基于MAS的车间管控系统中通用Agent建模与设计

在基于MAS的车间管理控制系统开发中,系统中各个Agent的建模与设计是其关键技术之一。该文提出了一种可满足MAS车间管控系统开发的通用Agent结构模型及其面向对象的表示与实现方法。基于该通用Agent的MAS系统不仅具有可集成性好、互操作性强、具有可重构能力等特点,而且可实现Agent程序的标准化,进而保障项目组协同开发的效率。     

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.     

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.     

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.     

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.     

一个基于多代理的并行工程设计系统模型研究

并行工程设计方法试图在产品的早期设计时期就考虑产品生命周期中的各方面因素,但是要真正实现却是很困难的,该文给出了一个基于多代理的并行工程设计系统框架,来解决“考虑制造的设计（ＤＦＭ）”这一总理２,同样地,并行工程的其它考虑,如可装配性,可维护性与可服务性都能以同样的方式集成到这个框架中来。     

Customized job shop scheduling application development

This paper discusses development of a customized job shop scheduling application. Effective shop floor control relies heavily on the scheduling of part production through work centers. Production without part schedules and sequences is inefficient for job shops. Standard off-the-shelf scheduling software will typically not do the job. Customized scheduling software was developed to both increase manufacturing control while meeting all the requirements of a job shop's production processes, using the least slack rule for sequencing parts in order to meet final assembly schedules.     

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.     

Smart shop floor scheduling using knowledge based simulation

In this paper, the need for knowledge-based simulation technique in shop floor scheduling are addressed. A prototype integrated system in Feed Mill manufacturing utilizing an integrated approach of Artificial Intelligence (AI) and simulation is discussed. The system is designed to support the production planner in scheduling and controlling the shop floor on real-time and on-line basis. System overview with the emphasis on knowledge-based simulation module is described.     

Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm

Most flexible job shop scheduling models assume that the machines are available all of the time. However, in most realistic situations, machines may be unavailable due to maintenances, pre-schedules and so on. In this paper, we study the flexible job shop scheduling problem with availability constraints. The availability constraints are non-fixed in that the completion time of the maintenance tasks is not fixed and has to be determined during the scheduling procedure. We then propose a hybrid genetic algorithm to solve the flexible job shop scheduling problem with non-fixed availability constraints (fJSP-nfa). The genetic algorithm uses an innovative representation method and applies genetic operations in phenotype space in order to enhance the inheritability. We also define two kinds of neighbourhood for the problem based on the concept of critical path. A local search procedure is then integrated under the framework of the genetic algorithm. Representative flexible job shop scheduling benchmark problems and fJSP-nfa problems are solved in order to test the effectiveness and efficiency of the suggested methodology. Received: June 2005 /Accepted: December 2005     

Petri net modeling and scheduling for cyclic job shops with blocking

Cyclic scheduling is an effective scheduling method in the repetitive discrete manufacturing environment. We investigate the scheduling problem for general cyclic job shops with blocking where each machine has an input buffer of finite capacity. We develop Petri net models for the shops. We propose a sequential buffer control policy that restricts the jobs to enter the input buffer of the next machine in a specified sequence. We show that the scheduling model of a cyclic shop with finite buffers under such a buffer control policy can be transformed into a scheduling model of a cyclic shop with no buffer that can be modeled as a timed marked graph. In addition, we characterize the structural properties for deadlock detection. Finally, we present a mixed integer programming model to find an optimal deadlock-free schedule that minimizes the cycle time.     

Applications of agent-based systems in intelligent manufacturing: An updated review

Agent technology has been recognized as a promising paradigm for next generation manufacturing systems. Researchers have attempted to apply agent technology to manufacturing enterprise integration, enterprise collaboration (including supply chain management and virtual enterprises), manufacturing process planning and scheduling, shop floor control, and to holonic manufacturing as an implementation methodology. This paper provides an update review on the recent achievements in these areas, and discusses some key issues in implementing agent-based manufacturing systems such as agent encapsulation, agent organization, agent coordination and negotiation, system dynamics, learning, optimization, security and privacy, tools and standards.     

A hybrid scheduling decision support model for minimizing job tardiness in a make-to-order based mould manufacturing environment

In the make-to-order (MTO) mode of manufacturing, the specification of each product is unique such that production processes vary from one product to another making the production schedule complex. In order to achieve high level productivity, the production flow is not arranged in sequence; instead, the job schedule of different production jobs is adjusted to fit in with the multiple-job shop environment. A poor scheduling of jobs leads to high production cost, long production time and tardiness in job performance. The existing of tardiness in the production schedule significantly affects the harmony among the multiple jobs on the shops floor. In order to provide a complete solution for solving MTO scheduling problems with job shifting and minimizing job tardiness, a hybrid scheduling decision support model (SDSM) is introduced. The model is combined by a Genetic Algorithm (GA) and an optimisation module. GA is adopted to solve the complex scheduling problem taking into consideration of the wide variety of processes while the optimisation module is suggested for tackling tardiness in doing the jobs in a cost effective way. The simulation results reveal that the model shortens the generation time of production schedules and reduces the production cost in MTO-based production projects.     

Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach

This paper presents an optimization via simulation approach to solve dynamic flexible job shop scheduling problems. In most real-life problems, certain operation of a part can be processed on more than one machine, which makes the considered system (i.e., job shops) flexible. On one hand, flexibility provides alternative part routings which most of the time relaxes shop floor operations. On the other hand, increased flexibility makes operation machine pairing decisions (i.e., the most suitable part routing) much more complex. This study deals with both determining the best process plan for each part and then finding the best machine for each operation in a dynamic flexible job shop scheduling environment. In this respect, a genetic algorithm approach is adapted to determine best part processing plan for each part and then select appropriate machines for each operation of each part according to the determined part processing plan. Genetic algorithm solves the optimization phase of solution methodology. Then, these machine-operation pairings are utilized by discrete-event system simulation model to estimate their performances. These two phases of the study follow each other iteratively. The goal of methodology is to find the solution that minimizes total of average flowtimes for all parts. The results reveal that optimization via simulation approach is a good way to cope with dynamic flexible job shop scheduling problems, which usually takes NP-Hard form.     

Operation allocation and part type selection in E-manufacturing: An auction based heuristic supported by agent technology

This paper enlightens some of the key issues involved in developing real schedule generation architecture in E-manufacturing environment. The high cost, long cycle time of development of shop floor control systems and the lack of robust system integration capabilities are some of the major deterrents in the development of the underlying architecture. We conceptualize a robust framework, capable of providing flexibility to the system, communication among various entities and making intelligent decisions. Owing to the fast communication, distributed control and autonomous character, agent-oriented architecture has been preferred here to address the scheduling problem in E-manufacturing. An integer programming based model with dual objectives of minimizing the makespan and increasing the system throughput has been formulated for determining the optimal part type sequence from the part type pool. It is very difficult to appraise all possible combinations of the operation-machine allocations in order to accomplish the above objectives. A combinatorial auction-based heuristic has been proposed to minimize large search spaces and to obtain optimal or near-optimal solutions of operation-machine allocations of given part types with tool slots and available machine time as constraint. We have further shown the effects of exceeding the planning horizon due to urgency of part types or over time given to complete the part type processing on shop floor and observed the significant increase in system throughput.     

求解考虑机器调整时间的并行机分批优化调度问题

基于目前车间调度问题是以单个或整批进行生产加工的并行机调度模型已不再符合实际工况下的车间生产。提出以最小化最大完工时间为优化目标,对遗传差分进化混合算法,灰狼差分进化混合算法进行了比较。为提高加工工件进行分批及分批之后子批的分配与排序效率,该问题是对不同规模的经典并行机调度问题进行求解并展示两种算法的求解,证明了灰狼差分进化混合算法在寻优性能上优于遗传差分进化混合算法,不仅具有更好的解的稳定性,而且具有更高的寻优精度。     

Capacitated lot sizing and sequence dependent setup scheduling: an iterative approach for integration

We consider here the lot sizing and scheduling problem in single-level manufacturing systems. The shop floor is composed of unrelated parallel machines with sequence dependent setup times. We propose an integer programming model embedding precise capacity information due to scheduling constraints in a classical lot-sizing model. We also propose an iterative approach to generate a production plan taking into account scheduling constraints due to changeover setup times. The procedure executes two decision modules per iteration: a lot-sizing module and a scheduling module. The capacitated lot-sizing problem is solved to optimality considering estimated data and aggregate information, and the scheduling problem is solved by a GRASP heuristic. In the proposed scheme the information flow connecting the two levels is managed in each iteration. We report a set of computational experiments and discuss future work.     

The design of an automated material handling system for a job shop

This paper describes the application of techniques developed for the management and control of Flexible Manufacturing Systems (FMS's) to a traditional job shop manufacturing sheet metal parts on typical sheet fabricating machines. The problem was the design and evaluation of an automated material handling subsystem (MHS) to support the shop. The MHS controlled the shop loading and floor control by its choice of orders to transport and enter the shop. Detailed simulation models were used to simulate the existing shop, the shop with a MHS, and several algorithms for loading and sequencing orders through the shop. This paper describes the shop, the models developed, the algorithms tested, and the simulation results. A highlight of the study was the ease and speed with which multiple models were developed using an interactive simulation generator, CAPS. Of particular interest is the procedure for shop scheduling, downstream pull, which uses heavily loaded machines to provide sequencing alternatives with the objective of “pulling” work through them to reach more lightly loaded machines. Shop output was estimated to increase from 26 orders per day to 45, a 73% increase, and turnaround time to decrease from ten days to two days.