Dynamic Manufacturing System Research Articles

Reactive Scheduling of a Decentralized Multi-Objective Material Transportation Tasks in a Dynamic Job Shop Manufacturing System

The increasingly challenging and dynamic nature of manufacturing industry has driven organizations to enhance their system efficiency. One of the most important aspects in manufacturing plant is the Material Transportation System (MTS). In order to address dynamic factors, MTS need to be equipped with rescheduling capability. This paper focuses on reactive transportation task assignment to a fleet of autonomous Automated Guided Vehicles (AGVs). This paper proposes a Mixed Integer Programming method to reschedule transportation tasks for a non-disruptive job shop manufacturing system based on Multi Agent System (MAS) architecture. The result shows that proposed rescheduling method is able to outperform conventional method significantly.

SOA-PLC – Dynamic Generation and Deployment of Web Services on a Programmable Logic Controller

The static and inflexible design of control software with current Programmable Logic Controllers (PLCs) and its integration in other automation systems do not meet the demands of agile and dynamic manufacturing systems with their rising complexity. Service-oriented Architecture (SOA) is a promising approach to overcome this shortcoming. Thus, a concept of a SOA-PLC is presented in this paper. The suggested concept allows a fully automated generation and deployment of field device functions as services implemented with the Devices Profile for Web Services (DPWS). Moreover, the concept is demonstrated in a case study using a conventional Phoenix Contact PLC and the DPWS orchestration tool JGrafchart.

Proactive Self-Adaptation of a Flexible Simulation Based Control System Using Forecasting

In recent years, intelligent self-adapting control mechanisms have become a center of research due to their many advantages in complex dynamic real world manufacturing and logistics systems. Their ability to modify control behavior based on detected changes in the environment allows for high flexibility and robustness. While existing self-adapting systems typically only react to changes a posteriori - resulting in a temporary performance drop - we propose a forecast of system parameters in order to maintain constantly high performance. The forecasting is used in context of a simulation based approach where the forecast results are used in a parameterized simulation model of the controlled system. This enables the control to proactively anticipate likely changes in the environment and be prepared in case of their realization. We specifically researched different forecasting methods from business applications for use in self-adapting control. We evaluate our approach in a proposed control system for a warehouse scenario using novel evaluation techniques in a custom prototypical software tool. We identified and adapted suitable forecasting methods and developed meaningful evaluation techniques based not only on parameter forecast, but actual resulting system states. The approach is validated using a prototypical Java implementation and integration into the material flow simulator d'fact.

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.

Evolution of Dynamic Manufacturing Systems and its Corresponding Operations Priorities and Corporate Performance

This paper explores evolution of dynamic manufacturing systems. Evolution of dynamic manufacturing systems is clustered into backward integration, forward integration, technological diversified integration and industrial diversified integration according to innovative capability and competitive environment. These four external corporate venturing modes are explored in terms of operations priorities and corporate performance. The research reveals that each mode operates in specific environments, develops different operations priorities, and attains diverse corporate performance. It shows which operations priorities successful firms adopt and how high performing firms sustain their advantages under specific innovative capabilities and competitive environments. We hope it will provide some useful guidelines to business managers.

Improving the Communication Performance in Multi-Agent Systems for Distributed Manufacturing Process Control

The distributed control of complex and dynamic manufacturing systems is a challenging task and the multi-agent system (MAS) becomes a suitable solution due to its flexibility in both distributing and integrating software functionalities across the controlled system. This paper investigates the potentials of improving agent message delivery by enhancing the underlying communication network with IP QoS mechanisms using service differentiation and early congestion avoidance techniques. The extensive simulation experiments demonstrated the effectiveness of our approach in providing small latency, small jitter and improved throughputs in ACL message delivery, whilst without deteriorating other data applications between distributed controllers.

An Intelligent Process Model for Manufacturing System Optimization

The paper aims to develop an intelligent modeling system using Microsoft Excel spreadsheet interface through mathematical language, mathematical reasoning and algorithms flow chart technique for manufacturing system optimization without human involvement. The paper begins to search for a mathematical theorem which is arithmetic series to represent a dynamic manufacturing system in a production floor using production time variable through numerical analysis and is validated using software simulation. The mathematical theorem is modeled with Industrial Engineering (IE) variables into spreadsheet to perform intelligent decision making. The model sets inventory target variable to be achieved with automated computation through the data input from users. Manual analysis from human can be transposed to mathematical language in order automate the system intelligently. The building of intelligent modeling system into spreadsheet using mathematical language sets a new platform for researchers to promote the next generation of modeling technique in the manufacturing field.

A multi-agent based agile manufacturing planning and control system

In today’s manufacturing enterprise, the performance of customer service level (e.g., short ordering-to-delivery time, low price) is highly dependent on the effectiveness of its manufacturing planning and control system (MPCS). However, most of the current MPCS, employed the hierarchical planning approach, may have some drawbacks, such as structural rigidity, difficulty of designing a control system, and lack of flexibility. Currently, RFID (Radio Frequency Identification) technology has been applied to enhance the visibility, accountability, track ability and traceability of manufacturing system whenever the accurate and detailed manufacturing information (e.g., raw material, WIP, products in factory and products in the down streams) of products will be followed in real-time basis by RFID technique. In addition, a multi-agent approach may be applied in a distributed and autonomous system which allows negotiation-based decision making. Therefore, the objective of this research is to study the application of RFID technique and multi-agent system (MAS) in developing an agent-based agile manufacturing planning and control system (AMPCS) to respond to the dynamically changing manufacturing activities and exceptions. In AMPCS, RFID-based manufacturing control (R-MC) module plays the role of controlling the manufacturing system in which production items and manufacturing resources attached with RFID tag may actively feedback production status to and receive production and operations schedule from advanced manufacturing planning (AMP) module. In addition, a bidding process and algorithm is developed to generate operations schedule by using the characteristics of MAS. Performance analysis (PA) module is responsible not only for evaluating the scheduling results but also for evaluating the performance of production execution. The development of an AMPCS for an automated manufacturing cell demonstrates that the integration of RFID technique and MAS in developing an agile manufacturing planning and control system can really possess the characteristics of visibility, accountability, track ability, responsiveness, and flexibility in a distributed and dynamic manufacturing system.

Performance evaluation of flexible manufacturing systems under uncertain and dynamic situations

The present era demands the efficient modelling of any manufacturing system to enable it to cope with unforeseen situations on the shop floor. One of the complex issues affecting the performance of manufacturing systems is the scheduling of part types. In this paper, the authors have attempted to overcome the impact of uncertainties such as machine breakdowns, deadlocks, etc., by inserting slack that can absorb these disruptions without affecting the other scheduled activities. The impact of the flexibilities in this scenario is also investigated. The objective functions have been formulated in such a manner that a better trade-off between the uncertainties and flexibilities can be established. Consideration of automated guided vehicles (AGVs) in this scenario helps in the loading or unloading of part types in a better manner. In the recent past, a comprehensive literature survey revealed the supremacy of random search algorithms in evaluating the performance of these types of dynamic manufacturing system. The authors have used a metaheuristic known as the quick convergence simulated annealing (QCSA) algorithm, and employed it to resolve the dynamic manufacturing scenario. The metaheuristic encompasses a Cauchy distribution function as a probability function that helps in escaping the local minima in a better manner. Various machine breakdown scenarios are generated. A ‘heuristic gap’ is measured, and it indicates the effectiveness of the performance of the proposed methodology with the varying problem complexities. Statistical validation is also carried out, which helps in authenticating the effectiveness of the proposed approach. The efficacy of the proposed approach is also compared with deterministic priority rules.

Use of machine learning for continuous improvement of the real time heterarchical manufacturing control system performances

Heterarchic manufacturing control system offer a significant potential in terms of capacity, adaptation, self-organisation and real time control for dynamic manufacturing system. In this paper, we present our steps to work out a manufacturing control system where the decisions taken by the system are the result of an agents group work, these agents ensure a continuous improvement of these performance, thanks to the reinforcement learning technique which was introduced to them. This technique of learning makes it possible for the agents to learn the best behaviour in their various roles (answer the requests (risks), self-organisation, plan, etc.) without attenuating the system real time quality. We also introduce a new type of agents called 'observant agent', which has the responsibility to supervise the evolution of the system's total performance. A computer implementation and experimentation of this model are provided in this paper to demonstrate the contribution of our approach.

Relationships among SFLP, DFLP and Robust Layout

Facility layout plays an important role in the performance of manufacturing systems. Firstly, this paper describes the derivations and classification of facility layout problem (FLP). The concepts of static facility layout problem (SFLP), dynamic facility layout problem (DFLP) and robust layout problem are introduced, and their main performance characteristics are compared with each other. Then, the procedure of rearrangement costs in DFLP is proposed. Some main characteristics of SFLP and DFLP, DFLP and robust layout are compared respectively. Lastly, the difference and connection among SFLP, DFLP and robust layout are analyzed. The research results indicate that the dynamic facility layout and robust layout are suitable for the dynamic and changeable manufacturing systems. The method for development of DFLP and its implementation are presented based on SFLP and robust layout.

Enhanced integrated manufacturing systems in an immersive virtual environment

Product innovation requires a dynamic manufacturing system to meet customers' demand with reduced cost. A time lag may exist in manufacturing systems between identifying a problem and finding solutions for the problem. There is a lack of decision tools for unseen problems in the manufacturing process and it is not well-integrated for product review and production evaluation in the current integrated manufacturing systems. An immersive virtual environment (VE) is employed in this research to enhance integrated manufacturing systems by the integration of product review and production evaluation, and by the integration of the system and users. The interaction between a manufacturing system and users is manipulated in a VE. The VE-based integration of manufacturing systems emphasizes the visualization and users' involvement, which enables a rapid and efficient process of the product development. The VE provides a means of efficient decision making over the entire process of product design and manufacturing and there is a complete integration of the system and users. Concurrent planning, sharing information, and users' involvement are three major features of the system, which is outlined in the current paper.

Ant colony optimization to select the best process plan in an automated manufacturing environment

In the present dynamic manufacturing systems, optimal process plan selection is quite an intricate task. Systematic determination of processing steps for the transformation of raw material to finished products is known as process planning. The complexity of process plan selection increases with the availability of alternative machines, alternative set-ups, and alternative processes for manufacturing the same part types. The ant colony optimization (ACO) algorithms are multiagent systems in which the behaviour of each ant is inspired by the foraging behaviour of real ants to solve the optimization problem. This paper presents yet another successful application of ACO to solve real-world problems by resolving the complex process plan selection (PPS) problem. The primary advantage of using ACO is that it stabilizes the solution with considerably less computational effort and time without any deterioration in the quality. The proposed strategy has been applied over a few benchmark problems from the literature, and the results establish the competitive advantage of ACO over the established strategies. Comparative analysis of the proposed strategy with other well-established methodologies confirms its effectiveness and superiority.

Data-Mining Approach to Production Control in the Computer-Integrated Testing Cell

This paper presents a data-mining-based production control approach for the testing and rework cell in a dynamic computer-integrated manufacturing system. The proposed competitive decision selector (CDS) observes the status of the system and jobs at every decision point, and makes its decision on job preemption and dispatching rules in real time. The CDS equipped with two algorithms combines two different knowledge sources, the long-run performance and the short-term performance of each rule on the various status of the system. The short-term performance information is mined by a data-mining approach from large-scale training data generated by simulation with data partition. A decision tree-based module generates classification rules on each partitioned data that are suitable for interpretation and verification by users and stores the rules in the CDS knowledge bases. Experimental results show that the CDS dynamic control is better than other common control rules with respect to the number of tardy jobs.

Using an empirical queueing approach to predict future flow times

The traditional method used to describe a steady-state manufacturing system is a queueing model; whereas the common tool used to predict the future performance of a dynamic manufacturing system is a simulation model. This study proposes an empirical queueing approach to obtain the flow time performance measures of a complex dynamic manufacturing system, such as semiconductor wafer fabrication. This approach is easier to implement than a simulation model and more straightforward than a queueing model. Initially, the empirical queueing curve of each work station, which defines the relationship between the utilization rate and the expected waiting time of a small time period, is obtained from the historical database. Then, an iterative scheme is used to predict the future system behavior. Several latest researches have reported that the prediction of future flow times is important in the operation management of a complex manufacturing system. The approach proposed in this study can be easily implemented for such purposes, and the experimental results show that this approach can accurately predict the future flow times.

A comparative study of dispatching rules in dynamic flowshops and jobshops

This paper presents a comparative study on the performance of dispatching rules in the following sets of dynamic manufacturing systems: flowshops and jobshops, and flowshops with missing operations and jobshops. Three new dispatching rules are proposed. We consider a total of 13 dispatching rules for the analysis of the relative performance with respect to the objectives of minimizing mean flowtime, maximum flowtime, variance of flowtime, proportion of tardy jobs, mean tardiness, maximum tardiness and variance of tardiness. First, we carry out the simulation study in flowshops with jobs undergoing processing on all machines sequentially and in jobshops with random routeing of jobs. The results of the study reveal some interesting observations on the relative performance of the dispatching rules in these two types of manufacturing systems. Next, we consider flowshops with missing operations on jobs and jobshops with random routeing of jobs. We observe some interesting results in the sense that the performance of dispatching rules is being influenced by the routeing of jobs and shopfloor configurations.

Aggregate scheduling and network solving of multi-stage and multi-item manufacturing systems

This paper is concerned with the modeling of aggregate scheduling problems in multi-stage and multi-item dynamic manufacturing systems with storage space limitations and production capacity constraints of workstations. The model aims to minimize the total cost which is the sum of all costs incurred in holding, processing, transporting and schedule delay over the whole study horizon. In order to solve this model efficiently, we show that it can be transferred into an equivalent static job assignment problem with multiple job classes over a space-time network which is a temporal expansion of the base workstation network. Then, an adapted network algorithm based on Frank-Wolfe's is proposed to solve the equivalent problem. The results are illustrated with their application to a numerical example. The computation times on several problem types and problem sizes are investigated. The model solution represents a system optimum scheduling pattern and, hence, could help to improve the existing scheduling works.

Adaptive scheduling in dynamic flexible manufacturing systems: a dynamic rule selection approach

This paper develops an adaptive scheduling policy for dynamic manufacturing systems. The main feature of this policy is that it tailors the dispatching rule to be used at a given point in time to the prevailing state of the system. The inductive learning methodology used for constructing this state-dependent scheduling policy also provides an understanding of the relative importance of the various system parameters in determining the appropriate dispatching rule. Experimental studies indicated the superiority of the suggested approach over the alternative approach involving the repeated application of a single dispatching rule for randomly generated test problems as well as a real system, and under both stationary and nonstationary conditions. In particular, its relative performance improves further when there are frequent disruptions, and when disruptions are caused by the introduction of tight due date jobs and machine breakdowns.

Regular policies and stability of dynamic scheduling for manufacturing systems

In modern manufacturing systems, where parts and products are produced automatically, there are buffers on each machine in order to achieve the desired trade-off between conflicting goals such as maximizing production throughput and keeping work-in-process inventory at reasonable levels. In consequence, scheduling policies that establish buffer levels and machine operations dynamics are needed. The stability of dynamic manufacturing systems depends on the type of scheduling policy. Here, we suggest a class of policies that stabilize, in real time, all machine buffers of a dynamic manufacturing system, that is, all buffer levels remain lower than some upper bounds. Moreover, we develop a closed formula to determine a buffer capacity for each machine and part-type. For comparison purposes, we take scheduling policies proposed in the literature, and consider two performance criteria: upper bound and average buffer levels. Simulation results are also included to show the effectiveness of the proposed policies.

Optimal production and setup control of a dynamic two-product manufacturing system: Analytical solution

This paper deals with the optimal control of a one-machine two-product manufacturing system with setup changes, operating in a continuous time dynamic environment. The system is deterministic. A known constant setup time is incurred when production is switched from one product to the other. Each product or part has specified constant processing time and constant demand rate, as well as an infinite supply of raw material. The problem is formulated as a feedback control problem. The objective is to minimize the sum total of backlog and inventory costs incurred over a finite horizon. The optimal solution provides the optimal production rate and setup switching epochs as a function of the state of the system (backlog and inventory levels). For the steady state, the optimal cyclic schedule is determined. To solve the transient case, the system's state space is partitioned into mutually exclusive regions, such that, with each region the optimal control policy is determined analytically.