Dynamically Integrated Manufacturing Systems (DIMS)—A Multiagent Approach

Manufacturing businesses in today's market are facing immense pressures to react rapidly to dynamic variations in demand distributions across products and changing product mixes. To cope with the pressures requires dynamically integrated manufacturing systems (DIMS) that can manage optimal fulfillment of customer orders while simultaneously considering alternative system structures to suit changing conditions. This paper presents a multiagent approach to DIMS, where production planning and control decisions are integrated with systems reconfiguration and restructure. A multiagent framework, referred to as a hierarchical autonomous agent network, is proposed to model complex manufacturing systems, their structures, and constraints. It allows the hierarchical structures of complex systems to be modeled while avoiding centralized control in classical hierarchical/hybrid frameworks. Subsystems interact heterarchically with product orders to carry out optimal planning and scheduling. An agent coordination algorithm, operating iteratively under the control of a genetic algorithm, is developed to enable optimal planning and control decisions for order fulfillment to be made through interactions between agents. This algorithm also allows the structural constraints of systems to be relaxed gradually during agent interaction, so that planning and control are first carried out under existing constraints, but when satisfactory solutions cannot be found, subsystems are allowed to regroup to form new configurations. Frequently used configurations are detected and evaluated for system restructure. The approach also enables Petri-net models of new system structures to be generated dynamically and the structures to be evaluated through agent-based discrete event simulation.     

Design of expert systems for integrated production automation

An approach to the design of expert systems for integrated production automation is presented. The major components in an integrated manufacturing system consist of corporate planning, marketing planning, research and development, engineering design, production planning, manufacturing, warehousing, and product distribution. These components are linked by management information flow, technology information flow, as well as materials flow. An intelligent computer is used to integrate information flow and to control materials flow. This paper discusses characteristics of the future factory, elements of knowledge based systems, and the design of computer based expert systems for production planning, for engineering design, and for integrated manufacturing.     

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.     

A project scheduling approach to production and material requirement planning in Manufacturing-to-Order environments

Long- and medium-term production planning are tools to match production orders with resource capacity and that can also be used as a baseline for material procurement. The lack of a detailed schedule for the manufacturing operations, however, may cause difficulties in providing a proper material requirements planning and may affect the feasibility of the production plan itself. This paper proposes an approach, based on production process knowledge, to extract scheduling information from an aggregate production plan in order to support material procurement. The proposed approach is applied to an industrial case involving machining center production.     

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.     

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.     

Design of a feature-based order acceptance and scheduling module in an ERP system

Customer-oriented manufacturing competes on timely responses to customer requirements, and precise scheduling control for delivery. This challenge demands engineering design and production planning to be fully integrated via advanced enterprise resource planning (ERP) systems. This paper proposes a generic feature association method and a detailed framework that can unify product and process models in order to satisfy customer orders with small batch sizes and high variations. A conceptual solution is introduced by integrating two traditionally separate feature domains: design configuration features and manufacturing process features. To achieve the proposed method, a customer feature class is suggested for the characterization of customers’ profiles related to the manufacturer. An instantiated customer feature object functionally tracks each customer’s selection of product configurations related to its requirements, specific orders, and production schedules with dynamic associations to the live manufacturing capacity. With the new associative integration method, a preliminary order acceptance system (OAS) prototype system has been implemented within an ERP order management system and its conceptual structure model is demonstrated within a multi-facet feature framework.     

Reactive scheduling using a multi-agent model: the SCEP framework

Multi-agent systems have been successfully applied to the scheduling problem for some time. However, their use often leads to poorly unsatisfactory disappointing results. A new multi-agent model, called supervisor, customers, environment, producers (SCEP), is suggested in this paper. This model, developed for all types of planning activities, introduces a dialogue between two communities of agents leading to a high level of co-operation. Its two main interests are the following: first it provides a more efficient control of the consequences generated by the local decisions than usual systems to each agent, then the adopted architecture and behaviour permit an easy co-operation between the different SCEP models, which can represent different production functions such as manufacturing, supply management, maintenance or different workshops. As a consequence, the SCEP model can be adapted to a great variety of scheduling/planning problems. This model is applied to the basic scheduling problem of flexible manufacturing systems, and it permits a natural co-habitation between infinite capacity scheduling processes, performed by the manufacturing orders, and finite capacity scheduling processes, performed by the machines. It also provides a framework in order to react to the disturbances occurring at different levels of the workshop.     

A new model of scheduling in manufacturing: tasks, roles, and monitoring

For over 3 decades there was a belief that computer-based solutions would "solve" complex industrial scheduling problems, yet most manufacturing organizations still require human contributions for effective scheduling performance. We present a new model of scheduling for the development and implementation of effective scheduling systems within manufacturing companies. The model derives from investigating the work of 7 schedulers in 4 manufacturing environments using a qualitative field study approach, for which novel field-based data collection and analysis methods were developed. The results show that scheduling in practice comprises task, role, and monitoring activities and that the business environment influences a scheduler at work. A new definition of scheduling is presented that includes the significant facilitation and implementation aspects of human scheduling ignored by many computer-based scheduling approaches. The implications for this model extend across the domains of human factors and operations management, especially for the analysis and improvement of existing and new production planning and control processes and enterprise information systems. Actual or potential applications of this research include the analysis, design, and management of planning, scheduling, and control processes in industry; the selection, training, and support of production schedulers; and the allocation of tasks to humans and computer systems in industrial planning, scheduling, and control processes.     

Maintenance scheduling and production control of multiple-machine manufacturing systems

This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and non-identical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.     

Design of a synchronous manufacturing system with just-in-time production (SMS/JITP)

One of the goals of a manufacturing system is to minimize the cost of production. A significant source of manufacturing cost is attributable to material handling. The sources of material handling cost include equipment, labor, work-in-process, and floor space. One approach to reduce material handling cost is through the implementation of synchronous manufacturing with just-in-time production. Although, the concept of a synchronous manufacturing is known, techniques to design and plan such systems are yet to be formalized. In this paper, a quantitative modeling framework for the design and analysis of a synchronous manufacturing system with just-in-time production is presented. The approach is composed of principles and techniques drawn from scheduling, layout planning, material handling, and computer simulation.     

基于MRP汽车零部件生产计划系统研究与实现

为了提高汽车零部件生产效率，确保按时完成客户订单，将物料需求计划（MRP）技术应用于汽车零部件生产计划系统中．首先给出了汽车零部件行业的供应链结构，提出了基于MRP的对产品进行分组并对产品结构进行层次化分解的汽车零部件生产计划模式．在此基础上，详细介绍了利用客户订单、产品结构信息、工作日历以及库存信息等来生成零部件生产计划和所需物料的采购计划的实现过程，并对实例进行了分析．该系统已经应用于汽车零部件生产企业中．     

Reinforcement learning approach to goal-regulation in a self-evolutionary manufacturing system

Up-to-date market dynamics has been forcing manufacturing systems to adapt quickly and continuously to the ever-changing environment. Self-evolution of manufacturing systems means a continuous process of adapting to the environment on the basis of autonomous goal-formation and goal-oriented dynamic organization. This paper proposes a goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation. Individual goals are regulated by a neural network-based fuzzy inference system, namely, a goal-regulation network (GRN) updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). The GEN approximates the compatibility of goals with current environmental situation. In this paper, a production planning problem is also examined by a simulation study in order to validate the proposed goal regulation mechanism.     

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.     

A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops

Dynamic personalized orders demand and uncertain manufacturing resource availability have become the research hotspots of intelligent resource optimization allocation. Currently, the data generated from the manufacturing industry are rapidly expanding. Such data are multi-source, heterogeneous and multi-scale. Transforming the data into knowledge to optimize the allocation between personalized orders and manufacturing resources is an effective strategy to improve the cognitive intelligent production level of enterprises. However, the manufacturing processes in resource allocation is diversity. There are many rules and constraints among the data. And the relationship among data is more complicated. There lacks a unified approach to information modeling and industrial knowledge generation from mining semantic information from massive manufacturing data. The research challenge is how to fully integrate the complex data of workshop resources and mine the implicit semantic information to form a viable knowledge-driven resource allocation optimization method. Such method can then efficiently provide the relevant engineering information needed for resource allocation. This research presented a unified knowledge graph-driven production resource allocation approach, allowing fast resource allocation decision-making for given order inserting tasks, subject to the resource machining information and the device evaluation strategy. The workshop resource knowledge graph (WRKG) model was presented to integrate the engineering semantic information in the machining workshop. A distributed knowledge representation learning algorithm was developed to mine the implicit resource information for updating the WRKG in real-time. Moreover, a three-staged resource allocation optimization method supported by the WRKG was proposed to output the device sets needed for a specific task. A case study of the manufacturing resource allocation process task in an aerospace enterprise was used to demonstrate the feasibility of the proposed approach.     

Object-oriented and distributed approach for programming robotic manufacturing cells

Flexible manufacturing systems (FMS) are essential for small/medium batch and job shop manufacturing. These types of production systems are used to manufacture a considerable variety of products with medium/small production volumes. Therefore, the manufacturing platforms supporting these types of production must be flexible and organized in flexible manufacturing cells (FMC). Programming FMCs remains a difficult task and is an actual area of research and development. This paper reports an object-oriented approach developed for FMC programming. The work presented was first thought for application in industrial robot manipulators, and later extended to other FMC equipments just by putting the underlying ideas in a general framework. Initially, the motivation for this work was to develop means to add force control to a standard industrial robot manipulator. This problem requires remote access to the robot controller, remote programming and monitoring, as also is required to program and monitor any other FMC equipment. The proposed approach is distributed based on a client/server model and runs on Win32 platforms, i.e., Microsoft Windows and Windows NT. Implementation for the special case of industrial robot manipulators is presented, along with some application examples used for educational, research and industrial purposes.     

Heuristic knowledge representation of production scheduling: An integrated modeling approach

In spite of the efforts to automate production scheduling systems, most manufacturing plants rely on human schedulers for their practical production scheduling due mainly to the complexity and unpredictability of the manufacturing systems. One of the reasons for the difficulties in automating the production scheduling process is the lack of an explicit representation scheme of heuristic scheduling knowledge.

In this paper, an integrated modeling approach is presented to represent the complicated heuristic knowledge in production scheduling systems. The integrated modeling approach is based on object-oriented and event-driven modeling. It provides the explicit, declarative and executable representation of heuristic scheduling knowledge which aids the communication between human schedulers and system analysts. The declarative and executable representation of scheduling knowledge will enhance the flexibility of production scheduling systems and aid the development of production scheduling information systems.

A heuristic production scheduling model of a tire manufacturing system is used throughout the paper to illustrate the concepts and applicability of the approach.     

Real-time capacity requirement planning for make-to-order manufacturing with variable time-window orders

In a make-to-stock (MTS) manufacturing environment using material requirement planning (MRP), checking the capacity feasibility of a master production schedule (MPS) requires capacity requirement planning (CRP) that can be easily calculated. The time window of an order is the time interval from its ready date to its due date. In a make-to-order (MTO) manufacturing environment, the CRP method checks whether a set of orders with different time windows can be scheduled for timely completion. This corporate-level CRP problem has long perplexed MTO contract manufacturers, such as those in the fashion industry. This study therefore develops an efficient and effective CRP approach that considers orders with variable time windows. Real-time capacity feasibility can be checked on both the corporate planning and detailed operational scheduling levels by applying the preemptive earliest due date (PEDD) rule to a single machine problem. This simple and efficient dispatching rule can assess the impact on capacity consumption each time an inquiry order is received or select a set of pre-prioritized orders that can be feasibly scheduled. The efficiency of a supply chain network is affected by its overall lead time, which includes time spent on order processing, manufacturing, and transportation. The proposed approach significantly reduces the order processing time and enhances supply chain efficiency.     

Bilevel model for production–distribution planning solved by using ant colony optimization

This paper addresses a hierarchical production–distribution planning problem. There are two different decision makers controlling the production and the distribution processes, respectively, that do not cooperate because of different optimization strategies. The distribution company, which is the leader of the hierarchical process, controls the allocation of retailers to each depot and the routes which serve them. In order to supply items to retailers, the distribution company orders from the manufacturing company the items which have to be available at the depots. The manufacturing company, which is the follower of the hierarchical process, reacts to these orders deciding which manufacturing plants will produce them. A bilevel program is proposed to model the problem and an ant colony optimization based approach is developed to solve the bilevel model. In order to construct a feasible solution, the procedure uses ants to compute the routes of a feasible solution of the associated multi-depot vehicle route problem. Then, under the given data on depot needs, the corresponding production problem of the manufacturing company is solved. Global pheromone trail updating is based on the leader objective function, which involves costs of sending items from depots to retailers and costs of acquiring items from manufacturing plants and unloading them into depots. A computational experiment is carried out to analyze the performance of the algorithm.