Public Sector OR in Japan: Education, Research, and Applications

Tatsuo OYAMAProfessor, National Graduate Institute for Policy Studies

7-22-1 Roppongi, Minato-ku, Tokyo 106-8677 JAPAN

Phone : +81-3-6439-6007, Phone & Fax : +81-3-6439-6138

E-mail : oyamat@grips.ac.jp

ORSI2012, Dec. 20-22

Public Sector OR in Japan : Education, Research, and Applications

• Brief History of ORSJ• Government officials : Recent trends• Recruiting and Promoting High-level Government

Official • Sending Government Officials Abroad• Educating and Training Government Officials • Public sector OR in Japan• Education Activities in Public Sector OR• Research Activities in Public Sector OR• Future prospects for OR in public sector

Brief History of ORSJ• Operations Research Society of Japan (ORSJ) • 1955 : First meeting was held• 1956 : First journal Keiei Kagaku • (Management Sciences) • published• 1957 : Founding of ORSJ 　The Union of Japanese Scientists and Engineers (JUSE), one of the most representative consulting company in Japan, contributed much for the start of ORSJ. “Four years after the foundation of ORSA and TIMS”

　Number of memberships were around 350 in 1957, then constantly increasing, reached 1000 in 1967 and 2000 in 1975. After 1980, however, total number of

memberships has been slightly decreasing.

ORSJ: Trend of Memberships

　 In 1997 ORSJ had a 40th anniversary conference in Tokyo, inviting around 10 young researchers from Asian countries to the APORS (Asia-Pacific Operations Societies) session. 　 We published a newly revised version of the dictionary of OR including around 500 applications distributing a CD-ROM to all society members. 　 In the following years we published a 40th Anniversary Series of 15 books called “New Frontier of Management Sciences”, some of which follow parallel computation in the mathematical programming, combinatorial optimization － focusing on the meta heuristics － , queueing algorithm, fuzzy OR, mathematical modeling in marketing, public policy and OR, congestion and queue, logistics engineering, production scheduling and DEA.　 In 2007 we held a 50th anniversary conference in Tokyo again, and also had several memorial events.

1977 －1985

1986 －1995

1996 －2005

Total(55)

Math. Prog./Appl. 83.1 57.3 36 176.4Reliability 66.4 56.8 53 176.2Queue Th. 43.8 67 31 141.8Graph Network 22.5 49 34.5 106Comb. Optimization 11.6 38 42 91.6Game Th. 27 27.5 32 86.5Finance 0 24 45.5 69.5Dynamic Prog. 20 20.5 15 55.5Scheduling 10.5 23.5 21.2 55.2Statistics 32.7 11.3 6.5 50.5AHP 0 10.5 36.5 47DEA 0 9 33.5 42.5Management Sys. 19.8 6.5 13 39.3Production Plan. 5.3 15.5 17 37.8Simulation 16.7 14.5 5.5 36.7

1977 －1985

1986 －1995

1996 －2005

Total(55)

Traffics 2.3 12.5 21.3 36.2Regional 10.8 14.8 7.3 33Marketing 2 10 20 32Decision Making 0 23.3 8.5 31.8Inventory 15.8 6 9.5 31.3City Plan. 1 0.5 28.8 30.3Nonlinear Prog. 0 14 12.5 26.5Forecasting 11 4.8 10 25.8Total Operation 0 0 23 23Environments 11.2 7.8 2.8 21.8Transportation Logistics 0 2.5 17.8 20.3Markov Process 2.8 9 8.5 20.3Fuzzy 0 16 3 19Social System 6.2 10.5 0.5 17.2

1977 － 1985 1986 － 1995 1996 － 2005 Total(55)Information System 3.5 3.5 8 15Probability Model 5.3 7 2.5 14.8System 3.5 5 2 10.5OR General 0 9.5 1 10.5Data Mining 0 1 8.5 9.5Communication Ntwk 0 0 9 9Decision Theory 5.2 3 0 8.2Maintenance 4.5 1 2 7.5Search Th. 2 1.5 4 7.5Integer Prog. 1 0 6 7Facility Location 0 4.5 2.5 7Education OR Literacy 0 2 4 6Policy 0 2.5 3.5 6

1977 －1985

1986 －1995

1996 －2005

Total(55)

Policy 0 2.5 3.5 6Modeling 1 2 3 6Application 6 0 0 6Supply Chain 0 0 5.5 5.5System Dynamics 2 3.5 0 5.5PERT 3.3 2 0 5.3Software 0 2 3 5Public System 0.7 1 3 4.7Renewal 4.5 0 0 4.5Finance 0 1 3.5 4.5Linear Prog. 0 2 2.5 4.5Investment Portfolio 0.5 4 0 4.5Medical 0 0.5 3.7 4.2

Number of Sessions in Each AreaPeriod 1977-1985 1986-1995 1996-2005 TotalMath.Prog./Application 105.5 (21.81) 95.3 (14.61) 70.5 (9.82) 271.3 (14.63)Traffic/City Plan. 18.0 (3.72) 27.8 (4.26) 57.5 (8.01) 103.3 (5.57)

Dynamic Prog./Scheduling 34.8 (7.19) 50.0 (7.67) 54.2 (7.55) 139.0 (7.50)

Reliability 66.4 (13.72) 56.8 (8.71) 53.0 (7.38) 176.2 (7.50)Comb. Optimization 12.6 (2.60) 39.0 (5.98) 49.0 (6.82) 100.6 (5.43)Finance 0.5 (0.10) 28.0 (4.29) 45.5 (6.34) 74.0 (3.99)Management System 21.8 (4.51) 17.5 (2.68) 38.5 (5.36) 77.8 (4.20)AHP 0.0 (0.00) 10.5 (1.61) 36.5 (5.08) 47.0 (2.54)Graph/Network 22.5 (4.65) 49.0 (7.51) 34.5 (4.81) 106.0 (5.72)DEA 0.0 (1.00) 9.0 (1.38) 33.5 (4.67) 42.5 (2.29)Game Theory 27.0 (5.58) 27.5 (4.22) 32.0 (4.46) 86.5 (4.67)Queuing Theory 43.8 (9.05) 67.0 (10.27) 31.0 (4.32) 141.8 (7.65)Public/Policy 3.2 (0.66) 13.0 (1.99) 26.3 (3.66) 42.5 (2.29)Statistics/Simulation 62.3 (12.88) 34.2 (5.24) 23.0 (3.20) 119.5 (6.45)

Total Operation Project 0.0 (0.00) 2.0 (0.31) 23.0 (3.20) 25.0 (1.35)

Information System 3.5 (0.72) 6.5 (1.00) 18.0 (2.51) 28.0 (1.51)

Transportation Logistic 0.0 (0.00) 2.5 (0.38) 17.8 (2.48) 20.3 (1.09)

Production Planning 0.5 (0.10) 26.2 (4.02) 17.0 (2.37) 43.7 (2.36)

Data Analysis 0.0 (0.00) 3.0 (0.46) 11.5 (1.60) 14.5 (0.78)

Stochastic Process 8.0 (1.65) 12.5 (1.92) 10.5 (1.46) 31.0 (1.67)

Maintenance/Search 11.1 (2.29) 18.5 (2.84) 9.0 (1.25) 38.6 (2.08)

System Modeling 6.5 (1.34) 8.0 (1.23) 8.5 (1.18) 23.0 (1.24)

Education/OR Literacy 0.0 (0.00) 6.0 (0.92) 5.0 (0.70) 11.0 (0.59)

Environments/Resource 11.2 (2.32) 8.8 (1.35) 4.2 (0.58) 24.2 (1.31)

Social System 6.2 (1.28) 15.0 (2.30) 3.0 (0.42) 24.2 (1.31)

Probability Model 5.3 (1.10) 7.0 (1.07) 2.5 (0.35) 14.8 (0.80)

Case Study 7.0 (1.45) 2.0 (0.31) 2.0 (0.28) 11.0 (0.59)

OR General 6.0 (1.24) 9.5 (1.46) 1.0 (0.14) 16.5 (0.89)

Sessions (Total) 483.7 (100) 652.1 (100) 718.0 (100) 1853.8 (100)

Government officials : Recent trends• Employment examination for government officials 4.36 million government officials 　　 1.114 million (25%) national 　　 3.247 million (75%) local• Bureaucrats have been losing people’s trust• “New Public Management” (NPM) 1. Reducing control by the government, and utilizing the

　 market mechanism 2. Placing value on results rather than plans and processes• Economic and political impacts of “globalization” • Governance shift 1. Domestically : from vertical relation to horizontal relation 2. Internationally : from horizontal and independent type to

vertical network type

Recruiting and Promoting High-level Government Official

Table 1: Numbers of Universities according to the Type,

passed for Type I Examination

　 Year

Type ofUniversity

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

National

58(66.7)

66(66.0)

66(64.1)

61(58.1)

62(63.9)

68(65.4)

64(63.4)

71(69.6)

61(61.6)

55(63.2)

Private29

(33.3)34

(34.0)37

(35.9)44

(41.9)35

(36.1)36

(34.6)37

(36.6)31

(30.4)38

(38.4)32

(36.8)

() : % to the total

Table 2: Numbers of the applicants, the passed and the employed

　 2008 2009 2010

Type Applicants Passed Employe

dApplicant

s Passed Employed

Applicants Passed Employe

d

I21,200 1,545 605 22,186 1,494 604 26,888 1,314 499

(6,461) (297) (154) (6,903) (300) (125) (8,212) (272) (124)

II35,546 5,299 2,156 39,940 5,199 2,022 48,040 4,076 -

(11,195) (1,417) (549) (12,685

) (1,539) (566) (14,799) (1,159) -

III16,119 2,191 1,270 16,417 1,938 984 17,311 1,399 -

(5,308) (799) (468) (5,821) (736) (362) (6,094) (577) -

( ) : number of women

The promotion system for Japanese government officials is characterized by two keywords : “slow” and “prize accumulation”.

Brief History of Sending Government Officials Abroad• 1862 In the Edo period (1603-1867) Mr. Griffith’s comment Young Japanese government officials at that time were all “polite,

sincere, hard-working, earnest and capable” people with the potential to lead the future Japanese societyStrong will to study and work for themselves and for their own country

Dr. Heinrich Schliemann Young Japanese government officials’ sincerity and politeness • BUSHIDO : The Soul of Japan published in 1899 in the US and written

by a famous Meiji era Japanese scholar and author Dr. and Professor Inazo Nitobe

Moral standards of Samurai as consisting of such properties as sacrifice, faithfulness, purity, thrift or plainness, honor, affection and so on “noblesse oblige”

Samurai were considered and treated as elite with great privilege and great responsibility

Educating and Training Government Officials• 16,801 training courses provided and 186,838 government officials trained. 168 courses and trained 5,915 officials in total by the NPA aimed at giving necessary knowledge and techniques for them to carry out their duties and responsibilities. • More than 1,500 officials to short term courses in the US (1,100), Great Britain (222), France (116), Germany (50), Canada (34) and Australia (12). • 970 officials to the United States (481), Great Britain (193), Australia (61), Germany (48), France (47), Canada (46) and others (94).• Young government officials tend to quit more frequently • Authority and responsibility must be established for each position and government official• Need to establish our own rule and customs to evaluate each individual’s contribution to their work more clearly and explicitly• Need to “invent” an evaluation system

Figure 3: Total number of government officials sent abroad

19661968

19701972

19741976

19781980

19821984

19861988

19901992

19941996

19982000

20022004

20062008

20100

20

40

60

80

100

120

140

160 Figure 3.Total Number of Officials sent Abroad

Figure 4 Number of students majored in

policy-related areas

0100200300400500600700800900

1000

1965

1976

1989

1991

1993

1995

1997

1999

2001

2003

Year

# of

Stu

dent

s

NationalPrivate

Public sector OR in Japan　 Public sector segment is described by a huge amount of statistical data that are obtained by various forms of processes: surveys, sampling, and other collective means. 　“ Reliable” data needs to be used more efficiently and effectively as we now find the need for various kinds of policy planning, implementation, and evaluation becoming more important. 　 Past contributions of OR, both theoretical and technical, has not been strong enough in these areas, even though it has contributed in some degree. Here is where OR can be applied more actively and positively, and, thus, we see a bright future for OR in Japan, especially in the public sectors.

Education Activities in Public Sector OR

　 Education activities of OR are mainly seen in the graduate schools having policy-related programs such as public policy, public administration, public management, business administration, development study, urban planning, and so on. 　 Statistics courses are given in most policy-related public and private graduate schools in the various forms of introductory statistics, (intermediate) statistics, statistical analysis, quantitative data analysis, statistical methods for policy analysis introductory mathematics for statistics, and so on.

　 Technical courses : introduction to simulation, decision analysis, mathematical modeling for policy analysis, and theoretical methodology-oriented courses such as introduction to mathematical programming, optimization model analysis, (applied or introductory) game theory discrete optimization are taught in some graduate programs in GRIPS, University of Tsukuba and Nanzan University. 　 Specific statistical courses : categorical data analysis, experimental designs, multivariate analysis, and time series analysis are given in University of Tsukuba Graduate School of Business Administration and Public Policy.

Research Activities in Public Sector OR

　 In most policy-related graduate programs students are required to write master’s thesis whose research topics are mostly related to their own interests. In the case that students sent from their own central and local governments, they tend to choose their master’s thesis topics close to their work in their own offices as they are accessible to the available data.

　 Recent master’s thesis titles in some graduate schools are given as follows.

GRIPS・ Applying network model approaches to optimize city waste collection and transportation systems・ Mathematical modeling analysis on the optimal location-allocation of emergent fire department facilities and staffs based upon the regional data of the degree of risks・ Mathematical modeling analysis on the connectivity -robustness of the disaster prevention communication network system

University of Tsukuba・ On the relation between voting behavior and regional characteristics・ Some results for impossibility theorems of social welfare function and social choice function・ Congestion evaluation system using road user’s recognition of congestion

T.Oyama,1993; Saitekika moderu bunseki (Optimization Model Analyses), Nikka

Giren Publishing Co., 372p(in Japanese)

Table of Contents 1. 　 Model Analysis 1.1 System and Mathematical Model 1.2 Data and Model Solution 1.3 Model Manipulation and Sensitivity Analysis

2. 　 Linear Programming Model 2.1 Linear Programming 2.2 Input/Output Model 2.3 Optimal Labor Force Allocation Model

3. 　 Integer Programming Models 3.1 Integer Programming 3.2 Train Scheduling Model 3.3 Power Utilities Construction Planning Model

4. 　 Graph Optimization Model 4.1 Graph Theory 4.2 Maritime Salvage Boats Location Model 4.3 Public Facilities Location Model

5. 　 Network Optimization Models 5.1 Network Theory 5.2 Energy Network Model 5.3 Road Traffic Network Model

6. 　 Queueing Combined Integer Programming Models 6.1 Queueing Theory 6.2 Optimization Model for Information System

　 7. 　 Markov Models 7.1 Markov Process 7.2 Industrial Structure Model 7.3 Labor Force Transition Model

8. 　 Gravity Models 8.1 Law of Gravity and Gravity Model 8.2 Commodity Flow Model and Information Service Flow Model 8.3 Population Move Model

9. 　 Entropy Models 9.1 Entropy and Entropy Model 9.2 Commodity and Information Flow Model 9.3 Interregional Telephone Calls Flow Model

T.Oyama and T.Sueyoshi,2002; Public Policy and OR,

Asakura Shoten Publishing Co.,271p(in Japanese)

Ⅰ 　 Basic Theory for Mathematical Model Analysis

1. 　 Public Policy and Mathematical Model Analysis 1.1 Mathematical Model and Data 1.2 Mathematical Model Analysis

2. 　 Basic Mathematical Model 2.1 One-variable Mathematical Model 2.2 Continuous-type Stochastic Model 2.3 Discrete-type Stochastic Model

3. 　 Forecasting and Evaluation 3.1 Forecasting Methods 3.2 Regression Analysis 3.3 DEA

4. 　 Optimization and Simulation 4.1 Continuous-type Optimization Model 4.2 Discrete-type Optimization 4.3 Simulation

5. 　 Stochastic Phenomena and Game 5.1 Markov Model 5.2 Queueing Simulation 5.3 Game Theory

Ⅱ 　 Application of Mathematical Model to Public Sectors

6. 　 Application to Traffics and Transportaion 6.1 City Traffics Data Analysis 6.2 Mathematical Model Analysis for Planning City Bus Routes 6.3 P&R Panning via Shortest Path Counting Method

7. 　 Application to Quantitative Evaluation Problems 7.1 Regression Analysis for Measuring Salary Gaps 7.2 DEA Model Analysis for Measuring Efficiency of Electricity Generation Sectors

8. 　 Application to Public Facility Location Problems 8.1 Optimal Facility Location Model Analysis for the Health and Welfare Services for the Aged 8.2 Mathematical Model Analysis for the Optimal Location of Post Offices 8.3 Optimal Location Mathematical Model Analysis of P&R Facilities

9. 　 Application to Energy Policy and Public Utilities 9.1 Markov Model Analysis for the Electricity Supply and Demand 9.2 Mathematical Model Analysis for the Optimal Electricity Supply System 9.3 Optimization Model Analysis for Introducing Diversified Electricity Resource MGT

T. Oyama(ed.), 1998;Handbook of Public Policy OR(Translation from Operations Reserch and the Poblic Sector, Elsevier Science),Asakura Shoten

Publishing Co.,776p(in Japanese)

[1] H. Takasakura and T. Oyama, 1994; “Optimization of collection and transportation system of city wastes”, Communications of Operations Research, Operations Research Society of Japan, Vol.39, No.12, pp.653-658 (in Japanese).

[2] T. Oyama and T. Ichimori, 1995. "On the Unbiasedness of the Parametric Divisor Method for the Apportionment Problem", Journal of Operations Research Society of Japan, Vol.38, No.3, pp.301-321.

[3] M. Hata T. Oyama, 1995. "Application of hierarchical mathematical programming model for the optimal facility location problem of the aged", Proceedings of the RAMP Symposium ‘95, Mathematical Programming Research Group, Operations Research Society of Japan, pp.91-106 (in Japanese).

[4] T. Oyama, 1996. "Optimizing city waste collection and transportation systems via the application of network model approaches", Perspectives of Advanced Technology Society 3 : Urban Life and Traffic, Y. Matsuda and M. Fushimi (eds.), Maruzen Planet Co., Tokyo, Japan, pp.21-36.

[5] M. Miwa, T. Ishikawa and T. Oyama, 1998: “Basic investigation on building the transition process model of railway track irregularity”, Proceedings of J-RAIL’98, Federation of Railway Technology, pp.443-446 (in Japanese).

[6] T. Oyama, 1999; “Mathematical model analyses on the optimal location of advanced medical instruments MRI”, Chap. 3, Research Report on evaluating advanced medical instruments MRI, Medical Economy Research Association, Vol.7, pp.47-66 (in Japanese).

[7] T. Oyama, 1999; “Regional inequality analyses and mathematical model analyses for the location of advanced medical instruments MRI”, Communications of Operations Research, Operations Research Society of Japan, Vol.44, No.7, pp.361-364 (in Japanese).

[8] T. Oyama, H. Tamura, and T. Sano, 1999; “Research on the location of post office facilities”, Monthly Research Report of Post and Telecommunications”, Research Institute of Post and Telecommunications, No.11, pp.4-23 (in Japanese).[9] M. Miwa, T. Ishikawa and T. Oyama, 1999: “Building the transition process model and its application to the MTT operation planning”, Proceedings of J-RAIL’99, Federation of Railway Technology, pp.443-446 (in Japanese).

[10] Miwa, M., T. Ishikawa and T. Oyama, 1999: "Modeling the transition process of railway track irregurarity and its application to decision making for maintenance strategy", Proceedings of WCRR'99, Tokyo, 4p.

[11] T. Oyama, N. G. Hall and K. Kobayashi, 2000: “Generalizing divisor method for the apportionment problem --- theoretical development and numerical experiments”, Optimization : Modelling and Algorithm 13, Mathematical Statistics Research Institute, pp. 47-73.

[12] Nagano, S. and T. Oyama, 2000 : "Mathematical programming model analyses on minimizing regional gap of health care services and finding an optimal facility location for the elderly", Communications of Operations Research, Operations Research Society of Japan, Vol.45, No.12, pp.33-41 (in Japanese).

[13] Mizuno, K. and T. Oyama, 2001 : "Mixed integer programming model analyses for the optimal electricity supply system in the post-liberalization period", Communications of Operations Research, Operations Research Society of Japan, Vol.46, No.9, pp.33-41 (in Japanese).

[14] Miwa, M., T. Ishikawa and T. Oyama, 2001: "Modeling the transition process of railway track irregurarity and all-integer type mathematical programming model analyses for the optimal planning of the track maintenance schedule", Journal of Civil Engineering Society, No.681/IV-52, pp.51-65.

[15] Ojima, J. and T. Oyama, 2003 : “Index gap minimization model analyses for local promotion branches of Iwate Prefecture”, Communications of Operations Research, Operations Research Society of Japan, Vol.48, No.8, pp.567-573 (in Japanese).

Future prospects for OR in public sector

Policy evaluation has become more necessary, important, and also very common with the emerging attention to the new public management. (so-called NPM) Also, much quantitative data has been gathered and prepared by all Japanese governmental organizations.

Note that OR theory and techniques that take advantage of this data have not been applied effectively so far (this is also the case for public sector research in other countries).

There are also many policy issues and problems that have not been considered seriously by OR researchers, even though their solutions are important and needed in the near future.

These global problems arise in the areas of environment, energy resources, complex societal problems such as recycling, information technology industries, natural disasters and other emergent risk situations; they all require appropriate policy decision making. They are worthy challenges to OR researchers.

Thank you very much