A knowledge-driven multiobjective optimization algorithm for the transportation of assembled prefabricated components with multi-frequency visits

In modern construction industries, an efficient routing strategy is a challenging issue to satisfy the requirements of transportation bulk goods and minimization of different conflicting objectives. To address this problem, a multi-frequency vehicle routing problem for prefabricated components (MFVRP-PC) is introduced, where the conflicting costs of multiple-trucks and the dependencies of service time and truckloads are considered. To solve it, a knowledge-driven multiobjective optimization algorithm is developed to minimize the total transportation cost and makespan, simultaneously. First, two types of problem-specific knowledge are derived. Then, a three-dimensional vector is designed for the solution representation. Next, split-based search operators are devel-oped to enhance global and local search abilities. Moreover, a dynamic weight adjustment strategy is embedded to enhance knowledge interaction. Experimental results show that the proposed algorithm is effective compared with state-of-the-art algorithms for solving MFVRP-PC, where a set of efficient Pareto solutions can be provided for the decision-makers. This study extends the theoretical foundation for the deep integration of construction scheduling, optimization, and industrial applications.

Automated summary

In modern construction industries, the efficient routing strategy for transportation and cost minimization is a challenging issue. To address this, a multi-frequency vehicle routing problem for prefabricated components (MFVRP-PC) is introduced. A knowledge-driven multiobjective optimization algorithm is developed to minimize total transportation cost and makespan, achieving effective results compared to state-of-the-art algorithms. It provides efficient Pareto solutions for decision-makers and extends the theoretical foundation for construction scheduling, optimization, and industrial applications.