Researcher:
Akkan, Can

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Can

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Akkan

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Akkan, Can

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1996 - 199922000 - 20052

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Researcher Of Publications: search.filters.isAuthorOfPublication.a101f4a5-2219-4883-a401-a36f34fdf54e

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Now showing 1 - 4 of 4
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    Finite-capacity scheduling-based planning for revenue-based capacity management
    (Elsevier Science Bv, 1997) Department of Business Administration; Akkan, Can; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; N/A
    Finite-capacity scheduling can be argued to be a crucial component of revenue-based capacity management. In that case, one way to plan production is to reserve portions of capacity for incoming customer orders as they arrive, in real-time. In such a planning method, the way these work-orders are scheduled affects the useable capacity, due to fragmentation of the time-line. Assuming the work-orders are rejected if they cannot be inserted into the existing schedule, we develop heuristics to minimise the present-value of the cost of rejecting orders and inventory holding cost due to early completion. We perform simulation experiments to compare the performance of these heuristics in addition to some common heuristics used in practice.
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    Overtime scheduling: an application in finite-capacity real-time scheduling
    (Taylor & Francis, 1996) Department of Business Administration; Akkan, Can; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; N/A
    Negotiating and meeting due-times for work-orders is often the most important concern of managers of manufacturing systems. We propose a new approach called overtime scheduling that determines on which work-centres, when and how much overtime is required to meet a requested due-time with minimum overtime cost. This method would be used as a part of a finite-capacity real-time scheduling and planning system. We propose a work-order insertion based approach, where a new work-order is scheduled without substantially changing the schedule of previously scheduled work-orders. Based on this approach, we characterise the solution space and present experimental results on the performances of several heuristics.
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    Generating two-terminal directed acyclic graphs with a given complexity index by constraint logic programming
    (Elsevier Science Inc, 2005) Drexl, Andreas; Kimms, Alf; Department of Business Administration; Akkan, Can; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; N/A
    Two-terminal directed acyclic graphs (st-dags) are used to model problems in many areas and, hence, measures for their topology are needed. Complexity Index (CI) is one such measure and is defined as the minimum number of node reductions required to reduce a given st-dag into a single-arc graph, when used along with series and parallel reductions. In this research we present a constraint logic programming algorithm (implemented in ILOG's OPL-Optimization Programming Language) for the generation of st-dags with a given CI. To this end the complexity graph with a maximum matching of CI, the dominator tree, the reverse dominator tree and the st-dag are characterized by a set of constraints. Then a multi-phase algorithm is presented which searches the space described by the set of constraints. Finally, the computational performance of the algorithm is tested.
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    The two-machine flowshop total completion time problem: improved lower bounds and a branch-and-bound algorithm
    (Elsevier Science Bv, 2004) Department of Business Administration; Department of Business Administration; Akkan, Can; Karabatı, Selçuk; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; College of Administrative Sciences and Economics; N/A; 38819
    This paper presents a branch-and-bound algorithm for the two-machine flowshop scheduling problem with the objective of minimizing the sum of completion times. The main feature of the branch-and-bound algorithm is a new lower bounding scheme that is based on a network formulation of the problem. With extensive computational tests, we demonstrate that the branch-and-bound algorithm can solve problems with up to 60 (45) jobs, where processing times are uniformly distributed in the [1,10] ([1,100]) range.