A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy

Dynamic optimisation of a production-inventory control system with two pipelines feedback

Al-Khazraji, H ORCiD: 0000-0002-6290-3382Interest in the need to have a strong upstream and downstream integration within production-inventory systems has arisen recently as a means to gain a competitive market position. As to the methods of achieving this, companies need to investigate the dynamic implications of ordering strategies and different feedback control mechanisms to optimise inventory levels and reduce negative consequences such as order amplification. The dynamics of a production-inventory control system consisting of a single product, one manufacturer and one retailer was studied. The order rate related costs and inventory level related costs were considered as the two main factors to model the total costs of the production-inventory system. The dynamic analysis of the production-inventory control system was addressed via control theory and simulation as follows. A novel dynamic model for a production-inventory control system has been proposed. The dynamics were modelled as a linear continuous-time control system. The proposed model considers an extension and improvement to the Inventory and Order Based Production Control System (IOBPCS) models, and it utilises a new feedback flow of information in order to improve the efficiency of order rate decisions. A procedure to select a best system configuration was developed. A Multi-Objective Particle Swarm Optimisation (MOPSO) approach for generating Pareto-optimal solutions was used to optimise the overall dynamic performance of the system and filter-out all undesired operational performance. Optimal solutions were selected based on competing criteria of minimisation of the variance ratio (Var) between the order rate and the consumption, and minimisation of the Integral of Absolute Error (IAE) between the actual and the target level of inventory. The optimal system configuration with minimum cost was then chosen as the best system configuration. The efficiency of the proposed model was evaluated under several scenarios. A comprehensive simulation-based comparison between the proposed model and a model published in the literature (APIOBPCS) was conducted. First, three different demand patterns were considered (literature published demand data, computer generated random data, and natural inspired data such as weather based). The Pareto curves of the proposed model in comparison with results from the APIOBPCS model reveal that the proposed model provides a systematically better performance than the APIOBPCS model under the same considerations. For example, under thesis assumptions, the simulation using literature published demand data showed that the proposed model is capable of achieving a 6% cost reduction compared to the APIOBPCS model. The comparison result using randomly generated computer data illustrated that the proposed model achieved a 4% cost reduction. The comparison utilising a nature inspired demand pattern demonstrated that the proposed model accomplished a 3.5% cost reduction. Lastly, to ensure a realistic scenario, another set of simulation-based experiments under four different operational scenarios (normal, mismatched lead time, capacity constraint and initial condition) were performed. It was found that, under the mismatched lead time operation, both models are affected. However, the proposed model offers better inventory response indicating greater robustness. Moreover, the variance ratios under capacity constraint for both models are reduced, and these simulation results do not show any superiority of any model over the other. Finally, the variance ratios under an initial condition for both models are increased, and the inventory responsiveness for the proposed model gives better performance in this case. Overall, the proposed model therefore provides an improvement over currents model in the dynamic optimisation of a production-inventory control system

Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem

[EN] Many important problems in engineering management can be formulated as Resource Assignment Problem (RAP). The Workers Assignment Problem (WAP) is considered as a sub-class of RAP which aims to find an optimal assignment of workers to a number of tasks in order to optimize certain objectives. WAP is an NP-hard combinatorial optimization problem. Due to its importance, several algorithms have been developed to solve it. In this paper, it is considered that a manager is required to provide a training course to his workers in order to improve their level of skill or experience to have a sustainable competitive advantage in the industry. The training cost of each worker to perform a particular job is different. The WAP is to find the best assignment of workers to training courses such that the total training cost is minimized. Two metaheuristic optimizations named Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA) are utilized to final the optimal solution that reduces the total cost. MATLAB Software is used to perform the simulation of the two proposed methods into WAP. The computational results for a set of randomly generated problems of various sizes show that the FPA is able to find good quality solutions.Al-Khazraji, H. (2022). Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem. International Journal of Production Management and Engineering. 10(1):91-98. https://doi.org/10.4995/ijpme.2022.16736OJS9198101Abdel-Basset, M., & Shawky, L. A. (2019). Flower pollination algorithm: a comprehensive review. Artificial Intelligence Review, 52(4), 2533-2557. https://doi.org/10.1007/s10462-018-9624-4Ammar, A., Pierreval, H., & Elkosentini, S. (2013). Workers assignment problems in manufacturing systems: A literature analysis. In Proceedings of 2013 international conference on industrial engineering and systems management (IESM) (pp. 1-7). IEEE.Bouajaja, S., & Dridi, N. (2017). A survey on human resource allocation problem and its applications. Operational Research, 17(2), 339-369. https://doi.org/10.1007/s12351-016-0247-8Caron, G., Hansen, P., & Jaumard, B. (1999). The assignment problem with seniority and job priority constraints. Operations Research, 47(3), 449-453. https://doi.org/10.1287/opre.47.3.449Cattrysse, D. G., Salomon, M., & Van Wassenhove, L. N. (1994). A set partitioning heuristic for the generalized assignment problem. European Journal of Operational Research, 72(1), 167-174. https://doi.org/10.1016/0377-2217(94)90338-7Chu, P. C., & Beasley, J. E. (1997). A genetic algorithm for the generalised assignment problem. Computers & Operations Research, 24(1), 17-23. https://doi.org/10.1016/S0305-0548(96)00032-9Demiral, M. F. (2017). Ant Colony Optimization for a Variety of Classic Assignment Problems. In International Turkish World Engineering and Science Congress, Antalya.Halawi, A., & Haydar, N. (2018). Effects of Training on Employee Performance: A Case Study of Bonjus and Khatib & Alami Companies. International Humanities Studies, 5(2).Jia, Z., & Gong, L. (2008). Multi-criteria human resource allocation for optimization problems using multi-objective particle swarm optimization algorithm. In 2008 International Conference on Computer Science and Software Engineering, 1, 1187-1190. IEEE. https://doi.org/10.1109/CSSE.2008.1506Koleva, N., & Andreev, O. (2018, June). Aspects of Training in the Field of Operations Management with Respect to Industry 4.0. In 2018 International Conference on High Technology for Sustainable Development (HiTech) (pp. 1-3). IEEE. https://doi.org/10.1109/HiTech.2018.8566581Krokhmal, P. A., & Pardalos, P. M. (2009). Random assignment problems. European Journal of Operational Research, 194(1), 1-17. https://doi.org/10.1016/j.ejor.2007.11.062Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2), 83-97. https://doi.org/10.1002/nav.3800020109Lin, J. T., & Chiu, C. C. (2018). A hybrid particle swarm optimization with local search for stochastic resource allocation problem. Journal of Intelligent Manufacturing, 29(3), 481-495. https://doi.org/10.1007/s10845-015-1124-7Mahmoud, K. I. (2009). Split Assignment With Transportation Model for Job-Shop Loading (Case Study). Journal of Engineering, 15(2).Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008Pentico, D. W. (2007). Assignment problems: A golden anniversary survey. European Journal of Operational Research, 176(2), 774-793. https://doi.org/10.1016/j.ejor.2005.09.014Ross, G. T., & Soland, R. M. (1975). A branch and bound algorithm for the generalized assignment problem. Mathematical programming, 8(1), 91-103. https://doi.org/10.1007/BF01580430Ruiz, M., Igartua, J. I., Mindeguia, M., & Orobengoa, M. (2020). Understanding and representation of organizational training programs and their evaluation. International Journal of Production Management and Engineering, 8(2), 99-109. https://doi.org/10.4995/ijpme.2020.12271Satapathy, P., Mishra, S. P., Sahu, B. K., Debnath, M. K., & Mohanty, P. K. (2018, April). Design and implementation of whale optimization algorithm based PIDF controller for AGC problem in unified system. In International Conference on Soft Computing Systems (pp. 837-846). Springer, Singapore. https://doi.org/10.1007/978-981-13-1936-5_85Sharma, H. (2014). Importance and performance of managerial training in Indian companies-an empirical study. The Journal of Management Development, 33(2), 75-89. https://doi.org/10.1108/JMD-11-2013-0144Suliman, A. S. A. (2019). Using ant colony algorithm to find the optimal assignment. AL-Anbar University journal of Economic and Administration Sciences, 11(25).Ostadi, B., Taghizadeh Yazdi, M., & Mohammadi Balani, A. (2021). Process Capability Studies in an Automated Flexible Assembly Process: A Case Study in an Automotive Industry. Iranian Journal of Management Studies, 14(1), 1-37.Walsh, B. & Volini, E. (2017). Rewriting the rules for the digital age. Deloitte University Press. New York.Wang, Z., Li, S., Wang, Y., & Li, S. (2009, August). The research of task assignment based on ant colony algorithm. In 2009 International Conference on Mechatronics and Automation (pp. 2334-2339). IEEE.Xuezhi, Q., & Xuehua, W. (1996). Dynamic programming model of a sort of optimal assignment problem [J]. Mathematics In Practice and Theory, 3.Yadav, N., Banerjee, K., & Bali, V. (2020). A survey on fatigue detection of workers using machine learning. 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Dynamic optimisation of a production-inventory control system with two pipelines feedback

Interest in the need to have a strong upstream and downstream integration within production-inventory systems has arisen recently as a means to gain a competitive market position. As to the methods of achieving this, companies need to investigate the dynamic implications of ordering strategies and different feedback control mechanisms to optimise inventory levels and reduce negative consequences such as order amplification. The dynamics of a production-inventory control system consisting of a single product, one manufacturer and one retailer was studied. The order rate related costs and inventory level related costs were considered as the two main factors to model the total costs of the production-inventory system. The dynamic analysis of the production-inventory control system was addressed via control theory and simulation as follows. A novel dynamic model for a production-inventory control system has been proposed. The dynamics were modelled as a linear continuous-time control system. The proposed model considers an extension and improvement to the Inventory and Order Based Production Control System (IOBPCS) models, and it utilises a new feedback flow of information in order to improve the efficiency of order rate decisions. A procedure to select a best system configuration was developed. A Multi-Objective Particle Swarm Optimisation (MOPSO) approach for generating Pareto-optimal solutions was used to optimise the overall dynamic performance of the system and filter-out all undesired operational performance. Optimal solutions were selected based on competing criteria of minimisation of the variance ratio (Var) between the order rate and the consumption, and minimisation of the Integral of Absolute Error (IAE) between the actual and the target level of inventory. The optimal system configuration with minimum cost was then chosen as the best system configuration. The efficiency of the proposed model was evaluated under several scenarios. A comprehensive simulation-based comparison between the proposed model and a model published in the literature (APIOBPCS) was conducted. First, three different demand patterns were considered (literature published demand data, computer generated random data, and natural inspired data such as weather based). The Pareto curves of the proposed model in comparison with results from the APIOBPCS model reveal that the proposed model provides a systematically better performance than the APIOBPCS model under the same considerations. For example, under thesis assumptions, the simulation using literature published demand data showed that the proposed model is capable of achieving a 6% cost reduction compared to the APIOBPCS model. The comparison result using randomly generated computer data illustrated that the proposed model achieved a 4% cost reduction. The comparison utilising a nature inspired demand pattern demonstrated that the proposed model accomplished a 3.5% cost reduction. Lastly, to ensure a realistic scenario, another set of simulation-based experiments under four different operational scenarios (normal, mismatched lead time, capacity constraint and initial condition) were performed. It was found that, under the mismatched lead time operation, both models are affected. However, the proposed model offers better inventory response indicating greater robustness. Moreover, the variance ratios under capacity constraint for both models are reduced, and these simulation results do not show any superiority of any model over the other. Finally, the variance ratios under an initial condition for both models are increased, and the inventory responsiveness for the proposed model gives better performance in this case. Overall, the proposed model therefore provides an improvement over currents model in the dynamic optimisation of a production-inventory control system

Optimization and Simulation of Dynamic Performance of Production–Inventory Systems with Multivariable Controls

The production–inventory system is a problem of multivariable input and multivariant output in mathematics. Selecting the best system control parameters is a crucial managerial decision to achieve and dynamically maintain an optimal performance in terms of balancing the order rate and stock level under dynamic influence of many factors affecting the system operations. The dynamic performance of the popular APIOBPCS model and the newly modified 2APIOBPCS model for optimal control of production–inventory systems is examined in the study. This examination is based on the leveled ground with a new simulation scheme that incorporates a designated multi-objective particle swarm optimization (MOPSO) algorithm into the simulation, which enables the optimal set of system control parameters to be selected for achieving the situational best possible performance of the production–inventory system under study. The dynamic performance is measured by the variance ratio between the order rate and the sales rate related to the bullwhip effect, and the integral of absolute error related to the inventory responsiveness in response to a random customer demand. Our simulation indicates that the 2APIOBPCS model performed better than or at least no worse than, and more robust than the APIOBPCS model under different conditions

Multi-objective particle swarm optimisation approach for production-inventory control systems

Purpose: This paper aims to optimise the dynamic performance of production–inventory control systems in terms of minimisation variance ratio between the order rate and the consumption, and minimisation the integral of absolute error between the actual and the target level of inventory by incorporating the Pareto optimality into particle swarm optimisation (PSO). Design/method/approach: The production–inventory control system is modelled and optimised via control theory and simulations. The dynamics of a production–inventory control system are modelled through continuous time differential equations and Laplace transformations. The simulation design is conducted by using the state–space model of the system. The results of multi-objective particle swarm optimisation (MOPSO) are compared with published results obtained from weighted genetic algorithm (WGA) optimisation. Findings: The results obtained from the MOPSO optimisation process ensure that the performance is systematically better than the WGA in terms of reducing the order variability (bullwhip effect) and improving the inventory responsiveness (customer service level) under the same operational conditions. Research limitations/implications: This research is limited to optimising the dynamics of a single product, single-retailer single-manufacturer process with zero desired inventory level. Originality/value: PSO is widely used and popular in many industrial applications. This research shows a unique application of PSO in optimising the dynamic performance of production–inventory control systems. © 2018, Emerald Publishing Limited

Dynamics analysis of a production-inventory control system with two pipelines feedback

© 2017, © Emerald Publishing Limited. Purpose: The purpose of this study is to propose a new dynamic model of a production-inventory control system. The objective of the new model is to maximise the flexibility of the system so that it can be used by decision makers to design inventory systems that adopt various strategies that provide a balance between reducing the bullwhip effect and improving the responsiveness of inventory performance. Design/methodology/approach: The proposed production-inventory control system is modelled and analysed via control theory and simulations. The production-inventory feedback control system is modelled through continuous time differential equations. The simulation experiments design is conducted by using the state-space model of the system. The Automatic Pipeline Inventory and Order-Based Production Control System (APIOBPCS) model is used as a benchmark production-inventory control system. Findings: The results showed that the Two Automatic Pipelines, Inventory and Order-Based Production Control System (2APIOBPCS) model outperforms APIOBPCS in terms of reducing the bullwhip effect. However, the 2APIOBPCS model has a negative impact on Customer Service Level. Therefore, with careful parameter setting, it is possible to design control decisions to be suitably responsive while generating smooth order patterns and obtain the best trade-off of the two objectives. Research limitations/implications: This research is limited to the dynamics of single-echelon production-inventory control systems with zero desired inventory level. Originality/value: This present model is an extension and improvement to Towill’s (1982) and John et al.’s (1994) work, since it presents a new dynamic model of a production-inventory control system which utilises an additional flow of information to improve the efficiency of order rate decisions

Analysing the impact of different classical controller strategies on the dynamics performance of production-inventory systems using state space approach

Purpose: The purpose of this paper is to examine the impact of applying two classical controller strategies, including two proportional (P) controllers with two feedback loops and one proportional–integral–derivative (PID) controller with one feedback loop, on the order and inventory performance within a production-inventory control system. Design/methodology/approach: The simulation experiments of the dynamics behaviour of the production-inventory control system are conducted using a model based on control theory techniques. The Laplace transformation of an Order–Up–To (OUT) model is obtained using a state-space approach, and then the state-space representation is used to design and simulate a controlled model. The simulations of each model with two control configurations are tested by subjecting the system to a random retail sales pattern. The performance of inventory level is quantified by using the Integral of Absolute Error (IAE), whereas the bullwhip effect is measured by using the Variance ratio (Var). Findings: The simulation results show that one PID controller with one feedback loop outperforms two P controllers with two feedback loops at reducing the bullwhip effect and regulating the inventory level. Originality/value: The production-inventory control system is broken down into three components, namely: the forecasting mechanism, controller strategy and production-inventory process. A state-space approach is adopted to design and simulate the different controller strategy. © 2018, Emerald Publishing Limited