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Language: search.filters.language.EnglishSubject: search.filters.subject.Metaheuristic

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    Green two-echelon closed and open location-routing problem: Application of NSGA-II and MOGWO metaheuristic approaches
    (Springer Science+Business Media, 2022-06-02)
    Nowadays organizations outsource transportation of goods or services to reduce cost which leads to a particular type of problem called open location-routing. Also, each logistic organization possesses a limited number of specific vehicles that may not be enough in certain circumstances. This issue indicates the importance of simultaneously considering both open and closed routs. On the other hand, the growing concerns about the detrimental environmental impacts of human activities reveal the necessity of paying attention to environmental issues in logistics. In this study, a bi-objective mathematical programming model is proposed for two-echelon close and open location-routing problem (2E-COLRP) including two echelons of factories, depots and customers to minimize costs and CO2 emissions. The proposed model finds the optimal routs, optimal number of vehicles and facilities as well as the locations of facilities. The augmented epsilon constraint method is used as an exact method to solve the small-sized problems. Due to complexity of model, two metaheuristic algorithms named MOGWO and NSGA-II are utilized to tackle the problems. The efficiency of two aforementioned algorithms is evaluated in terms of several indices considering 22 problem instances with various sizes. The results show that MOGWO performs better than NSGA-II.
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    An integrated analytics approach to multi-project scheduling and material procurement with coordinated hub location
    (Elsevier B.V., 2026-03-01)
    This study presents an integrated framework combining multi-project scheduling, material procurement, and the hub location problem to simultaneously minimize project completion times and overall project and logistics costs. To address the challenge of allocating high-cost renewable resources, we incorporate rental options that balance the trade-off between additional rental expenses and potential project delays. A multi-objective optimization model is developed, integrating the scheduling of multiple projects with coordinated material procurement. To reduce logistics costs and improve delivery efficiency, consolidation hubs are introduced where materials from various suppliers are aggregated before being dispatched to project sites. The model considers the availability of renewable rental resources and storage space capacity while scheduling project activities. Due to the problem's computational complexity, two metaheuristic algorithms NSGA-II (Non-dominated Sorting Genetic Algorithm II) and MOSFS (Multi-objective Stochastic Fractal Search) are employed to obtain near-optimal solutions for large-scale scenarios. The proposed approach is validated through a real-world case study involving a bridge construction project and various benchmark instances of different sizes. Results indicate that while NSGA-II performs better on one performance metric, MOSFS consistently outperforms NSGA-II across most criteria, particularly in large-scale problems. The main contributions of this research include integrating project scheduling, material procurement, and hub location within a single unified framework. The model also incorporates renewable rental resources and realistic, type-specific storage capacity constraints that directly affect material flow and the initiation of project activities.
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