Omega 36 (2008) 919–932www.elsevier.com/locate/omega

A historic review of management science research in China

John Wanga,∗, Ruiliang Yanb, Kimberly Hollistera, Dan Zhuc

aDepartment of Management & Information Systems, Montclair State University, Montclair, NJ 07043, USAbSchool of Business, P.O. Box 9209, Virginia State University, Petersburg, VA 23806, USA

cIowa State University, Ames, IA 50011, USA

Received 14 January 2007; accepted 31 October 2007Available online 3 December 2007

Abstract

The development of management science (MS) in China has been a long and dynamic journey involving many unexpectedevents. Beginning in 1955 and continuing through the present, the revolution roughly consisted of four stages. The first stage(1955–1965) was characterized by large-scale campaigns. Critical path method (CPM) and optimum seeking method swept thecountry resulting in astonishing economic efficiencies. The Cultural Revolution halted MS during the second stage (1966–1976).The third stage (1977–1992), began when the door to the outside world was officially opened, but half-closed due to TiananmenIncident later, and re-opened again owing to spirit of Deng Xiaoping’s speech in Southern China. The fourth stage (1992–present)has pushed MS into almost every field, accelerating national modernization. The impact of research by many scholars is evidencedthrough history by examples that include conducting war, building dams, and developing postal service routes. 2007 Elsevier Ltd. All rights reserved.

Keywords: Management science (MS); Optimum seeking method; Chinese postman problem; Gray system theory; DEA/preference structuremodel

1. History

The early systematic formulation of operation re-search (OR) began in Great Britain as an independentdiscipline about 60 years ago. In the days that eventuallyled to World War II, the British Air Ministry was fac-ing the pressing challenge of the formidable Air Forceof Nazi Germany. Figuring out how to best deploy theRoyal Air Force to protect the homeland of Britain had

This manuscript was processed by Area Editor B. Lev.∗ Corresponding author. Tel.: +1 973 655 7519;

fax: +1 973 655 7678.E-mail addresses: j.john.wang@gmail.com (J. Wang),

ruiliangy@gmail.com (R. Yan), hollisterk@mail.montclair.edu(K. Hollister), dzhu@iastate.edu (D. Zhu).

0305-0483/$ - see front matter 2007 Elsevier Ltd. All rights reserved.doi:10.1016/j.omega.2007.10.004

forced the British to scientifically and quantitativelystudy the strategic and tactic problems involved in mili-tary operations [1]. After WWII, OR displayed tremen-dous growth and expansion.

The marriage of OR and general management intomanagement science (MS) has brought it far from itsorigin of military operations and into our daily lives.Many scholars visualize OR and MS interchangeablybecause they usually are studied concurrently. Thereare at least eight major OR academic journals beingpublished at the present time. In addition, academicinstitutions all over the world have established theirown MS/OR department. One of the most prevalentscenes in MS/OR is the constant tweaking of ORprincipals and algorithms to solve social or economicproblems.

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1.1. Some ancient traces

MS is a cross-functional, multi-disciplinary exami-nation of advances and solutions supporting enhancedstrategic planning, executing, controlling, feedbacking,and managing in the modern business world. It is in-teresting to note that some non-business applicationsof its principles can be seen most ancient cultures.In Ancient China, in his epic novel “Romance of theThree Kingdoms” (220–280 AC), the Chinese writerGuanzhong Luo vividly depicted applications of manyof the operations management principles in ancientwarfare. Traces of the same principles can be seen in thepopular Chinese book of Tzu Sun’s “The Art of War”(300 BC) which touched on many topics includinglogistical management and resource management [2].

A real life example of MS can be seen in the DujiangDam irrigation project; the oldest large-scale irrigationproject in the history of the world. It has been support-ing people in Sichuan Province, China, for more than2000 years. Why is the Dujiang Dam so durable? It isa historical wonder of science and technology as wellas an excellent example of how human beings can livein harmony with nature. In the year 256 BC, duringthe Warring States Period (475–221 BC), Li Bing andhis son directed the construction of the Dujiang Damto control flooding on the Chengdu Plains in SichuanProvince [3]. After more than 2264 years, this brilliantachievement in water conservancy made rationalized ir-rigation supply, flood diversion, and sand discharge pos-sible. Still today, the dam plays a tremendous role inthis regard. People could not survive without this!

The Dujiang Dam project is a terrific example of us-ing nature and science in harmony. The entire DujiangDam project was built on nature and completely dis-solved in it. All things with a shape are living beings andhave a life history. Their lives all have a process of for-mation, settlement and degeneration. When somethingcan completely dissolve into nature, its life will certainlyconnect with nature. If nature does not degenerate, itslife will not degenerate. These are all key factors in Du-jiang Dam’s durability. The Dujiang Dam is composedmainly of “flowing cages.” These “cages” are made ofbamboo, which have been previously soaked in oil andlime. This pre-treatment enhances their fiber’s stretch-ing force and its resistance to rot. Several one foot bymore than three feet wide cages are made out of thispre-treated bamboo. Then they fill the cages with scree,to build “flowing cages.” Every year they are checkedand any decaying cages are replaced with new ones.This method seems extremely simple, but we will seethe brilliancy of it.

Li Bing was a founder of many great ideas and wasa founding father of some of our most popular manage-ment principles. Maintenance has been done on the damannually since it was first built. When building DujiangDam, Li Bing, one of the dam’s founders, put a stonemeter in the inner river to be used as the depth gage forremoving sand during annual maintenance. The princi-ple idea of this maintenance was to “dig sands deep andbuild dams low.” “Dig sands deep” means to dig downto the level of the stone meter. Otherwise, the water vol-ume in the inner river will not be enough for irrigation.“Build dams low” means that the dam cannot be builttoo high. Otherwise it might cause problems divertingfloods and overflow. In contrast to the ridiculous ideaof “manpower overrides the heavens,” when human be-ings and nature care about each other, humans are thenliving in harmony with nature. This principle seemsto be easily abandoned in this era of modern science.The lesson seems so simple, yet it is very profound.For example, many irrigation experts from Germany,England, France, America, and other western countriescame to visit Dujiang Dam during the civil war. Theybelieved that replacing the “flowing cages” was tootroublesome.

The experts proposed to build a concrete dam usingprinciples of modern mechanics. However, the concretedam collapsed soon after it was built. Experts had torestore the original dam, using the original techniqueof Li Bing and his son. It is fortunate that they did notsucceed in changing the dam, so that this inter-livingand inter-caring relationship between humans and na-ture could continue. On the other hand, these early ideaslack the quantitative mathematic analysis characterizedby the modem MS.

1.2. Early developments

After the establishment of the People’s Republic ofChina in 1949, certain war techniques of MS were stud-ied and used in China. Despite its immediate useful-ness as an academic discipline, MS has been distrusteddue to its association with western corporate capital-ism. As early as the mid 1950s, Xuesen Qian, the so-called “father of the Chinese atom bomb” and otherscholars who returned to their homeland (mainly fromthe US after 1949), introduced MS/OR in China. Atfirst, OR was translated directly by the words: OR, andlater by the meaning: “Yun-Chou Xue”: the science ofplanning and maneuvering. Then, in 1957, linear pro-gramming (LP) began appearing in architecture, textileindustries, and many other fields. In 1958, a significanteffect was achieved particularly in transportation, the

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loading–uploading of materials, and the dispatching ofvehicles. For instance, the diagrammatic decompositionmethod, a more convenient method than the traditionalone, was initialized.

In the early 1960s, a group of mathematicians joinedthe ranks of research and application of MS/OR. Headedby Loogeng Hua (a famous scholar), a special groupvisited each province to demonstrate and spread theproject management technology and the optimum seek-ing method. After that, dramatic economic efficiencywas realized across the entire country, along with in-ternational recognition. From 1965 on, the critical pathmethod (CPM) and the program evaluation and reviewtechnique (PERT) gradually penetrated all sectors, fromarchitecture to agriculture and forestry to the petroleumindustry. In the 1970s, the methods of optimization de-sign were used in the design of optics, ships, aircrafts,architectural structures, electronic circuits, and chemi-cal engineering processes.

In the mid 1970s, queuing theory was used in minetransportation, telecommunications, seaports, and com-puter designs. After the 1970s, non-linear programming(NLP) began catching people’s attention, making majorcontributions in improving the quality of product de-sign. Unfortunately, several political campaigns, espe-cially the Cultural Revolution, had seriously damagedthe growth of MS in China. The distrust and suspicionto the western world intensified and the field of MS wasrejected almost entirely. The only notable exception issome applications of LP were cautiously used by theCentral Planning Committee and attached to the centralgovernment.

1.3. Education

Most managers, especially high-ranking officials,were nominated from party members, but not profes-sionals as soon as the Communist party controlled thepower in China. Loyalty to the party was the paramountstandard for the appointment and promotion of man-agers. In place of science, management was treated asa revolution.

In the early 1950s, “learning from big brothers ofthe (former) Soviet Union” was a popular slogan and aprejudiced policy in China. Management education inuniversities and colleges started in the middle 1950s.At this time, only a few management majors in institu-tions of higher education, such as the Harbin Institute ofTechnology, offered the related courses. Even TsinghuaUniversity (a Chinese equivalent of MIT) and BeijingUniversity (a Chinese equivalent of Harvard) did notoffer a single management major.

History took a surprising turn in 1976, with thepassing away of Chairman Mao. The notorious “gangof four” was soon arrested and China once againshifted its focus onto economic development, ratherthan spreading revolutionary ideology. By 1978, thecentral government announced a new and ambitious“Four Modernizations” plan to modernize its science,industry, agriculture, and defense. Universities all overChina were encouraged to apply advanced sciencesand technologies from the west. By early 1979, formalprograms of study (that include MS and OR) were es-tablished at a number of Chinese universities in Beijing,Shanghai, and many other cities. The State EconomicPlanning Committee introduced 18 methods (whichincluded LP, PERT, the optimum seeking method,etc.) in the 1980s in order to spread modern manage-ment knowledge. Various training classes and specialseminars for high-level management personnel wereconducted. MS/OR did not become a required coursefor most economics and management students until theearly 1980s. As basic theories received more emphasis,combined with the use of computer software and casestudies, MS/OR as a major was growing in popularity.

In the early days of MS/OR in China, their fac-ulties were dispersed among departments of mathe-matics, systems engineering, industrial management,industrial engineering, or computer sciences. The pres-ence of MS/OR in schools of business was establishedmore recently. Chinese universities and research insti-tutions have been historically independent from indus-tries and businesses, which is very different from theirforeign colleagues. Positions are mostly held in privateenterprises. This has propelled the public perceptionof MS/OR as being very “academically oriented” and“mathematical.” As a result, the Chinese OR Society(CORS) affiliates itself to the Institute of Applied Math-ematics (IAM) in the Chinese Academy of Science.In most established OR departments, professors andresearchers are mathematicians by training. However,the situations have changed recently due to a greaternumber of foreign educated professionals finding theirway back to the mainland, bringing with them formaltraining and practical knowledge [4].

2. Theoretical breakthroughs

2.1. The Chinese postman problem (CPP)

The CPP was first proposed by Mei-Ko Kwan, a Chi-nese mathematician in 1962 [5]. It was a question heardaround the world, since it is a common problem in a reallife environment. The question was that given a postal

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zone with a number of streets that must be served by apostal carrier, how does one develop a tour that coversevery street in the zone and brings the postman back tohis point of origin, having traveled the minimum possi-ble distance? In general, any problem that requires thatall of the edges of a graph be traversed at least oncewhile traveling the shortest total distance overall is aCPP.

The CPP has many real world applications whichare much beyond the original postal carrier scenario.For instance, we can treat those problems like the in-spection of pipes, cable or optic fibers, street cleaning,garbage collecting, meter reading, etc. as CPPs [6]. Cer-tainly, the CPP has its roots in the origins of mathe-matical graph theory. In 1736, Leonid Euler’s famousanalysis of a popular puzzle of that time on the Königs-berg bridge problem, the mathematician demonstrateda forerunner’s contribution [7].

Researchers who have followed Kwan’s initial workhave since developed many variants of the original CPP.In the CPP, the edges have no direction; otherwise it willbecome the directed postman problem. In the directedpostman problem, each of the edges has a direction as-sociated with it. The mixed postman problem refers to agraph that contains a mixture of both directed and undi-rected edges. In this case, a subset of the edges in thegraph must be traversed and the situation becomes theNP-hard rural postman problem. Also, the capacitatedCPP admits restrictions, with each edge’s non-zero de-mand and a limited capacity of postmen for supplyingservice.

The Hierarchical CPP (HCPP) is a variant of the clas-sical CPP, in which the arcs are partitioned into clus-ters and a precedence relation is defined on the clusters.Practical applications of the HCPP include snow and icecontrol on the roads and determination of optimal torchpaths in flame cutting. The HCPP is NP-hard in general,but polynomial-time solvable if the precedence relationis linear and each cluster is connected. For this case,an exact algorithm, requiring a lower computational ef-fort than previous procedures, is described recently byGhiani and Improta [8].

2.2. Optimum seeking method

The optimum seeking method was created by the Chi-nese mathematician Loogeng Hua in the 1960s [9–11].In 1953, J. Kieffer, an American mathematician, discov-ered that seeking experiment points according to the ruleof the golden section would make it possible to reachthe optimal state the fastest. His discovery was then re-fined by Loogeng Hua, who turned it into the optimum

seeking method, or the 0.618 method. The method waspopularized in China for a time [12] and such a cam-paign, based on the human-wave tactic, produced defi-nite impact. This episode demonstrated the prospect ofapplying the rule of the golden section in spheres otherthan the arts. Even before the emergence of the notionof consciously grasping the rule of the golden section,people have repeatedly applied it to their own spheresof practice on the basis of their instincts.

The amazing campaigns and battles in the history ofwar provide clear applications of the rule of the goldensection; examples of conforming to this rule are seenthroughout the military realm. For example, the shadowof 0.618 can be seen in such examples ranging from thearc of the cavalry sword to the apex of the flying trajec-tory of a bullet, shell, or ballistic missile. Also, 0.618 isdisplayed from the optimum bomb-release altitude anddistance for an aircraft in the dive bombing mode to therelationship between the length of the supply line andthe turning point in a war.

2.3. Gray system theory

In 1982, Deng introduced the gray system theory(GST). This multi-disciplinary theory deals with thosesystems for which we lack information. Examples ofsuch systems can be found in agriculture, economics,meteorology, hydrology, ecology, and management. Thefields covered by GST include system analysis, dataprocessing, modeling, prediction, decision-making andcontrol [13].

Numerous national academic conferences on GSThave been organized since 1984. The methods of thegray system have been used to determine the generalplanning for rational development in science, technol-ogy, society, and economy throughout the China. Aspecial course entitled, “GST and applications”, hasbeen offered in many universities and colleges. “TheJournal of Gray System” was issued internationally.“Gray System (Society, Economy)”, the first bookabout this theory, was published by the Chinese De-fense Industry Press in 1985 [14]. From 1985 to 1988,Huazhong University of Science and Technology Pressissued “Gray Control System,” “Gray Forecasting andDecision-Making,” “Fundamental Methods of the GraySystem,” and “Multi-Dimensional Gray Programming”[15–17].

Penetration of GST into traditional mathematical pro-gramming is a powerful algorithm when there is a short-age of historical data. Written by Sifeng Liou and Tian-bang Guo, “Gray System: Theory and Application,” isbased on the technical transformation of the industrial

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system and research on decision-making for food pro-duction in Henai Province. This book demonstratedgray LP, gray 0-1 programming, gray NLP, etc. withcomputer programs.

There are four cases regarding incomplete systeminformation: incomplete element (parameter) infor-mation; incomplete structure information; incompleteboundary information; and incomplete operation be-havior information. The gray number, of which weonly know a rough range but not the exact number, isthe basic element of the gray system. A gray number isan internal set or a number set. Gray system analysis,modeling, forecasting, programming, and controllingare the main subjects of research for GST. Regardingreasoning logic and problem solving, GST is entirelydifferent from probability theory.

The main targets of research are: gray number, grayunit, and gray relationship. Gray number and its oper-ations, gray matrix, and gray equation are the basics ofGST. All probabilistic numbers are treated as gray num-bers within a certain range. No matter how complicatedthe system or how scattered the data are interior rulesstill exist. GST determines the rules among the data, butdoes not look for a probability distribution. The methodof data generation, which includes generation throughaccumulated addition or subtraction, makes seeminglydisordered data showing a certain degree of regularity.

Generation through accumulated addition:Let

X(0) = X(0)(1), i = 1, n.

Then the data series are: X(1)(1) = x(0)(1),

X(1)(k) = X(1)(k − 1) + X(0)(k), 1 < kN .

E.g. X(0) = 3.278, 3.337, 3.39, 3.679, 3.85,X(1) = 3.278, 6.615, 10.05, 13.684, 17.534.

As to gray LP (GLP): when solving the GLP, C(),A(), the gray numbers, need to be “whitened.” Basedon the historical data of b1(), a GM (1, 1) model canbe formed. The forecasting values of b1(s + k), i =1, 2, . . . , n, can be calculated and used to replace b.Then the general LP solving methods can be used [18].

2.4. Data envelopment analysis (DEA)/preferencestructure model

DEA was introduced by Abraham Charnes andWilliam W. Cooper in 1978. This method is used toidentify and analyze relative efficiency of decision-making units (DMU). In the 1980s, DEA was intro-duced as a new domain of OR in China. It was set

up with a series of LP models for evaluating the per-formance of homogeneous entities (schools, hospitals,business firms, etc.) that convert inputs into outputs.

In 1996, Joe Zhu proposed the DEA/preference struc-ture model [19]. In his paper, Zhu noted the importanceof considering the DMUs or decision maker’s prefer-ence over the potential adjustments of various inputsand outputs when DEA is employed. Zhu developedsome weighted, non-radial CCR models by specifyinga proper set of “preference weights.” These “weights”reflect the relative degree of desirability of the poten-tial adjustments of current input or output levels. Itis shown that the preference structure prescribes fixedweights (virtual multiplier bounds) or regions that in-validate some virtual multipliers, hence generating pre-ferred (efficient) input and output targets for each DMU.In addition to providing the preferred target, the ap-proach gives a scalar efficiency score for each DMUto secure comparability. It is also shown how specificcases of his approach handle non-controllable factors inDEA and measure allocative and technical efficiency. Inaddition, Zhu has intensified his model and developedthe related software [20–22].

3. Some original practical applications

A variety of practical models and principles havebeen applied in different industries and services inChina. The following are some works carried out in-side the mainland by Chinese researchers. It should benoted that there is very little information available re-garding actual implementation of these models makingit is hard to tell whether they had an actual effect inchanging the underlying operations.

3.1. CPM/PERT

In the early 1960s, Xuesen Qian used PERT forresearch and management of missiles in the Chinesespace program. Loogeng Hua had accelerated thenational application of CPM in the management ofprojects, construction, and in the maintenance andrepair of large equipment. Starting in the 1970s, theapplication of computer software simplified tediouscalculations and draft drawing of the network. Since theimported software did not match Chinese visualization,the Northwestern University of Technology and theBeijing Institute of Computer Technology each devel-oped PERT software with better graphics in the middle1980s. Given the input node number and simulating themanual way, “CPERT Network Calculation—GraphicsProgram System” can produce various time parameters

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and critical path(s), balance resources, optimize results,and draw a chart with Chinese ideographs automati-cally. A network of up to 300 nodes can be designed ina PC, which satisfies the needs of most projects.

Later in the 1980s, Jiangsu Province sponsored aresearch project entitled “Planning Management andDecision-Making Supporting System for Large ScaleProjects.” This project allowed for databases to be-come the center of data collecting, counseling, statisticssummarizing, and chart printing. Efficiency had beenimproved by dynamic management and the control ofnetworks.

Sijun Bai’s book, “Computer-Aided Analysis for Ac-tivity Network Planning,” addresses design principles,methods, implementation and programming, skills ofcomputer-aided network parameters analysis, time, cost,and resource optimization. The practice of using com-puters as an aid to the activity network analysis and de-sign in project management practice was also discussed.In accordance with the input data and precedence re-lationship, this software had the following functions: itautomatically determines the node number for each ac-tivity; calculates ES, EF, LS, LF with given relation-ships or nodes; draws network, time, and bar charts fromgiven nodes; compromises between time and cost; opti-mizes resources for network planning; optimizes time;transforms time and calendar; prints report tables; andmanages files.

Assuming each activity and cost parameter follownormal distribution, Hougui Zhou developed a GERTmodel in 1989 for installing a temporary bridge over theupper stream of the Gerzhou dam [23]. This GERT wasa new development of CPM/PERT. This developmentcaught the attention of Chinese engineers and managerssince it can be beneficial for making decisions prop-erly, distributing resources reasonably, simulating theproduction process, and solving stochastic problems.

3.2. Mathematical programming

Xiangyun Gui conducted a research project cover-ing 16 oil fields, 46 refineries, 30 consumption zones,and 19 kinds of petrochemical products for the Insti-tute of the Petrochemical Industry [24]. Gui set up alarge-scale mathematical programming model contain-ing 17 hundred and 21 variables and 921 constraints.Six main products needed to be distributed and trans-ported based on the equilibrium of production and con-sumption. A computer system was formulated whichincluded databases for the optimal distribution of oil,the optimal transportation of petrochemical products,operating flows, programs and models, and the systemic

dictionary lexicon. The economic gain was 2–4 × 108

Yuan.Kejun Guo and Rulong Wang [25], of the Institute

of Computer Techniques of Hunan Province, simplifiedthe complex processes of a refinery as the following:input of oil → transformation and treatment → out-put of products. Their model contained seven types ofconstraints: (1) the balance of materials; (2) the amountof added hydrogen; (3) the quantity of products; (4)the quarto of diesel and gasoline; (5) the quality ofproducts; (6) the consumption of energy; and (7) self-consumption of fuel. The objective was to maximizethe net profit. This model is very general and easy touse in the normal design of production procedures, theformation of production plans, and in intermediate andlong term strategic planning.

Zhong Li [26] used a model of two-stage dynamicprogramming (DP) to automatically decide the direc-tion of cables in electrical power plants. In modernelectrical power plants, cables were crossover installedalong channel brackets. If each cross-connection pointof channel brackets is treated as a node and the bracketsfor each workshop, which are connected through nodes,are treated as a bracket network or a cable channelnetwork; the problem of cable routes becomes the selec-tion of the shortest feasible path among the cable chan-nel networks, given related technical requirements. Thesize of modern electrical power plants is so big that over400 network nodes and more than 6000 cables need tobe installed. Through this model, the average orienta-tion time for each cable was reduced by 99.83% to 2 s.Also, many engineers were interested in using NLP foroptimal design in the 1980s.

3.3. Multiple objective decision-making

Based on personal preferences and assigned weights,goal programming (GP) gives decision makers a rangeof optional plans with a certain degree of freedom. GPis very popular with practitioners as decision makerschoose their most satisfactory solution. Jingyan Chen[27] identifies an application of GP to the case of solv-ing the composition of cotton mixing among 240 kindsof raw cotton. Chen’s model gives six priorities basedon six physical indicators of raw cotton, consisting ofgrade, length, spot, strength, spinning number, and fine-ness; the incomplete collection of raw cotton due towarehouse capacity limitations and gross cost is alsotaken into consideration. The use of the GP solutionhelped a cotton spinning factory with 21 varieties of rawcotton readily meet the requirements of quality, quan-tity, and the cotton-mixing cycle time of 20 days saving

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the factory 0.03 Yuan per kilogram of cotton. When thetotal factory production of 1.7 × 104 ton of cotton peryear is considered, the economic savings is substantial.

The introduction of the analytic hierarchy process(AHP) (Thomas L. Saaty, 1971) into China raisedthe interest of MS/OR practitioners in governmentaldecision-making, business administration, transporta-tion, energy resources, education, and merchandising.A special journal on “Decision-Making and AHP” waspublished to stimulate theoretical research and aca-demic interchanging. In other more recent work, fuzzyset theory has been used to analyze situations withoutsufficient data. Qualitative evaluation and quantitativeanalysis have been combined resulting in work moreapplicable to China’s situation.

Based on 36 basic environmental elements, XiujuanYuan [28] constructed a four-level model that was eval-uated through the Delphi Method. A weight table, con-sisting of 22 natural environmental elements and 14socioenvironmental elements produced results that weremuch more accurate than previous guess-work.

Based on an LP model forage composition, JianpingZhang [29] introduced the “may be less than” fuzzyobjective function for gross price and “may be morethan” fuzzy constraints with their member function. Asa result, the feasible ranges for change were decidedand a fuzzy LP formulation can be solved with tradi-tional LP methods. The solutions can be restricted tothe pre-determined ranges and problems related to LPhard constraint can be circumvented.

3.4. Other models

Population explosion, especially in developing coun-tries, has been a big concern worldwide. Implementingstrategies of how to control this growth of populationand set a goal for growth is a basic national policy inChina. “The Forecasting and Control of Population,”written by Jian Song, is a representative work of therelated research. Differential equations and other math-ematical tools were used to describe the process of pop-ulation growth and the birth–death rate. Based on censusinformation, the author analyzed different birth ratesand gave the respective levels of Chinese populationafter the year 2000. For instance, one of the synthe-sized analyses is if the average birth rate remained at1.5, the gross national population would achieve 1.13billion and would not begin to decrease until the year2028. The results played a big role in Chinese popula-tion decision-making for the government.

In order to determine the general goal of populationgrowth, Wang [30] developed a new concept and related

method on “degree of possibility-satisfaction.” The opti-mum population was analyzed in respect to land, water,air pollution, energy resources, food, fish, and economicdevelopment. Twenty factors were evaluated based onthe analysis of the degree of possibility-satisfaction, in-cluding the need of food per capita, the need of en-ergy resources per capita, and the need of living spaceper capita. The six scenarios and the related indicatorsof degree of possibility-satisfaction by the year 2080had also been explored. The concepts and methods ofthis book can also be conveyed into other fields. Systemdynamics (SD) has been employed to formulate the ag-gregate quantitative model of national society and econ-omy, which consists of 18 sub-systems with more than550 equations.

Applications of MS are everywhere: from schedul-ing bulk-pickup-delivery vehicles in Shanghai [31] toanalytical models of strategic structure for developmentof sciences and technology and their applications [32];to the PC aided network technique [33]; and to greensupply chain management (GSCM) in China [34].

Modeling methods have been widely adopted in thepast 30 years, especially in socioeconomic and man-agerial areas. In 1990, the State Council’s Institute ofDevelopment sponsored the research project of “Chinain the year 2000.” The results were published, in an800,000 word report, concerning the complicated top-ics and questions about the economic development inChina. The project posed many great questions such as:How to achieve the strategic goal of triple GNP? Howto deal with the proportional relationship of accumula-tion and consumption? What are the effects of technicalprogress on the national economy?

The following are some completed national models:

• the econometric model,• multi-divisional expanded reproduction,• population and economy coordination develop-

ment,• the quantitative analysis model of economic struc-

ture,• long-term development trends,• the middle to long-term macroeconomic model,• the national education planning model,• energy resources supply systems and decision-

making,• energy resource supply and demand,• environmental prediction, and• production structure of national agriculture.

Many MS tools have been used in the develop-ment and solution of these models; methods include:

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mathematical programming, recursive programming,state evolution simulation technique, Cobb–DouglasFunction (to analyze technical progress), AHP, SD,Leontief’s input–output model, and conception of rea-soning from analogy (CRA). CRA combines traditionalengineering project analogy technique with modernforecasting methods. LP was used to analyze and opti-mize the supply of coal, electricity and oil with demandfor the purpose of setting strategic goals for energyresource development. GP was employed to optimizethe overall distribution and planning of energy resourcesupply systems. Integer programming (IP) was manip-ulated to determine the exploitation and disposition ofenergy resource supply locations.

The future’s uncertainty is a predicament. Stochas-tic elements and uncontrollable factors make long-termplanning extremely difficult. The combination of differ-ent models has been used to reduce the risk in planningunder uncertainty. Four large-scale LP models, witheach having more than 3000 variables and 100 con-straints, were set up in a project to develop an optimalproduction plan for crops and livestock in a country ofShandong Provence [35]. Gray Theory was then utilizedto improve the optimal plan’s robustness to weatherconditions.

The application of mathematical programming re-quires significant data; which can be an obstacle inmodeling real systems. Since the interruption of 10-year havoc in China, lack of data is a typical problem.Allowing Fuzzy Set and Gray Theory to penetrate intotraditional mathematical programming is an interestingdevelopment helping to alleviate the data scarcity issue.At the same time, multiple criteria decision-making hasbeen attracting more and more attention.

4. Some recent contributions

4.1. New models and algorithms

In GP problem, the balance between general equi-librium and optimization is difficult to achieve. Toaddress this, Hu et al. [36] at Shanghai Jiao TongUniversity propose a generalized varying-domain op-timization method for fuzzy GP (FGP) incorporatingmultiple priorities; they present three varying-domainoptimization methods. Co-evolutionary genetic algo-rithms (GAs), called GENOCOPIII, are used in thesolution of the problem. The generalized varying-domain optimization method used by Hu et al. hasother real-world decision-making applications.

In recent years, artificial neural networks which of-fer the advantage of clearer visualization have attracted

considerable attention. Computer scientists and engi-neers are developing neural network representations ofexisting problems for which new or alternative solu-tions can be generated. Li and Li [37] and Li and Xu[38] demonstrate the flexibility of neural networks formodeling and solving diverse mathematical problemsincluding Taylor series expansion, Weierstrass’s first ap-proximation theorem, LP with single and multiple ob-jectives, and fuzzy mathematical programming. Neuralnetwork representations of these problems may help toovercome current limitations in finding their solutions.Li [39] also discusses neural network representation oflinear fuzzy LP problems.

Novel solutions to variations of the general DEAmodel have also been sought. Chen [40] examinesthe non-linear imprecise DEA (IDEA) model whichoccurs when multiple inputs and outputs consist ofimprecise data such as bounded data, ordinal data orratio bound data. Chen addresses the non-linearity ofthe resulting problem by either using scale transforma-tions or variable alternations to convert it into a linearprogram or by solving it by using standard DEA byconverting imprecise data into a set of exact data. Inlater work, Chen [41,42] proposes a modified super-efficiency DEA model which addresses infeasibilityissues in the super-efficiency DEA model to correctlycapture super-efficiency represented by the input savingor the output surplus.

Wang et al. [43] propose a projection method forsolving a system of non-linear monotone equations withconvex constraints. Under standard assumptions, the au-thors show the global convergence and the linear con-vergence rate of the proposed algorithm. Preliminarynumerical experiments show that this method is efficientand promising.

To address issues in unimodal optimization, Pan [44]develops an alternative search plan to the golden ratiosearch with a “platinum ratio” of around 0.55. In a sim-ulation study Pan shows that the golden ratio search isthe best only in the sense of zero variation, but not forminimizing cost.

Li et al. [45] advocate the application of the equate-to-differentiate rule, an alternative to the family of ex-pected utility theory, to the prisoner’s dilemma. Also,the authors have successfully tested the theoretical pre-scriptions derived from theoretical developments in sixexperiments.

4.2. Applications in policy-making

Regional economic issues in China have been mod-eled by Zheng et al. [46]. Their work applies the basic

J. Wang et al. / Omega 36 (2008) 919–932 927

models of Gini and variation coefficients to analyze theeffect of time on China’s regional economic differences.Their work resulted in forecasting China’s regional eco-nomic differences as well as providing results for theadaptive control of regional per-capita income levelswhich can be used to determine an acceptable level ofincome inequality for policy making.

Hua et al. [47] estimate the ecological efficiency ofpaper mills along the Huai River in China. The model,which describes a new approach to defining referencesets, provides efficient input/output targets for DMUmanagers to improve DMUs’ efficiencies. The modelwas validated with data from 32 paper mills along theHuai River in China.

While deterministic input–output analysis has beenapplied to solving a variety of economic problems,stochastic models, which account for uncertainty, pro-vide a better representation of real decision-making.Wu and Chang [48] developed a solution methodologyfor the stochastic input–output model using genetic-algorithm-based (GA-based) gray mathematical pro-gramming techniques. They apply their methodologyto a model in assessing the impacts of recent pollutioncharges and water resource fees to a textile–dyeingfactory. Their research findings indicate that grayinput–output analysis is an applicable tool for theevaluation of environmental cost impacts needed forcorporate production planning and management.

Li et al. [49] attempt to model country performancein the Olympic Games. Previous models which appliedconventional DEA failed to capture the impact of eco-nomic status on the number of gold, silver, and bronzemedals earned by each country. Li et al. [49] use acontext-dependent assurance region (AR) DEA modelto analyze the achievements of nations during six sum-mer Olympic Games taking into account economic sta-tus. Multiple sets of AR restrictions were incorporatedinto the DEA. As a result, a fair comparison of differ-ent nations is achieved. It is shown that by scaling up ordown the outputs, multiple AR restrictions of differentgroups of nations can be transformed into a commonset of AR restrictions that is applicable to all nations.

4.3. Applications in manufacturing

In the current environment of technological innova-tion, diversity in demand, and intensifying market com-petition; the development of cost effective schedulingsystems has attracted a lot of attention. Tang and Liu[50] elaborated on a real-life order scheduling problemfor the production of steel sheets in Baosteel. The prob-lem was formulated as a separable mixed IP model.

The objective was to determine the starting and endingtimes of critical bottleneck operations for each orderto minimize the sum of weighted completion times ofall orders subject to capacity constraints and compli-cated precedence constraints. Tang and Liu [50] devel-oped a decomposition solution methodology based onLagrangian relaxation, LP, and heuristics to address theproblem. In addition to developing a novel formulationand solution methodology for the problem, Tang andLiu developed a production order scheduling simulationsystem for Baosteel. The simulation system’s function-ality included initial managing and scheduling of ordersusing the simulation and then the manual adjustment ofschedules.

In an effort to encourage modernization in business,the Chinese government has adopted a policy to sep-arate the management and ownership of state-ownedenterprises. Currently, state-owned enterprises’ contractsystem (SECS) is the leading enterprise managementsystem used in China today. Feng and Xu [51] provideinsight into the impact of various managerial strate-gies on encouraging cooperation between the enterpriseand state to maximize outcomes. Their overall objectiveis discovering how to encourage enterprises to engagein independent business decision-making with the goalof enhancing their operational capabilities while pur-suing technological innovation and long-term businessgrowth.

Another issue involving manufacturing is the large-scale effort to upgrade production and operations man-agement systems of major iron and steel companies inChina. Traditionally, production scheduling is done us-ing a greedy serial method which results in very highsetup costs. Tang et al. [52] propose a parallel strategyto model the scheduling problem and solve it using anew modified GA (MGA). As in the simulation devel-oped by Tang and Liu [50], Tang et al. [52] combineoutput from an operations model with manual interac-tion to develop a scheduling system. Utilization of theirmodel in Shanghai Baoshan Iron & Steel Complex re-sulted in 20% improvement in 1 year over the previousmanual based system.

The rescheduling of production lines is a commonand frustrating problem for manufacturing firms. Dis-turbances come from many sources including: incor-rect work, machine breakdowns, rework due to qual-ity problem, or rush orders; disturbances are difficultto predict due to their fuzzy and random nature. Toaddress this issue, Li et al. [53] develop a productionrescheduling expert simulation system; their system in-tegrates many techniques and methods, including simu-lation, artificial neural networks, expert knowledge, and

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dispatching rules. The system developed by Li et al.[53] was deployed in a Chinese manufacturing firm withsatisfactory rescheduling results.

Railroad dispatching is another area within the man-ufacturing sector that has attracted OM attention. To ad-dress both the yards’ output objective and the customerservice on-time objective, He et al. [54] developed afuzzy mathematical model to handle the conflicting ob-jectives. They employed a hybrid approach of GAs andlocal search techniques to develop a solution. In vali-dating their model, they conducted a test on a practicalproblem. When compared to results published in an ear-lier study, the results from their problem indicated thatit is a promising method for analyzing the railyards’dispatching problem.

Uncertainties in manufacturing are common; Xuet al. [55] address the robust stabilization problem foruncertain systems with delayed states. In their workthey develop two approaches for addressing the stochas-tic nature of systems. A linear matrix inequality (LMI)approach is used to achieve robust stability solutionsin the case of a nominal unforced system. For anotherclass of systems with uncertainties in delay, solutionsare developed using linear memoryless state feedbackcontrol.

Fuzzy systems are now used to describe the majorscientific domain that began with fuzzy set theory.Applications of fuzzy systems can be seen in manyareas including both manufacturing and agriculture.Recent years have seen significant research in the areaof fuzzy control systems. Tang et al. [56] propose anadaptive control algorithm, based on the fuzzy param-eter identification. Their algorithm can be applied toa class of time-varying systems in which conventionalcontrol techniques have been used for many years.

Wang [57] considers the effects of learning anddeterioration on single-machine scheduling problems.Wang’s work demonstrates that when the learning ef-fect is introduced with deteriorating jobs (i.e., jobswhose processing times are an increasing function oftheir starting time) the solution to the single-machinetime-minimization scheduling problem remains poly-nomially solvable. Wang and Xia [58] address no-waitor no-idle flow shop scheduling problems with deteri-orating jobs. They assume a simple linear deteriorationfunction with some dominating relationships betweenmachines.

Li et al. [59] consider flow shop scheduling prob-lems with flowtime minimization. In order to reduce theCPU time of flowtime computation to 33.3%, which isthe main computational burden of most heuristics, theyemploy general flowtime computing (GFC). In related

work, He [60] introduces a general algorithm, calledALG, for online and semi-online scheduling problem.

4.4. Applications in supply chain management

Research in the area of supply chain managementhas grown considerably in recent years. Quality im-provement is a key topic in improving supply chainfunctionality. Zhu et al. [61] investigate the interactionsbetween quality-improvement decisions and operationaldecisions such as the buyer’s order quantity and thesupplier’s production lot size. They explore the impactsof multiple parties in improving quality in the supplychain. For example, the buyer’s quality standards havesignificant impact on the profits of both the buyer andthe supplier. Buyers cannot cede all responsibilities forquality improvement to the supplier.

The incorporation of risk preferences when establish-ing supply chain coordination contracts is very impor-tant. Choi et al. [62] address the issue of quantifyingdifferent risk preferences for the retailer and supplychain coordinator in a vertically integrated two-echelonsupply chain under a stochastic demand environment;they propose the use of the MV (mean-variance) for-mulation. Choi et al. [62] demonstrate how the supplychain coordinator can set a wholesale price for achiev-ing channel coordination with respect to the specificrisk preference of the retailer.

The greening of the supply chain is an importantconcern for many business enterprises and a challengefor logistics management. Zhu et al. [63] investigate thecorrelation of organizational learning and managementsupport with the extent of adoption of GSCM practicesin Chinese manufacturing firms. In all cases consid-ered, both inbound and outbound logistics activities arepotential polluters to the environment. Zhu et al. [63]confirm that, after controlling for a number of outsideinfluences, there is a significant positive relationshipbetween organizational learning and managementsupport and the greening of the supply chain.

To reflect the retailer’s power in supply chain man-agement, Hua and Li [64] propose retailer-dominantnon-cooperative game models for the newsvendor prob-lem by introducing the sensitivity of the retailer’s or-der quantity to the manufacturer’s wholesale price. Huaand Li [64] use the Nash bargaining mode to investigatetwo cooperative scenarios between a manufacturer anda retailer in a two-echelon supply chain.

The impact of cost and demand disruptions on thesupply chain is an important area of study. Xiao andQi [65] study the coordination problem for a supplychain with two competing retailers. They focus on the

J. Wang et al. / Omega 36 (2008) 919–932 929

impact of both cost disruption and demand disruptionson supply chain coordination mechanisms.

Li et al. [66] examine the impact of postponementstrategy on a manufacturer in a supply chain withplanned backorders. In their research, they demon-strate that using postponement strategy can result in alower total average cost under certain circumstances.Li et al. [66] identify that the variance of the ma-chine utilization rates and the variance of the back-order costs are key factors in making postponementdecisions.

A product mix flexibility model considering flexibil-ity in labor, machine, routing, and information technol-ogy is developed by Gong and Hu [67]. Outputs fromthe model can be useful in making production decisionsfor multiple products under uncertainty. The developedmode can also assist in making enterprise flexibilitypromotion decisions.

Ding and Chen [68] model coordination in a singleperiod, three level supply chain that sells short life cy-cle products. In their model, the contract between thedownstream firms is negotiated before the contract withupstream firms. Ding and Chen [68] construct a flex-ible return policy by postponing the determination ofthe final contract prices and only setting pricing rules.They conclude that multi-level supply chain can be fullycoordinated if each pair of adjacent firms implementsflexible return policies.

Zhang et al. [69] evaluate a more general three-tierlinear supply chain model via simulation and providean approach to quantify the value of shared shipmentinformation. Their model aids supply chain managersevaluate cost–benefit trade-offs during information sys-tem construction.

In managing a multiple source, multiple productsupply chain, another difficult question to address ishow to simultaneously determine the optimal numberof suppliers to use and how to optimally allocate orderquantity to each of these suppliers. The decision canbe further complicated by the consideration of suppliercapacity constraints and the multi-criteria nature of thesupplier selection problem. Xia and Wu [70] proposean integrated analytical hierarchy process approachimproved by rough sets theory and multi-objectivemixed IP.

4.5. Applications in services

Chinese research in MS/OR has made significant im-provements in the Chinese rail systems. Prior to thecomputerization of the national railroad system, rail-ways worked off a fixed optimized schedule; a delay of

just one train produced a cascading delay on many latertrains. Today, the Ministry of Railways has real-timeinformation on its network of more than 5000 railwaystations and the more than 2000 trains that depart daily[71]. With real-time information, the ministry can re-spond quickly to minimize the impacts of unexpecteddelays and temporary demand “hiccups.”

In practical situations, queuing systems face a greatdeal of uncertainty. The precise values of many param-eters are not known precisely; consequently, the mini-mal expected total cost per unit time becomes fuzzy. Toaddress the fuzzy nature of cost coefficients and actualarrival rates, Chen [72] proposes a mathematical pro-gramming approach to find the membership function ofthe fuzzy minimal expected total cost per unit. Chen’swork is based on Zadeh’s extension principle. Solutionsto Chen’s model provide decision makers with more in-formation for designing queuing systems.

Service organizations face complex issues in develop-ing staffing plans. Li and Li [73] present a multi-skilledstaff planning model that considers staff flexibility.Prior research failed to capture the balance of cost andbenefits of staff flexibility in developing staff planningmodels. Li and Li [73] apply multi-objective GP toanalyze several diversified goals in the case of staffplanning at a Chinese clinic; their model can be appliedto many types of service organizations.

Developing comprehensive maintenance and replace-ment plans in the transportation services sector is adynamic and multiple time-period problem. Lai et al.[74] address this issue with the application of the se-quential method to determine optimal policies for im-plementing preventive engine maintenance or enginereplacement. Lai et al. [74] use engine data from theKowloon Motor Bus Company Limited in Hong Kongto test their model. The sequential approach to the prob-lem considered states in an engine’s lifetime and theresponse of each state to corrective maintenance. Us-ing their model, three optimal maintenance and replace-ment policies with respect to three different criteria weredetermined.

The financial services sector faces uncertainty inoptimal portfolio selection. Following the idea ofMarkowitz’s mean variance model, Li and Xu [75]develop a model to address the fuzzy nature of theproblem. Their model incorporates both the judgmentof experts and the subjective opinions of investors infuture securities. Using real data from the ShanghaiStock Exchange, Li and Xu [75] demonstrate that theirportfolio selection model generates an efficient frontierbased upon the investor’s degree of optimism regardinginvestments.

930 J. Wang et al. / Omega 36 (2008) 919–932

5. Trends

Clearly one of the most obvious trends in the shortMS/OR history has been its dramatic increase in itspopularity in China. MS/OR has been either an oddobsession of “Ivory Tower” mavericks or high stakedecision-making that affects the lives of millions [76].Today, with the widespread software availability andendless supply of college educated workers, MS/OR hasbecome an overnight success. Private enterprise is in-creasingly feeling the squeeze of fierce competition andthe ever-increasing cost of labor and raw material andis turning to MS/OR in search of higher operations effi-cacy and competitive advantages. Most of these privateenterprises willingly accept the fact that change, some-times rather difficult ones, have to be made to survivein the present Chinese economic system. State-ownedstrategic manufacturing and service industries (such assteel, petrochemical, banking, and railroad), have alsowitnessed widespread adoption of MS/OR principles inproduction scheduling, supply chain management, de-livery, and inventory management.

The Chinese economic life has been dominated bystate owned enterprises and as a result most MS re-search and practical applications have been conductedwithin these enterprises. It was not until very recentlythat purely private enterprises were also involved inMS research and most importantly in its practical ap-plications. Most MS research has been conducted withfairly well-defined mathematical models and optimiza-tion objectives, which have resulted in measurable per-formance improvements. The explosive growth in theinformation technology industry and information net-works has made it possible to make near real-time op-timization and increase MS/OR flexibility [77]. We cansee this in examples all over the world.

The rigid social structure and the Chinese ten-dency of categorical formalism have also hindered theMS/OR development. This rigidness occurs both inthe academic and practical realms. From the “the FourModernizations” to “Ten Do’s and Do Not’s” to “FourThoughts,” the Chinese fondness of such paradigms isendless. The traditional division between academic re-search and labor has encouraged researchers to performmainly pure mathematical studies. Until a short timeago, papers on practical implementation on MS/ORhave been deemed “unfit” to be published in majoracademic journals. Maybe this was back in the bookburning days! This was considered due to the “lack-ing of theoretical study.” How unfortunate! Luckily,Chinese society has gone through transformationalchanges recently. It is pleasing and wonderful to see

the relaxation of traditional formalism and increasingpopularity of practical research [78,79].

We have witnessed the promising trend of increasingawareness of the importance of operational efficiency.Workers have shown increased willingness towardchanges and new ideas. MS/OR research in foreignsubsidiaries located in China has been the bright sideof Chinese MS/OR history. Unburdened by ambiguousownership and conflicting interests that is common inmost state owned enterprises, most MS/OR projectshave been very promising and successful. These for-eign subsidiaries also have the advantage of greaterflexibility in accessing foreign MS/OR workers andinformation technologies [80].

The other trend, which is unique to the Chinese, is there-vitalization of traditional Chinese wisdom. Do not fixsomething that is not broken! Today, MS/OR workersin China have recognized that most MS/OR projects aremulti-dimensional problems and encompass many areassuch as mathematics, sociology and, management. MostMS/OR workers have found themselves inevitably in-volved in persuading behavioral changes and balancingconflicting interests. It is in this context that most Chi-nese workers resort to ancient wisdoms to make MS/ORmore user “friendly.” Due to their wisdom, the uniquecombination of modern MS/OR and traditional Chinesethinking made the changes appear just, adhered to thesocial norms, and within the confinement of the socialstructure. These approaches undoubtedly increased theeffectiveness of MS/OR in China.

6. Conclusion

There have been many discoveries, enlightenments,and real world applications due to the hard work ofmany scholars. The development of MS/OR has beenexpanding since the mid 1900s. To sum up, the earliestknown public sector of MS/OR research in China datesback to the mid-1950s. Initial MS/OR research concen-trated on practical applications such as transportationand textiles. In the 1960s, with the establishment ofthe economic and mathematics group in the Instituteof Mathematics of the Chinese Academy of Science,preliminary research was carried out on combiningMS/OR with national economic planning. The CulturalRevolution brought MS/OR to a standstill for almost 10years until it ended in the 1970s. Finally, progress wasmade in the 1970s when MS/OR started being appliedin state owned enterprise planning and management.Input–output analysis was used in national planningand the control of population growth, which is a hugeissue for China. We witnessed the proliferation of

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practical application in the academic world in the mid-1980s. There are many application areas which includeenergy, population, agriculture, environment, ecology,national economic planning, defense, business admin-istration, large-scale scientific projects, education, andtraditional Chinese medical science.

China’s history is spectacular and they have madegreat contributions to the world. From the origins ofcivilization to the present day, 4000 years of China’shistory is amazing. There are about 20,000 researchersin the Fuzzy Set Theory arena around the world, half ofthem are Chinese! The potential contribution of Chinesescholars to the area is very optimistic. MS/OR has beenand will be playing a significant role in China’s fourmodernizations and harmony society!

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