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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]

Simulation-based optimization for industrial processlines is discussed in this paper. Our approach combinesmultidisciplinary modeling, modern sensitivity analysismethodology as well as multiobjective optimization bymeans of object-oriented software design principles. As aresult, a simulation and optimization approach that can beextended and modified due to users' needs can be developed.Our approach is illustrated by a real-world examplefrom papermaking industry. [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]

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]

With the development of casting process and its numerical simulation technique, many demands of realism and clearness in post-processing have arisen. This paper introduces the implicit surface to post-processing for the results of casting simulation so as to meet these demands. In this paper, a robust and fully automatic algorithm is proposed for extracting the implicit surface in the signed distance function calculated from the import stereolithography files. Then several applications of implicit surface in the post-processing of casting simulation are listed in two categories. The applications in the first category include fast rendering of massive data, section view in arbitrary direction, visualization of residual liquid, and casting deformation, giving clear and pretty visualization of the results in simulation, and making it possible for engineers and process designers to look through the opaque mold, gain insights to casting process, and locate possible defects. The applications in the second category involve thickness distribution and modulus calculation, which offer important characteristics of castings and facilitate the optimization of casting processes. [ABSTRACT FROM AUTHOR]

In complex systems like Large-ScaleDistributed Systems (LSDSs) the optimization of resource control is an open issue. The large number of resources and multicriteria optimization requirements make the optimization problem a complex one. The importance of resource control increases with the need of use for industrial process and manufacturing, being a key solution for QoS assuring. This paper presents different solutions for multiobjective decentralized control models for tasks assignment in LSDS. The transaction in real-time complex system is modeled in simulation by tasks which will be scheduled and executed in a distributed system, so a set of specifications and requirements are known. The paper presents a critical analysis of existing solutions and focuses on a genetic-based algorithm for optimization. The contribution of the algorithm is the fitness function that includes multiobjective criteria for optimization in different way. Several experimental scenarios, modeled using simulation, were considered to offer a support for analysis of near-optimal solution for resource selection. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the technical and economic potential of energy-intensive industries to provide demand-side management (DSM) in electricity and balancing markets through 2030. Increasing shares of renewables will lead to a rising demand for ancillary services at the same time that less conventional plants will be available to provide these services. This paper makes projections on the extent to which DSM from industrial processes can provide economic benefits in electricity markets with renewables by providing tertiary reserve capacity. Different industrial processes and their specific technical and economic properties are investigated and compared with other storage devices and electricity generation technologies. Based on an extension of an existing European electricity market model, simulations are used here to make long-term forecasts for market prices, dispatch and investments in the electricity markets through linear optimization. [ABSTRACT FROM AUTHOR]

Discrete event simulation (DES) has been widely applied to modelling and simulation of computer and engineering systems and is an active field of research that has now evolved from 2D to 3D discrete event simulation. This paper attempts to address several key issues in a successful implementation of DES models based on our own and the previous experiences of others. It describes the common basis, which forms the core for the application of modelling and simulation methodologies that are available to support manufacturing systems analysis, design and performance evaluation. Through a comprehensive literature survey, this paper summarises and compares the most widely used optimisation techniques for simulation of manufacturing systems; an overview of the recent and popular simulation languages and packages available for the modelling and simulation community and the classification of their utility for modelling and simulation of manufacturing systems is also given. Finally, this paper summarises and reports the latest development in the most exciting world wide web (www)-based simulation techniques that represent a future that may completely change the nature and future exploitation of modelling and simulation technology in industry. [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]

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]

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]

For a manufacturing equipment, any unplanned breakdown during the production period results into a high production loss. To keep the manufacturing facilities in good condition, preventive maintenance is planned. However, because of limited time and availability of resources, not all the system components can be or need to be repaired/replaced during a planned opportunity. Hence, the unplanned breakdowns can also be considered as an opportunity to do the maintenance activities for other components to take the advantage of economic dependency in multi-component system. However, when the system is under maintenance, it is very conservative to take the decision of maintenance actions on the components because of limited available time and resources. For such situation, this paper consider an opportunistic maintenance model for a multi-component system to take maintenance decision with a constraint on available time and the system availability requirements. The maintenance decisions for each component involves one of the three actions namely, repair, replace or do nothing to achieve the target availability with minimum maintenance cost. The model also considers the effect of component failures on the quality of product being manufactured as well as the production schedule on the machine. The cost of rejections is considered in the total failure cost along with the maintenance and downtime costs. The production schedule delay factor is considered as a constraint for the maintenance decision to account for the effect on production schedule delay. The optimal solution for the model is obtained using three solution methodologies namely simulated annealing, genetic algorithm and sequence heuristics. Using a real-life example of high pressure die casting machine, the opportunistic maintenance approach is demonstrated and results are discussed. [ABSTRACT FROM AUTHOR]

Due to the continuous growth in the application of networks in manufacturing, quality of service (QoS) has become an important issue. In this paper, the concept of QoS for manufacturing networks is discussed. To provide overall performance assurance for manufacturing networks, a service framework integrating the QoS mechanisms of the networked resource service management function and the communication networks is proposed. The novel framework maps an application to resource services and then to communication networks, adopts an intelligent optimisation algorithm for QoS management of resource services, and provides QoS schemes for data transfer across communication networks. A prototype implementation has been realised and a set of simulation experiments conducted to evaluate the validity of the framework. The results obtained demonstrate the ability of the framework to satisfy the various performance requirements posed by such applications and provide efficient overall performance assurance for manufacturing networks. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to perform the optimal design of a novel press system with Stephenson-I mechanism for deep drawing, and validate its feasibility. First, the system is presented and its advantages are discussed. Then, kinematic analysis is conducted by using the vector loop method. Moreover, the kinematic dimensions of the variable coupling are synthesized by using an optimization method. Furthermore, the proposed approach is illustrated by a design example. Finally, the solid model of the proposed design is established, and then kinematic simulation is performed by employing ADAMS software. The results show that proposed new press system is feasible and of better output motion for deep drawing. In addition, it has the advantages of easy manufacture, lower cost, higher precision, and easy adjustability. [Copyright &y& Elsevier]

Numerous methodologies have been developed for the plant-wide control (PWC) of industrial processes. These methodologies can be classified into heuristics, optimization, mathematical, and mixed approaches. However, most of these methodologies do not involve the extensive usage of rigorous process simulators as part of the control system development. Also, in recent years, the focus has shifted toward the use of mixed approaches, which builds synergies between different techniques. As part of the continuing search for more effective PWC system design methods, an integrated methodology is proposed in this paper. This eight-level framework incorporates heuristics and optimization, together with the use of simulation throughout the procedure. The main intuitive feature of this framework is the identification of key controlled variables for optimal operation; this is achieved by detailed analysis of disturbances, computation of the new optimal set points by reoptimization, and cost breakdown analysis for throughput changes. Another important feature is the use of dynamic performance measures to aid in analyzing the effects of integration. This framework is then applied to the styrene monomer plant case study presented earlier [Vasudevan et al. Ind. Eng. Chem. Res.2009, 48, 10941–10961] to develop a viable control structure. The performance of the resulting control system is compared with the control system developed using the integrated framework of simulation and heuristics. While both the control structures are found to be stable and robust in the face of disturbances, the control structure developed in the present work performs significantly better, in terms of steady-state profit and the dynamic economic index based on deviation from the production target. [ABSTRACT FROM AUTHOR]

This paper proposes methodologies for modeling and assessment of equipment and industrial processes susceptibility to voltage sags and short interruptions. Voltage sag severity indices are proposed that translate physical disturbance events into expected equipment behavior, allowing fast identification of possible process failure. Based on these indices, probabilistic and fuzzy-based methodologies are developed to provide realistic equipment representation and to incorporate flexibility for customized process modeling. Extensive simulations showed that the proposed models and methodologies yield greater accuracy in assessments compared to conventional approaches. [ABSTRACT FROM AUTHOR]

A component, which has an optimized combination of different materials in its different portions for a specific application, is considered as the component made of a multiphase perfect material. To fabricate such components, a hybrid layered manufacturing process was proposed and applies spraying, engraving, and refinishing technologies, among which the spraying step is the key technology for generating a coating of hybrid materials with their required volume fractions in every unit volume. To manufacture such a coating, it is important to study the spraying characteristics. This paper intends to establish the behavior model of plasma spraying, to implement the virtual manufacturing according to the behavior model, to analyze the volume fraction error of material constituents and to optimize the related technological parameters to eliminate the volume fraction error, thus providing the reliable basis for future real manufacturing. [Copyright &y& Elsevier]