A heuristic approach for profit oriented disassembly lot-sizing problem

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2011

Öğrenci: MELİKE KAYA

Danışman: ZEYNEP PELİN BAYINDIR

Özet:

In this thesis, we work on adisassembly lot-sizing problem for multiple products with parts commonality,i.e., general product structure. We assume that supply of discarded products is infinite. When a product (or a subassembly) is disassembled, all its immediate child items are obtained,i.e., complete disassembly case.Intermediate and leaf items obtained are demandedbyexternal suppliers or remanufacturers. The maximum possible salesfor each intermediate and leaf item are known.Sales of the intermediate and leaf items are the revenue sources. The discarded products are purchased ata unit purchasing cost. The disassembly operation incurs a fixed and a variable disassembly cost. Due to this cost structure, intermediate and leaf items can be stocked incurring an inventory holding cost. We develop an integer programming formulation to determine the time and quantity of the discarded products to be purchased;thetime and quantity of the discarded products and the intermediateitemsto be disassembled; and the time and quantity of intermediate and leaf items to be soldin order tomaximizethe total profit over a finite planning horizon. We state that ourproblem is NP-hard by refering the study of Kim et. al. (2009). We propose a heuristic solution approach that solves the problem in a reasonable computational time and generates near optimal solutions. The solution approach is based on the idea of sequentially solving a relaxed version of the problem and one-period integer programming models.In a computational study, the performance of the heuristic approach is assessed for a number ofrandomly generated problem instances.The results of the computational study show that the solutions of the heuristic approacharevery close to the optimal and the best feasible solutions obtained within the time limit.