Optimal design of single-tuned passive filters using response surface methodology

This paper presents an approach based on Response Surface Methodology (RSM) to find the optimal parameters of the single-tuned passive filters for harmonic mitigation. The main advantages of RSM can be underlined as easy implementation and effective computation. Using RSM, the single-tuned harmonic filter is designed to minimize voltage total harmonic distortion (THDV) and current total harmonic distortion (THDI). Power factor (PF) is also incorporated in the design procedure as a constraint. To show the validity of the proposed approach, RSM and Classical Direct Search (Grid Search) methods are evaluated for a typical industrial power system

Balancing of mixed-model two-sided assembly lines with underground workstations: A mathematical model and ant colony optimization algorithm

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordMixed-model assembly lines allow the production of different product variants in mass quantities on the same assembly line. In studies addressing mixed-model assembly with two-sided lines, assembly line stations are classified as left-side or right-side stations depending on the operation side to which they are allocated. However, underground stations are also utilized in industry to perform tasks that need to be done underneath the product being assembled on the line. This paper introduces and mathematically formulates a mixed-model, two-sided assembly line balancing problem considering underground stations. The precedence relationships between tasks being performed in the three types of stations are defined and considered in the model. A numerical example is solved in GAMS (with CPLEX solver) and the detailed balancing solution is provided. A new ant colony optimization algorithm, in which the parameters are optimized using response surface methodology, is also developed to solve real-world problems. A total of 78 test problems are derived from the literature and their lower bounds are calculated to test the performance of the ACO algorithm. ACO finds optimum solutions for the majority of small and medium-sized test problems. In comparing the ACO results to the lower bounds for the large-sized problems, ACO finds near optimum solutions in majority of the test cases

Reinforcement Learning Methods for Operations Research Applications: The Order Release Problem

An important goal in Manufacturing Planning and Control systems is to achieve short and predictable flow times, especially where high flexibility in meeting customer demand is required. Besides achieving short flow times, one should also maintain high output and due-date performance. One approach to address this problem is the use of an order release mechanism which collects all incoming orders in an order-pool and thereafter determines when to release the orders to the shop-floor. A major disadvantage of traditional order release mechanisms is their inability to consider the nonlinear relationship between resource utilization and flow times which is well known from practice and queuing theory. Therefore, we propose a novel adaptive order release mechanism which utilizes deep reinforcement learning to set release times of the orders and provide several techniques for challenging operations research problems with reinforcement learning. We use a simulation model of a two-stage flow-shop and show that our approach outperforms well-known order release mechanism.(VLID)3401079Accepted versio