Integration of lot sizing and scheduling models to minimize production cost and time in the automotive industry

Lot planning and production scheduling are important processes in the manufacturing industry. This study is based on the case study of automotive spare parts manufacturing firm (Firm-A), which produces various products based on customer demand. Several complex problems have been identified due to different production process flows for different products with different machine capability considerations at each stage of the production process. Based on these problems, this study proposes three integrated models that include lot planning and scheduling to minimize production costs, production times, and production costs and time simultaneously. These can be achieved by optimizing model solutions such as job order decisions and production quantities on the production process. Next, the genetic algorithm (GA) and the Taguchi approach are used to optimize the models by finding the optimal model solution for each objective. Model testing is presented using numerical examples and actual case data from Firm-A. The model testing analysis is performed using Microsoft Excel software to develop a model based on mathematical programming to formulate all three objective functions. Meanwhile, GeneHunter software is used to represent the optimization process using GA. The results show production quantity and job sequence play an essential role in reducing the cost and time of production by Rp 42.717.200,00 and 31392.82 minutes (65.4 days), respectively. The findings of the study contribute to the production management of Firm-A in helping to make decisions to reduce the time and costs of production strategically, where it provides a guideline for complex production activities

A Machine Learning Approach for Predicting Clinical Trial Patient Enrollment in Drug Development Portfolio Demand Planning

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded vs. unblinded, sponsor CRO selection, enrollment quarter, and enrollment country values to predict patient enrollment characteristics in clinical trials. The model was tested using a dataset consisting of 5,000 data points and yielded a high level of accuracy. This development in patient enrollment prediction will optimize portfolio demand planning and help avoid costs associated with inaccurate patient enrollment forecasting

Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector

[EN] Lack of homogeneity in the product (LHP) is a problem when customers require homogeneous units of a single product. In such cases, the optimal allocation of inventory to orders becomes much more complex. Furthermore, in an MTS environment, an optimal initial allocation may become less than ideal over time, due to different circumstances. This problem occurs in the ceramics sector, where the final product varies in tone and calibre. This paper proposes a methodology for the reallocation of inventory to orders in LHP situation (MERIO-LHP) and a model-based decision-support system (DSS) to support the methodology, which enables an optimal reallocation of inventory to order lines to be carried out in real businesses environments in which LHP is inherent. The proposed methodology and model-based DSS were validated by applying it to a real case at a ceramics company. The analysis of the results indicates that considerable improvements can be obtained with regard to the quantity of orders fulfilled and sales turnover.Oltra Badenes, RF.; Gil Gómez, H.; Merigó, JM.; Palacios Marqués, D. (2019). Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector. PLoS ONE. 14(7):1-19. https://doi.org/10.1371/journal.pone.0219433S119147Alarcón, F., Alemany, M. M. E., Lario, F. C., & Oltra, R. F. (2011). La falta de homogeneidad del producto (FHP) en las empresas cerámicas y su impacto en la reasignación del inventario. Boletín de la Sociedad Española de Cerámica y Vidrio, 50(1), 49-58. doi:10.3989/cyv.072011Wanke, P., Alvarenga, H., Correa, H., Hadi-Vencheh, A., & Azad, M. A. K. (2017). Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels. 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Expert Systems with Applications, 41(1), 39-49. doi:10.1016/j.eswa.2013.07.009Protopappa-Sieke, M., Sieke, M. A., & Thonemann, U. W. (2016). Optimal two-period inventory allocation under multiple service level contracts. European Journal of Operational Research, 252(1), 145-155. doi:10.1016/j.ejor.2016.01.013Luo, K., Bollapragada, R., & Kerbache, L. (2017). Inventory allocation models for a two-stage, two-product, capacitated supplier and retailer problem with random demand. International Journal of Production Economics, 187, 168-181. doi:10.1016/j.ijpe.2016.12.014Zhao, H., Huang, E., Dou, R., & Wu, K. (2019). A multi-objective production planning problem with the consideration of time and cost in clinical trials. Expert Systems with Applications, 124, 25-38. doi:10.1016/j.eswa.2019.01.038Kang, K., Pu, W., Ma, Y., & Wang, X. (2018). Bi-objective inventory allocation planning problem with supplier selection and carbon trading under uncertainty. 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A DSS approach to managing customer enquiries for SMEs at the customer enquiry stage. International Journal of Production Economics, 103(1), 332-346. doi:10.1016/j.ijpe.2005.08.008Mahdavi Pajouh, F., Xing, D., Zhou, Y., Hariharan, S., Balasundaram, B., Liu, T., & Sharda, R. (2013). A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates. Interfaces, 43(6), 503-517. doi:10.1287/inte.2013.0693Yang, W., & Fung, R. Y. K. (2014). An available-to-promise decision support system for a multi-site make-to-order production system. International Journal of Production Research, 52(14), 4253-4266. doi:10.1080/00207543.2013.877612Castiglione, C., Alfieri, A., & Pastore, E. (2018). Decision Support System to balance inventory in customer-driven demand. IFAC-PapersOnLine, 51(11), 1499-1504. doi:10.1016/j.ifacol.2018.08.288Mhiri, E., Jacomino, M., Mangione, F., Vialletelle, P., & Lepelletier, G. (2015). Finite capacity planning algorithm for semiconductor industry considering lots priority. IFAC-PapersOnLine, 48(3), 1598-1603. doi:10.1016/j.ifacol.2015.06.314Alemany, M. M. E., Lario, F.-C., Ortiz, A., & Gómez, F. (2013). Available-To-Promise modeling for multi-plant manufacturing characterized by lack of homogeneity in the product: An illustration of a ceramic case. Applied Mathematical Modelling, 37(5), 3380-3398. doi:10.1016/j.apm.2012.07.022ALEMANY, M. M. E., A., A., BOZA, A., & FUERTES-MIQUEL, V. S. (2015). A MODEL-DRIVEN DECISION SUPPORT SYSTEM FOR REALLOCATION OF SUPPLY TO ORDERS UNDER UNCERTAINTY IN CERAMIC COMPANIES. Technological and Economic Development of Economy, 21(4), 596-625. doi:10.3846/20294913.2015.1055613Grillo, H., Alemany, M. M. E., & Ortiz, A. (2016). A review of mathematical models for supporting the order promising process under Lack of Homogeneity in Product and other sources of uncertainty. 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A multi-objective production planning problem with the consideration of time and cost in clinical trials

Under increasingly challenging circumstances, pharmaceutical companies try to reduce the overproduction of clinical drugs, which is commonly seen in the pharmaceutical industry. When the overproduction is simply reduced without an efficient coordination of the inventories in the supply chain, the stock-outs at clinical sites and clinical trial delay can hardly be avoided. In this study, we propose a multi-objective model to optimize the production quantity, where the clinical trial duration and the total production and operational costs are minimized. The problem is formulated as a multi-stage stochastic programming model to capture the dynamic inventory allocation process in the supply chains. Since this problem's solving time and required memory can increase significantly with the increase of the stage and scenario numbers, the progressive hedging algorithm is applied as the solution approach in this paper. In the numerical experiments, we study this algorithm's performance and compare the solving efficiency with the direct solution approach. In addition, we identify the optimal production quantity of clinical drugs and give a discussion about the tradeoffs between the clinical trial delay and total cost.Economic Development Board (EDB)This research is supported in part by the GSK-Singapore Partnership for Green and Sustainable Manufacturing under Grant M406884