Machine Learning
Mohammad Reza Nazabadi; Seyed Esmaeil Najafi; Ali Mohaghar; Farzad Movahedi Sobhani
Abstract
Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, ...
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Adopting an integrated production, maintenance, and quality policy in production systems is of great importance due to their interconnected influence. Consequently, investigating these aspects in isolation may yield an infeasible solution. This paper aims to address the joint optimal policy of production, maintenance, and quality in a two-machine-single-product production system with an intermediate buffer and final product storage. The production machines have degradation levels from as-good-as-new to the breakdown state. The failures increase the production machine's degradation level, and maintenance activities change the status to the initial state. Also, the quality of the final product depends on the level of degradation of the machines and the correlation between the degradation level of the production machines and the product's quality in the case that high degradation of the previous production machines leads to a high probability to produce wastage by the following machines is considered. The production system studied in this research has been modeled using the agent-based simulation, and the Reinforcement Learning (RL) algorithm has obtained the optimal integrated policy. The goal is to find an integrated optimal policy that minimizes production costs, maintenance costs, inventory costs, lost orders, breakdown of production machines, and low-quality production. The meta-heuristic technique evaluates the joint policy obtained by the decision-maker agent. The results show that the acquired joint policy by the RL algorithm offers acceptable performance and can be applied to the autonomous real-time decision-making process in manufacturing systems.
Data modeling
Mohammad Khodashenas; Hamed Kazemipoor; Seyed Esmaeel Najafi; Farzad Movahedi Sobhani
Abstract
In this paper, a two-stage model is designed for arranging and locating vehicle routes with simultaneous pickup and delivery. The model developed in the first stage optimizes the arrangement of products in packages and thus optimizes packages' length, width, and height for delivery to customers. In the ...
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In this paper, a two-stage model is designed for arranging and locating vehicle routes with simultaneous pickup and delivery. The model developed in the first stage optimizes the arrangement of products in packages and thus optimizes packages' length, width, and height for delivery to customers. In the second stage, the goal is to provide customers with vehicle in simultaneous pickup and delivery. In this part of the model, the location of distribution centers is potentially considered, and the demand and cost parameters are considered uncertain. To solve the problem, precise methods and meta-heuristic algorithms of PSO for the first stage and multi-objective meta-heuristic algorithms NSGA II and MOALO for the set have been used. The results of examining the efficiency of the algorithms in the second stage show the high efficiency of the MOALO algorithm with a valuable weight of 0.8388. Therefore, to implement the model in the real problem (Golrang Broadcasting Company), the MOLAO algorithm has been used, the management results include obtaining 15 efficient answers.
