Researcher:
Özyurt, Zeynep

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Master Student

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Zeynep

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Özyurt

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Özyurt, Zeynep

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Researcher Of Publications: search.filters.isAuthorOfPublication.5e0681c4-15ff-43e5-a499-e110b152c2d6

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    Solving the multi-depot location-routing problem with lagrangian relaxation
    (Springer, 2007) N/A; N/A; Department of Business Administration; Department of Business Administration; Özyurt, Zeynep; Aksen, Deniz; Master Student; Faculty Member; Graduate School of Sciences and Engineering; College of Administrative Sciences and Economics; N/A; 40308
    Multi-depot Location-Routing Problem (MDLRP) is about finding the optimal number and locations of depots while allocating customers to depots and determining vehicle routes to visit all customers. In this study we propose a nested Lagrangian relaxation-based method for the discrete uncapacitated MDLRP. An outer Lagrangian relaxation embedded in subgradient optimization decomposes the parent problem into two subproblems. The first subproblem is a facility location-like problem. It is solved to optimality with Cplex 9.0. The second one resembles a capacitated and degree constrained minimum spanning forest problem, which is tackled with an augmented Lagrangian relaxation. The solution of the first subproblem reveals a depot location plan. As soon as a new distinct location plan is found in the course of the subgradient iterations, a tabu search algorithm is triggered to solve the multi-depot vehicle routing problem associated with that plan, and a feasible solution to the parent problem is obtained. Its objective value is checked against the current upper bound on the parent problem's true optimal objective value. The performance of the proposed method has been observed on a number of test problems, and the results have been tabulated.
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    Solving the multi-depot locationrouting problem with lagrangian relaxation
    (Springer Nature, 2007) Department of Business Administration; N/A; Department of Business Administration; Aksen, Deniz; Özyurt, Zeynep; Faculty Member; Master Student; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 40308; N/A
    Multi-depot Location-Routing Problem (MDLRP) is about finding the optimal number and locations of depots while allocating customers to depots and determining vehicle routes to visit all customers. In this study we propose a nested Lagrangian relaxation-based method for the discrete uncapacitated MDLRP. An outer Lagrangian relaxation embedded in subgradient optimization decomposes the parent problem into two subproblems. The first subproblem is a facility location-like problem. It is solved to optimality with Cplex 9.0. The second one resembles a capacitated and degree constrained minimum spanning forest problem, which is tackled with an augmented Lagrangian relaxation. The solution of the first subproblem reveals a depot location plan. As soon as a new distinct location plan is found in the course of the subgradient iterations, a tabu search algorithm is triggered to solve the multi-depot vehicle routing problem associated with that plan, and a feasible solution to the parent problem is obtained. Its objective value is checked against the current upper bound on the parent problem's true optimal objective value. The performance of the proposed method has been observed on a number of test problems, and the results have been tabulated.
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    Open vehicle routing problem with driver nodes and time deadlines
    (Operational Research Society, 2007) Aras, Necati; Department of Business Administration; N/A; Department of Business Administration; Aksen, Deniz; Özyurt, Zeynep; Faculty Member; Master Student; College of Administrative Sciences and Economics; Graduate School of Sciences and Engineering; 40308; N/A
    In this paper, we consider a variant of the open vehicle routing problem in which vehicles depart from the depot, visit a set of customers, and end their routes at special nodes called driver nodes. A driver node can be the home of the driver or a parking lot where the vehicle will stay overnight. The resulting problem is referred to as the open vehicle routing problem with driver nodes (OVRP-d). We consider three classes of OVRP-d: with no time constraints, with a maximum route duration, and with both a maximum route duration as well as time deadlines for visiting customers. For the solution of these problems, which are not addressed previously in the literature, we develop a new tabu search heuristic. Computational results on randomly generated instances indicate that the new heuristic exhibits a good performance both in terms of the solution quality and computation time.