planning and scheduling
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2022 ◽  
Vol 30 (8) ◽  
pp. 0-0

Artificial Intelligence (AI) significantly revolutionizes and transforms the global healthcare industry by improving outcomes, increasing efficiency, and enhancing resource utilization. The applications of AI impact every aspect of healthcare operation, particularly resource allocation and capacity planning. This study proposes a multi-step AI-based framework and applies it to a real dataset to predict the length of stay (LOS) for hospitalized patients. The results show that the proposed framework can predict the LOS categories with an AUC of 0.85 and their actual LOS with a mean absolute error of 0.85 days. This framework can support decision-makers in healthcare facilities providing inpatient care to make better front-end operational decisions, such as resource capacity planning and scheduling decisions. Predicting LOS is pivotal in today’s healthcare supply chain (HSC) systems where resources are scarce, and demand is abundant due to various global crises and pandemics. Thus, this research’s findings have practical and theoretical implications in AI and HSC management.

Simone König ◽  
Maximilian Reihn ◽  
Felipe Gelinski Abujamra ◽  
Alexander Novy ◽  
Birgit Vogel-Heuser

AbstractThe car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.

2022 ◽  
pp. 1-18
Nan-Yun Jiang ◽  
Hong-Sen Yan

For the fixed-position assembly workshop, the integrated optimization problem of production planning and scheduling in the uncertain re-entrance environment is studied. Based on the situation of aircraft assembly workshops, the characteristics of fixed-position assembly workshop with uncertain re-entrance are abstracted. As the re-entrance repetition obeys some type of probability distribution, the expected value is used to describe the repetition, and a bi-level stochastic expected value programming model of integrated production planning and scheduling is constructed. Recursive expressions for start time and completion time of assembly classes and teams are confirmed. And the relation between the decision variable in the lower-level model of scheduling and the overtime and earliness of assembly classes and teams in the upper-level model of production planning is identified. Addressing the characteristics of bi-level programming model, an alternate iteration method based on Improved Genetic Algorithm (AI-IGA) is proposed to solve the models. Elite Genetic Algorithm (EGA) is introduced for the upper-level model of production planning, and Genetic Simulated Annealing Algorithm based on Stochastic Simulation Technique (SS-GSAA) is developed for the lower-level model of scheduling. Results from our experiments demonstrate that the proposed method is feasible for production planning and optimization of the fixed-position assembly workshop with uncertain re-entrance. And algorithm comparison verifies the effectiveness of the proposed algorithm.

Ingeniería ◽  
2022 ◽  
Vol 26 (3) ◽  
pp. 436-449
Carlos Andrés López Ayala ◽  
Wilson Jurado Valbuena ◽  
Eduyn Ramiro Lopez Santana

Context:  In the context of business organizations, every process in which the product is immersed has a cost and time associated with it. The area of maintenance planning and scheduling is no exception; however, it is an aspect in which few companies specialize, tending to be outsourced. In this sense, the application of combinatorial models is a tool with a high potential to improve the overall performance of the organization through the understanding of the integral maintenance process. Method: A two-phase (maintenance and routing) dynamic algorithm is proposed which considers a set of clients distributed in a maintenance network (distance), where each of the technicians start from the same central node (depot), which, in turn, is the endpoint of each assigned route. The objective is to minimize the total cost associated with the development of preventive and corrective maintenance of all machines to be evaluated. With this purpose, the formulation of the mathematical problem for each of the phases and its interrelation method is proposed. Then, performance measures are expressed to evaluate the achieved objectives. Results: The results satisfy a consistent alternative for the resolution of problems of the NP-Hard type, which generates a high level of complexity to the model. That is, it proposes a tool for solving problems of these characteristics in low computational response times and with appealing results. Conclusions: The combined maintenance and routing model using a dynamic algorithm addresses the maintenance and routing problem satisfactorily. The model shows good results with respect to the comparison optimization model in percentage gaps of performance measures lower than 5%. As for the computational time required, a reduction of up to 98% was achieved, which makes it an ideal alternative for highly complex scenarios. Finally, achieving a higher level of characterization, employing multi-objective decision criteria and a greater number of constraints to the problem, is proposed in future research. Acknowledgements: To the High-Performance Computing Center (CECAD - Centro de computación de Alto Desempeño) of Universidad Distrital Francisco José de Caldas for their support, as well as for providing us with a virtual machine to run the proposed mathematical model, which was an essential element in the results obtained.

2022 ◽  
Georges Labrèche ◽  
David Evans ◽  
Dominik Marszk ◽  
Tom Mladenov ◽  
Vasundhara Shiradhonkar ◽  

2022 ◽  
pp. 1-29
Carlos A. Parra ◽  
Adolfo Crespo Márquez ◽  
Vicente González-Prida ◽  
Antonio Sola Rosique ◽  
Juan F. Gómez ◽  

The chapter explains in detail the maintenance management model (MMM) taken as a reference for the development of the book. The chapter is based on the eight phases of the MMM. The first three blocks determine the effectiveness of the management; the following blocks assure the same efficiency and continuous improvement in the following way: Blocks 4 and 5 include actions for the planning and scheduling of maintenance, including, of course, the capacity of planning of department of maintenance. Blocks 6 and 7 are dedicated to the evaluation and control of the maintenance and the cost of assets throughout their life cycle. This chapter of introduction briefly summarizes the process and the reference frame necessary for the implementation of the MMM. This chapter also presents the relationship between the eight phases of the maintenance management model proposed and the general requirements of the asset management standard ISO 55000 to show how the gradual implementation of the MMM largely covers the requirements of the standard ISO 55000.

Ramona-Iuliana Popa ◽  
Maria Catana ◽  
Gabriel Cimpoesu ◽  
Lucian Burlea ◽  

Keeping the equipment and machinery in working order is a very important activity in the field of industrial production, being specific to the maintenance department within an enterprise. Ensuring an optimal in operation of the manufacturing systems is closely related to the prevention of defects, an action that must be found in a planning and scheduling of preventive-planned maintenance works. In the paper, in addition to the introductory part on the main objectives and implementation of the maintenance system and maintenance-specific operations, a case study is conducted for the planning and organization of maintenance in a company equipped with plastic injection machines. The main stages developed consisted in elaborating the maintenance plan, establishing the optimal moment for replacing the machine tools, choosing the optimal type of machine tool and establishing the elements of random wear of the machine tools. It is concluded that the optimal type of injection molding machine that will be chosen to replace the T10 injection molding machine is T14, because it has a minimum average cost of acquisition, maintenance and repair, respectively 42.286 UV.

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