An agent-based approach for e-manufacturing and supply chain integration

A major problem facing manufacturing organisations is how to provide efficient and cost-effective responses to the unpredictable changes taking place in a global market. This problem is made difficult by the complexity of supply chain networks coupled with the complexity of individual manufacturing systems within supply chains. Current systems such as manufacturing execution systems (MES), supply chain management (SCM) systems and enterprise resource planning (ERP) systems do not provide adequate facilities for addressing this problem. This paper presents an approach that would enable manufacturing organisations to dynamically and cost-effectively integrate, optimise, configure, simulate, restructure and control not only their own manufacturing systems but also their supply networks, in a co-ordinated manner to cope with the dynamic changes occurring in a global market. This is realised by a synergy of two emerging manufacturing concepts: Agent-based agile manufacturing systems and e-manufacturing. The concept is to represent a complex manufacturing system and its supply network with an agent-based modelling and simulation architecture and to dynamically generate alternative scenarios with respect to planning, scheduling, configuration and restructure of both the manufacturing system and its supply network based on the coordinated interactions amongst agents.     

Evaluating the impact of alternative plans on manufacturing performance

In recent years, process planners have become interested in the development of dynamic process planning systems that can interface to scheduling systems providing alternative process plans to increase flexibility in scheduling. However, deciding how many alternatives are needed has not been addressed in any previous studies. This paper presents the results of a simulation-based study aimed at characterizing the benefit provided from having alternative plans available for use in scheduling. This benefit is quantified in terms of the overall performance of a job-shop manufacturing environment. The results of this study indicate that the advantage gained by increasing the number of alternative process plans diminishes rapidly. In fact, under some conditions for the particular system studied, increasing the number of alternatives actually resulted in degraded system performance. Based on these results developers of process planning systems and methodologies need to evaluate carefully the benefit of expending time and resources on the generation of alternative plans or optimal plans.     

Agent-Based Systems for Intelligent Manufacturing: A State-of-the-Art Survey

Agent technology has been considered as an important approach for developing distributed intelligent manufacturing systems. A number of researchers have attempted to apply agent technology to manufacturing enterprise integration, supply chain management, manufacturing planning, scheduling and control, materials handling, and holonic manufacturing systems. This paper gives a brief survey of some related projects in this area, and discusses some key issues in developing agent-based manufacturing systems such as agent technology for enterprise integration and supply chain management, agent encapsulation, system architectures, dynamic system reconfiguration, learning, design and manufacturability assessments, distributed dynamic scheduling, integration of planning and scheduling, concurrent scheduling and execution, factory control structures, potential tools and standards for developing agent-based manufacturing systems. An extensive annotated bibliography is provided.     

An agent-based service-oriented integration architecture for collaborative intelligent manufacturing

The rapidly changing needs and opportunities of today's global market require unprecedented levels of interoperability to integrate diverse information systems to share knowledge and collaborate among organizations. The combination of Web services and software agents provides a promising computing paradigm for efficient service selection and integration of inter-organizational business processes. This paper proposes an agent-based service-oriented integration architecture to leverage manufacturing scheduling services on a network of virtual enterprises. A unique property of this approach is that the scheduling process of an order is orchestrated on the Internet through the negotiation among agent-based Web services. A software prototype system has been implemented for inter-enterprise manufacturing resource sharing. It demonstrates how the proposed service-oriented integration architecture can be used to establish a collaborative environment that provides dynamic resource scheduling services.     

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.     

Temporal reasoning for manufacturing: A hybrid representation and complexity management

Temporal considerations play a key role in the planning and operation of a manufacturing system. The development of a temporal reasoning mechanism would facilitate effective and efficient computer-aided process planning and dynamic scheduling. We feel that a temporal system that makes use of the expressive power of the integral language and the computational ease of the point language will be best suited to reasoning about time within the manufacturing system. The concept of a superinterval, or a collection of intervals, is used to augment a hybrid point-interval temporal system. We have implemented a reasoning algorithm that can be used to aid temporal decision making within the manufacturing environment. Using the quantitative results obtained by measuring our program's performance, we show how the superinterval can be used to partition large temporal systems into smaller ones to facilitate distributed processing of the smaller systems. The distributed processing of large temporal systems helps achieve real-time temporal decision-making capabilities. Such a reasoning system will facilitate automation of the planning and scheduling functions within the manufacturing environment and provide the framework for an autonomous production facility.     

Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model

Increasing individualization demands in products call for high flexibility in the manufacturing systems to adapt changes. This paper proposes a novel digital twin-driven approach for rapid reconfiguration of automated manufacturing systems. The digital twin comprises two parts, the semi-physical simulation that maps data of the system and provides input data to the second part, which is optimization. The results of the optimization part are fed back to the semi-physical simulation for verification. Open-architecture machine tool (OAMT) is defined and developed as a new class of machine tools comprising a fixed standard platform and various individualized modules that can be added and rapidly swapped. Engineers can flexibly reconfigure the manufacturing system for catering to process planning by integrating personalized modules into its OAMTs. Key enabling techniques, including how to twin cyber and physical system and how to quickly bi-level program the production capacity and functionality of manufacturing systems to adapt rapid changes of products, are detailed. A physical implementation is conducted to verify the effectiveness of the proposed approach to achieving improved system performance while minimizing the overheads of the reconfiguration process by automating and rapidly optimizing it.     

VirMIC—一个基于Internet的IC虚拟制造环境

该文展示了一个Internet环境下的集成电路虚拟制造环境VirMIC,着重于系统的三个核心模块,即电路性能仿真与成品率优化模块、工艺线仿真与调度模块和产品决策模块,电路性能仿真与成品率优化模块,建立了一个基于OO-DCE的技术CAD环境,以这个环境为依托,通过协调效益和成品率进行了工艺流程的可制造性优化设计;工艺线仿真与调度模块,以一定的工艺流程和制造系统配置为基础,建立制造系统Petri网模型,从而进行系统性能仿真和生产调度,对制造系统的整体性能进行优化;产品决策模块以前两个模块为基础,对如何选择合作伙伴,如何在合作伙伴之间分配用户提交的制造负荷,为构成“虚拟企业”提供指导性信息,以上三个模块构成了Vir-MIC系统的功能核心。     

Business process reengineering — A case study

Recently, many companies in China try their best to reform their own management systems from original central planning economic systems into market economic systems. Some design reengineering to win competition in the market. The paper gives a case study for business process reengineering in a large aircraft manufacturing company in China.

What they do are as followings: 1. To set up a new planning and scheduling system. The most important discipline is Just In Time. 2. The company change the production planning organizations from four levels into one level. 3. The production planning department work out the four kinds of schedules. 4. To set up a new personnel management system. 5. To redesign the assembly process. i.e., to improved the assembly equipment, reduced the positions from nineteen to eight. 6. To design and implement the computer information systems for supporting all these redesigning. There expert systems and group decision support systems are now running in the computer networks.     

An active learning genetic algorithm for integrated process planning and scheduling

In traditional approaches, process planning and scheduling are carried out sequentially, where scheduling is done separately after the process plan has been generated. However, the functions of these two systems are usually complementary. The traditional approach has become an obstacle to improve the productivity and responsiveness of the manufacturing system. If the two systems can be integrated more tightly, greater performance and higher productivity of a manufacturing system can be achieved. Therefore, the research on the integrated process planning and scheduling (IPPS) problem is necessary. In this paper, a new active learning genetic algorithm based method has been developed to facilitate the integration and optimization of these two systems. Experimental studies have been used to test the approach, and the comparisons have been made between this approach and some previous approaches to indicate the adaptability and superiority of the proposed approach. The experimental results show that the proposed approach is a promising and very effective method on the research of the IPPS problem.     

Distributed joint dynamic maintenance and production scheduling in manufacturing systems: Framework based on model predictive control and Benders decomposition

Scheduling the maintenance based on the condition, respectively the degradation level of the system leads to improved system's reliability while minimizing the maintenance cost. Since the degradation level changes dynamically during the system's operation, we face a dynamic maintenance scheduling problem. In this paper, we address the dynamic maintenance scheduling of manufacturing systems based on their degradation level. The manufacturing system consists of several units with a defined capacity and an individual dynamic degradation model, seeking to optimize their reward. The units sell their production capacity, while maintaining the systems based on the degradation state to prevent failures. The manufacturing units are jointly responsible for fulfilling the demand of the system. This induces a coupling constraint among the agents. Hence, we face a large-scale mixed-integer dynamic maintenance scheduling problem. In order to handle the dynamic model of the system and large-scale optimization, we propose a distributed algorithm using model predictive control (MPC) and Benders decomposition method. In the proposed algorithm, first, the master problem obtains the maintenance scheduling for all the agents, and then based on this data, the agents obtain their optimal production using the distributed MPC method which employs the dual decomposition approach to tackle the coupling constraints among the agents. The effectiveness of the proposed method is investigated on two case studies.     

Multi-agent reinforcement learning for online scheduling in smart factories

Rapid advances in sensing and communication technologies connect isolated manufacturing units, which generates large amounts of data. The new trend of mass customization brings a higher level of disturbances and uncertainties to production planning. Traditional manufacturing systems analyze data and schedule orders in a centralized architecture, which is inefficient and unreliable for the overdependence on central controllers and limited communication channels. Internet of things (IoT) and cloud technologies make it possible to build a distributed manufacturing architecture such as the multi-agent system (MAS). Recently, artificial intelligence (AI) methods are used to solve scheduling problems in the manufacturing setting. However, it is difficult for scheduling algorithms to process high-dimensional data in a distributed system with heterogeneous manufacturing units. Therefore, this paper presents new cyber-physical integration in smart factories for online scheduling of low-volume-high-mix orders. First, manufacturing units are interconnected with each other through the cyber-physical system (CPS) by IoT technologies. Attributes of machining operations are stored and transmitted by radio frequency identification (RFID) tags. Second, we propose an AI scheduler with novel neural networks for each unit (e.g., warehouse, machine) to schedule dynamic operations with real-time sensor data. Each AI scheduler can collaborate with other schedulers by learning from their scheduling experiences. Third, we design new reward functions to improve the decision-making abilities of multiple AI schedulers based on reinforcement learning (RL). The proposed methodology is evaluated and validated in a smart factory by real-world case studies. Experimental results show that the new architecture for smart factories not only improves the learning and scheduling efficiency of multiple AI schedulers but also effectively deals with unexpected events such as rush orders and machine failures.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling

Process planning and scheduling are two key sub-functions in the manufacturing system. Traditionally, process planning and scheduling were regarded as the separate tasks to perform sequentially. Recently, a significant trend is to integrate process planning and scheduling more tightly to achieve greater performance and higher productivity of the manufacturing system. Because of the complementarity of process planning and scheduling, and the multiple objectives requirement from the real-world production, this research focuses on the multi-objective integrated process planning and scheduling (IPPS) problem. In this research, the Nash equilibrium in game theory based approach has been used to deal with the multiple objectives. And a hybrid algorithm has been developed to optimize the IPPS problem. Experimental studies have been used to test the performance of the proposed approach. The results show that the developed approach is a promising and very effective method on the research of the multi-objective IPPS problem.     

A multi-agent system using iterative bidding mechanism to enhance manufacturing agility

The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed.     

基于数据的复杂制造过程调度

现代制造企业规模庞大、过程复杂等特征给制造过程的调度决策带来了极大的挑战. 一方面, 使用传统方法建立指导生产过程调度的精确数学模型变得越来越困难; 另一方面, 因缺乏准确、及时的模型参数而往往导致低下的模型使用效果. 在此情况下, 基于数据--信息--知识--决策的信息提炼轨迹, 有必要探寻新的基于数据的复杂制造过程的调度理论与方法. 在综述国内外相关研究的基础上, 提出了由数据层与模型层构成的基于数据的复杂制造过程调度架构, 并对该结构框架下的相关理论、方法及实施技术进行了探讨.     

Framework and deployment of a cloud-based advanced planning and scheduling system

Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application.     

Design for agility: a scheduling perspective

Agility is the ability of a company to produce a variety of products in a short time and at a low cost. This demands that products and manufacturing systems be simple, robust, and flexible to allow for quick response to the changing market. Scheduling of manufacturing systems in a changing environment is complex. This paper attempts to simplify scheduling of manufacturing systems through appropriate design of products and manufacturing systems. An attempt has been made to generate rules that allow to design products and systems for easy scheduling. Four design for agility rules are proposed in the paper. The first rule deals with decomposition of a manufacturing system. The rule simplifies the scheduling problem and reduces the total changeover cost. The second rule is concerned with design of products with robust scheduling characteristics. Product designs with robust scheduling characteristics can improve the response of a manufacturing system to the changes in the product demand and mix and reconfigurability of the system. The third rule results in a streamlined assembly line which has the type of product flow that simplifies scheduling. The fourth rule emphasizes the reduction of the number of stations in an assembly line. Examples are provided to demonstrate the benefits from using these rules. The implementation of the four rules is also discussed.     

Pricing and production lot-size/scheduling with finite capacity for a deteriorating item over a finite horizon

Although the lately evolved manufacturing technologies such as enterprise resource planning (ERP) provide a unified platform for managing and integrating core business processes within a firm, the decision-making between marketing and production planning still remains rather disjoint. It is due in large parts to the inherent weaknesses of ERP such as the fixed and static parameter settings and uncapacitated assumption. To rectify these drawbacks, we propose a decision model that solves optimally the production lot-size/scheduling problem taking into account the dynamic aspects of customer's demand as well as the restriction of finite capacity in a plant. More specifically, we consider a single product that is subject to continuous decay, faces a price-dependent and time-varying demand, and time-varying deteriorating rate, production rate, and variable production cost, with the objective of maximizing the profit stream over multi-period planning horizon. We propose both coordinated and decentralized decision-making policies that drive the solution of the multivariate maximization problem. Both policies are formulated as dynamic programming models and solved by numerical search techniques. In our numerical experiments, the solution procedure is demonstrated, comparative study is conducted, and sensitivity analysis is carried out with respect to major parameters. The numerical result shows that the solution generated by the coordinated policy outperforms that by the decentralized policy in maximizing net profit and many other quantifiable measures such as minimizing inventory investment and storage capacity.Scope and purposeWe consider a manufacturing firm who produces and sells a single product that is subjected to continuous decay over a lifetime, faces a price-dependent and time-varying demand function, shortages are allowed and a completely backlogged, and has the objective of determining price and production lot-size/scheduling so as to maximize the total profit stream over multi-period planning horizon. We develop a tactical-level decision model that solves the production scheduling problem taking into account the dynamic nature of customer's demand which is partially controllable through pricing schemes. As analogous to the sales and operations planning, the proposed scheme can be used as a coordination center of the APS system within a generic enterprise resource planning framework which integrates and coordinates distinct functions within a firm.This paper differs from the existing works in several ways. First, we propose a dynamic version of the joint pricing and lot-size/scheduling problem taking into account the capacitated constraint. Second, several key factors being considered in the model, such as the demand rate, deteriorating rate, production rate, and variable production cost are assumed time-varying that reflect the dynamic nature of the market and the learning effect of the production system. A third difference between the past research and ours is that the price can be adjusted upward or downward in our model, making the proposed pricing policy more responsive to the structural change in demand or supply.