Scalability planning for reconfigurable manufacturing systems

Scalability is a key characteristic of reconfigurable manufacturing systems, which allows system throughput capacity to be rapidly and cost-effectively adjusted to abrupt changes in market demand. This paper presents a scalability planning methodology for reconfigurable manufacturing systems that can incrementally scale the system capacity by reconfiguring an existing system. An optimization algorithm based on Genetic Algorithm is developed to determine the most economical way to reconfigure an existing system. Adding or removing machines to match the new throughput requirements and concurrently rebalancing the system for each configuration, accomplishes the system reconfiguration. The proposed approach is validated through a case study of a CNC-based automotive cylinder head machining system.     

A metamodel-based Monte Carlo simulation approach for responsive production planning of manufacturing systems

Production planning is concerned with finding a release plan of jobs into a manufacturing system so that its actual outputs over time match the customer demand with the least cost. For a given release plan, the system outputs, work in process inventory (WIP) levels and job completions, are non-stationary bivariate time series that interact with time series representing customer demand, resulting in the fulfillment/non-fulfillment of demand and the holding cost of both WIP and finished-goods inventory. The relationship between a release plan and its resulting performance metrics (typically, mean/variance of the total cost and the fill rate) has proven difficult to quantify. This work develops a metamodel-based Monte Carlo simulation (MCS) method to accurately capture the dynamic, stochastic behavior of a manufacturing system, and to allow real-time evaluation of a release plan's performance metrics. This evaluation capability is then embedded in a multi-objective optimization framework to search for near-optimal release plans. The proposed method has been applied to a scaled-down semiconductor fabrication system to demonstrate the quality of the metamodel-based MCS evaluation and the results of plan optimization.     

Towards online reinforced learning of assembly sequence planning with interactive guidance systems for industry 4.0 adaptive manufacturing

Literature shows that reinforcement learning (RL) and the well-known optimization algorithms derived from it have been applied to assembly sequence planning (ASP); however, the way this is done, as an offline process, ends up generating optimization methods that are not exploiting the full potential of RL. Today’s assembly lines need to be adaptive to changes, resilient to errors and attentive to the operators’ skills and needs. If all of these aspects need to evolve towards a new paradigm, called Industry 4.0, the way RL is applied to ASP needs to change as well: the RL phase has to be part of the assembly execution phase and be optimized with time and several repetitions of the process. This article presents an agile exploratory experiment in ASP to prove the effectiveness of RL techniques to execute ASP as an adaptive, online and experience-driven optimization process, directly at assembly time. The human-assembly interaction is modelled through the input-outputs of an assembly guidance system built as an assembly digital twin. Experimental assemblies are executed without pre-established assembly sequence plans and adapted to the operators’ needs. The experiments show that precedence and transition matrices for an assembly can be generated from the statistical knowledge of several different assembly executions. When the frequency of a given subassembly reinforces its importance, statistical results obtained from the experiments prove that online RL applications are not only possible but also effective for learning, teaching, executing and improving assembly tasks at the same time. This article paves the way towards the application of online RL algorithms to ASP.     

Hierarchical production planning for complex manufacturing systems

A hierarchical approach to production planning for complex manufacturing systems is presented. A single facility comprising a number of work-centers that produce multiple part types is considered. The planning horizon includes a sequence of time periods, and the demand for all part types is assumed known. The production planning problem consists of minimizing the holding costs for all part types, as well as the work-in-process and the backlogging costs for the end items. We present a two-level hierarchy that is based on aggregating parts to part families, work-centers to manufacturing cells and time periods to aggregate time periods. The solution at the aggregate level is imposed as a constraint to the detailed level problems which are formulated for each manufacturing cell separately. This architecture uses a rolling horizon strategy to perform the production management function. We have employed perturbation analysis techniques to adjust certain parameters of the optimization problems at the detailed level to reach a near-optimal detailed production plan. Numerical results for several realistic example problems are presented and the solutions obtained from the hierarchical and monolithic approaches are compared. The results indicate that the hierarchical approach offers major advantages in computational efficiency, while the loss of optimality is acceptable.     

A simulation optimization approach in production planning of failure prone manufacturing systems

In this paper, the implementation of a new method to control the production rate of manufacturing systems, based on the combination of stochastic optimal control theory, discrete event simulation, experimental design and response surface methodology is outlined. The system under study consists of several parallel machines, multiple-product manufacturing system. Machines are subject to failures and repairs and their capacity process is assumed to be a finite state Markov chain throughout the analytical control model. The problem is to choose the production rates so as to minimize the expected discounted cost of inventory/backlog over an infinite horizon. We first show that, for constant demand rates and exponential failure and repair times distributions of the machines, the hedging point policy is optimal. The structure of the hedging point policy is then parameterized by factors representing the thresholds of involved products. With such a policy, simulation experiments are combined to experimental design and response surface methodology to estimate the optimal control policy. We obtain that the hedging point policy is also applicable to a wide variety of complex problems including non-exponential failure and repair times distributions and random demand rates. Analytical solutions may not be easily obtained for such complex situations.     

An intelligent simulation environment for manufacturing systems

The manufacturing field is an area where the application of simulation is an essential tool for validating methods and architectures before applying them on the factory floor. Despite the fact that there are a great number of simulation tools, most of them do not take into account the specific requirements of the “new manufacturing era” such as distributed organization, interoperability, cooperation, scalability, fault tolerance and agility. On the other hand, Multiagent System technology has demonstrated its utility in manufacturing system modeling and implementation. Agenthood features such as proactivity, reactivity, and sociability may also be useful for associating them with the specific simulation needs of the new changing requirements for manufacturing systems. In this paper, an Agent-supported Simulation Environment for intelligent manufacturing systems is presented. The different roles that are played by the agents of the simulation environment are defined taking into account the specific dynamic features in manufacturing simulation and the requirements of the new manufacturing era. Moreover, the interaction and cooperation scenarios among these agents are specified to facilitate manufacturing simulation in an appropriate and flexible way. A detailed evaluation study, with regards to the new manufacturing era requirements, demonstrates the advantages of the proposed approach over current state-of-the-art proposals.     

A distributed task planning system for computer-integrated manufacturing systems

A formal mathematical framework for a distributed task planning method suitable for computerintegrated manufacturing systems is proposed. All pertinent algorithms are derived. A detailed timing analysis associated with primitive actions and activities (complex tasks) execution is presented. A formal language is designed for event tracking and error specification. Based on the derived language, an error recovery mechanism (automaton) is proposed. A case study demonstrates the applicability of the presented method with and without error occurrences.Dr Kokinaki is currently Science and Engineering Research Centre, De Montfort University, UK.     

Expert systems for manufacturing cell simulation and design

Computer Integrated Manufacturing (CIM) is approached by means of the application of Computer Aided Design (CAD), Computer Aided Manufacturing (CAM) and other CA techniques, methods and programs/program systems. These programs are often implemented as knowledge-based, or expert systems and in this way they became typical examples of engineering application of artificial intelligence. The production task of CIM systems is solved by using flexible manufacturing systems (FMSs). FMSs built up from smaller, complex units, i.e. from flexible manufacturing cells (FMCs) have several advantages. The design and the operation of manufacturing systems need new, sophisticated methods to utilize all the embedded benefits of the sophisticated and expensive elements installed for production purposes. New methods like knowledge processing technology, cooperative problem-solving techniques, etc., offer wide possibilities to design more reasonable systems. This paper describes prototype expert systems that make use of different knowledge-based tools and techniques to design (configure, reconfigure) and simulate manufacturing cells, taking into consideration technological plans and other relevant information.     

Coupling of interactive manufacturing operations simulation and immersive virtual reality

This paper presents a novel general-purpose simulation analysis application that combines concurrent operations simulation with the advanced data interrogation and user interaction capabilities of immersive virtual reality systems. The application allows for interactive modification of the simulation parameters, while providing the users with the available simulation information by effectively placing the operator in the midst of the environment being simulated. The major contribution of this research is the total integration of the immersive virtual reality environment with the simulation, allowing users in the environment to interactively change the inputs to the simulation as it is running. Implementation and functionality details of the developed application are presented. The experience of using the application to analyze a manufacturing operation in a collaborative scenario is also discussed.     

A heuristic procedure for production planning of modern manufacturing systems

The purpose of this paper is to present a new heuristic algorithm based on a feasible enumeration method, developed to solve the machine loading and product-mix decision problems for manufacturing systems based on group technology. It provides an efficient tool for machine load and product-mix analysis to optimally select parts to be manufactured in a limited amount of time available in a given production facility, by applying the group technology concept. A computational algorithm is developed, a sample numerical problem included, and computational results presented. It is shown that the algorithm herein proposed is very efficient from the computational view point. The heuristics imbedded in the feasible enumeration procedure is repreented by a priority rule which has been found to be independent of problem data and general for the class of problem analysed.     

Cyber coordinated simulation for distributed multi-stage additive manufacturing systems

Additive Manufacturing (AM) processes have been increasingly used to manufacture energy storage products with dedicated material preparation and post-processing stages to enhance product properties. Most researchers focus on selecting materials and improving processes, yet the system modeling and management has not been investigated so far. This paper extends the conventional single-stage AM processes to multi-STage distRibutEd AM (STREAM) systems. In STREAM, a batch of material produced at the pre-processing stage is jointly consumed by distributed AM printers, and then the printed parts are collected for the post-processing stage. Modeling and managing such complex systems have been challenging. We propose a novel framework for “cyber-coordinated simulation” to manage the hierarchical information in STREAM. This is important because simulation can be used to infuse data into predictive analytics, thus providing guidance for the optimization and control of STREAM operations. The proposed framework is hierarchical in nature, where the single-stage, multi-stage, and distributed productions are modeled through the integration of different simulators. We demonstrate the proposed framework with simulation data from Freeze Nano Printing (FNP) AM for the fabrication of energy storage products.     

An application of interactive computer graphics for simulation of vibratory systems

Computer simulation of two vibratory systems composed of mass. spring, and dashpot el ements is described. The first system has one degree of freedom and it undergoes damped-free vibration. The second system undergoes damped-forced vibration with two degrees of freedom. The use of refresh graphics with full interaction in real time facilitates a comprehensive parametric study of the system and the programs can be used as teaching and design aids.     

Using online simulation in Holonic manufacturing systems

This paper deals with the use of online simulation on Holonic manufacturing systems. Concepts needed for the use of online simulation in a classical hierarchical system were already defined, the observer being the central one. The behavior's differences between both classes of systems are studied to determine the best way to adapt these concepts to this new environment. In the product resource order staff approach (PROSA) reference architecture, staff holons were chosen to welcome the simulation models and the observer. An application on an industrial sized Holonic manufacturing system is described to demonstrate the validity of the approach.     

Earliness/tardiness production planning approaches with due-window for manufacturing systems

Although the earliness/tardiness production planning approaches for manufacturing systems with due-date have appeared in the literature, in practice, customers prefer a time duration rather than an exact due-date. This kind of due-date is called due-window. This paper focuses on the production planning problems to minimize the total earliness and tardiness penalties with a due-window subject to the manufacturing resource constraints. Two models, one for mass manufacture and another for one-of-a-kind product (OKP) manufacture, are discussed separately. By means of mathematical deduction, the model for mass manufacture is translated into a linear programming problem and solved by a simplex method. In the case of OKP manufacture, the problem is reduced to a linear 0–1 programming model, using the elaborate definition of variables. The computational results show that both algorithms achieve the optimal production planning and are applicable to practical manufacturing systems.     

From observation to simulation: generating culture-specific behavior for interactive systems

In this article we present a parameterized model for generating multimodal behavior based on cultural heuristics. To this end, a multimodal corpus analysis of human interactions in two cultures serves as the empirical basis for the modeling endeavor. Integrating the results from this empirical study with a well-established theory of cultural dimensions, it becomes feasible to generate culture-specific multimodal behavior in embodied agents by giving evidence for the cultural background of the agent. Two sample applications are presented that make use of the model and are designed to be applied in the area of coaching intercultural communication.     

A model for manufacturing systems simulation with a control dimension

The objective of this article is related to the potential improvement of computer simulation as applied to manufacturing systems. Through our contacts with the operational environment, we have observed that simulation is not used to its full potential. One remark is that existing tools are not adapted to modelling the decision process: they fall short of offering effective integration into the control process of production. Control is usually limited to scheduling and does not lend itself to practical application. In order to enhance the capabilities of computer simulation and make it more responsive to today’s industrial needs, we present a way of introducing such control into simulation by pursuing generic and applicable concepts. The core concepts that constitute the framework of our research are a global structure supporting the co-ordination and co-operation relations; a local structure presenting a typology of industrial control adapted to our needs; a control centre, the main concept used to introduce control into simulation. The modelling language used is UML and the model is implemented using the object-oriented language JAVA. An industrial application was carried out in the company Alcatel with the help of the Apollo platform.     

A framework and a simulation generator for kanban-controlled manufacturing systems

A generalized framework for the definition of kanban-controlled manufacturing systems and a simulation generator based on this framework are introduced in this paper. Kanbans are used between and within workstations to trigger product flow or production. The simulation generator is capable of modeling complex manufacturing systems with multiple parts, multiple work centers and multiple processes at each work center. Multiple process definition capability enables users to define convergent and divergent points in a manufacturing system. Several decision rules are defined and incorporated into the generator. The simulation generator can be used as a completely interactive tool that is data-driven and requires no programming skills.