ORIGINAL_ARTICLE
Congestion Pricing: A Parking Queue Model
Drivers in urban neighborhoods who patrol streets, seeking inexpensive on-street parking create a significant fraction of measured traffic congestion. The pool of drivers patrolling at any time can be modeled as a queue, where ‘queue service’ is the act of parking in a recently vacated parking space and queue discipline is SIRO – Service In Random Order. We develop a queueing model of such driver behavior, allowing impatient drivers to abandon the queue and to settle for more expensive off-street parking. We then relate the model to the economic theory of congestion pricing, arguing that price differentials between on-street and off-street parking should be reduced in order to reduce traffic congestion. Reducing the number of “patrolling drivers” often can reduce urban road congestion significantly, in some cases as effectively as technologically expensive road pricing schemes that cordon off the center city.
http://www.jise.ir/article_4018_b3602eb0f54ec792f1f7eb7840cdcf91.pdf
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1
17
Queuing
Traffic
Parking
Markovian model
Richard C.
Larson
true
1
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA
Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 USA
AUTHOR
Katsunobu
Sasanuma
true
2
Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA
Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA
Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213 USA
AUTHOR
[1] Andreatta G., Odoni A.R. (2003), Analysis of Market-Based Demand Management Strategies for
1
Airports and En Route Airspace; Operations Research in Space and Air, Ciriani, T.(ed.), Kluwer
2
Academic Publishers; Boston.
3
[2] Carlin A., Park R.E. (1970), Marginal Cost Pricing of Airport Runway Capacity; American Economic
4
Review 60; 310-318.
5
[3] Cox D.R., Smith W.L. (1954), On the Superposition of Renewal Processes; Biometrika 41; 91-99.
6
[4] Downs A. (2004), Still Stuck in Traffic: Coping with Peak Hour Traffic Congestion; Brookings
7
Institution Press, Washington, D.C.
8
[5] Kaplan E.H. (1987), Analyzing Tenant Assignment Policies; Management Science 33; 395-408.
9
[6] Kaplan E.H. (1988), A Public Housing Queue with Reneging and Task-Specific Servers; Decision
10
Sciences 19; 383-391.
11
[7] Larson R.C., Sasanuma K. (2010), Urban Vehicle Congestion Pricing: A Review; Journal of Industrial
12
and Systems Engineering 3; 227-242.
13
[8] Lyons G., Dudley G., Slater E., Parkhurst G., Slater L. (2004), Evidence-Base Review – Attitudes to
14
Road Pricing; Final Report to the Department for Transport, UK. Bristol: Centre for Transport and
15
[9] Shoup D.C. (2005), The High Cost of Free Parking; APA (American Planning Association) Planners
16
Press, Chicago.
17
[10] Vickrey W. (1969), Congestion Theory and Transport Investment; American Economic Review
18
Proceedings 59; 251-260.
19
[11] Vickrey W. (1994), Statement to the Joint Committee on Washington, D.C., Metropolitan Problems,
20
The Economizing of Curb Parking Space – A Suggestion for a New Approach to Parking Meters;
21
Journal of Urban Economics 36; 42-65 (republished).
22
[12] Wolff R.W. (1982), Poisson Arrivals See Time Averages; Operations Research 30; 223-231.
23
[13] Odoni A.R., Larson R.C. (2006), Lecture Notes in Logistical & Transportation Planning Methods,
24
Massachusetts Institute of Technology, Cambridge, MA. http://web.mit.edu/urban_or_book/www/
25
[14] Shoup D.C. (2006), Cruising for parking; Transport Policy 13; 479-486.
26
http://shoup.bol.ucla.edu/Cruising.pdf
27
[15] Transportation Alternatives (February 2007), No Vacancy: Park Slope’s Parking Problem and How to
28
Fix It. http://www.transalt.org/campaigns/reclaiming/novacancy.pdf
29
[16] Victoria Transport Policy Institute, Road Pricing: Congestion Pricing, Value Pricing, Toll Roads and
30
HOT Lanes. http://www.vtpi.org/tdm/tdm35.htm
31
ORIGINAL_ARTICLE
Budgetary Constraints and Idle Time Allocation in Common-Cycle Production with non-zero Setup Time
Economic lot size scheduling problem (ELSP) for a multi-product single machine system is a classical problem. This paper considers ELSP with budgetary constraint as an important aspect of such systems. In the real world situations the available funds for investment in inventory is limited. By adopting the common cycle time approach to ELSP, we obtain the optimal common cycle which minimizes the total inventory ordering and holding costs for the case of nonzero setup times. One aspect of the scheduling is to decide what should be the sequence of production runs and how the idle times shall be distributed in the common cycle time. For such a sequencing problem, we consider two cases: a) the common cycle time is given, and b) the common cycle time is a decision variable. In the literature, scheduling rules are introduced for both cases, which assume that the total idle time is located at the end of each cycle. This paper relaxes this assumption and provides: i) a rule to optimize the production sequence and the length of idle times before (or after) producing each item, for both cases (a) and (b), and ii) the optimal common cycle for case (b). The presented rule is interestingly general, simple and easy-to-apply.
http://www.jise.ir/article_4019_17ac6384ec52dded9c646a4c8e6d3227.pdf
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32
ELSP
sequencing
Inventory control
Rasoul
Haji
haji@sharif.edu
true
1
Dept. of Industrial Engineering, Sharif University of Technology, Teharan, Iran
Dept. of Industrial Engineering, Sharif University of Technology, Teharan, Iran
Dept. of Industrial Engineering, Sharif University of Technology, Teharan, Iran
AUTHOR
Alireza
Haji
ahaji@sharif.edu
true
2
Dept. of Industrial Engineering, Sharif University of Technology, Teharan, Iran
Dept. of Industrial Engineering, Sharif University of Technology, Teharan, Iran
Dept. of Industrial Engineering, Sharif University of Technology, Teharan, Iran
AUTHOR
Ali
Ardalan
true
3
College of Business and Public Administration, Old Dominion University, Virginia 23529
College of Business and Public Administration, Old Dominion University, Virginia 23529
College of Business and Public Administration, Old Dominion University, Virginia 23529
AUTHOR
[1] Boctor P.P. (1982), The Two-Product, Single Machine, Static Demand, Infinite horizon Lot Scheduling
1
Problem; Management Science 27; 798-807.
2
[2] Carreno J.J. (1990), Economic Lot Scheduling for Multiple Products on Parallel Identical Processors;
3
Management Science 36; 348-358.
4
[3] Cook W.D., Saipe A.L., Seiford L.M. (1980), Production Runs for Multiple Products: The Full-Capacity
5
Heuristic; Operations Research 31; 405-412.
6
[4] Dobson G. (1987), The Economic Lot-Scheduling problem: Achieving Feasibility Using Time-Varying Lot
7
Sizes; Operations Research 35; 764-771.
8
[5] Elmaghraby S.F. (1978), The Economic Lot Scheduling Problem (ELSP): Review and Extensions;
9
Management Science 24; 587-631.
10
[6] Fujita S. (1978), The Application of Marginal Analysis to the Economic Lot Scheduling Problem; AIIE
11
Transactions 10; 354-361.
12
[7] Goyal S.K. (1973), Scheduling a Multi-Product Machine System; Operational Research 31; 405-412.
13
[8] Goyal S.K. (1984), Determination of economic Production Quantities for a Two-Product Single Machine
14
System; International Journal of Production Research 22; 121-126.
15
[9] Graves S.C. (1979), on the Deterministic Demand Multi-Product Single Machine Lot Scheduling Problem;
16
Management Science 25; 267-280.
17
[10] Gunter S.I., Swanson L.A. (1986), A Heuristic for Zero Setup Cost Lot Sizing and Scheduling Problems;
18
Presented at the ORSA-TIMS Conference, October 27-28; Miami, Florida.
19
[11] Haessler R.W. (1979), An Improved Extended Basic Period Procedure for Solving the Economic Lot
20
Scheduling Problem; AIIE Transactions 11; 336-340.
21
[12] Haji R. (1994), Optimal Allocation of idle times between production runs of a multi-item production
22
system; Reaserch Proceedings of Department of Industrial Engineering, Sharif University of Technology;
23
Tehran, Iran (in Persian).
24
[13] Haji R., Mansouri M. (1995), Optimum Common Cycle for Scheduling a Single-Machine Multi-product
25
System with a Budgetary Constraint; Production Planing and Control 2; 151-156.
26
[14] Haji A., Haji R. (2002), Optimum aggregate inventory for scheduling multi-product single machine system
27
with zero setup time; International Journal of Engineering 15(1); 41-48.
28
[15] Hanssmann F. (1962), Operations Research in Production and Inventory; John Wiley and Sons; New York.
29
[16] Hsu W. (1983), On the General Feasibility Test of scheduling Lot Size for several Products on One
30
Machine; Management science 29; 93-105.
31
[17] Jones P.C., Inmann R.R. (1989), When is the Economic Lot Scheduling Problem Easy?; IIE Transactions
32
21; 11-20.
33
[18] Johnson L.A., Montgomery D.C. (1974), Operation Reaserch in Production Planning, Scheduling and
34
Inventory Control; John Wiley; New York.
35
[19] Park K.S., Yun D.K. (1984), A Stepwise Partial Enumeration Algorithm for The Economic Lot Scheduling
36
Problem; IIE Transactions 16; 363-370.
37
[20] Parsons R.J. (1966), Multiproduct Lot Size Determination When certain Restrictions are Active; Journal of
38
Industrial Engineering 17; 360-363.
39
[21] Zipkin P.H. (1988), Computing Optimal Lot Sizes in The Economic Lot Scheduling Problem; Working
40
Paper, graduate School of Business; Columbia University.
41
ORIGINAL_ARTICLE
Contour Crafting Process Plan Optimization Part I: Single-Nozzle Case
Contour Crafting is an emerging technology that uses robotics to construct free form building structures by repeatedly laying down layers of material such as concrete. The Contour Crafting technology scales up automated additive fabrication from building small industrial parts to constructing buildings. Tool path planning and optimization for Contour Crafting benefit the technology by increasing the efficiency of construction especially for complicated structures. The research reported here has aimed at providing a systematic solution for improving the overall system efficiency and realizing the Contour Crafting technology for building customdesigned houses. In Part-I of this paper, an approach is presented to find the optimal tool path for the single nozzle Contour Crafting system. The model developed incorporates the physical constraints of the technology as well as some practical construction issues. In Part-II several algorithms are presented to find the collision-free tool paths for the multiple-nozzle system based on the algorithm developed for the single nozzle approach.
http://www.jise.ir/article_4020_933aa9953ee6a7acf0f3b91a4c251c55.pdf
2010-04-01T11:23:20
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33
46
Contour crafting
Tool path planning
optimization
Jing
Zhang
true
1
Department of Industrial & Systems Engineering, University of Sothern California, USA
Department of Industrial & Systems Engineering, University of Sothern California, USA
Department of Industrial & Systems Engineering, University of Sothern California, USA
AUTHOR
Behrokh
Khoshnevis
true
2
Department of Industrial & Systems Engineering, University of Sothern California, USA
Department of Industrial & Systems Engineering, University of Sothern California, USA
Department of Industrial & Systems Engineering, University of Sothern California, USA
AUTHOR
[1] Han Y.-K, Jang C.-D (1999), An Approach to Efficient Nesting and Cutting Path Optimization of
1
Irregular Shapes; Journal of ShipProduction 15(3); 129-135.
2
[2] Helsgaun K. (2000), An effective implementation of the Lin-Kernighan traveling salesman heuristic;
3
European Journal of Operational Research 126(1); 106-130.
4
[3] Israni S., Manber U. (1984), Pierce Point Minimization and Optimal Torch Path Determination in
5
Flame Cutting; Journal of Manufacturing Systems 3(1); 81-89.
6
[4] Issa, Raja R.A. (1999), State of the Art Report: Virtual Reality in Construction; International Council
7
for Research and Innovation in Building and Construction (CIB).
8
[5] Khoshnevis B. (1999), Contour Crafting - State of Development; Solid Freeform Fabrication
9
Proceedings 1999; 743-750.
10
[6] Khoshnevis B. (2004), Automated construction by Contour Crafting - Related robotics and information
11
technologies; Automation in Construction 12; 5-19
12
[7] Lawler E. L., Jan Karel Lenstra, Rinnooy Khan A.H.G., Shmoys D.B. (1985), The Traveling Salesman
13
Problem; A Guided Tour of Combinatorial Optimization.
14
[8] Lin S., Kernighan B. (1973), An Effective Heuristic Algorithm for the Traveling Salesman Problem;
15
Operations Research 21; 498-516.
16
[9] Pease III, L.F. (1998), Rapid Prototyping Methods; ASM Handbook Volume 7:Powder Metal
17
Technologies and Applications.
18
[10] Tang K., Pang A. (2003), Optimal connection of loops in laminated object manufacturing; CAD
19
Computer Aided Design 35(11); 1011–1022.
20
[11] Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest (1990), Introduction to Algorithms; The
21
MIT Press; 465-467.
22
[12] Trager M. (1998), Optimizing Laser Cutting; Industrial Laser Review.
23
[13] Wah P.K., Murty K.G., Joneja A., Chiu L.C. (2002), Tool path optimization in layered manufacturing;
24
IIE Transactions 34(4); 335-347.
25
[14] Yeh Z. (2003), Trowel-Path Planning For Contour Crafting; Ph.D. Dissertation, University of Southern
26
California.
27
[15] Concorde TSP Solver, http://www.tsp.gatech.edu/concorde.html, January 2005.
28
ORIGINAL_ARTICLE
A Two-Phase Robust Estimation of Process Dispersion Using M-estimator
Parameter estimation is the first step in constructing any control chart. Most estimators of mean and dispersion are sensitive to the presence of outliers. The data may be contaminated by outliers either locally or globally. The exciting robust estimators deal only with global contamination. In this paper a robust estimator for dispersion is proposed to reduce the effect of local contamination when estimating the parameters. The results have shown that the introduced estimator is more precise in estimating the dispersion when there are outliers within the subgroups. Simulation results indicate that robustness and efficiency of the proposed dispersion estimator is considerably high and its sensitivity to the changes in mean and standard deviation of any subgroup is roughly lower than the other estimators being compared.
http://www.jise.ir/article_4021_1ea1b7cce16a3bf07ce9f7fd2aadc3d0.pdf
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47
58
Dispersion Estimator
Local Contamination
Global Contamination
Robust
Estimator
M-estimator
Bisquare Function
Hamid
Shahriari
true
1
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
AUTHOR
Orod
Ahmadi
true
2
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
AUTHOR
Amir H.
Shokouhi
true
3
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran
AUTHOR
[1] Huber P.J. (1981), Robust Statistics; John Wiley; New York.
1
[2] Langenberg P., Iglewicz B. (1986), Trimmed mean X and R charts; Journal of Quality Technology
2
18; 152-161.
3
[3] Maronna A.R. (2006), Robust statistics theory and methods; John Wiley; New York.
4
[4] Montgomery D.C. (2005), Introduction to statistical quality control; 5th Edition, Wiley; New York.
5
[5] Omar M. (2008), A simple robust control chart based on MAD; Journal of Mathematics and Statistics
6
4(2); 102-107.
7
[6] Rocke D.M. (1989), Robust control charts; Technometrics 31;173–184.
8
[7] Rocke D.M. (1992), X Q and Q R charts: robust control charts; The Statistician 41; 97-104.
9
[8] Shahriari H., Maddahi A., Shokouhi A.H. (2009), A robust dispersion control chart based on Mestimate;
10
Journal of Industrial and system engineering 2; 297-307.
11
[9] Tatum L.G. (1997), Robust estimation of the process standard deviation for control charts;
12
Technometrics 39; 127–141.
13
ORIGINAL_ARTICLE
The Effect of Gauge Measurement Capability and Dependency Measure of Process Variables on the MCp
It has been proved that process capability indices provide very efficient measures of the capability of processes from many different perspectives. These indices have been widely used in the manufacturing industry for measuring process reproduction capability according to manufacturing specifications. In the past few years, univariate capability indices have been introduced and used to characterize process performance, but are comparatively neglected for multivariate processes where multiple dependent characteristics are involved in quality measurement. Also, most of researches related to process capability indices have assumed no gauge measurement errors. Unfortunately, such an assumption does not reflect real situations accurately even with highly sophisticated advanced measuring instruments. Conclusions drawn from process capability analysis are hence unreliable. In this paper, we consider the effect of process variables correlation coefficient on the multivariate process capability index (MCp) for different gauge measurement capabilities. Also, with respect to correlation coefficient and measurement capability we investigate the statistical properties of the estimated MCp. The results indicate that gauge measurement capability has an important role in determining process capability. This factor would increase the effect of correlation coefficient on estimating the process capability, such that for different gauge measurement capabilities, correlation coefficients will change the results of estimating and testing the process capability.
http://www.jise.ir/article_4022_28507418f1472aa715e15c8d149f9714.pdf
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76
Capability analysis
Correlation coefficient
Critical value
Hypothesis testing
Multivariate process
Gauge measurement errors
Davood
Shishebori
true
1
Department of Industrial Engineering; Isfahan University of Technology; Iran
Department of Industrial Engineering; Isfahan University of Technology; Iran
Department of Industrial Engineering; Isfahan University of Technology; Iran
AUTHOR
Ali
Zeinal Hamadani
hamadani@cc.iut.ac.ir
true
2
Department of Industrial Engineering; Isfahan University of Technology; Iran
Department of Industrial Engineering; Isfahan University of Technology; Iran
Department of Industrial Engineering; Isfahan University of Technology; Iran
AUTHOR
[1] Boyles R.A. (1996), Exploratory Capability Analysis; Journal of Quality Technology 28; 91–98.
1
[2] Chan L.K., Cheng S.W.; Spiring F.A. (1991), A Multivariate Measure of Process Capability; Journal
2
of Modeling and Simulation 11; 1–6.
3
[3] Chang Y.C, Wei Wu, Chien. (2008), Assessing process capability based on the lower confidence
4
bound of Cpk for asymmetric tolerances; European Journal of Operational Research 190; 205-227.
5
[4] Chen H. (1994), A multivariate process capability index over a rectangular solid tolerance zone;
6
Statistica Sinica 4; 749–758.
7
[5] Karl D.P., Morisette J.; Taam W. (1994), Some Applications of a Multivariate Capability Index in
8
Geometric Dimensioning and Tolerancing; Quality Engineering 6; 649–665.
9
[6] Kotz S., Johnson N.L. (2002), Process capability indices – a review, 1992-2000; Journal of Quality
10
Technology 34(1); 1-19.
11
[7] Montgomery D.C. (1996), Introduction to Statistical Quality Control; 3rd ed, John Wiley & Sons;
12
NewYork, NY.
13
[8] Montgomery D.C., Runger G.C. (1993), Gauge Capability and Designed Experiments, Part I: Basic
14
Methods; Quality Engineering 6(1); 115-135.
15
[9] Pearn W.L., Kotz S., Johnson N.L. (1992), Distributional and inferential properties of process
16
capability indices; Journal of Quality Technology 24; 216–231.
17
[10] Pearn W.L., Liao M.Y. (2005), Measuring process capability based on Cpk with gauge measurement
18
errors; Microelectronics Reliability 45; 739–751.
19
[11] Pearn W.L., Kotz S. (2006), Encyclopedia and Handbook of Process Capability Indices. Series on
20
Quality, Reliability and Engineering Statistics, Vol. 12; World Scientific publishing Co, Pte. Ltd.
21
[12] Pearn W.L., Liao M.Y. (2007), Estimating and testing process precision with presence of gauge
22
measurement errors; Quality and Quantity; Forthcoming.
23
[13] Pearn W.L., Wang F.K., Chen (2007), Multivariate Capability Indices: Distributional and Inferential
24
Properties; Journal of Applied Statistics 34(8); 941–962.
25
[14] Shahriari H., Hubele N.F., Lawrence F.P. (1995), A Multivariate Process Capability Vector;
26
Proceedings of the 4th Industrial Engineering Research Conference, Institute of Industrial Engineers;
27
pp 304–309.
28
[15] Shishebori D., Hamadani A.Z. (2008), The Effect of Gauge Measurement Errors on Multivariate
29
Process Capability, Proceedings of the 3th World Conference on Production and Operations
30
Management (POM), Tokyo, 5-8 August 2008, Chapter 17; pp.2425-2432.
31
[16] Taam W., Subbaiah P., Liddy J.W. (1993), A Note on Multivariate Process Capability Indices; Journal
32
of Applied Statistics 20(3); 339-351.
33
[17] Vannman K., Hubele N.F. (2003), Distributional Properties of Estimated Capability Indices Based on
34
Subsamples; Quality and Reliability Engineering International 19; 111–128.
35
[18] Wang F.K., Du T.C.T. (2000), Using Principal Component Analysis in Process Performance for
36
Multivariate Data; OMEGA, the International Journal of Management Science 28; 185-194.
37
[19] Wang F.K.; Miskulin J.D, Shahriari H. (2000), Comparison of Three Multivariate Process Capability
38
Indices; Journal of Quality Technology 32(3l); 263-275.
39
[20] Wang F.K., Chen J. (1998), Capability index using principal component analysis; Quality Engineering
40
11; 21–27.
41