13 research outputs found
Developing a Bi-objective Model to Configure a Scalable Manufacturing Line Considering Energy Consumption
No full textInternational audienc
Dynamic predictive and preventive maintenance planning with failure risk and opportunistic grouping considerations: A case study in the automotive industry
No full textInternational audienceRecently, the Predictive Maintenance (PdM) concept has gotten increasing attention in industrial practices and academic research. It is often used with real-time data to monitor the health status, or in the best case, estimate the Remaining Useful Life (RUL) of certain components. Apart from the technical and economic challenges of data acquisition and RUL calculation, estimating RUL is not the final goal in a maintenance management system. As in industry, there are other types of maintenance activities, industrial constraints, and complexities that should be managed together to define overall maintenance planning. In this paper, a simultaneous PdM and Preventive Maintenance (PvM) planning problem of multi-machine and multi-component systems is studied. In this context, a mathematical programming model is proposed to minimize direct and indirect maintenance costs, considering opportunistic grouping of maintenance activities, unused life losses of spare parts, and breakdown costs related to failure risk. To validate the proposed method, a case study from the automotive industry (FPT Industrial) is used, and a comprehensive sensitivity analysis is provided. The results indicate that consideration of the mentioned aspects could significantly impact maintenance planning and overall maintenance costs. Finally, the applicability of the proposed approach is discussed in managerial insights
Sequencing Problem in a Paced Mixed-Model Assembly Line with Multiple Flexible Operators
No full textFlexible Programming Model for Efficient Workload Control in the Car Sequencing Problem
No full textInternational audienceIn the era of increasing product customization, mixed-model assembly lines (MMALs) stand out, enabling the efficient production of diverse products. However, products with work-intensive characteristics may result in work overload when sequenced closely. The Car Sequencing Problem (CSP) utilizes spacing rules to address this challenge in MMALs. This paper presents a flexible CSP mixed-integer programming model within the context of flexible (fuzzy) linear programming. The results demonstrate that the proposed method empowers decision-makers to effectively manage workloads and prevent intolerable work overloads in MMALs through efficient control of sequencing rule violations.Keywords: Car sequencing problem, Mixed-model assembly line, Flexible linear programming, Flexible constraint, Work overload control, Cross-ratio constrain
A bi-level school bus routing problem with bus stops selection and possibility of demand outsourcing
No full textA Ship Routing and Scheduling Problem Considering Pickup and Delivery, Time Windows and Draft Limit
No full textDynamic Distributed Job-Shop Scheduling Problem Consisting of Reconfigurable Machine Tools
No full textPart 8: New Reconfigurable, Flexible or Agile Production Systems in the Era of Industry 4.0International audienceKeeping pace with rapidly changing customer requirements forces companies to increase the capability of adaptation of their production systems. To fulfill the market requirements in a reasonable time and cost, distributed manufacturing has been emerged as one of the efficient approaches. Moreover, the ability of reconfigurability makes manufacturing systems and tools to be more adaptable. This research deals with a dynamic production scheduling problem simultaneously in several different shop-floors consisting of reconfigurable machine tools (RMTs) by utilizing the real-time data extracted from a cyber-physical system (CPS). First, a mathematical programming model is presented for the static state. Thereafter, by utilizing the CPS capabilities, a dynamic model is extended to schedule new jobs, in which there have already been some other jobs in each facility. A numerical example is solved to illustrate the validation of the model. Finally, some potential solving approaches are proposed to make the model implementable in real-world applications
An Improvement in Master Surgical Scheduling Using Artificial Neural Network and Fuzzy Programming Approach
No full textPart 4: Data-Driven Applications in Smart Manufacturing and Logistics SystemsInternational audienceIn this study, a new mathematical model is presented for the master surgical scheduling (MSS) problem at the tactical level. The capacity of the operating room for each specialty is determined in the previous level and used as an input for the tactical level. In MSS, elective surgeries are often performed in a cycle for a cycle. However, this problem considers both elective and emergency patients. The model of this problem is specifically designed to achieve this tactical plan to provide emergency care, as it provides the possibility of reserving some capacity for emergency patients. The current study, forecast emergency patients by applying an artificial neural network, and reserve capacity for them are based on the demand. Fuzzy chance-constraint programming is employed to handle the uncertainty in the model. The data of a private hospital in Iran is used to solve the problem using GAMS software. The results show that the performance of the proposed method against the solution in the hospital performed better
New approaches in metaheuristics to solve the fixed charge transportation problem in a fuzzy environment
No full textA multi-modal competitive hub location pricing problem with customer loyalty and elastic demand
No full textInternational audienc
