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    A closed vendor managed inventory system under a mixed fleet of electric and conventional vehicles
    (Pergamon-Elsevier Science Ltd, 2021) Soysal, Mehmet; Belbag, Sedat; Sel, Cagri
    In a closed-loop supply chain, where a Vendor Managed Inventory system is executed, the Closed-Loop Inventory Routing Problem is one of the main problems confronted by logistics decision-makers. This study addresses a Closed-Loop Inventory Routing Problem under a mixed fleet of electric and conventional vehicles. The problem is formulated as a Mixed-Integer Linear Programming model and a Fix&Optimize algorithm is developed to tackle larger problem instances. The proposed decision support models incorporate comprehensive estimation approaches for energy consumption of both electric and conventional vehicles that allow to better estimate fuel and electric cost and transportation emissions. The models respect uncertain reverse returnable transport items flow from customers as well. The numerical analyses demonstrate the benefits that could be obtained by means of the provided models. The Fix&Optimize heuristic yields in 5.72% lower costs within 59.23% shorter computation times on average compared to the Mixed-Integer Linear Programming model. The proposed models are capable to provide trade-off analyses for sustainable logistics management.
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    A heuristic approach for green vehicle routing
    (Edp Sciences S A, 2021) Soysal, Mehmet; Cimen, Mustafa; Sel, Cagri; Belbag, Sedat
    This paper addresses a green capacitated vehicle routing problem that accounts for transportation emissions. A Dynamic Programming approach has been used to formulate the problem. Although small-sized problems can be solved by Dynamic Programming, this approach is infeasible for larger problems due to the curse of dimensionality. Therefore, we propose a Dynamic Programming based solution approach that involves the ideas of restriction, simulation and online control of parameters to solve large-sized problems. The added values of the proposed decision support tool have been shown on a small-sized base case and relatively larger problems. Performance comparisons of the proposed heuristic against other existing Dynamic Programming based solution approaches reveal its effectiveness, as in most of the instance-setting pairs, the proposed heuristic outperforms the existing ones. Accordingly, the proposed heuristic can be used as an alternative decision support tool to tackle real routing problems confronted in sustainable logistics management.
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    INVIGILATORS ASSIGNMENT IN PRACTICAL EXAMINATION TIMETABLING PROBLEMS
    (Univ Cincinnati Industrial Engineering, 2022) Cimen, Mustafa; Belbag, Sedat; Soysal, Mehmet; Sel, Cagri
    This paper addresses an invigilator assignment problem. The problem deals with a set of exams, each of which requires a given number of invigilators. The aim is to prepare a conflict-free schedule where all invigilator requirements of the exams are met. In this study, the conflict-free schedule is determined by a mixed-integer linear programming model and a heuristic algorithm that accounts for the following real-life concerns; assignment of the invigilators responsible for an exam, reduction of the number of successive invigilation duties, fair distribution of total workload and unfavorable workload among invigilators and, prioritization of the assignments based on invigilators??? profession. The applicability of the proposed model and the heuristic algorithm has been shown on eight different real-life problems of leading public universities in Turkey and further eight larger-sized examinations set up based on the real settings. In universities, the real schedules are manually prepared by a faculty team. Compared to the assignment of the faculty team responsible for the examination of timetabling in which balancing only the numbers of duties, we achieved to 86% decrease in the total positive error of invigilation hours by fairly distributing the invigilation duties in the model results. Besides, the following improvements are achieved by applying the proposed model; a 56% decrease in the total number of successor assignments, a 44% decrease in the total unfavorable time, and a 23% increase in the total number of department-based assignments. The heuristic algorithm improves the team schedules by 4% in terms of the total positive error of total invigilation hours and 57% in terms of the total number of successive exam assignments. Accordingly, the proposed model and the heuristic algorithm can be used as a decision support tool by the faculty team.