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    Prioritizing highway safety improvement projects using a stochastic optimization model with robust constraints
    (Springer, 2023) Dadashi, Ali; Mirbaha, Babak; Atan, Zumbul; Tirkolaee, Erfan Babaee
    Road authorities need efficient tools to assign a limited budget to safety improvement projects. The process of prioritizing safety improvement projects needs to predict the benefits and costs of projects. In real-life situations and also because of variances of crash frequency, crash modification factor (CMF), and imposed expenses, the forecast of project costs/benefits would be highly affected by uncertainty. Hence, this work develops a model based on stochastic binary programming with robust constraints to treat the inherent uncertainties to prioritize road safety improvement projects. Robust optimization approach ensures a high probability of feasibility for the solutions obtained under uncertain conditions. Efficiency of the suggested model is assessed using data collected from a real case study. Numerical results reveal that neglecting stochastic analysis would lead to a profit loss up to 15% of the total benefits of safety plan and the importance of considering stochastic nature of the problem increases as the budget of a safety plan decreases. Furthermore, to make a comparative analysis, the developed methodology is compared with some conventional methods, e.g., integer programming model and incremental benefit-cost analysis. Main findings demonstrate some deviations concerning how each approach copes with uncertainty which leads to differences in the list of the selected projects with respect to budget limitation. Finally, the developed methodology is recommended to managers as a flexible and robust tool to assess and select projects through setting the level of robustness against cost data uncertainty, which is done according to the decision-makers' attitudes.
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    A robust optimization model to design an IoT-based sustainable supply chain network with flexibility
    (Springer, 2023) Goli, Alireza; Tirkolaee, Erfan Babaee; Golmohammadi, Amir-Mohammad; Atan, Zumbul; Weber, Gerhard-Wilhelm; Ali, Sadia Samar
    Supply chain network design is one of the most important issues in today's competitive environment. Moreover, the ratio of transportation costs to the income of manufacturing companies has increased significantly. In this regard, strategic decisions, as well as tactical decisions making, are of concern for supply chain network design. In this research, a flexible, sustainable, multi-product, multi-period, and Internet-of-Things (IoT)-based supply chain network with an integrated forward/reverse logistics system is configured where the actors are suppliers, producers, distribution centers, first- and second-stage customers, repair/disassembly centers, recycling centers, and disposal centers. In order to create flexibility in this supply chain, it is possible to dispatch directly to customers from distribution centers or manufacturing plants. For direct shipping, the application IoT system is taken into account in the transportation system to make them able to manage direct and indirect delivery at the same time. The options and considerations are then incorporated into a Multi-Objective Mixed-Integer Linear Programming model to formulate the problem which is then converted into a single-objective model using Goal Programming (GP) method. Moreover, in order to deal with uncertainty in the demand parameter, robust optimization approach is applied. The obtained results from a numerical example reveal that the proposed model is able to optimally design the supply chain network whose robustness is highly dependent on the budgets of uncertainty whereas up to 213.528% increase in the GP objective function is observed.