the defect of local optimum in the literature viewpoint, and the optimization algorithms can effectively solve the location-routing problem.INDEX TERMS Location-routing problem, fuzzy demand, genetic algorithm, tabu search, fuzzy c-means algorithm.
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