For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.
In this paper we present a multi-optimization technique based on genetic algorithms to search optimal cuttings parameters such as cutting depth, feed rate and cutting speed of multi-pass turning processes. Tow objective functions are simultaneously optimized under a set of practical of machining constraints, the first objective function is cutting cost and the second one is the used tool life time. The proposed model deals multi-pass turning processes where the cutting operations are divided into multi-pass rough machining and finish machining. Results obtained from Genetic Algorithms method are presented in Pareto frontier graphic; this technique helps us in decision making process. An example is presented to illustrate the procedure of this technique.
This study is on the integrated planning problem of maintenance and production within the frame work of a system subject to periodic preventive replacements with minimal repairs in case of unplanned failures. A model was developed using the overall cost by considering the interdependence between the maintenance plan and the production schedule. The overall cost contains two parts: the costs of launching a product, production, storage and breaking on the demand and the preventive and corrective maintenance costs for multi-periods and multi-products systems. The purpose of this integration is to find simultaneously the optimal cycle T at which the preventive maintenance takes place and the optimal values of lot-size by adding the setup time constraint. Using the mixed integer linear programming these optimal values minimize the total cost over a finite horizon. The results show that the proposed model performs quite well and opens new research direction for future improvements.
This article discusses the issue of integrated planning of maintenance activities and production operations at the tactical level for multi-line systems with separate resources and introducing the breaking on demand constraint and that of setup time. The maintenance policy offers preventive replacements in the beginning of each cycle and minimal repairs in case of random failure. The model defined an objective function that reduces the overall cost and can simultaneously determine the optimal production plan (producing, lunching, storing and breaking costs) and the moment of replacement. The resolution is made with the mixed integer linear solver CPLEX. Then we provide a numerical example to illustrate the results and represent the economic gap between the separate and integrated planning.
In spite of the interdependence between them, production and maintenance planning decisions are generally studied and used independently in the majority of the manufacturing systems. Our contribution is summarized to obtain a maintenance policy including preventive replacement in each maintenance cycle and minimal repair in case of unplanned failure, and on the other side, for a set of products and in each period, specify the quantity to be produced and when is the production set up, also the stock and the breaking on demand level, so that to minimize the total cost. The purpose of the research was aimed at achieving the optimization of an integrated planning of preventive maintenance and production in a multi-period, multiproduct, and single-line production system. To achieve this purpose, our model is configured as a mixed integer linear programming and solved by IBM ILOG CPLEX OPL studio 12.6 (USA), and we propose our own genetic algorithms (GAs) using Python solver with respect to resolution time and the quality of results. Then, to find the performance of the model and the usefulness of the proposed resolution method, a numerical example is considered to produce two products for a finite horizon with 11 periods. The results of the analysis show that this GA provides a new tool for the integrated planning in the industrial sector. These results reflect the experiences of single-line system and further studies are needed for generalizability in the multiline cases, also we will compare the proposed GA with other evolutionary algorithms.
Today, in the modern era, and under the pressure of rapid development around the kingdom regions, electric utilities are confronted with a myriad of challenges that include aging infrastructure, enhanced expectation of reliability, reduced cost, and coping effectively with uncertainties and changing regulation requirements. Indeed, for distribution systems, HV/MV substations represent a complex and critical physical asset that requires a careful evaluation of their maintenance practices aiming at more cost-effective tasks. The first concern of the electric power distribution company in Morocco is the optimization of the distribution network maintenance scheduling, to minimize system operating costs and ensure that the system is running most economically. There is significant pressure on power providers for greater system reliability and improvement of customer satisfaction, while similar emphasis is placed on cost reduction. These cost reductions focus on reducing operating and maintenance expenses, and minimizing investments in new plants and equipment. However, the present of the electricity distribution, influenced by a number of technical restrictions and strict regulatory requirements to meet, leads distribution utilities to search new ways of optimizing the maintenance management, make an effort to maximize profit by reducing their electricity supply and operation costs while maintaining their reliability, developing more effective activities, with lower costs. Furthermore, maximal HV/MV substation asset value and minimal maintenance cost are typical economic objectives of the electric utilities. The optimization of maintenance is one possible technique to reduce maintenance costs while improving reliability and utilities need to implement new strategies for more effective maintenance techniques and Asset Management (AM) methods programs to manage inspections and maintenance activities in order to control HV/MV substation equipment conditions. However, development of strategies to make sound decisions in order to effectively improve equipment and system reliability while meeting constraints such as a maintenance budget is a challenge. Finally, this M. Mahmoudi et al. 237 paper describes the application of a new approach based Reliability Centered Maintenance (RCM) methodology to optimize maintenance resources and to the development of maintenance plan for HV/MV distribution substations. For this purpose, a case study was performed on the equipment of power distribution systems for the benefit of a holding company (ONEE Electricity Distribution) in Morocco. The present study was conducted in Oujda Exploitation Maintenance Service (OEXMS), in charge of operation and maintenance of distribution networks.
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