From Computers in Industry , Yen-Ching Chuang, Yee Ming Chen Presenter ：CHEN,YOU-SHENG (Shane) 2022/06/10

1. From Computers in Industry , Yen-Ching Chuang, Yee Ming Chen
Presenter ：CHEN,YOU-SHENG (Shane)
2022/06/10
Digital servitization of symbiotic service
composition in product-service systems

2. Vocabularies 1/4
/40
1
P. English Chinese
1
Cyber-physical systems
(CPSs)
虛實整合系統
1 cyberspace 網路空間
1 dynamic manufacturing
physical spaces
動態製造
實體空間
1 symbiotic simulations
system (S3)
共生模擬系統
1 inherent heterogeneity 固有異構性
1 service composition
and optimization se-
lection (SCOS)
服務組合和
優化選擇
P. English Chinese
1 prescriptive analytics 指示性分析
1 course of action 行動方針
2 operations research
(OR)
作業研究
2 equates 相當於/意味
2 well-established 成熟
2 polynomial function 多項式函數
2 cope with 應對
2 enlightening
implications
啟發意義

3. Vocabularies 2/4
/40
2
P. English Chinese
2 impractical 不切實際
2 machining tool wear 切削刀具磨損
2 milling 銑ㄒㄧㄢˇ
2 mitigate 減輕
2 actuator entities 操作器實體
2 fueling 推動
2 high-fidelity 保真
3 parasitism, mutualism,
and commensalism
寄生狀態、互惠
共生、共生
3 external perturbations 外部擾動
P. English Chinese
3 arrivals 引進
4 contextual data 上下文資料
4 aggregated 聚合
4 invoking 調用
4 encapsulate 封裝
4 flawed 缺陷
4 uptime of a service
服務正常運作
時間
4 up-and down-time 正常和停機時間
4 Subject to 前提是

4. Vocabularies 3/4
/40
3
P. English Chinese
6 directed graph 有向圖
6 feasible path 可行路徑
6 nontrivial 不平凡
6 trial-and- error
interaction
試錯互動
6 intermittently
disseminates
間接散佈
7 service enactment 服務設定狀態
7 atomic service 微粒服務
7 Specifically 具體而言
P. English Chinese
7 sequentially 依次地
7 hands off 移交
7 predecessor 前置
9 fictitious 虛擬的
9 synthetic 綜合的
9 corresponding 相應的
9 randomly discrete
monitoring
隨機離散檢測
9 Gaussian distribution 高斯分佈
9 arbitrarily 任意

5. Vocabularies 4/4
/40
4
P. English Chinese
9 tolerates 容忍
9 topological 拓樸
10 re-execution 重新執行
10 nontrivial theoretical 重要理論
11 former normalizes 前者規範
11 normalization step 正規化步驟
11 criterion 準則
11 policy derived 策略推導
P. English Chinese
12
Dijkstra-based
algorithm
基於狄克斯特拉
的演算法（用於
有向圖最短路徑
分析）
12 multi-objective 多目標
12 throughput 吞吐量
12 pseudocode 虛擬碼

6. JCR
/40
5
For Computers in Industry

7. CONTENTS
Introduction
01
Related works
02
Service composition
and optimal selection
process in CMfg
03
Computational
experiments and results
04
/40
6
Conclusions
05

8. Purpose
Methodology
Findings
/40
7
A symbiotic simulations to perform
efficient service combinations in
CMfg (Cloud manufacturing)
• Using data analytics
techniques
• Created a fictitious
case study (generated
a synthetic dataset)
• Service composition and optimization selection
(SCOS) challenge in CMfg during cloud information
sharing (Bouzary and Chen, 2018; Ghomi et al., 2019)
• How to determine the appropriate combination of
manufacturers services for specific requirements,
especially to deal with the fluctuated service

9. Originality
Outcome
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8
• It presents a framework for a multi-agent system-based
symbiotic simulation platform
• To make full use of the advantages of different
computational algorithms
• Can embedded symbiotic simulation performing the
SCOS challenge
• the results of the symbiotic simulations demonstrate the
performance of the approach in terms of reduced
combined resources and wait times
• reduce the performance fluctuation of CMfg applications

10. Introduction
01
/40
9

11. Product service systems offer
a bundle of product-service
combinations that aim to be
competitive by satisfying
customer needs
Introduction
/40
10
PSSs
CMfg
SPSS
Cloud manufacturing offers
the potential to extract cloud
information sharing from
bundled product-service-
combination manufacturing
Their potential for supporting sustainable manufacturing
• Various stakeholders
• Connected products
• Smart services
• Smart CMfg platform
In order to meet individual
customer needs

12. Introduction
/40
11
SPSS
Service
composition
(SC)
Optimization
selection (OS)
An effective solution for SCOS is to eliminate the conflict between mass-customized
production and flexible market demands
This necessitates the use of optimization and simulation techniques to provide
prescriptive analytics, explore several possible actions, and suggest a course of action
using cloud data analytics techniques

13. Related works
02
/40
12

14. SCOS problem in SPSS /knowledge-driven
/40
13
2019 Ghomi et al.
The SCOS challenge has proven to be a primary technique for
tackling issues covering a broad spectrum of service compositions
2016 Chen et al.
For OR (operations research), several solutions have been
developed by leveraging computational algorithms
2012 Cardellini et al.
Linear programming (LP) is often adopted to address service
composition in the OR domain
2018 Min et al.
An efficient heuristic algorithm for graph-based approaches has
enlightening implications
2006 Ter Beek et al.
It is usually impractical to describe a manufacturing system (e.g.,
PSSs, CMfg) completely by knowledge-driven modeling, because
the running process of most systems in the real manufacturing
world is not sufficiently clear

15. SCOS problem in SPSS /data-driven & integrating
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14
2020 Ullah
Owing to the development of big data, several researchers have
employed ML approaches in various fields to predict the future
behavior of the target system
ML is well suited for factory signals for which no clear
mathematical formulation emerges (video, audio)
Limitations :
• solely describes the correlation of the data
• build models that rely thoroughly on real data
• an accuracy limitation
▶︎ A new symbiotic simulation approach integrating both
knowledge-driven and data-driven modeling is proposed

16. Symbiotic simulation for CMfg platform
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15
2008 Aydt et al.
Symbiotic simulation is a CPS paradigm in which a physical system
is closely coupled with a simulation system that utilizes sensors
and actuators
2012
Chertow and
Ehrenfeld
Can be employed to initialize and drive high-fidelity simulations of
physical systems
Aydt et al. (2008) designated five types of
symbiotic simulation systems:
SS control systems (SSCS)
SS decision support systems (SSDSS)
SS forecasting systems (SSFS)
SS model validation systems (SSMVS)
SS anomaly detection systems (SSADS)

17. Service composition
and optimal selection
process in CMfg
03
/40
16

18. SCOS process in CMfg
/40
17
A symbiotic simulation can potentially enable OR practitioners and researchers to build
multiagent system (MAS), run experiments faster, or perform more execution in CMfg
Fig. 1. Framework of embedded symbiotic simulation performing CMfg operation for SCOS process.

19. Sub-
system
Component Explanation
Cyber
space
Data collect Extracting, transforming and loading data from online or off-line
Data analytics
• Designed to respond to data when the simulation is running
• Used in CMfg belong to statistical models or data mining models
CMfg operation module
• Governed by task decomposition, service discovery, and
manufacturing (SCOS)
Computational algorithms Computational algorithms from knowledge-driven to data-driven
Symbiotic simulation MAS based symbiotic simulation platform
Physical
space
Actuator
• Convert digital information into an action in the physical space.
• Created a tremendous amount of contextual data
• Can offer or implement QoS aware service composition to meet
customer requirements
SCOS process in CMfg
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18
Table 2 CMfg framework core components. (key point version)

20. SCOS process in CMfg /CMfg operation module
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19
Task decomposition
• complex manufacturing task into several subtasks
• physical resources into virtual resources
Service discovery
• determining all candidate services
• each subtask can be associated with a set of candidate manufacturing cloud services
(CMS)
Manufacturing SCOS
• Selecting one service with optimal QoS
• MSs are selected according to the non-functional properties of the MS
• CMS plan is selected through the extraction, comparison, and assessment of the QoS

21. SCOS process in CMfg
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20
Fig. 2. CMfg operation module in framework of CMfg platform.
Step 1: Customers encapsulate the requirement as a task and
submit it to the CMfg platform.
Step 2: SCOS process for CMfg (Fig. 1) data collection and data
analytics, the task submitted by customers, and decomposes the
complexity manufacturing workflow: Task = {T1 , T2 , ..., Tn }
Step 3: For each subtask Tn, a set of manufacturing services (MS)
on the cloud service pool satisfying the functional requirements: To
achieve the task, at least one candidate is selected for each Tn,
which is denoted by CMSn from each MSn.
Step 4: If only one candidate composite service is contained in the
CMSn, this composite service is the optimal composite service.
Otherwise, the aggregated QoSs are selected in the CMS according
to the value of the QoS criteria calculated for the composite model.
Step 5: The aggregation QoS is fed back to customers. After
customer selection and confirmation, the selected manufacturing
cloud services are labelled in the cloud service pool.
Subsequently, the overall process is controlled and monitored by
the CMfg platform.

22. SCOS process in CMfg
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21
2019 Li et al.
Determine the service selection that satisfies the QoS and cost
constraints
2012 Alrifai et al.
The attributes of QoS are non-functional properties (including
service cost, security, reliability, usability, runtime, etc.) that describe
the degree of user satisfaction with the web service
2 General service attributes are chosen for this study: response time and availability
Response time
• the time taken to display a response when sending a request.
Availability
• the ratio of the total time a service can be used during a given interval to the length of the interval.

23. SCOS process in CMfg
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22
The optimal service selection is to determine a service with less response time but
higher availability, as illustrated below
• F = optimal service selection result
• a = MS = q
• IF A(a) ≥ Amin then is true
• E(R(a)) represents the expected
value of the random value R(a)
• Amin is the minimum average
availability
Following basic assembly structure and analyzes its nonfunctional properties (Tao et al., 2013).
sequence
pattern
•parallel
pattern
•loop
pattern
•conditiona
l pattern
(1)

24. SCOS process in CMfg
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23
(2a)
(2b)
(3a)
(3b)

25. SCOS process in CMfg
/40
24
(4a)
(4b)
(5a)
(5b)

26. SCOS process in CMfg
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25
(6)
The complex manufacturing workflow network is converted into a directed graph, and the
graph path is constrained by QoS constraints and resource availability conditions
The following cost function was adopted to evaluate the feasible path:
• (Qr represents any of the above attributes, and
R is the total value)
• wi (p) is the summation of the i-th dimension
QoS parameter along p
• Ri is the total constraint of the i-th dimension
QoS value
• Min(aggQos) has the greatest possibility of
being a feasible path

27. /40
26
Symbiotic simulation support CMfg
operational module
Increased on-demand and scalability of service composition
• requires significant computing resources
• a large number of candidate services
• more likely to become less stable
• maintaining a low efficiency for adaptive
To combine machine learning (ML) and multi-agent
technologies
• dynamically choosing the best service
• without complete knowledge of the environment
Q learning is a commonly for planning and control in a
dynamic environment (Vakili and Navimipour, 2017)

28. Symbiotic simulation support CMfg
operational module
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27
Fig. 7. Symbiotic simulation support SCOS process of CMfg.
Online
Offine
The overall procedure for the SCOS of CMfg
physical space mainly
receives the user’s task
decomposes the task into
series sub-tasks
each subtask can determine
the corresponding MSs
satisfying the function
initializing agent (init,
execution, monitoring)
ensure that the sub-
processes status and
managed (peer)

29. Symbiotic simulation support CMfg
operational module
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28
Fig. 7. Symbiotic simulation support SCOS process of CMfg. (agent part)
Online
the proposed multiagent-based symbiotic simulation with a decentralized control architecture
is based on a set of software agents that are distributed across multiple hosts
Init
• initializing agent normally interacts
• responsible for delivering the final results
Peer
• manage the execution of an atomic service
• can exchange messages
Moniter
• the executionof individual manufacturing
services
• continuously scanning the state

30. /40
29
Symbiotic simulation support CMfg
operational module
• Suppose that Customer-A
(Host) plans to find a
manufacturing service, which
is a rush request
• Unfortunately, Customer-B
submitted a similar
requirement the previous day
• Customer-A tried to find
another request suitable for
both of them
Fig. 8. Message exchange in multiagent based symbiotic simulation.

31. Computational
experiments
and results
04
/40
30

32. Case study for a small scale example
/40
31
The first case study of a manufacturing
value chain project
3 sub-tasks:
• R&D (T1) –CMS1
• Parts manufacturing (T2) –CMS2
• Assembly (T3) –CMS3
If the QoS constraint predecessor
satisfies the current examined service
→ directed edge is added
Run the Dijkstra-like heuristic algorithm
Fig. 9. Illustration of manufacturing value chain candidate service.
3.6616
3.7942
3.9278

33. /40
32
• Not obtaining real data
• Created a fictitious case study of an automotive
cyber physical manufacturing system
• Generated a synthetic dataset
• The CMfg operation module was applied to
compose services based on their functions and
QoS constraints
• To verify the stability and scalability of our
approach in various service situations
• 2 experiments were performed
Extended experiments
design
raw material purchase
parts manufacturing
vehicle assembly
testing
selling services

34. /40
33
2 personal computers were employed
• run the service composition system and
selection algorithm
• operate the service repository
A. Verification of stability in Experiment 1
• 2 QoS attributes: availability and
response time (split to Scenario 1 & 2)
• define availability deviation as the
difference between need and actual
availabilities in duration
B. Verification of scalability in Experiment 2
• 4 types of challenge scales were utilized
 Randomly discrete monitoring
 Simulated the service composition processes
for each agent over 300 rounds
Experiment design

35. /40
34
Results of Experiment 1 and scenario analysis
The lower the service availability deviation, the better the quality of the elementary services
• the heuristic is much lower than the Q-learning and LP modeling under various request workloads
• the heuristic has the lowest QoS availability deviation, approximately 15% lower than those of the others

36. /40
35
Results of Experiment 1 and scenario analysis
Simulation results prove that the
Dijkstra-based heuristic algorithm
best tolerates topological
variation and uniformly achieves
the highest QoS.
A lower response time deviation indicates the trustworthiness of the agents in terms of the on-time response
• the heuristic is much lower than the Q-learning and LP modeling

37. /40
36
Results of Experiment 2 and scenario analysis
Using large-scale compositions would
increase the multi-agent share resources
and autonomously adapt to the dynamicity
composition.
4 Scenario :
(1) small tasks of small available services
(2) small tasks of larger available services
(3) larger tasks of small available services
(4) a larger ratio of available services per task

38. /40
37
Results of Experiment 2 and scenario analysis
The time increment was caused mainly by the fact that the higher the number of services
The lower occurrences of failures in large-scale scenarios
• In Scenarios 2 and 3, when the number of services was increased by 10x, the time increased by approximately 2x
• The number of services per task was x2 (Scenario 4), the service composition time increased by approximately 15%
• The LP performed better than the heuristic and Q- learning algorithms on services that comprised time and
successful services at different challenge scales
• The Q-learning algorithm is better than the heuristic algorithm in terms of the successful composition

39. Conclusions
05
/40
38

40. Conclusions
/40
39
Presented a framework for CMfg to
support the life-cycle in PSSs.
A new opportunity for multiagent-
based symbiotic simulation was
proposed
The performance of the approach in
terms of reduced combined
resources and wait times

41. Limitations & Future
/40
40
New integration
frameworks
• capable of supporting
informed decision-making
in short computing times
• new approaches for
validation and verification
of models
Computing
requirements
• performed in parallel
• use specialized hardware
The framework was
synchronized at each time
• will cause various errors are to
be further investigated in depth
pending improvements
• specific solutions and algorithms
are key issues

42. T H A N K S

43. Appendix 1

44. Appendix 2

45. Appendix 3

46. • Yen-Ching Chuang, Yee Ming Chen, Digital servitization of symbiotic service composition in
product-service systems, Computers in Industry, Volume 138, 2022, 103630, ISSN 0166-3615,
https://doi.org/10.1016/j.compind.2022.103630.
• Vector Designed By Windy from Powerpoint from PPTdaily
• P19, 26, 33, 40 Microsoft Stock images (royalty-free images)
• Other pictures from Bing search and using CC
Resources

47. • 產品服務系統 https://baike.baidu.hk/item/產品服務系統/4321254
• Integer Programming 整數規劃 https://medium.com/ycpan/integer-programming-
整數規劃-f7c589c3c05b
• 整數規劃 https://wiki.mbalib.com/zh-tw/整数规划
• QoS（Quality of Service，服务质量） https://baike.baidu.com/item/qos/404053
• 黑盒子模型（英語：Black box model）
https://thebusinessprofessor.com/en_US/investments-trading-financial-
markets/black-box-model-definition
• 白箱或黑箱：如何依照場合選擇機器學習模型？
https://blog.pulipuli.info/2017/11/white-box-or-black-box-choosing-machine.html
Extended learning

48. • 作業研究( Operations Research, OR ) https://wiki.mbalib.com/zh-tw/作业研究
• 启发式算法 (Heuristic Algorithms) https://leovan.me/cn/2019/04/heuristic-
algorithms/
• 什麼是強化學習中的Ｑ learning演算法?
https://www.cupoy.com/qa/club/ai_tw/0000016D6BA22D97000000016375706F795
F72656C656173654B5741535354434C5542/0000017C0D6897CD000000296375706
F795F72656C656173655155455354
• Dijkstra算法 https://wiki.mbalib.com/wiki/Dijkstra%E7%AE%97%E6%B3%95
Extended learning