Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

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

A novel approach for integrating the optimization of the lifetime and cost of manufacturing of a new product during the design phase

Maximum lifetime and minimum manufacturing cost for new products are the primary goals of companies for competitiveness. These two objectives are contradictory and the geometric dimensions of the products directly control them. In addition, the earlier design errors of new products are predicted, the easier and more inexpensive their rectification becomes. To achieve these objectives, we propose in this article a novel model that makes it possible to solve the problem of optimizing the lifespan and the manufacturing cost of new products during the phase of their design. The prediction of the life of the products is carried out by an energy damage method implemented on the finite element (FE) calculation by using the ABAQUS software. The manufacturing cost prediction is carried out by applying the ABC cost estimation analytical method. In addition, the optimization problem is solved by the method of genetic algorithms. The proposed model can be successfully applied for the optimization of new mechanical products made by subtractive manufacturing. The products that mostly benefits from this model are those used in machines and in the automotive or aeronautic fields. The proposed approach can be directly used by the designer for an optimal preliminary design of new products whose manufacture is done by the same company or subcontracted entirely or partially by other companies

Optimizing the integrated production and maintenance planning using genetic algorithm

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

New model of planning and scheduling for job-shop production system with energy consideration

This paper reports a new multi-item planning and scheduling problem in a job-shop production system with the consideration of energy consumption. A mixed integer linear programming is proposed to integrate planning and scheduling with the consideration of energy aspect. In this study a new operational constraint is considered in the tactical level because of the huge interest given to energy consumption and its strong link existing with production system. To evaluate the performance of this model, computational experiments are presented, and numerical results are given using the software CPLEX and then discussed

Planning and scheduling of production system in conditioning line: industrial application, optimizationand simulation approach

In this article we present an industrial application of our mathematical model that integrates planning and scheduling. Our main objective is to concretize our model and compare the reel results with the theoretical ones. Our application is realized on a conditioning line of pharmaceutical products at the ECAM EPMI production laboratory. For this reason and to save time, we used Witness simulation tool. It gives an overall idea of how the line works, the Makespan of each simulation and it highlights areas for improvement. We looked for the best resulting sequence which corresponds to the minest Makespan and total production cost. Then this sequence is applied on the conditioning line of pharmaceutical products for simulation. On the other hand, we program our mathematical model with the parameters of the conditioning line under python in version 3.6 and we adopt a simulation/optimization coupling approach to verify our model