Just in Time open shop scheduling considering stability risks

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Just in Time open shop scheduling considering stability risks

ارائه دهنده: Provider: Marjan Majidi

اساتید راهنما: Supervisors: dr. Javad Behnamian

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: dr. kheirkhah - dr. dezfoulian

زمان و تاریخ ارائه: Time and date of presentation: 06/20/2022

مکان ارائه: Place of presentation: room no 10

چکیده: Abstract: In real-world production environments, scheduling often encounters unexpected events that run the risk of scheduling inefficiencies and instability of the production system. This study minimizes risks in both performance and stability for open shop scheduling under random machine breakdowns. In this research, the operation-block based limited buffer time insertion approach is used to produce a predictive schedule; Which allows additional idle time to be inserted to control stability risks. On the other hand, due to the importance of on time delivery of products or services in many industries, using Just In Time (JIT) scheduling can be a good way to reduce system costs (corruption, insurance, etc.). This work is presented in the form of a mixed integer non-linear programming aiming to find a predictive schedule with two objectives of minimizing the earliness and tardiness. In order to solve the model in small dimensions, the augmented epsilon constraint Method and Gams software were utilized. Since the considered problem is an NP-Hard problem, the Nondominated Sorting Genetic Algorithm (NSGA-II) was developed to solve large samples. Finally, comparative criteria for the sample problems solved by the main algorithm in competition with the multi-objective variable neighborhood search algorithm (MOVNS) were calculated and the obtained results were analyzed using the Kruskal-Wallis nonparametric test. The analysis revealed negligible difference between the performance of two algorithms based on diversity and spacing criteria. While the MOVNS algorithm has less running time, the NSGA-II algorithm offers better performance in both Number of Pareto Solution and Mean Ideal Distance criteria.

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