Journal of Advanced Mechanical Design, Systems, and Manufacturingered/ME482/Paper_Topics/Rebarchik... · 2010-12-06 · Systems, and Manufacturing Vol. 2, No. 4, 2008 719 Integrated

Journal of Advanced Mechanical Design, Systems, and

Manufacturing

Vol. 2, No. 4, 2008

719

Integrated Dynamic Process Planning and Scheduling in Flexible Manufacturing Systems

via Autonomous Agents*

Hossein TEHRANI NIK NEJAD**, Nobuhiro SUGIMURA**, Koji IWAMURA** and Yoshitaka TANIMIZU**

**Japan Society of Mechanical Engineering Graduate School of Engineering, Osaka Prefecture University

1-1 Gakuen-cho, Nakaku, Sakai, Osaka, 599-8531, Japan Email: [email protected]

Abstract Process planning and scheduling are important manufacturing planning activities which deal with resource utilization and time span of manufacturing operations. The process plans and the schedules generated in the planning phase shall be modified in the execution phase due to the disturbances in the manufacturing systems. This paper deals with a multi-agent architecture of an integrated and dynamic system for process planning and scheduling for multi jobs. A negotiation protocol is discussed, in this paper, to generate the process plans and the schedules of the manufacturing resources and the individual jobs, dynamically and incrementally, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks discussed in the previous paper, and the suitable process plans and schedules are searched and generated to cope with both the dynamic status and the disturbances of the manufacturing systems. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans and schedules in the dynamic manufacturing environment. A simulation software has been developed to carry out case studies, aimed at verifying the performance of the proposed multi-agent architecture.

Key words: Multi Agent System, Integrated Process Planning and Scheduling, Real Time Scheduling, Flexible Manufacturing System, Petri Nets.

1. Introduction

Process planning and scheduling are important manufacturing planning activities which deal with resource utilization and time span of manufacturing operations. In order to cope with competitiveness and globalization of today’s business environment, supply chains become more complex, and manufacturing processes have become more advance, however products have to be manufactured in higher varieties and smaller batches. It is essential to establish effective and efficient process plans and production schedules to cope with the highly dynamic manufacturing requirements. Some automobile manufacturers are gradually adopting their production ways to support the diversity of the customer needs and increase the changing speed for the developing the new products (1).

The process planning and scheduling tasks are very complicated and time consuming, if it is applied to the dynamically changing FMSs (Flexible Manufacturing Systems). To facilitate the dynamic and adaptable manufacturing activities in the FMS, a new methodology with appropriate architecture is needed to handle the process planning tasks,

*Received 21 Mar., 2008 (No. 08-0210) [DOI: 10.1299/jamdsm.2.719]

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in order to cope with dynamic changes of the products and the manufacturing processes in the FMSs.

The multi-agent approaches are becoming increasingly important for the FMSs to increase the productivity and the profitability through the greater shop floor agility and flexibility (2). The multi-agent systems provide us a systematic way to deal with the dynamic changes of the products and the manufacturing processes in the FMSs.

This research applies a multi-agent architecture to a real time integrated process planning and scheduling system in the FMSs, which generate suitable process plans and schedules based on the status of the FMSs. The objective of the paper is to develop a multi-agent architecture for the process planning and scheduling of multi jobs that rapidly adopt the process plans and schedules to cope with the job changes and the unexpected disturbances in the FMSs. A process plan network and a heuristic search algorithm are proposed to generate suitable process plans and schedules real-timely.

2. Literature review

In recent years, Multi-Agent Systems (MAS) have been widely applied in manufacturing applications because of its flexibility, re-configurability, and scalability (3-5). Gu et al. (3) proposed a multi-agent system where process routes and schedules of a part are accomplished through the contract net bids. The system addresses some practical issues for merging the CAPP with shopfloor scheduling such as the parts feature representation and operation specification. The task allocation and process alternative selection are achieved through the hierarchical bidding processes between machine agents and shop floor manager, between upper level machine agents and lower level machine agents, and between machine agents and tool agents, etc. A cascading auction protocol (6) was proposed as a framework for integrating process planning and heterarchical shop floor control. The integration of the real-time online process planning (alleviating the selection of routing alternatives, resource alternatives, detail process planning, etc.), and shop floor control (resource allocation, scheduling, transportation, tooling, and fixturing) is accomplished progressively through a recursive auction process carried out in parallel among part management agent and multiple resource management agents.

IDCPPS (7) is an integrated, distributed and cooperative process planning system. The process-planning tasks are separated into three levels, namely, initial planning, decision-making, and detail planning. The results of these three steps are general process plans, a ranked list of near-optimal alternative plans and the final detailed linear process plans, respectively. The integration with scheduling is considered at each stage with process planning. Wang and Shen (8) presented a new Distributed Process Planning (DPP) methodology by integrating machining feature-based planning, function block-based control, and agent-based distributed decision making. It proposes to use two-level decision-making supervisory planning and operation planning. The supervisory planning focuses on product data analysis, machine selection, and machining sequence planning, and the operation planning considers the detailed working steps of the machining operations inside each process plan and is accomplished by intelligent NC controllers. Lim and Zhang (9) have developed an agent-based integrated dynamic process planning and scheduling system to increase the responsiveness of the manufacturing systems. This system does not only effectively integrate the dynamic process planning and scheduling, but also optimizes the machine utilization and provides a platform to assess the reconfiguration of the manufacturing systems.

Sugimura et al. (10) proposed a basic architecture for integrated process planning and scheduling from the view points of the distributed decision making.


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Figure 1. Configuration of the target manufacturing system

A systematic approach was proposed to select suitable combination of the process plans and to generate suitable production schedules for all the jobs by applying the genetic algorithm and the dispatching rules. Wong et al. (11) proposed an agent-based approach for the dynamic integration of the process planning and scheduling functions. In consideration of the alternative processes and alternative machines for the production of the parts, the actual selection of the schedule and the allocation of the manufacturing resources is achieved through the negotiation among the part and machine agents which represent the parts and the manufacturing resources, respectively.

The multi-agent architecture has been widely applied to the process planning and the scheduling, as discussed in (12). The methods proposed in the literatures deal mainly with the process planning and scheduling tasks in the static environment in which the jobs specifications and the manufacturing system status are stable. However, it is now required to develop an integrated process planning and scheduling systems applicable to the dynamic environment in which some unforeseen disturbances may occur. The objective of the research is to propose a multi-agent based integrated system for process planning and scheduling in the dynamic environment, in order to cope with the jobs specification changes and the unforeseen disruptions, such as the malfunction of the machine tools. The following issues are discussed in the paper. (1) Target flexible manufacturing systems. (2) Multi-agent system for dynamic process planning. (3) Flexibility and robustness of the proposed architecture. (4) Synchronization (5) Simulation software and experimental results.

3. Target Flexible Manufacturing System

The basic configuration of the target FMSs are shown in Fig. 1. The manufacturing systems consist of a set of machine tools, preparation stations, input and output buffers, AGVs (Automated Guided Vehicles), fixtures and cutting tools. The input jobs are transformed by a process plan to the finished products through the manufacturing processes including machining, transportation, and fixturing processes. The inputted jobs are firstly fixed on the fixtures at the preparation stations and transmitted to the input buffers by the AGVs. The jobs are then transported to the machine tools in order to carry out the machining processes required. If the refixturing is required to change the positions and orientations of the jobs against the fixtures, the jobs are transported to the preparation stations and the re-fixturing process is carried out at the preparation stations. When all the required machining processes of the jobs are finished, the finished jobs are transported to the preparation stations to separate the jobs from the fixtures, and the jobs are transmitted to the output buffers.

Preparation Stations

Turning Center

Machining Center


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Figure 2. UML Class diagram of physical agents and their relations.

4. Multi Agent Architecture

A multi-agent architecture is proposed to carry out the integrated process planning and scheduling. The agent system incrementally generates suitable process plans and production schedules based on the dynamic status of the FMSs. In the following sections, the system architectures proposed here are discussed from the viewpoints of the agent definitions and the negotiation protocols among the agents.

4.1 Agents Definition

Two types of agents are considered in this research to develop the process planning and scheduling systems. They are, physical agents and information agents. The physical agents represent jobs, the manufacturing resources and the machining processes in the FMSs. The manufacturing resources considered here are the machine tools, the preparation stations, the AGVs (Automated Guided Vehicles), the fixtures and the cutting tools. The information agents are virtual agents which are responsible for governing the negotiation protocol and decision-making.

4.1.1 Physical agents

The UML class diagram of the physical agents is summarized in Fig. 2. The contents of the physical agents are summarized in the following.

Job agents

The job agents represent the jobs to be manufactured in the FMSs. The role of the job agents in the process planning is to certify the correct machining processes of the jobs. The job agents include the following information to describe the orders and the machining features. (1) Job information:

The job information section describes the order information, the locations and the progresses of the machining processes of the jobs.

(2) Machining features The machining feature section gives the machining features of the jobs and their technical data such as the types, the tolerances and the roughness. These technical data are required to select appropriate machining processes.

(3) Process plan networks The process plan networks represent the generated process plans in non-linear and hierarchical ways. It includes all the alternative process plans that satisfy the


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technological requirements of the jobs. (4) Job Status

We consider the following status for the job agents. • Idle: The job agent is idle and waiting for the next machining operations. • Machining operation: The job agent is under machining processes on the machine

tools. • Transportation and re-fixturing: The job agent is transported and/or re-fixtured for

its next machining operations.

Machine tool agents The machine tool agents represent the machine tools. The agents representing such

resources as the preparation stations and the AGV are not considered, at present, since only the machining processes are discussed in the present research. The machine tool agents are responsible for generating proposals to the machining processes required from the job agents. The proposals include the machining time, the transportation time and the re-fixturing time needed to carry out the required machining processes of the job agents. The machine tool agents include the following information to represent the machine tools in the FMSs. (1) Machine tool information

The machine tool section specifies the shape generation functions, which are represented by the cutting motions, the spindle directions, the feed motions and the maximum product size.

(2) Machine tool status We consider the following status for the machine tool agents in the simulation • Idle: The machine tool is idle and negotiating with job agents for next machining

operation • Machining operation: The machine tool is machining the job agent • Breakdown: The machine tool has been broken and is under recovery process

(3) Cutting tool The characteristics of the cutting tools are described in the cutting tool section, which includes the information about the cutting tool types, the tool sizes and the cutting edge types.

(4) Fixture The fixture section describes the fixture types, and the positions of the fixtures against the spindle axis.

Machining process agents The machining process agents represent the machining processes of machining features

of the jobs, which are carried out by the machine tools. The agents include the following information. (1) Machining process ID which is the combination of the ID of the machine tools, the ID

of the fixtures and the ID of the cutting tools. (2) Machining process types and machining features types, which can be generated by the

machining processes (3) Surface roughness, tolerances and material removal rate of the machining processes. (4) Machining process status

The status represents the dynamic status of the machining processes in the FMSs. The machining process agents have two statuses. • inactive: if one of the machine tool, the cutting tool and the fixture related to the

machining process are broken-down • active: otherwise


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Figure 3. Communication with other agents

4.1.2 Information agents

The information agents are virtual agents for governing the negotiation protocol and decision-making.

Production engineering agents The production engineering agents generate the job agents, the machine tool agents, and

the machining process agents to specify the geometric and technological information of the jobs, the machine tools and the machining processes of the FMSs. The agents play a key role for initializing the information of the physical agents.

Job order agents The job order agents represent the manufacturing tasks. They are information agents,

which carry out the negotiation processes between the job agents and the machine tool agents to generate suitable process plans. The agents have crucial influence on the system performance by deploying efficient decision-making mechanism to select the appropriate machine tools for the individual machining features of the jobs.

4.2 Negotiation Protocol

A negotiation protocol among the agents is required to coordinate the distributed decisions of the individual agents for solving the complex problems in the process planning and scheduling of the FMSs. The problems to be solved here are as follows. (1) Selection of candidate machining processes for individual machining features. (2) Selection of suitable combinations of the machine tools, the cutting tools and the

fixtures. (3) Selection of suitable machining sequences of the machining features.

We apply the contract net protocols (13) which are widely used in the agent based scheduling problems. In the protocol proposed here, the individual agents have three types of boards named “ request boards ”, “ proposal boards ” and “status boards” for the communication among the agents. Figure 3 shows the role of the boards. The requests from the other agents are firstly sent to the request boards, and the agents scan and receive the requests from the boards, every fixed time intervals named RTIP (reading time interval period). The individual agents secondly generate the proposals to the requests, and store them in the proposal boards. The statuses of the agents are changed and stored in the status board, if necessary.

The communications between the boards and the other agents are controlled by a communication system, which is common to all the agents. The agents communicate with each other through the messages. The general format of the messages and their attributes is summarized in the Table 1. The symbol “×” in the table means the characters representing the contents of the messages. Figure 4 summarizes the negotiation protocol proposed here to generate suitable process plans and schedules by the distributed decision-makings of the individual agents and the negotiations among the agents. The negotiation processes are carried out through the following steps.


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Table 1. General format for messages

Attribute Size ( Character)

Description

Z× 1 Message Status V××××× 5 Valid time window for message N××××× 5 Message unique ID T×× 2 Message type C×× 2 Current position of requester S×× 2 Sender ID R×× 2 Receiver ID P×× 2 Machining process ID F×× 2 Machining feature IDs X××××× 5 Manufacturing time of next operationE××××× 5 Estimation of completion time

JobOrder

Agents

Machine Tool

Agents

ProductionEngineering

Agent

MachiningProcessAgent

Step 1: Initialization

Remaining Machining Features

Available Machining Processes

Nodes of Process Plan Networks Send Requests

Send Proposals

Jobs ScheduleMachine Tools

Schedule

Step 2: Request for available machining processes

Step 3: Requests generation by the job order agent

Step 4: Proposal preparation by the machine tool agent

Step 5: Selection the appropriate proposal

Step 6: Preparation for next operation

Messages for expansion of the next level of the process plan network

Calculated minimum estimationof completion time

Request for calculating minimum estimation of completion time

Job Agents

Figure 4. Negotiation protocol among agents

Step 1: Initialization

The production engineering agents firstly generate all the job agents, the machine tool agents and the machining process agents to initialize the status of the target FMSs. They also define the machining features, which can be generated simultaneously by the same combinations of the machine tools, the cutting tools and the fixtures, and assign them to the job agents.

Step 2: Requests for available machining processes The job agents select a set of the machining features, which can be machined in the

next machining process, based on the precedence constraints among the machining features. For example, let us consider a case shown in Fig. 5. This part consists of three machining features (see Fig. 5(b)). They are, one slot MF1, and two holes MF2 and MF3. The precedence constraints for this example is shown in Fig. 5(c). In the first step, the machining features MF1 and MF3 are sent to the machining process agents, and they select a set of available alternative machining processes for the individual machining features, based on the specifications of the machining features, such as the geometries, the sizes, the surface roughness, and the tolerances as shown in the Table 2.


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MF1

MF2 MF3

(a) part geometry (b) machining features

MF1 MF2

(c) precedence constraints Figure 5. An example of parts

Table 2. Alternative machining processes for machining features MF1 and MF3

Machining Features

Machining Process ID

Machine Tool ID

Fixture ID

Cutting Tool ID

MF1 mp1 mt1 fi1 ct1 MF1 mp4 mt2 fi2 ct1 MF3 mp3 mt1 fi2 ct2 MF3 mp6 mt2 fi1 ct2

mt1 : Vertical machining center , mt2 : Horizontal machining center ct1 : End Milling , ct2 : Drill

N0RMF = {MF1,MF2,MF3} AMF= {MF1,MF3}

N1mp1=(mt1, fi1,ct1) -> MF1RMF = {MF2,MF3} AMF= {MF2,MF3}MT = 738ECT = 2070

N2mp3=(mt1, fi2,ct2) -> MF3RMF = {MF1,MF2} AMF= {MF1}MT = 1397ECT = 2227

N3mp4=(mt2, fi2,ct1) -> MF1RMF = {MF2,MF3}AMF= {MF2,MF3}MT = 685ECT = 2525

N4mp6=(mt2, fi1,ct2) -> MF3RMF = {MF1,MF2} AMF= {MF1}MT = 1312ECT = 2440

N5mp2=(mt1, fi1,ct2) -> MF2RMF = {MF3} AMF= {MF3}MT = 435N6mp3=(mt1, fi2,ct2) -> MF3RMF = {MF2}AMF= {MF2}MT = 897

N7mp5=(mt2, fi2,ct2) -> MF2RMF = {MF3}AMF= {MF3}MT = 943

N8mp6=(mt2, fi1,ct2) -> MF3RMF = {MF2} AMF= {MF2}MT = 1341

End

RMF = Remaining machining features setAMF = Available machining features set for the next operation

First Level

N9mp3=(mt1, fi2,ct2) -> MF3RMF = {} AMF = {}MT = 897

N10mp6=(mt2, fi1,ct2) -> MF3RMF = {} AMF = {}MT = 1531

MT = Manufacturing timeECT = Estimation of minimum completion time

Second Level Third Level Figure 6. Process plan network.

The selected machining processes include the information about the machine tools, the

cutting tools and the fixtures. The selected machining processes for the individual machining features are sent back to the job agents.

The job agents generate a set of groups of the machining features, which can be machined by the same combinations of the machine tools, the cutting tools and the fixtures. This means that the grouped machining features are machined by one machining process concurrently, and the job agents generate the nodes representing all the grouped machining features in the process plan networks as shown in the first level nodes 1N to 4N in Fig.6.

The contents of the process plan networks are described in the previous paper (14). The individual nodes in the process plan networks represent a set of the machining features which could be machined by one machining process. An algorithm also generates nodes


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Table 3. Generated requests by job order agents

Process plan network nodes Generated requests by job order agents

1N Z1V01000N00001T01C00S01R01P01F01

2N Z1V01000N00002T01C00S01R01P03F03

3N Z1V01000N00003T01C00S01R02P04F01

4N Z1V01000N00004T01C00S01R02P06F03

representing the machining sequences of the machining features based on the precedence constraints. The information of the generated nodes is sent to the job order agents for the negotiations.

Step 3: Request generation by job order agents

The job order agents create requests for the machining process execution for the individual nodes of the process plan networks, which are the groups of the machining features that can be generated by same machine tools. The generated requests are sent to the request boards of the corresponding machine tool agents. The content of request includes the machining features and selected machine tool, cutting tool and fixture. As you can see in Fig. 6, there are four nodes 1N to 4N in the first level of the process plan network. For each of them, the requests shown in the Table 3 are generated according to the message format in Table 1, and sent to the related machine tools MT1 and MT2.

Step 4: Proposal preparation by machine tool agents

The machine tool agents read all the requests from the request boards every RTIP (Reading Time Interval Period). The machine tool agents analyze the request messages, and generate appropriate proposals to all the requests. In this paper, we consider a heuristic algorithm to estimate minimum completion time for generating appropriate proposal for each request by the machine tool agents, as shown in the followings.

Minimum completion time The machine tool agents need to estimate the completion time of the remaining machining features of the job agents. A procedure is developed and given to the job agents to estimate the minimal completion time of the remaining machining features, based on the process plan networks shown in Fig. 6. When a machine tool agent requires a job agent to estimate the minimum completion time, the job agent starts the procedures from the start node which is specified by the machine tool agent, and repeat to generate and to select suitable successive nodes with the minimum machining time. When all the machining features are included in the process plan networks, the job agent find both the machining sequences of the machining features and the estimated minimal completion time. Consider a case where we are at node iN of the process plan network and we are going to estimate the manufacturing time from the node iN to the end node. The algorithm for calculating the estimated minimum completion time from node iN to the end node is summarized in the followings.

Initialization: Set RMF and AMF. The RMF is the set of the remaining machining features. The

AMF is the set of the available machining features that do not have any preceding machining features and could be done firstly considering the precedence constraints among the nodes of the process plan networks.( RMFAMF ⊆ ).

Put the node iN in ECTS set. The ECTS is the set of the nodes in the path from node the iN to the end of the process plan network, which has the minimum manufacturing time.


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Table 4. Generated proposals by machine tool agents

Received requests Generated proposals by machine tool agents

Z1V01000N00001T01C00S01R01P01F01 Z1V01080N00005T02C00S01R01P01F01X738E2070

Z1V01000N00002T01C00S01R01P03F03 Z1V01080N00006T02C00S01R01P03F03X1397E2227Z1V01000N00003T01C00S01R02P04F01 Z1V01080N00007T02C00S02R01P04F01X685E2525

Z1V01000N00004T01C00S01R02P06F03 Z1V01080N00008T02C00S02R01P06F03X1312E2440

Set a initial node iN . In the iN , RMF = {set remaining machining features},

AMF= {set of machining features without any successors}.

(1) Generate a set of successor nodes },...2,1{ SNjNSN j == of the node iN for all feasible machining processes Rrctfxmtmp tfir ,...,1),,,( == , ( =R total number of available machining processes) by applying the following algorithm.

Cluster all features of the AMF set of the node iN that could be machined with the machining process rmp ,

Generate a new node jN representing a set of machining features which can be machined by the machining process rmp and put it in the SN set. The links to the nodes jN , which are successor nodes, are stored in the node iN for further processing,

Estimate the manufacturing time for node jN that includes the time of the machining, the transportation and the re-fixturing processes,

Update the RMF and AMF sets for the node jN ,

(2) Select a successor node kN from the SN set which has the minimum machining time for the next step of extension, and move it to the ECTS set. (3) If RMF set of kN is not empty consider node kN as node iN and go to (1). (4) If RMF set of kN is empty, it means that we are in the end of the process plan network. The sum of the manufacturing time for the nodes in ECTS set is the estimation of the minimum completion time from node iN to the end.

Let us consider a case where we are going to calculate the estimation of minimum completion time for node 1N at the process plan network shown in Fig 6. We start with node 1N , and there are four successor nodes 5N , 6N , 7N , 8N from the node 1N as shown in Fig. 6. We select the node 5N which has the minimum manufacturing time, and we put it in the ECTS set. We expand the node 5N at the next stage of the algorithm and there are two successor nodes 9N , 10N . The node 9N is selected and which has the minimum manufacturing time, we put it in the ECTS set. As you can see in Fig. 6, for the node 9N the RMF set is empty and the algorithm stops. It is because that there are no remaining machining features in the node 9N . The sum of the manufacturing time for the nodes in ECTS set is the estimation of the completion time from node 1N until end.

Following this, the job agent returns the estimated completion time to the machine tool agent. As you can see in the Fig. 6, the estimation of completion time for all nodes 1N ,

2N , 3N , 4N are calculated and these values are returned to the machine tool agent. This procedure can estimate the completion time of all the remaining machining features, however it requires the additional communications between the machine tool agents and the job agents.

The machine tool agents generate proposals for each request as shown in Table 4, based on the minimal completion time of the remaining machining features and send them back to the related job order agents.


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Figure 7: Synchronizing the agents for generating requests and proposals. Step 5: Selection of appropriate proposal by job order agents

The job order agents scan all the received proposals from the machine tool agents every RTIP, and select an appropriate proposal based on the either the minimum manufacturing time or the estimated minimum completion time. The job agent selects the node 3N according to the minimum manufacturing time, or the node 1N according to the estimated minimum completion time. The job order agents inform both the job agents and the machine tool agents that the machining features sent from the job agents shall be machined by the machine tools of the selected node. This means that the job order agents dynamically generate the process plans and the production schedules of the job agents and the machine tool agents.

The job agents and the machine tool agents selected here carry out the requested machining processes in the next step. Therefore, the statuses of these agents are changed, and the status data are stored in the status boards. All the agents monitor the status data if necessary.

Step 6: Preparation for next operation

When the machine tool agents complete the machining operations of the job agents, the job agents modify their process plan networks. That is, the job agents delete the corresponding nodes representing the group of the machining features which was completed by the machine tool agents. New nodes of the process plan networks are generated to specify the groups of the machining features to be machined in the next step. The procedures presented in Steps 2 to 6 are repeated until the job agents do not have any remaining machining features.

4.3 Synchronization

The Petri nets (15) are used, in the research, for synchronizing the messages and the negotiation protocols between the different agents. This Petri nets control both the sequence and the timing of the interaction and the messages between the agents. Each Petri net represents one agent or interacting agents. Fig. 7 shows an example of the interaction between the agents for generating and sending the requests to the request board of the machine tool agents and generating the proposals by the machine tool agents. These Petri nets are linked with each other with global transition (See transitions, 1714842 ,,,, ttttt in Fig. 7).


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Figure 8. Simulation system

5. Flexibility and robustness

The flexibility and the robustness of the proposed method are discussed from the viewpoints of the response to the unforeseen changes of the jobs and the machining processes. The job agents utilize the process plan networks representing all candidate machining process routes of the jobs, and generate suitable process plans incrementally, referring to the status of the machine tools. Therefore, any pre-defined process plans are not required, and the process plans are dynamically generated from the candidate machining process routes specified in the process plan networks. The proposed architecture increases the flexibility and the robustness of the FMSs.

The proposed architecture also supports the modifications of the jobs and the machine tools. The production engineering agents may modify the geometric and the technological specifications of the jobs to be machined. In this case, the content of the job agents are modified, in particular, the contents of the machining features are modified to represent the new specifications. In some cases, it is possible to realize the job specification changes during the manufacturing processes of the jobs. For the cases where some machine tools are added and/or removed, the contents of the machine tool agents and the machining process agents are added and/or removed from the agent systems. If some machine tools are broken down, the corresponding machining processes become inactive, and the contents of the machining process agents are modified.

6. Simulation and case study

A prototype of the agent based integrated process planning and scheduling system have been developed for the case studies, as shown in Fig. 8. The system developed here is able to simulate the distributed decision-makings of the agents, and the negotiation among the agents, and to generate suitable process plans and a schedule for a set of inputted jobs. Some case studies have been carried out to verify the applicability of the proposed system to the integrated process planning and scheduling problems in the FMSs. The FMS considered here includes 7 machine tools and 50 different machining processes. Figure 9 shows the shapes of the job agents for the case studies. The detailed information of the machining features and the machining resources of the case studies are brought in the previous paper (14). The RTIP in the simulation is set to be 2 sec. for the machine tool agents


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MF3,MF8,MF10

MF1

MF2

MF12

MF13

MF14

MF16

MF17

MF18

MF15

MF20

MF21

MF22

MF23

MF5,MF9,MF11

MF24 MF10MF11,MF25,MF30

MF9

MF2 MF1

MF8

MF31

MF22MF15MF29

MF19

MF27

MF28

MF17,MF23MF32

MF14 MF18

MF26

(a) (b)

MF19

MF1,MF2

MF3,MF4

MF5,MF9MF6,MF10

MF7,MF11

MF8,MF12MF13

MF14

MF15

MF16,MF20MF17

MF18

MF12

MF2，MF6，MF21MF7，MF10，MF20

MF4

MF15

MF5

MF9

MF1，MF17，MF23

MF6，MF19，MF22

MF8

MF11

MF3

MF14

MF13

MF18 (c) (d)

Figure 9. Jobs considered in case studies.

and 4 sec. for the job order agents. It was shown through the case studies, that the proposed multi-agent architecture is capable to generate appropriate process plans and schedules for all the jobs, as shown for some job agents in the Table 5 and Gantt chart of Fig. 10 (a).

6-1 Robustness against malfunction of machine tools and job specification changes

An additional experiment is also carried out to assess the robustness of the proposed architecture against the malfunction of the machine tools. The original process plans are shown for some job agents in the Table 5 and Gantt chart of Fig. 10 (a). In the experiment, the machine tool “MT14” is broken down at simulation time 4811 sec. and the recovery time is set to be 5000 sec. As you can see in Table 5 and the Gantt chart of Fig. 10 (b), the proposed architecture can dynamically generate alternative process plans and schedule to cope with the malfunctions of the machine tools. The job agents can be dynamically allocated to another manufacturing route in the process plan networks.

In the other experiments, the following unforeseen changes have been considered in the job specifications.

• Change the roughness of the machining features o Job 03, MF 16 at simulation time 3000 o Job 10, MF 18 at simulation time 10000

• Add a new machining feature to the job o Job 02, MF 21 at simulation time 7000 o Job 04, MF 24 at simulation time 5000 o Job 05, MF 25 at simulation time 2900

• Change the size of machining feature o Job 10, MF16 at simulation time 10000 o Job 03 , MF 21 at simulation time 6500

The results are shown in Table 5 and the Gantt chart of Fig. 10 (c). As shown in Gantt chart Fig. 10 (c) and Table 5, the proposed architecture can dynamically generate updated process plans to cope with the changes of job specifications.

8. Conclusion

A multi-agent based integrated process planning and scheduling system was proposed for the integrated process planning and scheduling systems for the FMSs. The following remarks are concluded.


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Table 5. Generated process plans and for jobs for case studies

Note: [(MT×, FI×, CT×)|(MF×,…)] indicate [(Machine tool number, Fixture number, Cutting tool number)|(Machining feature number,…..)]

(1) A multi-agent system consisting of five basic agents and a negotiation protocol among

the agents were proposed to carry out the various tasks in the process planning and the scheduling. The individual agents have the capability for the distributed decision-making and the communications with the other agents.

(2) A systematic procedure was proposed to generate suitable process plans of the jobs and

suitable schedules of the machine tools. The proposed method is able to solve the process planning and scheduling problems concurrently and dynamically, with use of the search algorithms of the process plan networks.

Job

No

Job

Type Conditions Process Plans Flow Time

Without

unforeseen

changes

[(MT14,FI1,CT135)│(MF6,MF14,MF17)]→ [(MT14,FI1,CT135)│(MF10)]→

[(MT15,FI2,CT149)│(MF15)]→[(MT15,FI2,CT145)│(MF3)]→

[(MT15,FI2,CT145)│(MF4)]→

[(MT14,FI1,CT133)│(MF1,MF5,MF7,MF8,MF13,MF16,MF18,MF19)]→

[(MT14,FI1,CT135)│(MF20)]→ [(MT14,FI1,CT133)│(MF2,MF9,MF11,MF12)]

35924

Malfunction of

machine tool

[(MT15,FI2,CT149)│(MF15)]→ [(MT15,FI2,CT145)│(MF3)]→

[(MT15,FI2,CT145)│(MF4)]→[(MT12,FI1,CT117)│(MF13,MF14,MF16,MF18,MF19)]

→[(MT12,FI1,CT117)│(MF20)]→[(MT14,FI1,CT133)│(MF1,MF5,MF6,MF7,MF8,MF1

7)]→ [(MT14,FI1,CT135)│(MF10)]→[(MT14,FI1,CT133)│(MF2,MF9,MF11,MF12)]

30676 2 (c)

Job specification

changes

[(MT15,FI2,CT149)│(MF15)]→ [(MT15,FI2,CT149)│MF21)]→

[(MT15,FI2,CT145)│(MF3)]→ [(MT15,FI2,CT145)│(MF4)]→

[(MT12,FI1,CT117)│(MF13,MF14,MF16,MF18,MF19)]→[(MT12,FI1,CT117)│(MF20)]

→ [(MT14,FI1,CT133)│(MF1,MF5,MF6,MF7,MF8,MF17)]→

[(MT14,FI1,CT135)│(MF10)]→[(MT14,FI1,CT133)│(MF2,MF9,MF11,MF12)]

34346

Without

unforeseen

changes

[(MT9,FI1,CT81)│(MF9,MF10,MF11,MF12,MF13,MF20,MF23,MF24,MF27,MF29)]→

[(MT9,FI1,CT81)│(MF16,MF30,MF32)]→



[(MT17,FI1,CT163)│(MF21)]→ [(MT17,FI2,CT169)│(MF17,MF25)]

16900

Malfunction of

machine tool





[(MT17,FI2,CT169)│(MF17,MF25)]

17792 3 (b)

Job specification

changes



[(MT17,FI2,CT169)│(MF17,MF25)]→


[(MT14,FI1,CT133)│(MF1,MF2,MF3,MF4,MF5,MF6,MF7,MF8)]

20780

Without

unforeseen

changes

[(MT3,FI1,CT27)│(MF1,MF2,MF3,MF4,MF5,MF6,MF7,MF12,MF13,MF15,MF21,MF2

3)]→ [(MT3,FI1,CT27)│(MF8,MF9)]→


[(MT17,FI1,CT165)│(MF11)]→[(MT17,FI1,CT163)│(MF10)]

8683

Malfunction of

machine tool


3)]→[(MT3,FI1,CT27)│(MF8,MF9)]→[(MT17,FI1,CT165)│(MF11)]→

[(MT17,FI1,CT163)│(MF10)]→[(MT12,FI1,CT117)│(MF14,MF16,MF17,MF18,MF19,

MF20,MF22,MF24)]

25500 5 (a)

Job specification

changes


3)]→[(MT3,FI1,CT27)│(MF8,MF9)]→[(MT3,FI1,CT23)│(MF25)]→[(MT17,FI1,CT165

)│(MF11)]→[(MT17,FI1,CT163)│(MF10)]→[(MT14,FI1,CT133)│(MF14,MF16,MF17,

MF18,MF19,MF20,MF22,MF24)]

17922


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(a) Original schedule without unforeseen changes

(b) Modified schedule for malfunction of machine tool “MT14”

(c) Modified schedule for job specification changes

Figure 10. Gantt chart for case studies

(3) Some case studies have been carried out to verify the applicability of the proposed

method to the integrated process planning and scheduling problems in the FMSs including 7 machine tools and 10 jobs. It was shown, through the case studies, that the proposed multi-agent architecture is capable to generate appropriate process plans and schedules. It was also shown that the proposed architecture generates alternative process plans dynamically, to cope with the malfunctions of the machine tools and unforeseen job specification changes.

Idle and Negotiation □Transportation and ■ refixturing Machine Tool 03 ■ Machine Tool 06 ■ Machine Tool 09 ■ Machine Tool 12 ■ Machine Tool 14 ■ Machine Tool 15 ■ Machine Tool 17 ■




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Vol. 2, No. 4, 2008

References

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