DPP: An agent-based approach for distributed process planning

A changing shop floor environment characterized by larger variety of products in smaller batch sizes requires creating an intelligent and dynamic process planning system that is responsive and adaptive to the rapid adjustment of production capacity and functionality. In response to the requirement, this research proposes a new methodology of distributed process planning (DPP). The primary focus of this paper is on the architecture of the new process planning approach, using multi-agent negotiation and cooperation. The secondary focus is on the other supporting technologies such as machining feature-based planning and function block-based control. Different from traditional methods, the proposed approach uses two-level decision-making—supervisory planning and operation planning. The former focuses on product data analysis, machine selection, and machining sequence planning, while the latter considers the detailed working steps of the machining operations inside of each process plan and is accomplished by intelligent NC controllers. By the nature of decentralization, the DPP shows promise of improving system performance within the continually changing shop floor environment.     

A methodology for joint optimization for maintenance planning, process quality and production scheduling

Performance of a manufacturing system depends significantly on the shop floor performance. Traditionally, shop floor operational policies concerning maintenance scheduling, quality control and production scheduling have been considered and optimized independently. However, these three aspects of operations planning do have an interaction effect on each other and hence need to be considered jointly for improving the system performance. In this paper, a model is developed for joint optimization of these three aspects in a manufacturing system. First, a model has been developed for integrating maintenance scheduling and process quality control policy decisions. It provided an optimal preventive maintenance interval and control chart parameters that minimize expected cost per unit time. Subsequently, the optimal preventive maintenance interval is integrated with the production schedule in order to determine the optimal batch sequence that will minimize penalty-cost incurred due to schedule delay. An example is presented to illustrate the proposed model. It also compares the system performance employing the proposed integrated approach with that obtained by considering maintenance, quality and production scheduling independently. Substantial economic benefits are seen in the joint optimization.     

The order fulfillment planning problem considering multi-site order allocation and single-site shop floor scheduling

The order fulfillment planning process in the thin film transistor–liquid crystal display panel industry is analyzed in this study. A two-phase order fulfillment planning structure is proposed, including the multi-site order allocation among module factories and single-site shop floor scheduling in each factory. In the first phase, the order allocation problem is solved using a mathematical programming model considering practical characteristics, including product structures, customer preferences, alternative bill-of-material, and production constraints. In the second phase, a constraint-based simulation scheduling algorithm is developed to address the scheduling problem in each module factory for determining the ideal order release time. Since production planning and scheduling are dealt with different time scales, the major challenge for the integration lies in the large problem size of the optimization model and becomes intractable. Most of the time bucket-based planning methods in the past literature simplify their scheduling models, but in this paper the detailed shop floor operations and processing behaviors are considered, such as changeover time, processing sequence of orders, and machine characteristics. Finally, a practical case in Taiwan will be employed to testify the feasibility of the proposed order fulfillment planning process; meanwhile, through the analysis of experiments, the adaptability and comparison of different planning approaches in an environment of various market demands are discussed.     

Real-time scheduling for hybrid flowshop in ubiquitous manufacturing environment

This paper discusses the implementation of RFID technologies, which enable the shop floor visibility and reduce uncertainties in the real-time scheduling for hybrid flowshop (HFS) production. In the real-time HFS environment, the arriving of new jobs is dynamic, while the processes in work stages are not continuous. The decision makers in shop floor level and stage level have different objectives. Therefore, classical off-line HFS scheduling approaches cannot be used under these situations. In this research, two major measures are taken to deal with these specific real-time features. Firstly, a ubiquitous manufacturing (UM) environment is created by deploying advanced wireless devices into value-adding points for the collection and synchronization of real-time shop floor data. Secondly, a multi-period hierarchical scheduling (MPHS) mechanism is developed to divide the planning time horizon into multiple shorter periods. The shop floor manager and stage managers can hierarchically make decisions for their own objectives. Finally, the proposed MPHS mechanism is illustrated by a numerical case study.     

Significance of the dimensional view for visualizing relevant aspects of a production system in a co-operative planning process

Pictorial visualization is expected to facilitate communication between industrial professionals when planning working environments and production systems. This hypothesis was investigated by studying how 24 participants including managers, supervisors, machine operators, and occupational health and safety officials, judged three types of computer animated visualization varying in dimensional view (scale and scope of a production line): shop floor view/survey of shop floor; production unit view/semi-survey of production unit; and workplace view/close-up of workplace, in relation to a set of planning issues. The participants participated in a controlled 2-day planning workshop, redesigning a fictitious manufacturing process by means of computer graphics, and then responded to a questionnaire. It can be concluded that shop floor view as well as production unit view are significant for survey planning issues, while all 3-dimensional views are significant for close-up planning issues. Analogously, all dimensional views are significant for technocentric planning issues, whereas only the workplace view is valuable for anthropocentric planning issues.     

Designing function blocks for distributed process planning and adaptive control

The objective of this research is to develop methodologies and a framework for distributed process planning and adaptive control using function blocks. Facilitated by a real-time monitoring system, the proposed methodologies can be applied to integrate with functions of dynamic scheduling in a distributed environment. A function block-enabled process planning approach is proposed to handle dynamic changes during process plan generation and execution. This paper focuses mainly on distributed process planning, particularly on the development of a function block designer that can encapsulate generic process plans into function blocks for runtime execution. As function blocks can sense environmental changes on a shop floor, it is expected that a so-generated process plan can adapt itself to the shop floor environment with dynamically optimized solutions for plan execution and process monitoring.     

Simulation-based planning and control: From shop floor to top floor

This paper illustrates how simulation-based shop-floor planning and control can be extended to enterprise-level activities (top floor). First, the general planning and control concept are discussed, followed by an overview of simulation-based shop-floor planning and control. Analogies between the shop floor and top floor are discussed in terms of the components required to construct simulation-based planning and control systems. Analogies are developed for resource models, coordination models, physical entities, and simulation models. Differences between the shop floor and top floor are also discussed in order to identify new challenges faced for top-floor planning and control. A major difference between the top floor and the shop floor is the way a simulation model is constructed for use in planning, depending on whether time synchronization among member simulations becomes an issue or not. Another difference is in the distributed communication/computing platform. This work uses a distributed computing platform using Web services technology to integrate heterogeneous simulations and systems in a distributed top-floor control environment. The research results reveal that simulation-based planning and control is extensible to the top-floor environment’s evolving new research challenges.     

Significance of the dimensional view for visualizing relevant aspects of a production system in a co-operative planning process

Pictorial visualization is expected to facilitate communication between industrial professionals when planning working environments and production systems. This hypothesis was investigated by studying how 24 participants including managers, supervisors, machine operators, and occupational health and safety officials, judged three types of computer animated visualization varying in dimensional view (scale and scope of a production line):shop floor view/survey of shop floor; production unit view/semi-survey of production unit; and workplace view/close-up of workplace, in relation to a set of planning issues. The participants participated in a controlled 2-day planning workshop, redesigning a fictitious manufacturing process by means of computer graphics, and then responded to a questionnaire. It can be concluded that shop floor view as well as production unit view are significant for survey planning issues, while all 3-dimensional views are significant for close-up planning issues. Analogously, all dimensional views are significant for technocentric planning issues, whereas only the workplace view is valuable for anthropocentric planning issues.     

Artificial intelligence approaches to determination of CNC machining parameters in manufacturing: a review

In Computer Numerical Control (CNC) machining, determining optimum or appropriate cutting parameters can minimize machining errors such as tool breakage, tool deflection and tool wear, thus yielding a high productivity or minimum cost. There have been a number of attempts to determine the machining parameters through off-line adjustment or on-line adaptive control. These attempts use many different kinds of techniques: CAD-based approaches, Operations Research approaches, and Artificial Intelligence (AI) approaches. After describing an overview of these approaches, we will focus on reviewing AI-based techniques for providing a better understanding of these techniques in machining control. AI-based methods fall into three categories: knowledge-based expert systems approach, neural networks approach and probabilistic inference approach. In particular, recent research interests mainly tend to develop on-line or real-time expert systems for adapting machining parameters. The use of AI techniques would be valuable for the purpose.     

An on-line relative position and orientation error calibration methodology for workcell robot operations

Relative position and orientation inaccuracy always exists between a robot and the equipment with which it operates, especially in batch-type production cells that are subjected to dynamic changes. This inaccuracy causes robot relative positioning errors, and may even result in operation failure if the off-line programmed moving path is implemented without adjustment. To make use of off-line programming and simulation tools, an on-line calibration methodology for robot relative positioning inaccuracy was developed in this study. This methodology eliminates the need for time-consuming off-line calibrations relying on accurate models and expensive devices. An industrial robot system was enabled to detect and compensate automatically for relative positioning errors by incorporating a vision system, a 3-D force/torque sensor, and control strategies involving neural networks. The experimental results showed that this methodology is valid and robust in calibrating the relative position and orientation errors automatically without the need for mathematical models and complex off-line calibration procedures for model parameters. Consequently, batch-type production cells would be more flexible, adaptable and intelligent in accommodating dynamic workcell changes with less human effort.     

A two-level advanced production planning and scheduling model for RFID-enabled ubiquitous manufacturing

Radio frequency identification (RFID) technology has been used in manufacturing industries to create a RFID-enabled ubiquitous environment, in where ultimate real-time advanced production planning and scheduling (APPS) will be achieved with the goal of collective intelligence. A particular focus has been placed upon using the vast amount of RFID production shop floor data to obtain more precise and reasonable estimates of APPS parameters such as the arrival of customer orders and standard operation times (SOTs). The resulting APPS model is based on hierarchical production decision-making principle to formulate planning and scheduling levels. A RFID-event driven mechanism is adopted to integrate these two levels for collective intelligence. A heuristic approach using a set of rules is utilized to solve the problem. The model is tested through four dimensions, including the impact of rule sequences on decisions, evaluation of released strategy to control the amount of production order from planning to scheduling, comparison with another model and practical operations, as well as model robustness. Two key findings are observed. First, release strategy based on the RFID-enabled real-time information is efficient and effective to reduce the total tardiness by 44.46% averagely. Second, it is observed that the model has the immune ability on disturbances like defects. However, as the increasing of the problem size, the model robustness against emergency orders becomes weak; while, the resistance to machine breakdown is strong oppositely. Findings and observations are summarized into a number of managerial implications for guiding associated end-users for purchasing collective intelligence in practice.     

Integrating manufacturing resources planning (MRP II) with flexible manufacturing systems (FMS)

This paper will describe the gap that exists between the Manufacturing Resources Planning (MRP II) system and the Flexible Manufacturing System (FMS) and discuss several approaches for closing the gap. Considerable effort has gone into developing and integrating the various modules in the MRP II system. The modules for business planning, production planning, master production scheduling, material requirement planning, capacity requirements planning, and shop floor control have been integrated into a comprehensive computer package for planning and controlling manufacturing. At the same time, a similar effort has been underway to develop and integrate the several subsystems of a FMS. In particular, a number of computer control systems have been developed to automatically plan and control the operations of NC machines with automated tool changers, automated material handling, and automated test and inspection equipment. Unfortunately these two efforts have proceeded relatively independently with little interaction between the two groups. If computer integrated manufacturing (CIM) is to become a reality, the resulting gap must be closed, and closed rapidly.     

A featured-based dynamic process planning and scheduling

The paper presents a dynamic featured-based process planning and scheduling system which is currently under development utilizing a commercial CAD system.

In this system, the product features are extracted using an AI-based feature extractor according to the shop floor capabilities. Using this system, the products can be dynamically process planned and scheduled.     

Distributed Reinforcement Learning Control for Batch Sequencing and Sizing in Just-In-Time Manufacturing Systems

This paper presents an approach that is suitable for Just-In-Time (JIT) production for multi-objective scheduling problem in dynamically changing shop floor environment. The proposed distributed learning and control (DLC) approach integrates part-driven distributed arrival time control (DATC) and machine-driven distributed reinforcement learning based control. With DATC, part controllers adjust their associated parts' arrival time to minimize due-date deviation. Within the restricted pattern of arrivals, machine controllers are concurrently searching for optimal dispatching policies. The machine control problem is modeled as Semi Markov Decision Process (SMDP) and solved using Q-learning. The DLC algorithms are evaluated using simulation for two types of manufacturing systems: family scheduling and dynamic batch sizing. Results show that DLC algorithms achieve significant performance improvement over usual dispatching rules in complex real-time shop floor control problems for JIT production.     

A genetic algorithm for dynamic advanced planning and scheduling (DAPS) with a frozen interval

This paper investigates a dynamic advanced planning and scheduling (DAPS) problem where new orders arrive on a continuous basis. A periodic policy with a frozen interval is adopted to increase stability on the shop floor. A genetic algorithm is developed to find a schedule such that both production idle time and penalties on tardiness and earliness of both original orders and new orders are minimized at each rescheduling point. The proposed methodology is tested on a series of examples. A representative example is illustrated to indicate that the suggested approach can improve the schedule stability while retaining efficiency.     

Dynamic Drum-Buffer-Rope approach for production planning and control in capacitated flow-shop manufacturing systems

Drum-Buffer-Rope-based production planning and control (PPC) approaches provide production managers with effective tools to manage production disruptions and improve operational performance. The corner stone of these approaches is the proper selection of time-buffers which are considered as exogenously defined constant. However, the majority of real-world manufacturing systems are characterized by the dynamic change of demand and by stochastic production times. This fact calls for a dynamic approach in supporting the decision making on time-buffer policies. To this end, we study a capacitated, single-product, three-operation, flow-shop manufacturing system. We propose a dynamic time-buffer control mechanism for short/medium-term PPC with adaptive response to demand changes and robustness to sudden disturbances in both internal and external shop environment. By integrating the control mechanism into the flow-shop system, we develop a system dynamics model to support the decision-making on time-buffer policies. Using the model, we study the effect of policies on shop performance by means of analysis of variance. Extensive numerical investigation reveals the insensitivity of time-buffer policies to key factors related to demand, demand due date and operational characteristics such as protective capacity and production times.     

ASP: An Adaptive Setup Planning Approach for Dynamic Machine Assignments

This paper presents a decision-making approach towards adaptive setup planning that considers both the availability and capability of machines on a shop floor. It loosely integrates scheduling functions at the setup planning stage, and utilizes a two-step decision-making strategy for generating machine-neutral and machine-specific setup plans at each stage. The objective of the research is to enable adaptive setup planning for dynamic job shop machining operations. Particularly, this paper covers basic concepts and algorithms for one-time generic setup planning, and run-time final setup merging for dynamic machine assignments. The decision-making algorithms validation is further demonstrated through a case study. Note to Practitioners-With increased product diversification, companies must be able to profitably produce in small quantities and make frequent product changeovers. This leads to dynamic job shop operations that require a growing number of setups in a machine shop. Moreover, today's customer-driven market and just-in-time production demand for rapid and adaptive decision making capability to deal with dynamic changes in the job shop environment. Within the context, how to come up with effective and efficient setup plans where machine availability and capability change over time is crucial for engineers. The adaptive setup planning approach presented in this paper is expected to largely enhance the dynamism of fluctuating job shop operations through adaptive yet rapid decision makings.     

The development of a hybrid hierarchical/heterarchical shop floor control system applying bidding method in job dispatching

Advancement in computer technology has brought shop floor control into a new era. Hierarchical and heterarchical control structures are two of the most popular control frameworks applied by industries and academics. However, the loading on the central controller may be very heavy in a hierarchical control structure, as the number of equipment in the shop floor increases. On the other hand, in a heterarchical configuration, the inter-cell communication will be very busy making control strategies to achieve. In this research, a hybrid hierarchical/heterarchical structure is proposed. This approach reduces the loading on the shop floor controller by empowering each cell controller to determine the production schedules of its corresponding equipment. However, the shop floor controller still is responsible for the global conditions of the shop such as deciding the job finishing time and balancing the utilization rate of equipment. A bidding method based on the required production costs is also proposed and implemented under the developed control structure.     

Multi-objective scheduling of dynamic job shop using variable neighborhood search

Dynamic job shop scheduling that considers random job arrivals and machine breakdowns is studied in this paper. Considering an event driven policy rescheduling, is triggered in response to dynamic events by variable neighborhood search (VNS). A trained artificial neural network (ANN) updates parameters of VNS at any rescheduling point. Also, a multi-objective performance measure is applied as objective function that consists of makespan and tardiness. The proposed method is compared with some common dispatching rules that have widely used in the literature for dynamic job shop scheduling problem. Results illustrate the high effectiveness and efficiency of the proposed method in a variety of shop floor conditions.     

A hybrid approach for dynamic routing planning in an automated assembly shop

Highly turbulent environment of dynamic job-shop operations affects shop floor layout as well as manufacturing operations. Due to the dynamic nature of layout changes, essential requirements such as adaptability and responsiveness to the changes need to be considered in addition to the cost issues of material handling and machine relocation when reconfiguring a shop floor’s layout. Here, based on the source of uncertainty, the shop floor layout problem is split into two sub-problems and dealt with by two modules: re-layout and find-route. GA is used where changes cause the entire shop re-layout, while function blocks are utilised to find the best sequence of robots for the new conditions within the existing layout. This paper reports the latest development to the authors’ previous work.