Seru order acceptance and scheduling in the distributed production network considering maintenance

نوع: Type: thesis

مقطع: Segment: masters

عنوان: Title: Seru order acceptance and scheduling in the distributed production network considering maintenance

ارائه دهنده: Provider: Parisa Hajipour

اساتید راهنما: Supervisors: Ph.D Javad Behnamian

اساتید مشاور: Advisory Professors:

اساتید ممتحن یا داور: Examining professors or referees: Ph.D Amir Saman Kheirkhah Gheh , Ph.D Hamid Reza Dezfoolian

زمان و تاریخ ارائه: Time and date of presentation: 2024

مکان ارائه: Place of presentation: 62

چکیده: Abstract: Abstract: In this research, a scheduling and order acceptance problem is examined within a distributed production network, where customer orders must be allocated across factories, and some must be processed in production environments equipped with seru systems according to the needs and constraints of the problem. The need for optimization and increased efficiency in production networks has intensified, particularly in competitive and dynamic environments where demand shifts rapidly. Here, fast response and flexibility in production systems are crucial. Seru systems, by dividing production lines into smaller, independent units, allow manufacturers to respond more effectively to varying demands. Additionally, preventive maintenance, as part of the production process, reduces unexpected stoppages and extends equipment life, ultimately improving overall system productivity. Seru systems provide high flexibility in responding to variable demands by splitting production lines into small, independent units. Furthermore, preventive maintenance is incorporated into the production process to prevent sudden stops. Operational decision-making includes optimal worker allocation, production scheduling across factories, and implementation of maintenance strategies. To achieve optimal solutions, a mixed-integer nonlinear programming (MINLP) model is designed and solved using GAMS software and exact solvers. For solving large-scale problems, two types of memetic algorithms were employed: Memetic Type 1, which uses simulated annealing for local search, and Memetic Type 2, which utilizes hill-climbing local search. The results indicated that Memetic Type 2 generally outperformed in terms of solution time, although Memetic Type 1 occasionally achieved higher-quality solutions. The genetic algorithm, one of the most well-known optimization methods, was also employed as a baseline and competitor in this problem, and its results were compared with both types of memetic algorithms to ensure the proposed algorithm’s efficiency. This comparison demonstrated that both memetic algorithms were more efficient than the genetic algorithm in terms of solution quality and solution time. In large-scale problem-solving, a suitable combination of local searches leads to improved results.

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