TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4

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Department: search.filters.department.İEÜ, Mühendislik Fakültesi, Endüstri Mühendisliği BölümüSubject: search.filters.subject.İmalat Mühendisliği

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  • Article
    Design and Analysis of Bi-Objective Flexible Job Shop Scheduling Problem: a Case Study in Construction Equipment Manufacturing Industry
    (Pamukkale Univ, 2020) Resat, Hamdi Giray
    This paper presents a design and development of mixed-integer linear optimization model for scheduling of flexible job-shop production problem under capacity constraints by using exact solution algorithm. Modelling approach is designed in order to introduce data analysis in real situations, minimize production time in production lines, reduce total production costs, and reveal important features of mathematical programming problem in detail. The main purpose of this study is to obtain faster and efficient Pareto solution sets for bi-objective problem by using epsilon-constraint method. Generated Pareto frontier using real life data is shared with decision makers. The GAMS programming language is used during the solution phase of a mixed-integer linear optimization model for bi-objective problem and production efficiency of the company is increased around 16.6% in terms of production cost.
  • Article
    Citation - WoS: 1
    Minimization of Number of Tool Switching Instants in Automated Manufacturing Systems
    (Gazi Univ, 2022-03-01) Gokgur, Burak; Özpeynirci, Selin
    This study addresses the problem of minimizing tool switching instants in automated manufacturing systems. There exist a single machine and a group of jobs to be processed on it. Each job requires a set of tools, and due to limited tool magazine capacity, and because it is not possible to load all available tools on the machine, tools must be switched. The ultimate goal, in this framework, is to minimize the total number of tool switching instants. We provide a mathematical programming model and two constraint programming models for the problem. Because the problem is proven to be NP-hard, we develop two heuristic approaches, and compare their performance with methods described in the literature. Our analysis indicates that our constraint programming models perform relatively well in solution quality and execution time in small-sized problem instances. The performance of our greedy approach shows potential, reaching the optimal solution in 82.5% of instances. We also statistically demonstrate that the search algorithm enhances the quality of the solution obtained by the greedy heuristic, particularly in large sets. Hence, the solution approach, i.e., the greedy heuristic and the search algorithm proposed in this study is able to quickly reach near-optimal solutions, showing that the method is appropriate for manufacturing settings requiring sudden adjustments.