Browsing by Author "Sancar Edis R."

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Mixed integer programming approaches to partial disassembly line balancing and sequencing problem
    (Elsevier Ltd, 2022) Edis E.B.; Sancar Edis R.; Ilgin M.A.
    Product recovery has received greater attention in recent years mainly due to increased environmental awareness of consumers and stricter environmental regulations imposed by governments. In product recovery, disassembly of the product into its constituent parts is the most significant activity and generally performed on a disassembly line. During disassembly, a complete or partial disassembly of the product may be preferred. In complete disassembly, all parts must be disassembled, while partial disassembly allows to disassemble a subset of parts (e.g., the ones with relatively high revenues). This study deals with a partial disassembly line balancing and sequencing (PDLBS) problem considering revenues of parts to be disassembled, general workstation cost, additional cost of workstation(s) with hazardous parts, and cost of direction changes. For the PDLBS problem, a generic mixed integer programming (MIP) model, with the aim of maximizing total profit, is developed. To strengthen the MIP formulation, two sets of valid inequalities are proposed. The computational results show that the MIP model with valid inequalities is able to provide optimal solutions for the PDLBS problems with up to 30 tasks. To obtain near-optimal solutions for large-sized problems, a MIP-based solution approach is proposed. The proposed approach decomposes the entire MIP model into selection and assignment (SA) and sequencing (SEQ) models. The SA model is an exact relaxation of the MIP model (with valid inequalities) obtained by removing all the sequencing variables and constraints. Hence, SA model also produces an efficient upper bound for the PDLBS problem. The SEQ model, accordingly, aims to find an optimal sequence of tasks subject to the fixed selection and assignment of tasks provided by the SA model. The computational results show that the proposed MIP-based solution approach provides efficient solutions with small optimality gaps for large-sized problems. © 2021 Elsevier Ltd
  • No Thumbnail Available
    Item
    Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure
    (Institute for Ionics, 2023) Edis E.B.; Uzun Araz O.; Eski O.; Sancar Edis R.
    Warehouse operations have a significant role to survive in today’s competitive world. Hence, companies introduce various solutions to improve efficiency of warehouses which is mainly affected by the performance of storage operations. This study deals with a real-life storage location and assignment problem encountered in a fastener company where several orders consisting steel coils are to be assigned into storage areas. Three individual objective functions; minimizing the number of lanes to be used, minimizing area usage, and maximizing volume utilization are considered. For the investigated problem, first, an integer linear programming (ILP) model is developed. Then, a greedy randomized adaptive search procedure (GRASP) which provides quick and efficient solutions is proposed. The proposed methods are applied to the real problem case and the results are compared with the current storage assignment. Moreover, through an extensive computational study, the performances of proposed methods are evaluated on a set of test problems with different range of characteristics. The computational results show that the ILP model proves optimality in most of the problem instances within reasonable computation times, while the GRASP gives quick solutions with small optimality gaps. © 2022, The Author(s) under exclusive licence to Sociedad de Estadística e Investigación Operativa.