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An optimization of the production process is defined as the search for solutions with improved production efficiency. Process optimization should be one of the main components of a business strategy that not only delivers benefits to customers but also helps increase the performance of production processes and benefits the entire business. The traditional approaches to job shop scheduling are based on the exact mathematically formalized model. If the number of model parameters is high and the environment is rather uncertain, the practical applications are quite restricted. That is why the theory proposes an approach based on a large-scale computer simulation. The main goal of this paper is to show that in a concrete company case, the simulation-based approach provides increased productivity. The presented study proposes the practical application of the Tecnomatix software used in the research to optimize the production system. The partial aims of the paper are as follows: (1) to create a simulation model of the production system with the help of the Plant Simulation module, (2) to model the current state of matters in the company, and (3) to propose a solution to the problem. Ultimately, we show that the simulation approach to the production line control provides rather effective solutions when compared to the intuitive one based on trial-and-error experience. The improvement includes a significant (1) shortening of the production cycle and (2) increase in productivity. [ABSTRACT FROM AUTHOR]

This paper presents a research that investigates solutions and algorithms for determining the optimum machine layout served by autonomous material handling system, like mobile robot or automated guided vehicle. Unlike previous works which solved the layout problem by optimizing the distance between facilities, in this paper the machine layout is addressed based on optimizing the travel time of the material handling system. The proposed approach can include boundary kinematic constraints of vehicle while optimizing the objective function such as velocity, acceleration, orientation, and trajectory curvature. The nonlinear constrained model is transformed to unconstrained problem using penalty method. Then, a simulated annealing-based algorithm is used to search for the optimum locations of machines among all possible feasible layouts. The simulation results showed that the proposed approach was efficient enough to use in real factories due to including a vehicle path planner integrated in an overall layout design scheme that involves searching of vehicle control parameters. [ABSTRACT FROM AUTHOR]

Composites manufacturing processes usually involve multiscale models in both space and time, highly non-linear and anisotropic behaviors, strongly coupled multiphysics and complex geometries. In this framework, the use of simulation for real-time decision making directly in the manufacturing facility is still precluded nowadays, in spite of the impressive progresses reached in numerical analysis and computer science during the last decade. In this paper, a process-specific simulation tool based on reduced order modeling is introduced, the Simulation App. This concept is presented through a practical case involving a multi-physics and coupled problem describing the manufacturing process of a composite outlet guide vane. We show that several manufacturing settings can be simulated in few seconds with the Simulation App, thus enabling fast process optimization. Finally, the advantages over general-purpose simulation software, in the context of process simulation, are discussed. [ABSTRACT FROM AUTHOR]

Individualized production, which is a major goal of many high-wage countries, describes a production process in which all elements of a production system are designed in such a way that they enable a high level of product variety at mass production costs. This paper demonstrates recent advances in the individualized production with die-based manufacturing processes, namely high-pressure die casting and plastics profile extrusion. Within these application areas, the chosen approach aiming at individualized production is based on the use of numerical die and process design. The design procedure relies on numerical process simulations based on a nonlinear optimization library and a spline-based geometry kernel. All components interact automatically without requiring user interaction; thus, a completely independent optimization cycle can be achieved. The numerical optimization helps to reduce-or even eliminate-the so far very characteristic manual reworking steps of an original die or process design. These reworking steps are a major cost factor when it comes to individual production. Their abolishment through the presented numerical approaches therefore represents a large step towards the concept of individualized production. [ABSTRACT FROM AUTHOR]

The five-axis milling is widely applied to complex surface machining. When cutting forces of milling processes increase, the consequent workpiece and tool deflections may result in poor machining quality and high processing cost. There are a lot of researches on three-axis milling processes simulation, but very few about five-axis milling. To solve these disadvantages, this paper presents an integrated system containing modeling, simulation, and optimization of five-axis milling processes. The system has three major applications: (1) simulation verification of milling processes, (2) cutting forces prediction, and (3) cutting parameters (feedrate) optimization. The material removal process simulation used for verifying the five-axis milling is based on the three-dexel (depth element) model, and the cutter-workpiece engagement regions are extracted from the geometric model. According to the extracted cutter-workpiece engagement regions, the instantaneous cutting forces could be predicted. The feedrate is off-line modified for balancing the given maximum or the reference cutting forces with the predicted cutting forces on different machining steps. The developed system is validated experimentally to show that the modeling, simulation, and optimization methods could improve the accuracy and efficiency of five-axis milling processes. [ABSTRACT FROM AUTHOR]

Simulations are widely used in manufacturing system design, production planning and decision making. The aim of this paper is to present the possibility of using Monte Carlo simulations in the production plan optimizing and in the project risk management. Optimization is accomplished through two different approaches which principles and results are mutually compared. According to the first approach, production optimization is performed via a deterministic model using the Generalized Reduced Gradient algorithm. The second approach is based on the stochastic model. The optimized production plan is submitted to risk analysis. Two approaches are demonstrated in order to reduce the rate of risk. The first way is modifying the production plan to increase the forecast reliability; the second approach is limiting the uncertainty of key variables. The detailed methodology enables implementing presented approaches in solving various optimization tasks. [ABSTRACT FROM AUTHOR]

Multi-objective constrained optimization problems which arise in many engineering fields often involve computationally expensive black-box model simulators of industrial processes which have to be solved with limited computational time budget, and hence limited number of simulator calls. This paper proposes two heuristic approaches aiming to build proxy problem models, solvable by computationally efficient optimization methods, in order to quickly provide a sufficiently accurate approximation of the Pareto front. The first approach builds a multi-objective mixed-integer linear programming (MO-MILP) surrogate model of the optimization problem relying on piece-wise linear approximations of objectives and constraints obtained through brute-force sensitivity computation. The second approach builds a multi-objective nonlinear programming (MO-NLP) surrogate model using curve fitting of objectives and constraints. In both approaches the desired number of approximated solutions of the Pareto front are generated by applying the ɛ -constraint method to the multi-objective surrogate problems. The proposed approaches are tested for the cost vs. life cycle assessment (LCA)-based environmental optimization of drinking water production plants. The results obtained with both approaches show that a good quality approximation of Pareto front can be obtained with a significantly smaller computational time than with a state-of-the-art metaheuristic algorithm. [ABSTRACT FROM AUTHOR]

In the context of modern industry, optimization emerges as one of the most powerful tools, allowing decision-makers to allocate their resources more assertively and deal with complex manufacturing problems. Moreover, manufacturing systems usually involve activities' interdependency and high stochastic levels, which are necessary to associate optimization and simulation techniques to solve problems. Although simulation optimization is a powerful technique, it can converge on a good solution, which often limits its use in day-to-day operations. As an alternative, metamodels may be used to replace simulation models in the optimization process. In recent years, with the development in the machine learning area, algorithms with high learning capacity have emerged, making the metamodel-based simulation optimization (MBSO) a promising study field. Based on the latest theoretical research on the theme, MBSO techniques have been widely used to solve manufacturing problems. However, there is still no consensus about the experimental design, the learning algorithms, and the importance of the hyperparameter optimization step. Then, the article evaluates the performance of six machine learning algorithms trained with and without hyperparameter optimization, two experimental designs, and five different sample sizes to build metamodels in three real manufacturing cases. Based on the results, the random forest algorithm and the random design with 40 × sample size expressed the better performance to metamodels' development. Furthermore, the hyperparameter optimization step reduced the metamodels' error in about 32.83%. [ABSTRACT FROM AUTHOR]

Abstract: This study addressed the issue of automated material handling systems (AMHS) in the photolithography zone of a 300mm (12-in.) wafer fab facility. The lithography process accounts for 40–50% of the time required to produce wafers. Therefore, managing the AMHS in the photolithography zone is a challenging task. This paper examines the dispatching rule and the number of vehicles in variable wafer input cases. With a stochastic and complex manufacturing process, a photobay simulation may lead to excessive iterations and wasted computation time. The most frequently used approach for process management in the literature is performance analysis with a model that simulates each alternative for N times. However, this approach becomes time consuming as the number of variables and iterations increases. To address this issue, we use Optimal Computing Budget Allocation (OCBA) and extend OCBA by adding particle swarm optimization (PSO). With this combined approached, the number of iterations of each alternative is determined by OCBA, and the optimal solution in the domain of feasible solutions is identified through PSO. This research provides a useful reference to optimally allocate lithographical resources and the number of iterations with random parameters for both scholars and practitioners. Results demonstrate the superiority of PSOOCBA in terms of searching quality and robustness. [Copyright &y& Elsevier]