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In today's competitive world, due to factors such as stochastic demand and failure of machines, managers have been attention more to the importance of production and inventory control. Network Failure Prone Manufacture Systems are such as production systems that include failure of machine. This paper considers multi-products network failure manufac...
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... of complexity of these systems, hedging point policy is utilized as control policy. In this paper, the control policy that is presented in Xie’s article (Xie, 1989) to a network of FPMS with multiple non- identical machines and connections between them is generalized. Production rate of each machine is related to i that is as optimal inventory threshold level of each machine, so Z ∗ is defined as decision variable (Sajadi, 2011). Simulation model in Network Failure Prone Manufacture Systems is used to learn optimum production level of each machine, and to obtain data to plug into ARENA14.0 in order to simulate production and satisfy demand systems in 10 replications that each replication takes over 960 minutes. We consider a NFPMS system with four machines. In this part of paper, Figure 2 illustrates the simulation modeling of production of final machine. Parts are generated on a constant inter-arrival rate of 1 /mpr i hours. Then the variables are defined such as: production machine rates, holding and shortage inventory costs, production coefficient, demand rate, etc., to their initial values. After that the state of machine and inventory level of previous production are checked by first Decide Module decides whether machine 4 is busy, idle or not. By the next three Decide Modules, we check number of parts of previous products that is consumed by machine 4 to produce one unit of product4.. Then control policy is defined by Decide Module that is defined in Section 3. After production, product is stocked in warehouse, if the product isn’t use until it’s expire date, it is considered as perishable item and it is unusable, otherwise it is usable. Simulation modeling of previous machines is the same as simulation of final machine. But in previous machine sho rtage and perishable items aren’t allowed. Producing of final product loop is related to Demand and shortage loop. As showed in Figure 3, customers inter the system with 1 /d n rate, then a signal is sent to Producing of final product loop, and the customers have to wait for product. It may the customers receive perishable items, so is defined a share queue that the customers who receive perishable item (red shape) are placed in priority queue of the customers who are faced with shortage (green ...
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Citations
... Its main objective is to determine the optimal production rates that minimize the average total costs, including shortage, production, storage, and perishable costs. Due to the uncertainty and complexity of this system, the optimization simulation of this system was conducted using ARENA software 14 [16]. ...
... Table 16 and Figure 15 show that using blockchain leads to a greater reduction in cost than in time. ...
With the emergence of the fourth industrial revolution, the use of intelligent technologies in supply chains is becoming increasingly common. The aim of this research is to propose an optimal design for an intelligent supply chain of multiple perishable products under a vendor-managed inventory management policy aided by IoT-related technologies to address the challenges associated with traditional supply chains. Various levels of the intelligent supply chain employ technologies such as Wireless Sensor Networks (WSNs), Radio Frequency Identification (RFID), and Blockchain. In this paper, we develop a bi-objective nonlinear integer mathematical programming model for designing a four-level supply chain consisting of suppliers, manufacturers, retailers, and customers. The model determines the optimal network nodes, production level, product distribution and sales, and optimal choice of technology for each level. The objective functions are total cost and delivery times. The GAMS 24.2.1 optimization software is employed to solve the mathematical model in small dimensions. Considering the NP-Hard nature of the problem, the Grey Wolf Optimizer (GWO) algorithm is employed, and its performance is compared with the Multi-Objective Whale Optimization Algorithm (MOWOA) and NSGA-III. The results indicate that the adoption of these technologies in the supply chain can reduce delivery times and total supply chain costs.
... [15] studies also control problems for reliable manufacturing systems for perishable products having a deterministic shelf-life and a random demand rate. As for work that include machine failure, we cite Sajadi et al. [16] and Tavan and Sajadi [17] that applied the classical HPP for perishable products with a deterministic shelf-life. The work of Malekpour et al. [18] and Hatami-Marbini et al. [19] deal with production control problems for unreliable manufacturing systems composed of a network of machines where the final product perishable. ...
... We propose the structure of production control policy described by Eqs. (15) to (17). It consists of building and maintaining safety stocks of each sub-stock ( x 1 and x 2 ) based on multiple hedging levels. ...
... The production control policy (block 3) allows the manufacturing system to determine the production rate based on the different inventory levels following Eqs. (15) to (17). The preventive maintenance control policy (block 4) is responsible for providing changes in the state of the system based on the two control parameters: age of the machine and the level of inventory. ...
The problem of integrated production and maintenance control of unreliable manufacturing systems evolving in a stochastic and dynamic environment is studied in this paper. The considered system is subject to degradation and the produced products are perishable with random shelf-lives. The literature of operations management for perishable products reports a correlation between the shelf-life of the product and the machine degradation. In fact, the latter results in shelf-lives reduction. Ignoring this correlation effect may result in inaccurate values of the shelf-lives and inefficient control policies. The objective of this paper is to develop a joint production and maintenance control policy that minimizes the total cost composed of backlog, inventory holding, disposal, and maintenance costs. The proposed parametrized joint control policy combines a multi hedging point policy and an age-based preventive maintenance policy. The optimization of the parameters of the proposed joint control policy is obtained using a simulation-based optimization approach and sensitivity analyses are provided to confirm its robustness. The obtained results show that the correlation between machine degradation and shelf-life reduction has a major influence on the control parameters and that preventive maintenance interventions can lead to increasing the shelf-life of products and minimizing the total cost. The proposed joint control policy is then compared to three other polices for a wide range of system and cost data. The obtained results show that the proposed joint control policy outperforms the other polices in terms of total incurred costs.
... Only a few articles deal with problems of optimal control of perishable products. Sajadi et al. (2011) and Tavan and Sajadi (2015) consider failure-prone manufacturing systems and use the classical HPP for production control of perishable products with a deterministic shelf life. The works of Malekpour et al. (2016) and Hatami-Marbini et al. (2020) deal with production control problems for unreliable manufacturing systems composed of a network of machines, where the final product is perishable in nature. ...
This paper develops an optimal control policy for unreliable manufacturing systems evolving in a stochastic and dynamic environment and producing perishable products with limited and random shelf lives. The developed optimal control policy aims to minimize the total cost, composed of backlog, inventory holding and disposal costs. To do this, we develop a stochastic dynamic programming model and the obtained optimality conditions are solved using a numerical approach to determine the parametrized structure of the resulting optimal control policy. A simulation-based optimization approach is used to optimize the parameters of the developed control policy. This policy is subsequently analyzed using multiple sensitivity analyses and compared to other policies from the literature. We also study shelf life variability and its influence on the system performance. Numerical examples show that in the case of a random shelf life, the developed control policy presents a lower total cost in comparison with other policies from the literature.
... Hedjar et al., 2007 studies also control problems for reliable manufacturing systems for perishable products having a deterministic shelf-life and a random demand rate. As for work that include machine failure, we cite Sajadi et al. (2011) and Tavan and Sajadi (2015) that applied the classical HPP for perishable products with a deterministic shelf-life. The work of Malekpour et al. (2016) and Hatami-Marbini et al. (2020) deal with production control problems for unreliable manufacturing systems composed of a network of machines where the final product perishable. ...
The problem of integrated production and maintenance control of unreliable manufacturing systems evolving in a stochastic and dynamic environment is studied in this paper. The considered system is subject to degradation and the produced products are perishable with random shelf-lives. The literature of operations management for perishable products reports a correlation between the shelf-life of the product and the machine degradation. In fact, the latter results in shelf-lives reduction. Ignoring this correlation effect may result in inaccurate values of the shelf-lives and inefficient control policies. The objective of this paper is to develop a joint production and maintenance control policy that minimizes the total cost composed of backlog, inventory holding, disposal and maintenance costs. The proposed parametrized joint control policy combines a multi hedging point policy and an age-based preventive maintenance policy. The optimization of the parameters of the proposed joint control policy is obtained using a simulation-based optimization approach and sensitivity analyses are provided to confirm its robustness. The obtained results show that the correlation between machine degradation and shelf-life reduction has a major influence on the control parameters and that preventive maintenance interventions can lead to increasing the shelf-life of products and minimizing the total cost. The proposed joint control policy is then compared to three other polices for a wide range of system and cost data. The obtained results show that the proposed joint control policy outperforms the other polices in terms of total incurred costs.
... There are different methods for quantifying the robustness and resiliency of the transportation systems, e.g. mathematical models (Chen and Miller-Hooks, 2012;Hassannayebi et al., 2016d), analytical approaches (Duy et al., 2019;Omer et al., 2012), simulation techniques (Hassannayebi et al., 2016a;Sajedinejad et al., 2011), simulation-optimization methods (Tavan and Sajadi, 2015;Gholamian, 2017;Bahramian and Bagheri, 2015;Ilati et al., 2014;Kim et al., 2013), robust optimization , stochastic optimization (Hassannayebi et al., 2016b;Khan and Zhou, 2010;Shakibayifar et al., 2017a;Hassannayebi et al., 2014), heuristic algorithms (Pavlov et al., 2017), meta-heuristic algorithms Hassannayebi et al., 2018;Qi et al., 2018). Lai et al. (2020) proposed a public transport system efficiency metric based on the system efficiency function. ...
Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1 st line of Tehran underground rail rapid transit. All fitness functions are validated using the analysis of variance (ANOVA) by applying the hypothesis testing method. Also, a validated discrete-event computer simulation model is applied to examine the average waiting time per passenger as the key performance measure under different scenarios generated using full factorial design of experiments. The validity of the obtained optimal solution, i.e., train headway times is confirmed at a 95% level of reliability. Also, simulation outcomes indicated that the proposed response surface meta-model could efficiently provide a more reliable train operation plan to ensure a desirable level of system resiliency on the presence of random disturbances. The numerical results indicated that wait time could be reduced by 14.8% for passengers as compared with the baseline train headway plan.
... The performances of proposed VNS algorithms are compared to several algorithms, i.e., ACO, PSO and VNS algorithms that are explained in previous literature. Tavan and Sajadi (2015) proposed a paper about determination of production rate in NFPMS. 1 Because of complexity and uncertainly of these systems, ARENA software is used for simulation optimisation. Then design of experiment with Taguchi test for choosing the best answer of production rate is done. ...
In this paper, robust and stable scheduling for a flexible job-shop
problem with random machine breakdowns has been discussed. A two-stage genetic algorithm is used to generate the predictive schedule. The first stage optimises the primary objective, which minimises the makespan, where all data is considered to be deterministic with no expected disruptions. The second stage optimises two objectives, makespan and stability, function in the presence of random machine breakdowns. For the second stage two different versions of multi-objective genetic algorithm, non-dominated sorting genetic algorithm II and non-dominated ranking genetic algorithm, is used. A simulator is proposed to simulate random machine breakdowns. An experimental study and analysis of variance is conducted to study the results of each multi-objective algorithm
and breakdown simulator. The results of their comparison indicate that, non-dominated ranking genetic algorithm (NRGA) performs better and also shows a significant difference between various repair times in the proposed breakdown simulator.
... The performances of proposed VNS algorithms are compared to several algorithms, i.e., ACO, PSO and VNS algorithms that are explained in previous literature. Tavan and Sajadi (2015) proposed a paper about determination of production rate in NFPMS. 1 Because of complexity and uncertainly of these systems, ARENA software is used for simulation optimisation. Then design of experiment with Taguchi test for choosing the best answer of production rate is done. ...
In this paper, robust and stable scheduling for a flexible job-shop problem with random machine breakdowns has been discussed. A two-stage genetic algorithm is used to generate the predictive schedule. The first stage optimises the primary objective, which minimises the makespan, where all data is considered to be deterministic with no expected disruptions. The second stage optimises two objectives, makespan and stability, function in the presence of random machine breakdowns. For the second stage two different versions of multi-objective genetic algorithm, non-dominated sorting genetic algorithm II and non-dominated ranking genetic algorithm, is used. A simulator is proposed to simulate random machine breakdowns. An experimental study and analysis of variance is conducted to study the results of each multi-objective algorithm and breakdown simulator. The results of their comparison indicate that, non-dominated ranking genetic algorithm (NRGA) performs better and also shows a significant difference between various repair times in the proposed breakdown simulator.
In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.
