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جستجوی مقالات مرتبط با کلیدواژه

simulation-optimization

در نشریات گروه صنایع
تکرار جستجوی کلیدواژه simulation-optimization در نشریات گروه فنی و مهندسی
تکرار جستجوی کلیدواژه simulation-optimization در مقالات مجلات علمی
  • MohammadReza Nazabadi, Seyed Najafi *, Ali Mohaghar, Farzad Movahedi Sobhani

    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.

    Keywords: Agent-based modeling, Reinforcement Learning, simulation-optimization, Production Planning, maintenance, Quality Control
  • Mojtaba Hajian Heidary *
    Increasing uncertainties in the supply chains have caused more attentions to the supply chain risk management approaches. Because of the inherent turbulences in the international transactions, these uncertainties in the global context are more important. On the other hand, due to competitive pressures, businesses has been prepared themselves to operate in a global context to take advantage of the international markets. In addition, supplier selection is a challenge for purchasing managers by having more uncertainties in supply from the foreign supplier (exchange rate risk, extended lead times, regional risks). On the other hand, lower price procurement and having more diversified suppliers are the benefits that a company could obtain from global supply chains. In this paper a scenario based supply chain model for global purchasing of substitutable products is introduced and as a solution method a simulation-optimization approach is proposed. The model is applied on the modified data adopted from a case study and sensitivity analyzes (on the risk attitude of retailers, product substitutability and exchange rate) are presented for different amounts of parameters.
    Keywords: Supply chain, risk analysis, simulation-optimization, substitutable product, global factors
  • Mojtaba Hajian Heidary *
    During the last decade, many researchers have been attracted to study the role of uncertainties in their supply chain designs. Two important uncertainties of a supply chain are demand uncertainty and supply disruption. The basic concept of the proposed model of this paper is based on the newsvendor problem. The model consists of many retailers and many suppliers as two types of autonomous agents that interact with each other considering demand and supply uncertainties. To cope with the uncertainties, retailers have three choices: a forward contract, an option contract, and purchasing from the spot market. Retailers maybe risk sensitive or risk neutral. A new simulation optimization approach is developed to find the best behavior of a risk sensitive retailer in contrast with the other risk neutral retailers during the multiple contract periods. In this model two objectives are defined to find the best behavior of the risk sensitive retailer: the maximization of the profit and the service level. In order to optimize the agent based simulation, an NSGA-II approach is used. The proposed simulation based NSGA-II is further developed in two directions: the one is different realization numbers of the uncertain parameters, and the other is preference points. Under the different preference points and different number of realizations, Pareto optimal solutions are discovered by the collaboration of the agents. Results of the numerical studies showed that adopting more risk averse policies during the contract periods will result in a larger service level and smaller profit rather than adopting more risk taking policies.
    Keywords: stochastic supply chain, Newsvendor problem, Agent Based Modeling, Simulation Optimization, NSGA-II
  • Mehdi Iranpoor, S. M. T. Fatemi Ghomi *

    Preventive maintenance is the essential part of many maintenance plans. From the production point of view, the flexibility of the maintenance intervals enhances the manufacturing efficiency. On the contrary, the maintenance departments tend to know the timing of the long term maintenance plans as certain as possible. In a single-machine production environment, this paper proposes a simulation–optimization approach which establishes periodic flexible maintenance plans by determining the time between the maintenance intervals and the flexibility (i.e., length) of each interval. The objective is the minimization of the estimated total costs of the corrective and preventive maintenance, the undesirability of the flexibility (i.e., uncertainty) in maintenance timing, and the tardiness and long due date costs of jobs. Two mixed continuous-discrete variations of the ant colony optimization algorithm and the particle swarm optimization algorithm are developed as the solution approaches. Numerical studies are used to compare the performance of these algorithms. Further, the average reduction of the total costs gained from the flexibility of maintenance intervals on a wide range of parameters is reported.

    Keywords: Periodic flexible maintenance planning, Random breakdown, Single-machine setting, Simulation-optimization, Mixed continuous, discrete metaheuristics
  • Amir Parnianifard *, Siti Azfanizam Ahmad, Mohd Khairol Anuar Ariffin, Mohd Idris Shah Ismail
    Computational complexity and time-consuming iteration of simulation for tuning of Proportional-Integral-Derivative (PID) controller is a common drawback in many types of existing methods. This paper aims to propose a new method for achieving an optimal design for PID gains parameters with the least number of simulation runs. To achieve this purpose, we combine polynomial regression and Latin Hypercube Sampling (LHS) in order to Design and Analyze of Computer Experiments (DACE). In this method, the LHS is performed three times to design the associated sample points for different usage that includes training sample points to fit polynomial regression as a common surrogate model; validating sample points to scale standardized residuals; grid search sample points for investigating optimal point over whole design space. To show the flexibility and applicability of the proposed method, we serve a numerical case in the tuning of PID controller for linear speed control of Direct Current (DC) motor. Four different polynomial regression fit input/output (I/O) data over separately four model’s performances that includes Integral-Square-Error (ISE), Integral-Absolute-error (IAE), Integral-Time-Square-Error (ITSE), and Integral-Time-Absolute-Error (ITAE). Comparison of the result with two existing approaches such as traditional Zeigler-Nichols method and Taguchi-Gray Relational Analysis (Taguchi-GRA) confirms the reliability and superiority of the proposed method.
    Keywords: Polynomial Regression, PID Controller, DC Motor, Simulation Optimization, Computer experiments, Latin Hypercube Sampling
  • Zohreh Omranpour, Farhad Ghassemi, Tari
    In this article a new algorithm is developed for optimizing computationally expensive simulation models. The optimization algorithm is developed for continues unconstrained single output simulation models. The algorithm is developed using two simulation optimization routines. We employed the nested partitioning (NP) routine for concentrating the search efforts in the regions which are most likely contained the global optimum, and we used the experimental design concept for selecting most promising points. Then we integrated the Particle Swarm Optimization (PSO) routine as the searching mechanism of the developed algorithm to single out the best point (optimal or a near optimal solution). Through these integrations, an algorithm was developed which is capable of optimizing digital simulation models. The efficiency of the developed algorithm was then evaluated through a computationalexperiment. Ten test problems were selected from the literature and the efficiency of the PSPO algorithm was compared by two well-known algorithms. The result of this experiment revealed that the developed algorithm provided a more accurate result comparing to these algorithms.
    Keywords: Simulation optimization, Nested Partition Algorithm, Sequential Experimental Design, Particle Swarm Optimization Algorithm
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