2017
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Userbased Vehicle Route Guidance in Urban Networks Based on Intelligent Multi Agents Systems and the ANTQ Algorithm
http://www.ijte.ir/article_43833.html
10.22119/ijte.2017.43833
1
Guiding vehicles to their destination under dynamic traffic conditions is an important topic in the field of Intelligent Transportation Systems (ITS). Nowadays, many complex systems can be controlled by using multi agent systems. Adaptation with the current condition is an important feature of the agents. In this research, formulation of dynamic guidance for vehicles has been investigated based on the characteristics of ITS and using the userbased approach to meet drivers’ satisfaction. As a result of timevarying flows on traffic networks, a multi agent model and the routing algorithm based on artificial intelligence techniques emphasized on a hybrid algorithm combining Ant Colony and Reinforcement Learning is proposed. The critical result of this paper is the ability of designing an algorithm for better trip planning, routing decisions in a dynamic urban transportation. Finally, the validity of the proposed algorithm is shown by implementation on a subnetwork extracted from Tehran traffic map.
0

147
161


Alireza
Eydi
Assistant Professor, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Iran
eydi81@yahoo.com


Susan
Panahi
MSc. Grad., Department of Industrial Engineering, Tarbiat Modares University, Tehran, Iran
Iran
susan.panahi@yahoo.com


Isa
iNakhai Kamalabadi
Professor, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
Iran
nakhai.isa@gmail.com
Urban transportation network
route guidance
ANTQ algorithm
Intelligent Agents
userbased system
[ Adler, J. L. and Blue, V. J. (1998) “Toward the design of intelligent traveler information systems”, Transportation Research Part C, Vol.6, No.3, pp.157172.## Adler J. L. and Blue, V. J. (2002) “A cooperative multiagent transportation management and route guidance system”, Transportation Research, Part C, Vol.10, No.56, pp.433454.## Cai, C. Q. and Yang, Z. S. (2007) "Study on urban traffic management based on multiagent system", Proceedings of the Sixth International Conference on Machine Learning and Cybernetics, Hong Kong, pp.2529.## Cao, Z., Guo, H., Zhang J., Fastenrath, U. (2016) “Multi agentbased route guidance for increasing the chance of arrival on time”, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp.38143820.## Chabrol, M., Sarramia, D. and Tchernev, N.(2006) "Urban traffic systems modeling methodology", Int. J. Production Economics, Vol.99, No.12, pp.156176.## Che, K., Li, L.L., Niu, X.T. and Xing, S.T. (2009) “Research of software development methodology based on selfadaptive multiagent systems”, IEEE International Symposium on IT in Medicine & Education (ITME 2009), Vol. 1, pp. 235–240.## Chen, B. and Cheng, H. H. (2010) “A Review of the applications of agent technology in traffic and transportation systems”, IEEE Transactions on Intelligent Transportation Systems, Vol.11, No.2, pp. 485497.## Claes, R., Holvoet, T. and Weyns, D. (2011) “A decentralized approach for anticipatory vehicle routing using delegate multi agent systems”, IEEE Transactions on Intelligent Transportation Systems, 10.1109/TITS.2011.2105867.## Colorni A., Dorigo M. and Maniezzo V. (1991) “Distributed optimization by ant colonies”, Proceedings of ECAL91  European Conference on Artificial Life, Paris, France, F. Varela and P. Bourgine (Eds.), Elsevier Publishing, pp.134–142.## Deflorio, F. P. (2003) “Evaluation of a reactive dynamic route guidance strategy”, Transportation Research Part C, Vol.11, No.5, pp.375388.## Dong, W. (2011) “An overview of invehicle route guidance”, Proceedings of the Australasian Transport Research Forum, Adelaide, Australia.## Fu, L. (2001) “Adaptive routing algorithm for invehicle route guidance systems with realtime information”, Transportation Research Part B: Methodological, Vol. 35, No. 8, pp. 749765.## Fu, L P., Sun, D. and Rillet, L. R. (2006) “Heuristic shortest path algorithms for transportation applications: State of the art”, Computers and Operations Research, Vol.33, No.11, pp.33243343.## Gambardella L. and M. Dorigo. (1995) “AntQ: A reinforcement learning approach to the traveling salesman problem”, Proceedings of ML95, Twelfth International Conference on Machine Learning, Tahoe City, CA, A. Prieditis and S. Russell (Eds.), Morgan Kaufmann, pp.252–260.## Gu, J., Lin, E., Liu, Y. and Zhang, N. (2010) “Study on improved ant colony algorithm in dynamic multipaths route guidance system”, Applied Mechanics and Materials, Vol. 2023, pp.243248.## Jiong, S. and Zhao, J. (2011) “Qlearning based multiintersection traffic signal control model”, International Conference on System Science, Engineering Design and Manufacturing Informatization, Guiyang, China, pp. 280283.## Karimi, A., Hegyi, A. De Schutter, B. Hellendoorn, J. and Middelham, F. (2004) "Integrated model predictive control of dynamic route guidance information systems and ramp metering," Intelligent Transportation Systems, pp. 491 496.## Lee, D. C (2006) “Proof of a modified Dijkstra's algorithm for computing shortest bundle delay in networks with deterministically timevarying links”, IEEE Communications Letters, Vol.10, No.10, pp.734736.## Levinson, D. (2003) “The value of advanced traveler information systems for route choice”, Transportation Research Part C, Vol.11, No.1, pp.7587.## Marti, I., Tomas, V.R., Saez, A. and Martinez, J. J. (2009) “A rulebased multiagent system for road traffic management”, International Joint Conference on Web Intelligence and Intelligent Agent Technology, Vol. 3.## Masoomi, Z., Sadeghi Nearki, A. and Mesgari, A. (2011) “Designing and using a multiobjective route planning algorithm in Intelligent Transportation Systems” (In Persian), Journal of Transportation Research, Vol.8, No.1, pp.4762.## Nadi, S. and Delavar, M. R. (2011) “Multicriteria, personalized route planning using quantifierguided ordered weighted averaging operators”, International Journal of Applied Earth Observation and Geoinformation, Vol.13, pp.322–335.## Nadi, S. and Delavar, M. R. (2010) “Locationbased services for invehicle route guidance with real time traffic information”, The 12th World Conference on Transport Research.## Ng, K. M., Ibne Reaz, M. B., Mohd Ali, M.A. and Chang, T. G. (2013) “A brief survey on advances of control and intelligent systems methods for trafficresponsive control of urban networks”, Technical Gazette, Vol. 20, No.3, pp. 555562.## Nakhai Kamalabadi I. and Eydi, A. (2009) “Vehicle routing in dynamic route guidance system”( In Persian), Journal of Transportation Research, Vol.6, No.3, pp.269286.## Pahlavani P. and Delavar, M. (2014) “Multicriteria route planning based on a driver’s preferences in multicriteria route selection”, Transportation Research Part C 40, pp.14–35.## Papageorgiou, M., Diakaki, C., Dinopoulou, V. , Kotsialos, A. and Wang, Y.(2003) “Review of road traffic control strategies”, Proceedings of the IEEE, Vol. 91, No. 12, pp. 20432067.## Pahlavani P., Samadzadegan, F. and Delavar, M. (2006) “A GISBased Approach for Urban Multicriteria Quasi Optimized Route Guidance by Considering Unspecified Site Satisfaction”, Geographic Information Science , Lecture Notes in Computer Science Volume 4197, pp 287303.## Park D., Laurence, R. and Choi, C. (2007a) “A class of multi criteria shortest path problems for realtime invehicle routing, Canadian journal of civil engineering, Vol.34, No.9, p.1096.## Park, K., Bell, M. Kaparias, I. and Bogenberger, K. (2007b) “Learning user preferences of route choice behaviour for adaptive route guidance”, Special Issue: Selected papers from the 13th World Congress on Intelligent Transport Systems and Services, IET Intell. Transp. Syst., Vol.1, No.2, pp. 159–166.## Sadek, A. and Chowdhury, M. A. (2003) “Fundamentals of intelligent transportation systems planning”, Boston, Artech House.##Sadeghi Niaraki, A. and Kim, K. (2009) “Ontology based personalized route planning system using a multicriteria decision making approach”, Expert Syst. Appl., Vol.36, pp.2250–2259.## Schmitt, E. J. and Jula, H. (2006) “Vehicle route guidance systems: classification and comparison”, Proceedings of the IEEE Intelligent Transportation Systems conference, Toronto, Canada. pp. 242 –247.## Shi, A., Na, C. and Chunbin, H. (2008) “Simulation and analysis of route guidance strategy based on a multiagent game approach”, 15th International conference on Management science & Engineering, Long Beach, USA, pp.140146.## Vandebon, U. and Upadhyay, P. K. (1997) “Simulation modeling of route guidance concept”, Transportation Research Record, 1573, pp.4451.## Wu, J. J., Sun, H. J. and Gao, Z. Y. (2008) “Dynamic urban traffic flow behavior on scalefree networks”, Physica A: Statistical Mechanics and its Applications 387, pp.653660. ## Wunderlich, K.E., Kaufman, D. E. and Smith, R. L. (2000) "Link travel time prediction for decentralized route guidance architectures," Intelligent Transportation Systems, Vol. 1, No. 1, pp. 414.## Yamashita, T., Izumi, K. and Kurumatani, K. (2004) "Car navigation with route information sharing for improvement of traffic efficiency," Intelligent Transportation Systems, pp. 465 470.## Yoshikawa, M. and Otani, K. (2010) “Ant colony optimization routing algorithm with tabu search”, Proceeding of International Multi conference of Engineers and Computer Scientists Hong Kong: IMECS, pp. 21042107.## Zolfpour, M., Selamat, A., MohdHashim, S. and Selamat, M. (2011) “Route guidance system based on selfadaptive multiagent algorithm”, Computational Collective Intelligence. Technologies and Applications, Lecture Notes in Computer Science Volume 6923, pp. 9099.##]
1

Part A Experimental: Experimental Analysis of Crack Propagation in Prestressed Concrete Sleepers by Fracture Mechanics
http://www.ijte.ir/article_43834.html
10.22119/ijte.2017.43834
1
This study investigates propagation of mode I crack in B70 prestressed concrete sleepers by fracture mechanics approach. A new experimental analysis is done for notched B70 prestressed concrete sleepers with Replica test and image analysis. A scanning electron microscope test (SEM) and an image analysis are applied for the Replica test in order to determine crack length and crack mouth opening displacement (CMOD). The experimental data extracted from the threepoint bending load test of B70 sleepers are analyzed with fracture mechanics method. In this study, the fracture mechanics parameters of a sleeper are investigated based on nonlinear fracture mechanics (NLFM) principles for concrete material. Sleepers with initial crack width of 8 mm and different initial crack lengths of 5 mm to 45 mm, with a 10 mm increasing step, are tested. Five specimens’ of each group are loaded under threepoint bending load test, in order to determine the propagated crack length, crack mouth opening displacement (CMOD), final load and the specimens’ energy. The results showed that by increasing the cracktodepth ratio, both final load and specimens’ energy values are decreased linearly. Also, these analyses confirm that the structural behavior of the prestressed concrete sleepers can be predicted by a simple fracture mechanics test, such as beams in bending, provided that the related structural conditions like initial crack length and CMOD, are known.
0

163
177


Seyed Mohammad
Farnam
PhD Candidate, Department of Civil Engineering, BuAli Sina University, Hamedan, Iran
Iran
seyed.farnam@yahoo.com


Freydoon
Rezaie
Associate Professor, Department of Civil Engineering, BuAli Sina University, Hamedan, Iran
Iran
frrezaie94@gmail.com
Fracture mechanics
Crack propagation
prestressed concrete sleeper b70
Crack length
CMOD
[ Aikawa, J. (2013) “Determination of dynamic ballast characteristics under transient impactloading”, Electronic J of Structural Eng. Vol 13, No. 1, pp.. 17–34. ## AREMA, Evolution tests for tie systems, Chapter 30, Part 4, concrete ties, “American Railway Engineering and Maintenance of Way Association”, 2010.##AS1085.14 Railway Track Material Part 14 (2012) “Prestressed concrete sleepers”, Standard Australia## Chen, Z., Shin, M., Andrawes, B. and Edwards, R. J. (2014) “Parametric study on damage and load demand of prestressed concrete crosstie and fastening systems”, Eng. Fail. Anal. Volume 46, pp. 4961.##Chen, Z., Shin, M., Andrawes, B. and Edwards, R. J. (2014) “Finite element modeling and validation of the fastening systems and concrete sleepers used in North America”, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, Volume 228, No 6, pp. 59060.## GonzálezNicieza, C., ÁlvarezFernández, M. I., MenéndezDíaz, A., ÁlvarezVigil, A. E. and AriznavarretaFernández, F. (2008) “Failure analysis of concrete sleepers in heavy haul railway tracks”. Eng. Fail. Anal. Volume 15, Issues 1–2, pp. 90–117.## Dong, W., Wu, Z., Zhou, X.. and Wang, C. (2016).. “A comparative study on two stress intensity factorbased criteria for prediction of modeI crack propagation in concrete”, Eng. Fract. Mec, Volume 158, pp. 3958.## Grassl, P. and Davies, T. (2011) “Lattice modelling of corrosion induced cracking and bond in reinforced concrete”, Cement and Con. Composites, Volume 33, No 9, pp. 918–924.## IR code 301. Pavement general guidelines of railway (2005) “Management and Planning Organization of Islamic Republic of Iran”, I*ranian Code of Practoce.## IR code 355. Supervision of Railway Track Construction (2005) “Management and Planning Organization of Islamic Republic of Iran”, Iranian Code of Practice## Jokūbaitisa, A., Valivonisa, J. and Marčiukaitisa, G. (2016) “Analysis of strain state and cracking of cocnrete sleepers”, J of Civil Eng and Management, Volume 22, No 4, pp. 564572## Kaewunruen, S. and Remennikov, A. M. (2009) “Progressive failure of prestressed concrete sleepers under multiple highintensity impact loads”, Eng. Struct. Volume 31, Issue 10, pp. 2460–2473.## Kaewunruen, S. and Remennikov, A. M. (2011) “Experiments into impact behaviour of railway prestressed concrete sleepers”, Eng. Fail. Anal. Volume 18, Issue 8, pp. 2305–2315.## Kaplan, M. E. (1961) “Crack propagation and the fracture concrete”, ACI. J. Volume 58, pp. 596610.## Kim, H., Wagoner, P. M. and Buttlar, W. G. (2009) “Numerical fracture analysis on the specimen size dependency of asphalt concrete using a cohesive softening model”, Construc and Building Mater, Volume 23, No 5, pp. 21122120.## Mechtcherine, V. (2009) “Fracture mechanical behavior of concrete and the condition of its fracture surface”, Cement and Concrete Res, Volume 39, No 7, pp. 620628.## Ohno, K. and Ohtsu, M. (2010) “Crack classification in concrete based on acoustic emission”, Construc and Building Mater, Volume 24, No 12, pp. 2339–2346.## Rezaei, F. and Farnam, S. M. (2015). “Fracture mechanics analysis of prestressed concrete sleepers via investigating crack initiation length”, Eng. Fail. Anal, Volume 58, pp. 267–280.## Rezaei, F., Bayat, A. M.R. and Farnam, S. M. (2016) “Sensitivity analysis of prestressed concrete sleepers for longitudinal crack prorogation effective factors”. Eng. Fail. Anal. Volume 66, pp. 385–397.## Rezaei, F., Shiri, M. R. and Farnam, S. M. (2012) “Experimental and numerical studies of longitudinal crack control for prestressed concrete sleepers”. Eng. Fail. Anal. Volume 26, pp. 21–30.## Shah, S. P. and MacGarry, F.J. (1971) “Griffith fracture criterion and concrete”, J. Eng. Mech. Division, Volume 97, pp. 16631676##  Shahani, A. R. and Moayeri Kashani, A. (2014) “Fracture mechanicsbased life prediction of a riveted lap joint”, J of Computational and App.lied Res, Volume 4, No 1, pp. 1–17.## Taherinezhad, J., Sofi, M., Mendis, P. A. and Ngo, T. (2013) “A review of behaviour of prestressed concrete sleepers”, Electronic J. of Structural Eng. Volume 13. No 1, pp. 1734.## Wang, Y., Hu, X., Liang, L. and Zhu, W. (2016) “Determination of tensile strength and fracture toughness of concrete using notched 3pb specimens”, Eng. Fract. Mec, Volume 160, pp. 6777.## Zhang, X. X., Ruiz, G., Yu, R. C. and Tarifa, M. (2009). “Fracture behaviour of highstrength concrete at a wide range of loading rates”, Int J of Impact Eng, Volume 36, No 1011, pp. 1204–1209.## Zhang, J. X., Shi, Y. W. and Tu, M. J. (1995) “Factors affecting the estimation of fracture mechanics parameters of centercracked weldment”, Eng. Fract. Mec, Volume 50, Issue 4, pp. 537543.##]
1

Application of A Route Expansion Algorithm for Transit Routes Design in Grid Networks
http://www.ijte.ir/article_43835.html
10.22119/ijte.2017.43835
1
Establishing a network of transit routes with satisfactory demand coverage is one of the main goals of transitagencies in moving towards a sustainable urban development. A primary concern in obtaining such anetwork is reducing operational costs. This paper deals with the problem of minimizing construction costsin a grid transportation network while satisfying a certain level of demand coverage. An algorithm isproposed following the general idea of “constructive algorithms” in related literature. The proposedalgorithm, in an iterative approach, selects an origindestination with maximum demand, generates a basicshortestpath route, and attempts to improve it through a route expansion process. The paper reports thescenarios and further details of the algorithm considered for expanding a transit route in a grid network. Arandom 6×10 grid network is applied to report the results. The results support that application of theproposed algorithm notably reduces the operational costs for various amounts of demand coverage.
0

179
196


Iran Khanzad
Khanzad
MSc Grad., Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran,
Iran
Iran


Amirali Zarrinmehr
Zarrinmehr
Ph.D Candidate, Department of Civil and Environmental Engineering, Tarbiat Modares University,
Tehran, Iran
Iran


Seyedehsan Seyedabrishami
Seyedabrishami
Assistant Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University,
Tehran, Iran
Iran


Mahmoud Saffarzadeh
Saffarzadeh
Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran,
Iran
Iran
saffar_m@modares.ac.ir
Transit routes
grid transportation network
demand coverage
operational costs
[ Baaj, M. H. and Mahmassani, H. S. (1991) “An AI‐based approach for transit route system planning and design”, Journal of Advanced Transportation, Vol. 25, No. 2, pp. 187209.## Baaj, M.H. and Mahmassani, H.S. (1995) “Hybrid route generation heuristic algorithm for the design of transit networks”, Transportation Research C: Emerging Technologies, Vol. 3, No. 1, pp. 31–50.## Badia, H., Estrada, M. and Robusté, F. (2014) “Competitive transit network design in cities with radial street patterns”, Transportation Research Part B: Methodological, Vol. 59, pp. 161181.## Cancela, H., Mauttone, A. and Urquhart, M.E. (2015) “Mathematical programming formulations for transit network design”, Transportation Research Part B: Methodological, Vol. 77, pp. 1737.## Ceder, A. and Wilson, N. H. (1986) “Bus network design”, Transportation Research Part B: Methodological, Vol. 20, No. 4, pp. 331344.## Farahani, R.Z., Miandoabchi, E., Szeto, W.Y. and Rashidi, H. (2013) “A review of urban transportation network design problems”, European Journal of Operational Research, Vol. 229, No. 2, pp. 281302.## Guihaire, V. and Hao, J. K. (2008) “Transit network design and scheduling: A global review”, Transportation Research Part A: Policy and Practice, Vol. 42, No. 10, pp. 12511273.## IbarraRojas, O.J., Delgado, F., Giesen, R. and Muñoz, J.C. (2015). “Planning, operation, and control of bus transport systems: A literature review”, Transportation Research Part B: Methodological, Vol. 77, pp. 3875.## Kepaptsoglou, K. and Karlaftis, M. (2009) “Transit route network design problem: review”, Journal of Transportation Engineering, Vol. 135, No. 8, pp. 491–505.## Kermanshahi, S., Shafahi, Y. and Bagherian, M. (2015). “Application of a new rapid transit network design model to bus rapid transit network design: case study Isfahan metropolitan area”, Transport, Vol. 30, No. 1, pp. 93102.## Mandl, C. E. (1980) “Evaluation and optimization of urban public transportation networks”, European Journal of Operational Research, Vol. 5, No. 6, pp. 396404.## Mauttone, A. and Urquhart, M. E. (2009) “A route set construction algorithm for the transit network design problem”, Computers and Operations Research, Vol. 36, No. 8, pp. 24402449.## Nie, Y. M. (2016) “NUTREND”, http://translab.civil.northwestern.edu/nutrend/?page_id=53. Accessed 1 February 2016.## Schöbel, A. (2012) “Line planning in public transportation: models and methods”, OR Spectrum, Vol. 34, No. 3, pp. 491510.## Snellen, D., Borgers, A. and Timmermans, H. (2002) “Urban form, road network type, and mode choice for frequently conducted activities: a multilevel analysis using quasiexperimental design data”, Environment and Planning A, Vol. 34, No. 7, pp. 12071220.## Stern, R. (1996) “Passenger transfer system review”, Transportation Research Board, Washington, D.C.## Zhao, F. and Zeng, X. (2006) “Simulated annealing–genetic algorithm for transit network optimization”, Journal of Computing in Civil Engineering, Vol. 20, No. 1, pp. 5768.##]
1

A Comprehensive Approach for Railway Crew Scheduling Problem (Case Study: Iranian Railway Network)
http://www.ijte.ir/article_43836.html
10.22119/ijte.2017.43836
1
The aim of this study is to propose a comprehensive approach for handling the crew scheduling problem in the railway systems. In this approach, the information of different railway trips are considered as input and the problem is divided to three separated phases. In phase I, we generate all feasible sequences of the trips, which are named as the pairings. A depthfirst search algorithm is developed to implement this phase. In phase II, the pairings constituting the optimal solution are to be obtained. Both mentioned phases are handled in a centralized decisionmaking system for the entire railway network. Phase III aims to locally assign the crew groups to the optimal pairings. To solve the problem in phase III, a new mathematical model is developed in this paper. The model can determine the minimum required crew groups, and optimally assign the crew groups to the selected pairings of each home depot. In order to evaluate the developed algorithm and model, the Iranian railway network is evaluated by consideration of all passenger trips of the network. The results show that the proposed approach is capable of efficiently generating the optimal schedules for the railway crew groups in a reasonable computation time.
0

197
210


Amin Khosravi
Khosravi
MSc. Student, Department of Transportation Engineering, Isfahan University of Technology,
Isfahan, Iran
Iran


Mohammad Tamannaei
Tamannaei
Assistant Professor, Department of Transportation Engineering, Isfahan University of Technology,
Isfahan, Iran
Iran
m.tamannaei@cc.iut.ac.ir


Mohammad
ReisiNafchi
Assistant Professor, Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran
Iran
reisi.m@cc.iut.ac.ir
railway
crew scheduling
trip
pairing
optimal solution
[Caprara, A., Fischetti, M., Guida, P.L., Toth, P. and Vigo D. (1999) “Solution of largescale railway crew planning problems: The Italian experience”, Computeraided transit scheduling, Vol. 471, pp. 118.##Caprara, A., Toth, P., Vigo, D. and Fischetti, M. (1998) “Modeling and solving the crew rostering problem”, Operations research, Vol. 46, No. 6, pp. 820830.##Ernst, A.T., Jiang, H., Krishnamoorthy, M., Nott, H. and Sier, D. (2001) “An integrated optimization model for train crew management”, Annals of Operations Research, Vol. 108, pp. 211224.##Ernst, A.T., Jiang, H., Krishnamoorthy M. and Sier, D. (2004) “Staff scheduling and rostering: A review of applications, methods and models”, European Journal of Operational Research, Vol. 153, No. 1, pp. 327.##Farhadfar, S. and Dolati A. (2015) “Modelling and solving the crew dispatching problem in Iranian railways”, In 4th International Conference on Recent Advances in Railway Engineering, Tehran, Iran (In Persian).##Freling, R., Lentink, R.M. and Odijk, M. A. (2001) “Scheduling train crews: A case study for the Dutch railways”, ComputerAided Scheduling of Public Transport, Vol. 505 of the series Lecture Notes in Economics and Mathematical Systems, pp. 153165.##Guillermo, C.G. and José, M.R.L. (2009) “Hybrid algorithm of tabu search and integer programming for the railway crew scheduling problem”, PACIIA 2nd AsiaPacific Conference on Computational Intelligence and Industrial Applications, Vol. 2, pp. 413416.##Hanafi, R., and Kozan, E. (2014) “A hybrid constructive heuristic and simulated annealing for railway crew scheduling”, Computers and Industrial Engineering, Vol.70, pp. 1119.##Jütte, S. and Thonemann, U. W. (2012) “Divideandprice: A decomposition algorithm for solving large railway crew scheduling problems”, European Journal of Operational Research, Vol. 219, No. 2, pp. 214223.##Kroon, L.G., Huisman, D., Abbink E., Fioole P., Fischetti M., Maroti G., Schrijver A., Steenbeek A. and Ybema, R. (2008) “The new Dutch timetable: The OR revolution”, Report / Econometric Institute, Erasmus University Rotterdam, Vol. 1, pp. 118.##Nishi, T., Muroi, Y. and Inuiguchi, M. (2011) “Column generation with dual inequalities for railway crew scheduling problems”, Public Transport, Vol. 3, No. 1, pp. 2542.##Pourseyedaghaiee, M. and Salehi, P. (2006) “Railway crew (conductors) programming using roundtrip and roster algorithms”, Journal of Transportation Research, Vol. 3, No. 2, pp. 163173.##Sepehri M. M. and Hajfathaliha, A. (2001) “A mathematical model for railway crew scheduling based on the network”, 1st Conference of Industrial Engineering, Tehran, Iran (In Persian).##Sepehri, M. M., Najmi, M. and Khoshalhan, F. (2004) “Solving railway crew scheduling problem using ant colony method: FBMMAS algorithm”, Conference of Industrial Engineering, Tehran, Iran (In Persian).##Shijun C. and Yindong S. (2013) “An improved column generation algorithm for crew scheduling problems”, Journal of Information and Computational Science, Vol. 10, No. 1, pp. 175183.##Shijun, C., Yindong, S., Xuan S. and Heming, C. (2013) “A crew scheduling with Chinese meal break rules”, Journal of Transportation Systems Engineering and Information Technology, Vol. 13, N0. 2, pp. 9095.##Yaghini, M. and Ghannadpour, S. F. (2009) “Railway crew scheduling using heuristic model”. Journal of Transportation Research, Vol. 6, pp.381–395 (In Persian).##Yaghini, M. and Fathipour, F. (2009) “Modelling and solving multiobjective crew scheduling problem by columngeneration approach”, 2nd International Conference on Recent Advances in Railway Engineering, Tehran, Iran (In Persian).##Yaghini, M., Mazinan, G. and Rostamabadi, A (2009) “Determination of optimal pairings by using minimumcost flow algorithm for crew planning problem”, 2nd International Conference On Recent Advances In Railway Engineering, Tehran, Iran (In Persian).##]
1

The Effects of Concrete Pavement Mix Design Parameters on Durability under Freeze and Thaw Condition
http://www.ijte.ir/article_43837.html
10.22119/ijte.2017.43837
1
This paper is based on an experimental research that examined the effects of concrete`s major parameters on durability of concrete pavements and curbs under freezing and thawing cycles. These parameters include concrete mix design parameters such as watercement ratio, fine aggregate percentage and using air entraining admixture and simulating real freezethaw cycles that infrastructures undergo by considering deicing salt and water flow. Four types of concrete samples were prepared and submerged in four different freezethaw conditions. Their weight and compressive strength were measured and the results were analyzed. Based on the results, regression analysis was used and two linear models were developed to predict the weight loss and compressive strength loss of concrete under freezethaw cycles. The results indicated that fine aggregate percentage is a key factor in durability of concrete, and concrete samples with 6% air underwent less deterioration in comparison to concrete with lower watercement ratio. In addition, water flow increases the deterioration of concrete under freezethaw cycles specially when deicing salt is present.
0

211
224


Saleh
Sharif Tehrani
Assistant Professor, Department of Civil Engineering, Kharazmi University, Tehran, Iran
Iran
saleh.tehrani@gmail.com


Seyed Hossein
hosseini Lavasani
Assistant Professor, Department of Civil Engineering, Kharazmi University, Tehran, Iran
Iran
hh_lavasani@yahoo.com
Concrete pavements
Freezethaw cycles
Watercement ratio
Fine aggregate percentage. Air entraining admixture
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1

Optimal Blood Transportation in Disaster Relief Considering Facility Disruption and Route Reliability under Uncertainty
http://www.ijte.ir/article_43838.html
10.22119/ijte.2017.43838
1
The blood supply chain as a part of healthcare systems play a substantial role in health improvement within societies, and blood supply for daily needs and especially in disasters is the challenges requiring more attention. This paper presents a fuzzystochastic mixed integer linear programming model to design blood supply chain network for disaster relief. To deal with the uncertainty in model parameters, a fuzzy programming approach is considered, and the combination of the expected value and the chance constrained programming is applied to solve the proposed model. Besides, a real case study in Iran is implemented to illustrate the applicability of the present model. The results implies that an appropriate adjustment in the capacity and coverage radius of blood facilities, the decrease in the disruption probability of facilities and transportation routes as well as referral rate can be applied as strategies to improve the supply chain costs. supply chain costs.
0

225
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Sara
Cheraghi
MSc. Student, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
Iran
sara_cheraghi@ind.iust.ac.ir


Seyyed Mahdi
HosseiniMotlagh
Assistant Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran,
Iran
Iran
motlagh@iust.ac.ir
blood supply chain
disaster
Fuzzycredibility
Chanceconstrained programming
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