ORIGINAL_ARTICLE
Urban Vehicle Congestion Pricing: A Review
Drivers in urban neighborhoods who cruise streets, seeking inexpensive on-street parking create a significant fraction of measured traffic congestion. The solution to this problem is to reduce the total traffic volume including cruising traffic by implementing a congestion pricing scheme: the imposition of a usage fee on a limited-capacity resource during times of high demand. We review the history of two alternatives for implementing congestion pricing scheme: road pricing (RP), which involves cordoning off a section of the center city and imposing a fee on all vehicles that enter it; and parking pricing (PP), which increases the costs of on-street and perhaps off-street parking. PP is needed in many environments where a significant fraction of drivers are simply cruising, looking for inexpensive on-street parking. In this paper, we propose a simple method to estimate the number of cruising drivers and the optimal parking price. Our survey in Boston shows that the number of cruising vehicles reaches 10-20% of the total number of parking spaces during peak hours and the required congestion charge (CC) for onstreet parking is at least about $1/ hour.
https://www.jise.ir/article_4013_990c8b56d5ea02480ac1309f19768664.pdf
2010-02-01
227
242
Vehicle Congestion
pricing
urban
Richard C.
Larson
1
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA
AUTHOR
Katsunobu
Sasanuma
2
Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA
AUTHOR
[1] Downs A. (2004), Still Stuck in Traffic: Coping with Peak Hour Traffic Congestion; Brookings
1
Institution Press, Washington, D.C.
2
[2] Katz B., Puentes R. (2005), Taking the High Roads: A Metropolitan Agenda for Transportation
3
Reform; Brookings Institution Press, Washington, D.C.
4
[3] Larson R.C. and Sasanuma K. (2010), Congestion Pricing: A Parking Queue Model; Journal of
5
Industrial and Systems Engineering; to appear.
6
[4] Litman Todd (2006), London Congestion Pricing: Implications for Other Cities, Victoria Transport
7
Policy Institute, http://www.vtpi.org/london.pdf.
8
[5] Lyons G., Dudley G., Slater E., Parkhurst G., Slater L. (2004), Evidence-Base Review – Attitudes to
9
Road Pricing; Final Report to the Department for Transport, UK. Bristol; Centre for Transport and
10
[6] Schrank D., Lomax T. (2005), The 2005 Urban Mobility Report; Texas Transportation Institute,
11
College Station, TX.
12
[7] Shoup D.C. (2006), Cruising for parking; Transport Policy 13; 479-486,
13
http://shoup.bol.ucla.edu/Cruising.pdf
14
[8] Shoup D.C. (2005), The High Cost of Free Parking; APA (American Planning Association) Planners
15
Press, Chicago.
16
[9] Swedish National Road Association (2002), Road Pricing in Urban Areas (VV Publication
17
2002:136E), http://www.transport-pricing.net/download/swedishreport.pdf
18
[10] Transport for London, Congestion Charging: Third Annual Monitoring Report,
19
http://www.tfl.gov.uk/assets/downloads/ThirdAnnualReportFinal.pdf, April 2005.
20
[11] Transportation Alternatives, No Vacancy: Park Slope’s Parking Problem and How to Fix It,
21
http://www.transalt.org/campaigns/reclaiming/novacancy.pdf, February 2007.
22
[12] Victoria Transport Policy Institute, Road Pricing: Congestion Pricing, Value Pricing, Toll Roads and
23
HOT Lanes, http://www.vtpi.org/tdm/tdm35.htm.
24
[13] Vickrey W. (1969), Congestion Theory and Transport Investment; American Economic Review
25
Proceedings 59; 251-260.
26
[14] World Conference on Transport Research Society (2004), Urban Transport and the Environment: An
27
International Perspective; Elsevier, New York.
28
ORIGINAL_ARTICLE
Optimal Batch Production for a Single Machine System with Accumulated Defectives and Random Rate of Rework
In this paper we consider an imperfect production system which produces good and defective items and assume that defective items can be reworked. Due to the nature of rework process we do not restrict the rework rate to be equal to normal production rate or constant and assume that it is a random variable with an arbitrary distribution function. We also assume as it is true in most real world situation that the unit rework processing cost need not to be equal to unit normal processing cost. We consider the case in which during a specified period of time consisting of several cycles the rework process takes place only in its last cycle. For practical reasons such as the ease of production scheduling and resource planning we require that the length of the last cycle must have the same length as the other cycles of this period. For this purpose we assume in the last cycle of this period first the normal production starts after which the rework of all defective units produced in the period begins. After the end of the last cycle of the period the whole process will start all over again. Further we assume that, for reduction of set up cost for rework per unit time, the total number of setups for rework must be as small as possible. For this system we derive the expected total cost function consisting of inventory holding, waiting time of defectives for rework, setup, and processing costs per unit time. Then we obtain the economic batch quantity which minimizes this expected cost.
https://www.jise.ir/article_4014_d611fdb41c3901364992d1b3f0214a3e.pdf
2010-02-01
243
256
Economic batch quantity
Accumulated rework
Random rework rate
Rasoul
Haji
haji@sharif.edu
1
Industrial Engineering, Sharif unversity of technology, Teharan, Tehran, Iran
AUTHOR
Babak
Haji
2
Industrial Engineering, Sharif unversity of technology, Teharan, Tehran, Iran
AUTHOR
[1] Buscher U., Lindner G. (2007), Optimizing a production system with rework and equal sized batch
1
shipments; Computers and Operations Research 34(2); 515-535.
2
[2] Chakrabarty S., Rao N.V. (1988), EBQ for a multi stage production system considering rework;
3
Opsearch 25(2); 75-88.
4
[3] Cheng T.C.E. (1991), An economic order quantity model with production processes; IIE Transactions
5
23(1); 23-28.
6
[4] Cárdenas-Barrón L.E. (2009), Economic production quantity with rework process at a single-stage
7
manufacturing system with planned backorders; Computers & Industrial Engineering 57; 1105–1113.
8
[5] Gupta T., Chakrabarty S. (1984), Looping in a multistage production system; International Journal of
9
Production Research 22(2); 299-311.
10
[6] Haji B., Haji A., Rahmati Tavakol A. (2008), Scheduling Accumulated Rework in a Normal Cycle:
11
Optimal Batch Production with Minimum Rework Cycles; Journal of Industrial and Systems
12
Engineering 2(3); 236-249.
13
[7] Haji R., Haji A., Sajadifar M., Zolfaghari S. (2008), Lot sizing with non-zero setup times for rework;
14
Journal of Systems Science and Systems Engineering 17(2); 230-240.
15
[8] Hayek P.A., Salameh M.K. (2001), Production lot sizing with the reworking of imperfect quality item
16
produced; Production Planning and Control 12(6); 584-590.
17
[9] Jamal A.M.M., Sarker B.R., Mondal S. (2004), Optimal manufacturing batch size with network
18
process at a single-stage production system; Computers & Industrial Engineering 47; 77-89.
19
[10] Johnson L.A., Montgomery D.C. (1974), Operations research in production planning and inventory
20
control; New York, John Wiley and Sons.
21
[11] Kalro A.H., Gohil M.M. (1982), A lot size model with backlogging when the amount received is
22
uncertain; International Journal of Production Research 20(6); 775–786.
23
[12] Lee H.L., Rosenblatt M.J. (1987), Simultaneous determination of production cycle and inspection
24
schedules in a production system; Management Science 33(9); 1125-1136.
25
[13] Lee H.L. (1992), Lot sizing to reduce capacity utilization in production process with defective item,
26
process corrections and rework; Management Science 38(9); 1314-1328.
27
[14] Lee H.L., Chandra M.J., Deleveaux V.J. (1997), Optimal batch size and investment in multistage
28
production systems with scrap; Production Planning and Control 8(6); 586-596.
29
[15] Love S. (1979), Inventory Control; McGraw-Hill, New York.
30
[16] Nahmias S. (2005), Production and Operations Analysis; McGraw-Hill, 5th ed; new York.
31
[17] Porteus E.L. (1986), Optimal lot sizing, process quality improvement and setup cost reduction;
32
Operations Research 34(1); 137–144.
33
[18] Rosenblatt M.J., Lee H.L.(1986), Economic production cycles with imperfect production processes;
34
IIE Transactions 18(1); 48-55.
35
[19] Salameh M.K., Jaber M.Y.(2000), Economic production quantity model for items with imperfect
36
quality; International Journal of Production Economics 64; 59–64.
37
[20] Sarker B.R., Jamal A.M.M., Mondal S.(2008), Optimal batch sizing in a process at multi-stage
38
production system with network consideration; European Journal of Operational research 184(3);
39
[21] Schwaller R.L. (1988), EOQ under inspection costs; Production and Inventory Management Journal
40
29(3); 22-24.
41
[22] Zhang X., Gerchak Y. (1990), Joint lot sizing and inspection policy in an EOQ model with random
42
yield; IIE Transactions 22(1); 41-47.
43
ORIGINAL_ARTICLE
Using Neural Networks with Limited Data to Estimate Manufacturing Cost
Neural networks were used to estimate the cost of jet engine components, specifically shafts and cases. The neural network process was compared with results produced by the current conventional cost estimation software and linear regression methods. Due to the complex nature of the parts and the limited amount of information available, data expansion techniques such as doubling-data and data-creation were implemented. Sensitivity analysis was used to gain an understanding of the underlying functions used by the neural network when generating the cost estimate. Even with limited data, the neural network is able produced a superior cost estimate in a fraction of the time required by the current cost estimation process. When compared to linear regression, the neural networks produces a 30% higher R value for shafts and 90% higher R value for cases. Compared to the current cost estimation method, the neural network produces a cost estimate with a 4.7% higher R value for shafts and a 5% higher R value for cases. This significant improvement over linear regression can be attributed to the neural network ability to handle complex data sets with many inputs and few data points.
https://www.jise.ir/article_4015_6e93fe6ecd22f43bbeed9f48493763b5.pdf
2010-02-01
257
274
Neural Network
cost estimation
Gary R.
Weckman
1
Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA
AUTHOR
Helmut W.
Paschold
2
School of Public Health Sciences and Professions, Ohio University, Athens, Ohio, USA
AUTHOR
John D.
Dowler
3
Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA
AUTHOR
Harry S.
Whiting
4
Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA
AUTHOR
William A.
Young
5
Department of Industrial and Systems Engineering, Ohio University, Athens, Ohio, USA
AUTHOR
[1] Divelbiss D. (2005), Evaluation of the impact of product detail on the accuracy of cost estimates;
1
Thesis, Ohio University; Athens, Ohio.
2
[2] Fine T. (1999), Feedforward Neural Network Methodology; Springer Series in Statistics; 1st edition,
3
[3] Günaydin, H. Murat, Doğan, S. Zeynep. (2004), A neural network approach for early cost estimation
4
of structural systems of buildings; International Journal of Project Management 22; 595-602.
5
[4] Kim K., Han I. (2003), Application of a hybrid genetic algorithm and neural network approach in
6
activity based costing; Expert Systems with Applications 24; 73–77.
7
[5] Layer A., Brinke E.T., Houten F., Kals H., Haasis S. (2002), Recent and future trends in cost
8
estimation; International Journal Computer Integrated Manufacturing 15(6); 499-510.
9
[6] Millie D., Weckman G., Pigg R., Tester P., Dyble J., Litaker R.W., Hunter J.C., Carrick H.J.,
10
Fahnenstiel G.L. ( 2006), Modeling phytoplankton aAbundance in Saginaw Bay, Lake Huron: Using
11
artificial neural networks to discern functional influence of environmental variables and relevance to a
12
Great Lakes observing system; Journal of Phycology 42(2); 336-349.
13
[7] National Aeronautics and Space Administration (2004), NASA Cost Estimation Handbook;
14
Washington.
15
[8] Prechelt L. (1998), Automatic early stopping using cross validation: quantifying the criteria; Neural
16
Networks 11; 761–767.
17
[9] Principe J.C., Euliano E.R., Lefebvre W.C. (1999), Neural and adaptive systems: Fundamentals
18
through simulations with cd-rom; John Wiley & Sons; New York.
19
[10] Schenker B., Agarwal M. (1996), Cross-validated structure selection for neural networks; Computers
20
and Chemical Engineering 20(2); 175-186.
21
[11] Seo K.K., Park J.H., Jang D.S., Wallace D. (2002), Approximate estimation of the product life cycle
22
cost using neural networks in conceptual design; International Journal of Advanced Manufacturing
23
Technology 19; 461-471.
24
[12] Shlub A., Versand R. (1999), Estimating the cost of steel pipe bending, a comparison between neural
25
networks and regression analysis; International Journal of Production Economics 62; 201-207.
26
[13] Smith A.E., Mason A.K. (1997), Cost estimation predictive modeling: Regression versus neural
27
network; The Engineering Economist 42(2); 137–161.
28
[14] Swingler K. (1996), Applying neural networks, A practical guide; 3rd edition, Morgan Kaufmann.
29
[15] Walpole, Myers, Myers, Ye. (2002), Probability & Statistics for engineers & scientists; 7th edition,
30
Prentice Hall.
31
[16] West D. (2000), Neural network credit scoring models; Computers & Operation Research 27; 1131-
32
[17] West D., Dellana S., Qian J. (2005), Neural network ensemble strategies for financial decision
33
application; Computers & Operations Research 32; 2543-2559.
34
[18] Yamin H.Y., Shahidehpour S.M., Li Z. (2004), Adaptive short-term electricity price forecasting using
35
neural networks in the restructured power markets; Electrical Power and Energy Systems 26; 571-581.
36
[19] Yuval (2000), Neural network training for prediction of climatological time series, regularized by
37
minimization of the generalized cross-validation function; Monthly Weather Review 128(5); 1456-
38
[20] Zhang Y.F., Fuh J.Y.H. (1998), A neural network approach for early cost estimation of packaging
39
products; Computers and Industrial Engineering 34(2); 433-450.
40
ORIGINAL_ARTICLE
Optimal Allocation of Ships to Quay Length in Container Ports
Due to the continuously increasing container trade, many terminals are presently operating at or close to capacity. An efficient terminal is one that facilitates the quick transshipment of containers to and from ships. In this sake, this paper addresses the ship assignments problem at a maritime container terminal, where ships are normally assigned to specific quay cranes until the work is finished. The paper’s target is to develop a new Continues Berth Allocation Problem (CBAP) in the form of a mixed integer nonlinear programming to achieve the best service time in a container terminal. For illustrating the accuracy of Proposed model (PM), Imai et. al. 's model (IM) (TRANSPORT RES B, 39 (2005) 199–221) was applied and a wide variety of computational test examples were conducted. The results of demonstrated that the presented BAPC reduces the number of nonlinear variables (constraints) and generates substantial savings in the CPU time.
https://www.jise.ir/article_4016_27c7a41be0ba5ebd5a231fd4ff7792af.pdf
2010-02-01
275
290
Optimal transportation
Continues Berth allocation Problem (CBAP)
mixed integer
nonlinear programming
H.
Javanshir
1
Dep. of Industrial Engineering, Islamic Azad University, South Tehran Branch, Iran
AUTHOR
S.R.
Seyed-Alizadeh Ganji
2
Dep. of Transportation Engineering, Islamic Azad University, Science and Research Branch, Iran
AUTHOR
[1] Brown G.G., Lawphongpanich S., Thurman K.P. (1994), Optimizing ship berthing; Naval Research
1
Logistics 41; 1–15.
2
[2] Brown G.G., Cormican K.J., Lawphongpanich S., Widdis D.B. (1997), Optimizing submarine berthing
3
with a persistence incentive; Naval Research Logistics 44; 301–318.
4
[3] Guan Y., Xiao W.-Q., Chueng R.K., Li C.-L. (2002), A multiprocessor task scheduling model for berth
5
allocation: Heuristic and worst case analysis; Operations Research Letter 30; 343–350.
6
[4] Imai A., Nagaiwa K., Chan W.T. (1997), Efficient planning of berth allocation for container terminals
7
in Asia; Journal of Advanced Transportation 31; 75–94.
8
[5] Imai A., Nishimura E., Papadimitriou S. (2001), The dynamic berth allocation problem for a container
9
port; Transportation Research Part B 35; 401–417.
10
[6] Imai A., Nishimura E., Papadimitriou S. (2003), Berth allocation with service priority; Transportation
11
Research Part B 37; 437–457.
12
[7] Imai A., Nishimura E., Hattori M., Papadimitriou S. (2007), Berth allocation at indented berths for
13
mega-containerships; European Journal of Operational Research 179(2); 579-593.
14
[8] Imai A., Sun X., Nishimura E., Papadimitriou S. (2005), Berth allocation in a container port: Using a
15
continuous location space approach; Transportation Research Part B 39; 199–221.
16
[9] Kim K.H., Moon K.C. (2003), Berth scheduling by simulated annealing; Transportation Research Part
17
B 37; 541–560.
18
[10] Lai K.K., Shih K. (1992), A study of container berth allocation; Journal of Advanced Transportation
19
26; 45–60.
20
[11] Li C.-L., Cai X., Lee C.-Y. (1998), Scheduling with multiple-job-on-one-processor pattern; IIE
21
Transactions 30; 433–445.
22
[12] Lim A. (1998), The berth planning problem; Operations Research Letters 22; 105–110.
23
[13] Nishimura E., Imai A., Papadimitriou S. (2001), Berth allocation planning in the public berth system
24
by genetic algorithms; European Journal of Operational Research 131; 282–292.
25
[14] Park K.T., Kim K.H. (2002), Berth scheduling for container terminals by using a sub-gradient
26
optimization technique; Journal of the Operational Research Society 53; 1054–1062.
27
[15] Park Y.-M., Kim K.H. (2003), A scheduling method for berth and quay cranes; OR Spectrum 25; 1–23.
28
[16] Seyedalizadeh-Ganji S.R., Javanshir H., Vaseghi F. (2009), Nonlinear Mathematical Programming for
29
Optimal Management of Container Terminals; International Journal of Modern Physics B 23(27);
30
5333-5342.
31
[17] Javanshir H., Seyedalizadeh-Ganji S.R. (2010), Yard crane scheduling in port container terminals
32
using genetic algorithm; Journal of Industrial Engineering International; In Press.
33
[18] Tavakkoli-Moghaddam R., Makui A., Salahi S., Bazzazi M., Taheri F. (2009), An efficient algorithm
34
for solving a new mathematical model for a quay crane scheduling problem in container ports;
35
Computers and Industrial Engineering 56(1); 241–248.
36
[19] Zhang C., Liu J., Wan Y.-W., Murty K.G., Linn R. (2003), Storage Space Allocation in Container
37
Terminals; Transportation Research Part B: Methodology 37B(10); 883–903.
38
[20] Vis I.F.A., Koster R.D. (2003), Transshipment of containers at a container terminal: An overview;
39
European Journal of Operational Research 147; 1–16.
40
[21] Bierwirth C., Meisel F. (2010), A survey of berth allocation and quay crane scheduling problems in
41
container terminals; European Journal of Operational Research 202; 615-627.
42
ORIGINAL_ARTICLE
Semi-Supervised Learning Based Prediction of Musculoskeletal Disorder Risk
This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those obtained by a backpropagation neural network. Comparison indicates an improved performance by the semi-supervised approach over the random forest classifier as well as neural network approach. Highest classification accuracy of 78.20% was achieved by the used semi-supervised approach with random forest as base classifier in comparison to an accuracy of 72.4% and 74.7% obtained by random forest and back propagation neural network approaches respectively. Thus results suggest that the proposed approach can successfully classify jobs into the low and high risk categories of low-back disorders based on lifting task characteristics.
https://www.jise.ir/article_4017_54396a475ae17312ac32ae0b3324ee47.pdf
2010-02-01
291
295
low-back disorders
semi-supervised learning
Backpropagation neural network
random forest classifier
Pankaj
Chandna
1
Mechanical Engineering Department, National Institute of Technology, Kurukshetra 136119, Haryana, INDIA
AUTHOR
Surinder
Deswal
2
Civil Engineering Department, National Institute of Technology, Kurukshetra 136119, Haryana, INDIA
AUTHOR
Mahesh
Pal
3
Civil Engineering Department, National Institute of Technology, Kurukshetra 136119, Haryana, INDIA
AUTHOR
[1] Breiman L. (1996), Bagging predictors; Machine Learning 26; 123-140.
1
[2] Breiman L. (1999), Random forests-Random Features; Technical Report 567; Statistics Department,
2
University of California, Berkeley, http://ftp.stat.berkeley.edu/pub/users/breimanf,4 July 2007.
3
[3] Bureau of labour statistics (1996), Characteristics of injuries and illnesses resulting in absences from
4
work, 1994 Washington, DC: US Department of Labour; Bureau of Labour Statistics, USDL; 96–163.
5
[4] Dempsey P.G., Ayoub M.M., Westfall P.H. (1995), The NIOSH lifting equations: a closer look. In:
6
Bitner A.C. and Champney P.C. (Ed); Advance in Industrial Ergonomics and Safety VII. Taylor and
7
Francis, Bristol, PA; 705–712.
8
[5] Dempsey P.G., Westfall P.H. (1997), Developing explicit risk models for predicting low-back
9
disability: a statistical perspective; International Journal of Industrial Ergonomics 19; 483–497.
10
[6] Driessens K., Reutemann P., Pfahringer B., Leschi C.(2006), Using Weighted Nearest Neighbour to
11
Benefit from Unlabeled Data.In:Wee Keong Ng,Masaru Kitsuregawa, Jianzhong Li, and Kuiyu Chang,
12
(Ed); Advances in Knowledge Discovery and Data Mining, 10th Pacific-AsiaConference, PAKDD
13
2006, 3918 of LNCS; 60-69.
14
[7] Feller W. (1968), An Introduction to Probability Theory and Its Application; third edition, Vol. 1,
15
Wiley; New York.
16
[8] Frymoyer J.W., Cats-Baril W.L. (1991), An overview of the incidence and costs of low-back pain;
17
Orthopaedic Clinics of North America 22; 262–271.
18
[9] Guo H., Tanaka S., Cameron L., Seligman P., Behrens V., Ger J. (1995), Back pain among workers in
19
the United States: national estimates and workers at high risks; American Journal of Industrial
20
Medicine 28; 591–602.
21
[10] Karwowski W., Zurada J., Marras W.S., Gaddie P. (1994), A prototype of the artificial neural networkbased
22
system for classification of industrial jobs with respect to risk of low back disorders. In:
23
Aghazadeh F. (Ed); Advances in Industrial Ergonomics and Safety VI, Taylor & Francis, Bristol, PA;
24
19–22.
25
[11] Killough M.K., Crumpton L.L., Calvert A., Bowden R. (1995), An investigation using neural networks
26
to identify the presence of carpal tunnel syndrome; 4th Industrial Engineering Research Conference
27
Proceedings, IIE, Norcross, GA; 659–667.
28
[12] Marras W.S., Lavender S.A., Leurgans S., Sudhakar L.R., Allread W.G., Fathallah F., Ferguson S.
29
(1993), The role of dynamic three dimensional trunk motion in occupationally-related low back
30
disorders; Spine 18; 617–628.
31
[13] Pal M., Mather P.M. (2003), An assessment of the effectiveness of decision tree methods for land
32
cover classification; Remote Sensing of Environment 86; 554–565.
33
[14] Quinlan J.R. (1993), C4.5: Programs for Machine Learning; Morgan Kaufmann, San Mateo.
34
[15] Seeger M. (2002), Learning with labeled and unlabeled data; Technical report, Institute for Adaptive
35
and Neural Computation, University of Edinburgh,
36
http://www.kyb.tuebingen.mpg.de/bs/people/seeger/papers/review.pdf, 13 May 2007.
37
[16] Tanaka S., Wild D., Seligman P., Halperin W., Behrens V., Putz-Anderson V. (1995), Prevalence and
38
work-relatedness of self-reported carpal tunnel syndrome among US workers: Analyses of the
39
occupational health supplement data to the 1988 National Health Interview Survey; American Journal
40
of Industrial Medicine 27; 451–470.
41
[17] Webster B.S., Snook S.H. (1994), The cost of compensable upper extremity cumulative trauma
42
disorders; Journal of Occupational and Environmental Medicine 36; 713–717.
43
[18] Zhu X. (2006), Semi-supervised learning literature survey; Technical Report 1530, Department of
44
Computer Sciences, University of Wisconsin-Madison,
45
http://www.pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf, 8 May 2007.
46
[19] Zurada J., Karwowski W., Marras W.S. (1997), A neural network-based system for classification of
47
industrial jobs with respect to risk of low back disorders due to work-place design; Applied
48
Ergonomics 28; 49–58.
49