Integration of neural networks and genetic algorithms for an intelligent ...

Computers ind. EngngVol. 29, No. 1-4, pp. 211-215, 1995 Copyright © 1995 Elsevier Science Ltd Printed in Great Britain. All rights reserved 0360-8352/95 $9.50 + 0.00

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Integration of Neural Networks and Genetic Algorithms for an Intelligent Manufacturing Controller Tammy Hoiter Ernst & Young LLP, One Columbus, 10 West Broad Street, Columbus, Ohio 43215-3400, USA

Xiaoqiang Yao Luis Carlos Rabelo Department of Industrial and Systems Engineering, Ohio University, Athens. Ohio 45701, USA

Albert Jones National Institute of Standards and Technology, Gaithersburg, Maryland 20899. USA Yuehwern Yih School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907, USA

Abstract. This paper addresses the development and implementation of a "controller" for a single manufacturing machine. This prototype will serve as an important tool to study the integration of several functions and the utilization of status data to evaluate scheduling and control decision alternatives. The emphasis is on creating a prediction capability to aid in assessing the long-term system performance impact resulting from decisions made and environmental changes. This prediction capability is implemented by using neural networks, simulation, and genetic algorithms. Neural networks predict the behavior of different sequencing policies available in the system. The contribution of the genetic algorithms to the decision-making process is the development of a "new" scheduling rule based on a "building blocks" procedure initiated by the neural networks

1. Introduction The effective integration and coordination of decision making and control will provide the required level of "intelligence" to respond to the ever changing manufacturing environment. Many approaches, ranging from traditional applications to artificial intelligence techniques, have been utilized for this problem with varying degrees of success [4]. These approaches have emphasized the concept that a manufacturing controller must be more intelligent by integrating effectively decision-making and control. Currently, no real applications of intelligent controllers exist. Most of the research is strictly theoretical. In our view, an intelligent controller performs the following five functions in overseeing the resource(s) it is attempting to control: planning, scheduling, monitoring, execution, and interfacing. An intelligent controller must, therefore, integrate decision-making (planning and scheduling) with control (monitoring and execution). The main purpose of this paper is to present a prototype of an intelligent single machine controller. The control architecture will be based upon the intelligent generic control architecture proposed by Davis et ai. [2] and Jones and Saleh

[6]. This architecture possesses the four production management functions required: planning, scheduling, monitoring, and execution (see Figure 1). Furthermore, it is integrated well with the capability to be mapped to a hierarchical structure. In addition, the optimization (i.e., scheduling) function will be based upon the work of Rabelo et al. [8].

2. Optimization Framework The optimization framework consists of three phases. Phase one deals with initial candidate rule selection. Phase two conducts simulation based on the selected rule candidates. Phase three analyzes the results from simulation, and applies GAs for further improvement 2.1. Candidate Rule Selection The first step in this process is to select a small list of candidate rules from a larger list of available rules. For example, we might want to find the best five scheduling policies (e.g., dispatching rules) from the list of all known scheduling policies so that each one maximizes (or minimizes) at least one of the performance measures, with no regard to the others. To carry out this part of the analysis, we have used neural 211

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the worst features of those rules, and 3) simultaneously achieves satisfactory levels of performance for all objectives. Consequently, we seek to generate a new schedule from these candidate schedules. To do this, we use a GA approach.

I Tasksand

3. Single Machine Controller Development

AssignedTasks I and LimitTimcs~

] Feedback

SUBORDINATE PROCESSES

Figure 1. The generic controller architecture networks. This approach extends earlier efforts by Rabelo [9] and Chryssoiouris et al. [1].

2.2 Real-Time Simulation After these R candidates have been determined, each of the selected rules (i.e., rules with the highest ranking) must be evaluated to determine the impact that each rule will have on the future evolution of the system as measured from the current state of the system. In other words, we must predict how it does against all of the desired performance measures simultaneously. The outputs from these simulation trials yield the projected schedule of events under each scheduling rule [3]. These schedules are then used to compute the values of the various performance measures and constraints imposed by the scheduling problem. 2.3 Compromise Analysis No matter how the utility function described above is constructed, only one rule from the candidate list can be selected. This causes an undesirable situation whenever there are negatively correlated performance measures, because no one rule can optimize all objectives simultaneously. Conceptually, one would like to create a new "rule" which 1) combines the best features of the most attractive rules, 2) eliminates

The prototype developed and presented in this paper enables control and decision making at the lowest level, the machine level. The Flexible manufacturing cell (FMC) scenario modeled consists of five workstations and one material handling robot as depicted in Figure 2. This cell is able to produce seven different types of products. Each product type has its own arrival behavior, process plans, processing time distributions, and set up dependencies. Table I illustrates the 7 types of products with their process plans and there are also sequence dependent setup requirements for workstation #1. There are buffers among the workstations and one input and output buffer for the cell. Jobs arrive at the cell randomly and the batch size is one. Workstation

Robot ~

f--/--3 I/O Buffer

Figure 2. Flexible manufacturing cell The control of a machine at a workstation (Workstation #1) will take place through a computer interface. Real time communications will occur between the controller and the machine such as passing the job sequences from the controller to the machine and passing equipment status and processing times from the machine to the controller.

3.1. Performance Measures To evaluate the performance of the controller and measure the benefits gained, performance

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criteria were defined for this research. Due to their importance in industrial scheduling problems, the following performance measures were considered: Maximum Flow Time, Mean Flow Time, Maximum Tardiness, Mean Tardiness, Work-InProcess Inventory, Resource Utilization, and Throughput

Table 1. Routings processing times

and workstation #1

Job Type

Processing T i m e

Process P l a n / R o u t i n g

1

4

# 1 - #3 - #5

2

6

#1 - #4

3

5

#1

4

3

#1 - # 2 - # 3 #1 - #3 - #4

5

10

6

8

#1

7

15

#1 - # 5

3.2. Scheduling Rules As defined in the optimization framework, scheduling rules were designed into the neural networks for initial candidate rule selection. By incorporating reliable dispatching rules, the number of iterations required by the GAs to find an optimal sequence can be significantly decreased. The following scheduling rules were selected for this study: SPT, LPT, FIFO, LIFO, SST, L S T , SPST, LPST, EDD, LDD, mSLACK, MSLACK, CR, and SLACK/RTi. 3.3. Hardware Considerations The hardware configuration for the prototype consists of two 486 compatible PCs @ 66 MHz communicating via RS232 ports. The first PC represents the intelligent controller for the machine (Workstation #1) and the second PC simulates the machine activities using a stochastic model. 3.4. Software Considerations The controller was primarily developed in Level 5 Object (an expert system development tool). The artificial neural networks, GAs, and serial communication programs were programmed in C++ for Windows. Performance measures are stored and graphed in Microsoft Excel using Dynamic Data Exchange (DDE) with Level 5 Object. Finally, the machine discrete-

213

event simulator (implementing the stochastic model) was developed in DOS C Code.

3.5 Controller lmplementation As implemented, the Assesment Function has three primary tasks. First, the Assesment function must retrieve the queue information from the machine and determine the process plan for each job entering the queue. Second, the Assesment Function must collect information from the supervisor regarding the performance measure to be evaluated and the status of subordinate workstations. Lastly, it must assess the jobs in the queue, compare the process plans with the status of subordinate workstations and filter out of the queue any jobs planned to proceed to a down machine. Software components providing the functionality for the Assessment Function include a C++ Windows program for serial communications to retrieve the queue information from the simulated shop floor, a Level 5 Object knowledge base containing the routing information for each job type, and a Level 5 Object expert system to temporarily removes jobs from the queue that are scheduled for a down machine. Every time the controller generates a new schedule, the status of subordinates will be assessed and if a previously down machine becomes available, the jobs which proceed to that machine will be rescheduled with the other jobs in the queue. The Optimization Function is responsible for generating a near-optimal schedule based on the performance criteria for the queue information passed down from the Assessment Function. This functionality was implemented as described in the optimization framework in Section Three. The neural networks program for candidate rule selection and the GAs program for further improvements were programmed in C++ for Windows and interfaced with the Level 5 Object controller. The Execuiton Function is responsible for communicating to the machine simulator the job which is to be processed. It is simply the interface between the controller and the machine simulator. This functionality is provided through a C++ communications program which dispatches the required orders (e.g., the job number to be processed) to the machine simulator and waits for

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the updated performance values, clock time, and new queue information. As implemented, the Monitoring Function is responsible for graphing and displaying performance values to the supervisor and monitoring the queue status to determine (with its corresponding knowledge-base) if a new schedule is required. Functionality for the Monitoring Function was provided using Level 5 Object and DDE with Microsoft Excel. To graph and display the performance measurements to the supervisor, Level 5 Object stores dynamic arrays containing current performance value information in its knowledge base. As implemented, the Monitoring Function compares the new queue information passed to the controller from the simulator with the old queue information. If any new jobs have arrived, the Monitoring Function sends a request to the Assessment Function to activate the expert system with the new queue information to begin the process of generating a new schedule. If the queue status has not changed, the Monitoring Function informs the Execution Function to process the next job.

4. Testing Example This Section includes a scenario to demonstrate the validity and the synergistic effect of the different techniques utilized to develop the single machine controller (for more testing procedures and details please see [5]). The example evaluates the performance of the Optimization Function (i.e., the integration of the neural networks and GAs).

4.1 OptimizationPerformance A scheduling example is considered to illustrate the integration of the different artificial intelligence techniques in the development of a schedule. This example uses Average Work In Process (WIP) as the performance measure. 1. For Table 2, the current clock time is 2055 and '7' was the previous job type. Notice that the data set contains a variety of job types and processing times vary from 3 to 15. 2. The candidate rule selector implemented, employing a modular neural network (one expert network for each performance measure), uses the system status and the performance criteria in order to select a small set of candidates from the

Table 2. Optimization performance example Job Job Arrival Processing Due Date Num~rl ,ilType i iTiime Time I 2 3 4 5 6 7 8 9 10

7 1 4 2 7 3 1 5 4 6

1958 2002 2006 2010 2015 2017 2028 2032 2047 2048

15 4 3 6 15 5 4 10 3 8

2143 2072 2078 2081 2204 2101 2099 2140 2119 2164

15 dispatching rules available. Each expert network in parallel ranks all rules. However, the expert network that minimize WIP is the only one considered. The networks developed in the C++ programming language take on average less than 1 ms (486 PC compatible @ 66 MHz) to give an answer to the problem (see Table 3). For this example, the Neural Network rated only two of the dispatching rule sequences as excellent. The top five rules selected by the neural networks were SPT, SPST, EDD, mSLACK, and SSLACK. After the fact analysis using simulation showed the neural network selections are exactly the top five dispatching rules for this scenario (in spite of being "unseen" data - not utilized in the training database - See Table 4). 3. The genetic algorithm is utilized having as initial populations the three top rated schedules from the candidate rule selector (SPT, SPST, EDD ) and some randomly generated schedules (Population size = 50 -- Increasing the population number increases the likelihood of the generation of a better schedule). The fitting function is WIP. The genetic algorithm takes on average seven iterations (350 schedules generated and evaluated in total) with an average time of less than 200 ms on a 4 8 6 P C @ 6 6 M H z . The new schedule is an optimal schedule and it has building blocks from the initial rules suggested by the candidate rule selection process. The "new" schedule generated by the genetic algorithm is superior (based on the performance criteria) to the initial schedules selected by the modular neural network (3 9 2 7 4 6 8 10 1 5 w i t h a W l P = 4.06742 ). However, the new sequence generated utilizes building blocks of SPT ( 3 9 7) and the relative ordering of (10 x l 5) from SPT and SPST (SPT and SPST were rated as "excellent" by the neural networks engine.

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T a b l e 3. C a n d i d a t e rule s e l e c t o r o u t p u t Ruh~ Selected

RULE SPT LPT FIFO LIFO SST LST SPST LPST EDD LDD mSLACK MSLACK CR SSLACK SLACK/RT

Excellent Very Low Low Low Low Low Excellent Low Good Low Good Low Good Good Good

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5. C o n c l u s i o n s The "intelligent controller" e m p l o y i n g the integration o f neural networks and G A s creates n e w alternatives not e n v i s i o n e d by the system designers for scheduling. This n e w k n o w l e d g e should be captured in order to m o d i f y the decision m a k i n g structure. That means: the system should not repeat the s a m e process w h e n similar situations arise. The system should be able to learn from past experiences. Recent d e v e l o p m e n t s in this research have p r o m p t e d for the utilization o f induction m e c h a n i s m s [7] to capture this k n o w l e d g e in English-like terms (suitable for validation and training o f production personnel).

T a b l e 4. D i s p a t c h i n g rules a n a l y s i s SCHD RULE SPT LPT FIFO LIFO SST LST SPST LPST EDD LDD mSLACK MSLACK CR SSLACK SLK/RT

Max Mean i Max ! Mean W I P Mach ThruTD T D Avg Utit put FT . . F. T. 170 74.6 6 0.6 4.080 0.830 0.114 132 97.7 60 23 6.860 0.849 0.116 112 90.2 19 6 5.479 0.777 0.106 192 89.2 61 15.9 5.316 0.768 0.105 36 8.1 5.770 0.839 0.115 112 88.9 152 94.8 76 20.7 5.784 0.753 0.103 170 74.8 6 0.6 4.102 0.830 0.114 128 98.2 57 23.5 6.685 0.820 0.112 0 0 4.326 0.768 0.105 164 79.8 145 98.8 75 24.6 6 . 5 3 3 0.793 0.109 0 0 4.394 0.777 0.106 150 80.0 152 97.7 74 24.1 6.484 0.802 0. I 10 145 83.0 2 0.2 4.615 0.760 0.104 175 79.2 0 0 4.355 0.785 0.108 145 83.0 2 0.2 4.615 0.760 0.104

References [1] G. Chryssolouris, M. Lee and M. Domroese, The use of neural networks in determining operational policies for manufacturing systems, Journal of Manufacturing Systems, 10, pp. 166-175, 1991. [2] W. Davis, A. Jones, and A. Saleh, A Generic Architecture For Intelligent Control Systems, National Institute of Standards and Technology Internal Report NISTIR 4521, February, 1991. [3] W. Davis and A. Jones, Issues in real-time simulation for flexible manufacturing systems, in Proceedings of the European Simulation Multiconference, Rome, Italy, 1989. [4] C. Harmonosky and S. Robohn, S. F., Real-time Scheduling in Computer Integrated Manufacturing: a Review of Recent Research, International Journal of Computer Integrated Manufacturing, Vol. 4, No. 6, 331340, 1991.

TD ~b 1 6 6 4 4 4 1 6 0 5 0 5 1 0 I

JOB SEQUENCE 39276410815 15810462739 12345678910 10987654321 15274639810 38910145627 27396410815 15810462379 2347698 1 105 51018967432 23467918105 51081967432 42367189105 42379681015 42367189105

[5] T. Holter, Development of a Prototype for the Integration of Scheduling and Control in Manufacturing Using Artificial Intelligence Techniques, MS Thesis, Ohio University, 1994. [6] A. Jones and A. Saleh, A multi-level/multi-layer architecture for intelligent shop floor control, International Journal of Computer Integrated Manufacturing, 3, 1, 1990, pp. 60-70. [7] L. Rabelo, Y. Yih, Yuehwern, A. Jones and J-S Tsai, Intelligent Scheduling for Flexible Manufacturing Systems, 1993 IEEE International Conference on Robotics and Automation, Vol. III, 810-815, 1993. [8] L. Rabelo, Y. Yih, A. Jones and G. Witzgall, Intelligent FMS scheduling using modular neural networks, in Proceedings of ICNN, 1993, pp. 1224-1229. [9] L. Rabelo, A hybrid artificial neural network and expert system approach to flexible manufacturing system scheduling. Ph.D. Dissertation, University of Missouri, 1990.