Abstract
Bu calismada esnek atolye tipi uretim cizelgeleme probleminin kapasite kisitlari altinda programlanmasi icin karmasik tamsayi dogrusal optimizasyon modelinin tasarlanmasi ve gelistirilmesi kesin cozum algoritmasi kullanilarak saglanmistir. Modelleme yaklasimi, gercek vakalar uzerinden veri analizini saglamak, uretim hatlarindaki uretim suresini en aza indirmek, toplam uretim maliyetlerini azaltmak ve matematiksel programlama probleminin onemli ozelliklerini detayli olarak ortaya koymak icin tasarlanmistir. Bu calismanin temel amaci, iki amacli cizelgeleme problemleri icin ϵ-kisit yontemini kullanarak daha hizli ve verimli cozum setleri elde etmektir. Gercek hayat verileri kullanilarak elde edilen Pareto cozum setleri karar vericiler ile paylasilmistir. Iki amacli cizelgeleme problemi icin gelistirilen karmasik tamsayi dogrusal optimizasyon modelinin cozum asamasinda GAMS programlama dili kullanilmistir ve sirketin uretim maliyetlerinde %16.6’lik bir iyilestirme gerceklestirilmistir.
Highlights
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 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
Summary
Considering global competition conditions and increased awareness to the environmental factors, companies begin to develop innovative and adaptive management and production mechanisms in their systems. According to perspective of companies, customer losses due to delayed orders are very critical topic and required to be taken preventive actions immediately. This competitive environment pushes companies to offer special solutions for their customers to serve in shorter lead times. A single machine-scheduling problem in order to minimize maximum completion time is studied by Van Wassenhove and Baker [3]. Kacem et al [6] introduces two new approaches to solve job-shop scheduling problems in the study; partial and full flexibility. All machines having same characteristics can be processed at the same time or different times in partial flexible job-shop problems. Some of these hybrid models can propose some feasible solution sets for combinatorial NP-hard problems
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