IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2697 10.22034/2016.3.01 operations planning,scheduling & control Defining Robust Recovery Solutions for Preserving Service Quality during Rail/Metro Systems Failure Defining Robust Recovery Solutions for Preserving Service Quality during Rail/Metro Systems Failure D'Acierno Luca Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Naples, Italy Placido Antonio D’Appolonia S.p.A., Naples, Italy Botte Mariisa Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Naples, Italy Gallo Mariano Department of Engineering, University of Sannio, Benevento, Italy Montella Bruno Department of Civil, Architectural and Environmental Engineering, Federico II University of Naples, Naples, Italy 01 11 2016 3 3 1351 1372 14 07 2016 07 01 2017 Copyright © 2016, Kharazmi University. 2016 http://www.ijsom.com/article_2697.html

In this paper, we propose a sensitivity analysis for evaluating the effectiveness of recovery solutions in the case of disturbed rail operations. Indeed, when failures or breakdowns occur during daily service, new strategies have to be implemented so as to react appropriately and re-establish ordinary conditions as rapidly as possible. In this context, the use of rail simulation is vital: for each intervention strategy it provides the evaluation of interactions and performance analysis prior to actually implementing the corrective action. However, in most cases, simulation tasks are deterministic and fail to allow for the stochastic distribution of train performance and delays. Hence, the strategies adopted might not be robust enough to ensure effectiveness of the intervention. We therefore propose an off-line procedure for disruption management based on a microscopic and stochastic rail simulation which considers both service operation and travel demand. An application in the case of a real metro line in Naples (Italy) shows the benefits of the proposed approach in terms of service quality.

Sensitivity Analysis Public Transport Management Rail System Travel Demand Estimation Quality of Service
Barrena E., Canca D., Coelho L.C. and Laporte G. (2016). Single-line rail rapid transit timetabling under dynamic passenger demand. Transportation Research Part B, Vol. 70, pp. 134-150. Botte M., Di Salvo C., Placido A., Montella B. and D’Acierno L. (2017). A Neighbourhood Search Algorithm for determining optimal intervention strategies in the case of metro system failures. International Journal of Transport Development and Integration, Vol 1, pp. 63-73. Cacchiani V., Huisman D., Kidd M., Kroon L., Toth P., Veelenturf L. and Wagenaar J. (2014). An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B, Vol. 63, pp. 15-37. Cacchiani V., Caprara A. and Fischetti M. (2009). Robustness in Train Timetabling. European Journal of Operational Research, Vol. 219, pp. 727-737. Cadarso L. and Marín A. (2011). Robust rolling stock in rapid transit networks. Computers & Operations Research, Vol. 38, pp. 1131-1142. Cadarso L. and Marín A. (2014). Improving robustness of rolling stock circulations in rapid transit networks. Computers & Operations Research, Vol. 51, pp. 146-159. Cantelmo G., Viti F., Tampére C.M.J., Cipriani E. and Nigro M. (2014). A two-step approach for the correction of the seed matrix in the dynamic demand estimation. Proceedings of the 93rd Annual Meeting of the Transportation Research Board, Washington (D.C.), USA, January 2014. Cascetta E. (2009). Transportation systems analysis: Models and applications. New York (NY), USA: Springer. CENELEC (1999). Railway applications - Specification and demonstration of Reliability, Availability, Maintainability and Safety (RAMS). EN50126.  Corman F., D’Ariano A., Pacciarelli D. and Pranzo, M. (2010a). A Tabu Search algorithm for rerouting trains during rail operations. Transportation Research Part B, Vol. 44, pp. 175-192. Corman F., D’Ariano A. and Hansen I.A. (2010b). Disruption handling in large railway networks. WIT Transactions on the Built Environment, Vol. 114, pp. 629-640. Corman F., D’Ariano A., Hansen I.A. and Pacciarelli D. (2011). Optimal multi-class rescheduling of railway traffic. Journal of Rail Transport Planning & Management, Vol. 1, pp. 14-24. Corman F. and D’Ariano A. (2012). Assessment of advanced dispatching measures for recovering disrupted railway traffic situations. Transportation Research Record, Vol. 2289, pp. 1-9. Corman F., D’Ariano A., Pacciarelli D. and Pranzo M. (2012). Bi-objective conflict detection and resolution in railway traffic management. Transportation Research Part C, Vol. 20, pp. 79-94. D’Acierno L., Gallo M., Montella B. and Placido A. (2013). The definition of a model framework for managing rail systems in the case of breakdowns. Proceedings of IEEE ITSC 2013 – 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, The Netherlands, October 2013, pp. 1059-1064. D’Acierno L., Gallo M. and Montella B. (2014). Application of metaheuristics to large-scale transportation problems. Lecture Notes in Computer Science, Vol. 8353, pp. 215-222. D’Acierno L., Placido A., Botte M. and Montella B. (2016). A methodological approach for managing rail disruptions with different perspectives. International Journal of Mathematical Models and Methods in Applied Sciences, Vol. 10, pp. 80-86. D’Ariano A. (2009). Innovative decision support system for railway traffic control. IEEE Intelligent Transportation Systems Magazine, Vol. 1, pp. 8-16. De Fabris S., Longo G., Medeossi G. and Presenti R. (2014). Automatic generation of railway timetables based on a mesoscopic infrastructure model. Rail Transport Planning & Management, Vol. 4, pp. 2-13. De Shutter B., Van den Boom T. and Hegyi A. (2002). Model predictive control approach for recovery from delays in railway systems. Transportation Research Record, Vol. 1793, pp. 15-20. Dollevoet T., Huisman D., Schmidt M. and Schöbel, A.(2012). Delay management with rerouting of passengers. Transportation Science, Vol. 46, pp. 74-89. Eickmann C., Kettner M. and Sewcyk B. (2003). Integrating microscopic and macroscopic models for railway network evaluation. Proceedings of ETC 2003 –European Transport Conference 2003, Strasbourg, France, October 2003. haemi N., Goverde R.M.P. and Cats O. (2016). Railway disruption timetable: Short-turnings in case of complete blockage. Proceedings of 2016 IEEE International Conference on Intelligent Rail Transportation (ICIRT), Birmingham, UK, August 2016. Goverde R.M.P. (2005). Punctuality of railway operations and timetable stability analysis. PhD thesis, TRAIL Thesis Series T2005/10, The Netherlands. Hansen I.A. and Pachl J. (2008). Railway timetable and traffic. Hamburg, Germany: Eurailpress. Kanai S., Shiina K., Harada S. and Tomii N. (2011). An optimal delay management algorithm from passengers’ viewpoints considering the whole railway network. Journal of Rail Transport Planning & Management, Vol. 1, pp. 25-37. Kecman P., Corman F., D’Ariano A. and Goverde R.M.P. (2013). Rescheduling models for railway traffic management in large-scale networks. Public Transport, Vol. 5, pp. 95-123. Kettner M. and Sewcyk B. (2002). A model for transportation planning and railway network evaluation”. Proceedings of the 9th World Congress on Intelligent Transport Systems, Chicago (IL), USA, 2002. Kunimatsu T., Hirai C. and Tomii N. (2012). Train timetable evaluation from the viewpoint of passengers by microsimulation of train operation and passenger flow. Electrical Engineering in Japan, Vol. 181, pp. 51-62. Marinov M. and Viegas J. (2011). A mesoscopic simulation modelling methodology for analyzing and evaluating freight train operations in a rail network. Simulation Modelling Practice and Theory, Vol. 19, pp. 516-539. Marzano V., Papola A. and Simonelli F. (2008). Effectiveness of origin-destination matrix correction procedure using traffic counts. Transportation Research Record, Vol. 2085, pp. 57-66. Mascis A. and Pacciarelli D. (2002). Job-shop scheduling with blocking and no-wait constraints. European Journal of Operational Research, Vol. 143, pp. 498-517. Meng X., Jia L. and Qin Y. (2010). Train timetable optimizing and rescheduling based on improved particle swarm algorithm. Transportation Research Record, Vol. 2197, pp. 71-79. Nash A. and Huerlimann D. (2004). Railroad simulation using Open-Track. WIT Transactions on The Built Environment, Vol. 74, pp.45-54. Nash A., Weidmann U., Bollinger S., Luethi M. and Buchmueller S. (2006). Increasing schedule reliability on the S-Bahn in Zurich, Switzerland: Computer analysis and simulation. Transportation Research Record, Vol. 1955, pp. 17-25. Pender B., Currie G., Delbosc A. and Shiwakoti N. (2013). If you fail to plan you plan to fail: a survey of passenger rail disruption recovery practices. Proceedings of the 92nd Annual Meeting of the Transportation Research Board, Washington (D.C.), USA, January 2013. Placido A., Cadarso L. and D’Acierno L. (2014). Benefits of a combined micro-macro approach for managing rail systems in case of disruptions. Transportation Research Procedia, Vol. 3, pp. 195-204. Placido A., D’Acierno L. and Botte M. (2015) Effects of stochasticity on recovery solutions in the case of high-density rail/metro networks. Proceedings of 6th International Conference on Railway Operations Modelling and Analysis – RailTokyo2015, Tokyo, Japan, March 2015. Placido A. (2015). The definition of a model framework for the planning and the management phases of the rail system in any kind of service condition, PhD Thesis. Pouryousef H. and Lautala P. (2015) Hybrid simulation approach for improving railway capacity and train schedules. Journal of Rail Transport Planning & Management. Vol. 5, pp. 211-224. Prinz R., Sewcyk B. and Kettner M. (2001) NEMO: Network Evaluation Model for the Austrian railroad (ÖBB)”, Eisenbahntechnische Rundschau. Vol 50, pp. 117-121. Quaglietta E., Corman F. and Goverde R.M.P. (2013). Impact of a stochastic and dynamic setting on the stability of railway dispatching solutions. Proceedings of IEEE ITSC 2013 – 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, The Netherlands, October 2013, pp. 1035-1040. Sato K., Tamura K. and Tomii N. (2013). A MIP-based timetable rescheduling formulation and algorithm minimizing further inconvenience to passengers. Journal of Rail Transport Planning & Management. Vol. 3, pp. 38-53. D'Acierno et al. Schmocker J., Cooper S. and Adeney W.E. (2005). Metro service delay recovery comparison of strategies and constraints across systems. Transportation Research Record, Vol. 1930, pp. 30-37. Schöbel A. (2007). Integer programming approaches for solving the delay management problem. Lecture Notes in Computer Science, Vol. 4359, pp. 145-170. Siefer T. and Radtke A. (2005). Railway simulation: key for better operation and optimal use of infrastructure”. Proceedings of the 1st International Seminar on Railway Operations Modelling and Analysis, Delft, The Netherlands, 2005. Umiliacchi S., Nicholson G., Zhao N., Schmid F. and Roberts C. (2016). Delay management and energy consumption minimisation on a single-track railway. IET Intelligent Transport Systems, Vol. 10, pp. 50–57. Veelenturf L.P., Kidd M.P., Cacchiani V., Kroon L.G. and Toth P. (2016). A railway timetable rescheduling approach for handling large scale disruptions. Transportation Science, Vol. 50, pp. 841-862. Wardman M. (2004). Public transport values of time. Transport Policy, Vol. 11, pp. 363–377.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2704 10.22034/2016.3.02 modelling & simulation Modeling and Solving the Multi-depot Vehicle Routing Problem with Time Window by Considering the Flexible end Depot in Each Route Modeling and Solving the Multi-depot Vehicle Routing Problem with Time Window by Considering the Flexible end Depot in Each Route Mirabi Mohammad Department of industrial engineering, Ayatollah Haeri University of Meybod, Meybod, Yazd, Iran Shokri Nasibeh Group of industrial engineering, ElM-O-Honar University, Yazd Iran Sadeghieh Ahmad Group of industrial engineering, Yazd University, Yazd, Iran 01 11 2016 3 3 1373 1390 17 04 2016 26 02 2017 Copyright © 2016, Kharazmi University. 2016 http://www.ijsom.com/article_2704.html

This paper considers the multi-depot vehicle routing problem with time window in which each vehicle starts from a depot and there is no need to return to its primary depot after serving customers. The mathematical model which is developed by new approach aims to minimizing the transportation cost including the travelled distance, the latest and the earliest arrival time penalties. Furthermore, in order to reduce the problem searching space, a novel GA clustering method is developed. Finally, Experiments are run on number problems of varying depots and time window, and customer sizes. The method is compared to two other clustering techniques, fuzzy C means (FCM) and K-means algorithm. Experimental results show the robustness and effectiveness of the proposed algorithm.

Vehicle routing problem Multi-Depot Flexible end depot Genetic Algorithm Clustering
Alvarenga. G.B., Mateus. G.R, de Tomi. G. (2007). “A Genetic and Set Partitioning Two-phase Approach for the Vehicle Routing Problem with Time Windows”.Computers & Operations Research, Vol. 34, pp. 1561– 1584. Banos.R, Ortega, Consolacion. G, Fernandez.A, de Toro.F. (2013). “A Simulated Annealing-based Parallel Multi- Objective Approach to Vehicle Routing Problems with Time Windows”, Expert Systems with Applications, Vol. 40,pp. 1696–1707. Cordeau J-F, Gendreau M, Laporte G. (1997). “A Tabu Search Heuristic for Periodic and Modeling and Solving the Multi-depot Vehicle Routing Problem with Multi-Depot Vehicl Routing Problems”. Networks Vol.30, pp. pp 105–19. Cordeau J-F,Maischberger. M, (2010) “A parallel iterated tabu search heuristic for vehicle routing problems”. Computers & Operational Research Vol.39, pp. 2033-2050. Crevier B, Cordeau J, Laporte G. (2007). “The Multi-Depot Vehicle Routing Problem With Inter-depot Routes”. European Journal of Operational Research, Vol.176, pp. 756–73. Dondo R, Cerda J. (2007). “A Cluster-based Optimization Approach for the Multi-Depot Heterogeneous Fleet Vehicle Routing Problem with Time Windows”, European Journal of Operational Research, Vol.176, pp, 1478– 1507. Dondo R, Cerda J. (2009). “A Hybrid Local Improvement Algorithm for Large Scale Multi-Depot Vehicle Routing Problems with Time Windows”, Computers and Chemical Engineering, Vol33, pp. 513–530. Eidi, A.R. AbdulRahimi, H. (2012). “Proposition and Solution of the Multi-periodic and Multi-depot Routing Problem Model with Flexibility in Determining the finished depot of each route”, International Journal of production management and Industries Engineering Vol, 23. No.3. pp, 334-349. Holland, J. (1975). “Adaptation in natural and artificial systems”, Ann Arbor: The University of Michigan Press. Ho W, Ho TS, Ji P, Lau CW. (2008). “A Hybrid Genetic Algorithm for The Multi-Depot Vehicle Routing Problem”. Engineering Applications of Artificial Intelligence, Vol. 21, pp. 548–557. Hosseininezhad.F, Salajegheh.A. (2012). “Study and Comparison of Partitioning Clustering Algorithms”, Iranian Journal of Medical Informatics, Vol 2, No. 1. Kek, A.G.H., Cheu, R.L., Meng, Q. (2008). “Distance- Constrained Capacitated Vehicle Routing Problems with flexible Assignment of Start and End Depots”, Mathematical and Computer Modeling, Vol. 47, No 1-2, pp. 140- 152. Kallehauge.B. (2008). “Formulations and Exact Algorithms For the Vehicle Routing Problem With Time Windows”, Computers & Operations Research, Vol. 35, pp. 2307 – 2330. Kritikos, M.N., Ioannou, G. (2010). “The balanced cargo vehicle routing problem with time windows, International Journal of Production Economics, Vol. 123(1), pp. 42-51. Maulik .U, Bandyopadhya. S. (2000). “Genetic algorithm-based clustering technique”, Pattern Recognition, Vol. 33, pp. 1455-1465. Mirabi, Shokri and Sadeghieh Mirabi. M, Ghomi. S. M. T. F, and Jolai. F. (2010). “Efficient Stochastic Hybrid Heuristics for the Multi-Depot Vehicle Routing Problem,” Robotics and computer integrated manufacturing, Vol. 26, pp. 564-569. Ombuki.B, Ross.B.J and Hanshar.F. (2006). “Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows”, Applied Intelligence, Vol. 24,pp. 17–30. Renaud J, Laporte G, Boctor FF. (1996). “A Tabu Search Heuristic for the Multi-Depot Vehicle Routing Problem”.Computers & Operations Research; Vol. 23, pp. 229–35. Salhi, S., Sari, M. (1997). “A Multi-level Composite Heuristic for the Multi-depot Vehicle Fleet Mix Problem”. European Journal of Operational Research, Vol. 103, pp. 95–112. Sivanandam.S.N.,.Deepa. S.N. (2008). “Introduction to Genetic Algorithms”, Springer-Verlag Berlin Heidelberg; Springer Berlin Heidelberg New York; chapter 2; ISBN 978-3-540- 73189-4; pp. 31-53. Sumaiya Iqbal, Kaykobad.M, Sohel Rahman. M. 2015.”Solving the Multi-Object Vehicle Routing Problem with Soft Time Window with the help of bees”.Swarm and Evolutionary Computation, Vol. 24, pp. 50-64. Thangiah. S.R. (1999). “A Hybrid Genetic Algorithms, Simulated Annealing and Tabu Search Heuristic for Vehicle Routing Problems With Time Windows”, in: L. Chambers (Ed.), Practical Handbook of Genetic Algorithms Complex Structures, Vol. 3, CRC Press, pp. 347–381. Tan K.C, Lee. L.H, Zhu K.Q, Qu. K. (2001). “Heuristic Methods for Vehicle Routing Problem with Time Windows”, Artificial Intelligence in Engineering 15, 281– 295. Toth, P., and Vigo, D. (2002). “The vehicle routing problem”, Society for Industrial and Applied Mathematics”, Philadelphia, PA. Xu .Y, Wang.L, Yang.Y. (2012). “A New Variable Neighborhood Search Algorithm For the Multi-Depot Heterogeneous Vehicle Routing Problem With Time Windows”, Electronic Notes in Discrete Mathematics, Vol. 39, pp, 289-296. Yu.B, Yang.Z.Z. (2011). “An Ant Colony Optimization Model: The Period Vehicle Routing Problem With Time Windows”, Transportation Research Part E, Vol.47,pp. 166–181. http://www.bernabe.dorronsoro.es/vrp/
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2707 10.22034/2016.3.03 operations planning,scheduling & control The Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem The Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem Pinha Denis West Virginia University, Morgantown, WV, USA Ahluwalia Rashpal West Virginia University, Morgantown, WV, USA Senna Pedro Federal Center for Technological Education of Rio de Janeiro, Rio de Janeiro, Brazil 01 11 2016 3 3 1391 1412 09 10 2016 12 03 2017 Copyright © 2016, Kharazmi University. 2016 http://www.ijsom.com/article_2707.html

This paper presents the formulation and solution of the Combinatorial Multi-Mode Resource Constrained Multi-Project Scheduling Problem. The focus of the proposed method is not on finding a single optimal solution, instead on presenting multiple feasible solutions, with cost and duration information to the project manager. The motivation for developing such an approach is due in part to practical situations where the definition of optimal changes on a regular basis. The proposed approach empowers the project manager to determine what is optimal, on a given day, under the current constraints, such as, change of priorities, lack of skilled worker. The proposed method utilizes a simulation approach to determine feasible solutions, under the current constraints. Resources can be non-consumable, consumable, or doubly constrained. The paper also presents a real-life case study dealing with scheduling of ship repair activities.

Resource constrained project scheduling Mathematical formulation Discrete event simulation decision support system
Abrantes, R., Figueiredo, J., (2015). Resource management process framework for dynamic NPD portfolios. International Journal of Project Management, Vol. 33(6),pp. 1274-1288. Alcaraz, J., Maroto, C., Ruiz, R., (2003). Solving the multi-mode resource-constrained project scheduling problem with genetic algorithms. Journal of the Operational Research Society, Vol.54, pp. 614–626. AlSehaimi, A., Koskela, L., and Tzortzopoulos, P., (2013). Need for Alternative Research Approaches in Construction Management: Case of Delay Studies. Journal of Management in Engineering, Vol.29(4), pp. 407–413. Araúzo, J., Pajares, J., Lopez-Paredes, A., (2010). , Simulating the dynamic scheduling of project portfolios. Simulation Modelling Practice and Theory, Vol. 18(10), pp. 1428-1441. Baumann, P., Trautmann, N., (2013). Optimal scheduling of work-content constrained projects. In Proceedings of the IEEE international conference on industrial engineering and engineering management. Belkaid, F., Sari, Z., & Souier, M. (2013). A genetic algorithm for the parallel machine scheduling problem with consumable resources. International Journal of Applied Metaheuristic Computing (IJAMC), Vol.4(2), pp. 17-30. Belkaid, F., Yalaoui, F., & Sari, Z. (2016). An Efficient Approach for the Reentrant Parallel Machines Scheduling Problem under Consumable Resources Constraints. International Journal of Information Systems and Supply Chain Management (IJISSCM), Vol. 9(3), pp. 1-25. Belkaid, F., Yalaoui, F., & Sari, Z. (2016). Investigations on Performance Evaluation of Scheduling Heuristics and Metaheuristics in a Parallel Machine Environment. In Metaheuristics for Production Systems (pp. 191-222). Springer International Publishing. Beşikci, U., Bilge, U., Ulusoy, G., (2015). Multi-mode resource constrained multi-project scheduling and resource portfolio problem. European Journal of Operational Research, Vol. 240(1), pp. 22-31. Bianco, L., Caramia, M., (2013). A new formulation for the project scheduling problem under limited resources. Flexible Services and Manufacturing Journal, Vol. 25, pp. 6–24. Pinha, Ahluwalia and Senna Bouleimen, K., Lecocq, H., (2003). A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. European Journal of Operational Research, Vol. 149(2), pp. 268–281. Browning, T., Yassine, A., (2010). Resource-constrained multi-project scheduling: Priority rule performance revisited. International Journal of Production Economics, Vol. 126(2), pp. 212–228. Brucker, P., Drexl, A., Mohring, R., Neumann, K., Pesch, E., (1999). Resource-constrained project scheduling: notation, classification, models, and methods. European Journal of Operational Research, Vol. 112, pp. 3–41. Carlier, J., Moukrim,A., & Xu, H. (2009). The project scheduling problem with production and consumption of resources: A list-scheduling based algorithm. Discrete Applied Mathematics, Vol. 157(17), pp. 3631-3642. Chen, P., Shahandashti, S., (2009). Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Automation in Construction, Vol. 18(4), pp. 434-443. Chryssolouris, G., (2005). Manufacturing Systems: Theory and Practice, 2nd Edition NewYork, Springer-Verlag. DoN, 2013. http://www.onr.navy.mil/~/media/Files/Funding-Announcements/BAA/2013/13-020.ashx Drexl, A., Nissen, R., Patterson, J., Salewski, F., (2000). Progen/px – An instance generator for resource-constrained project scheduling problems with partially renewable resources and further extensions. European Journal of Operational Research, 125(1), 59–72. Elmaghraby, S., (1977). Task networks: Project planning and control by network models. Wiley, New York. Fundeling, C., Trautmann, N., 2010. A priority-rule method for project scheduling with work-content constraints. European Journal of Operational Research, Vol. 203, pp. 568–574. Hartmann, S., Briskorn, D., (2010). , A survey of variants and extensions of the resource-constrained project scheduling problem, European Journal of Operational Research, Vol. 207(1), pp. 1-14. Józefowska, J., Weglarz, J., (2006). Perspectives in Modern Project Scheduling. Springer, New York. Jozefowska, J., Mika, M., Rozycki, R., Waligora, G., Weglarz, J., (2001). Simulated annealing for multi-mode resource-constrained project scheduling. Annals of Operations Research, Vol. 102, pp. 137–155. Kolisch, R., Drexl, A., (1997). Local for multi-mode resource-constrained project. IIE Transactions, Vol. 29(11), pp.  987–999. Laslo, Z., Goldberg, A., 2008. Resource allocation under uncertainty in a multi-project matrix environment: Is organizational conflict inevitable? International Journal of Project Management, Vol. 26(8), pp. 773-788. Lau, S., Lu, M., and Poon, C. (2014). Formalized Approach to Discretize a Continuous Plant in Construction Simulations. Journal of Construction Engineering and Management, Vol. 140(8), 04014032. Leadership, (2013), http://ec.europa.eu/enterprise/sectors/maritime/files/shipbuilding/leadership2020-final-report_en.p df Lee, K., Lei, L., Pinedo, M., & Wang, S. (2013). Operations scheduling with multiple resources and transportation considerations. International Journal of Production Research, Vol. 51(23-24), pp. 7071-7090. Maenhout, B., Vanhoucke, M., (2015), “An exact algorithm for an integrated project staffing problem with a homogeneous workforce”, Journal of Scheduling, p-1-27, August-2015. MARAD, (2013). http://www.marad.dot.gov/documents/MARAD_Econ_Study_Final_Report_ 2013.pdf MP (2015), http://office.microsoft.com/en-us/project/ Naber, A., Kolisch, R., 2014. MIP models for resource-constrained project scheduling with flexible resource profiles. European Journal of Operational Research, Vol. 239(2), pp. 335-348. NRC (2009), http://www.nationalacademies.org/nrc/ NSRP, (2013), http://www.nsrp.org/2-Solicitation_Documents/RA%2012-01_FINAL-v2.pdf Peteghem, V., Vanhoucke, M., (2010). A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research, Vol. 201(2), pp. 409–418. Pinha, D., (2015), “Short-Term Resource Allocation and Management”, Ph.D. dissertation, West Virginia University. Pinha, D., Ahluwalia, R., Carvalho, A., (2015), “Parallel Mode Schedule Generation Scheme”, 2015 IFAC Symposium on Information Control in Manufacturing, Ottawa, Canada. Pinha, D., Ahluwalia, R., Carvalho, A., Senna, P., (2015), “Supply Chain Scheduling: A Motorcycle Assembly Case Study”, 2015 IFAC Symposium on Information Control in Manufacturing, Ottawa, Canada. Pinha, D, Ahluwalia, R, (2014), “Decision Support System for Production Planning in the Ship Repair Industry”. Industrial and Systems Engineering Review Journal, [S.l.], Vol. 2, No. 1, pp. 52-61, jul. 2014. ISSN 2329-0188 Pinha, D., Ahluwalia, R, (2013), Proceedings of the Industrial and System Engineering World Conference, “Decision Support System for Repair Shipyard Industry”, Las Vegas, USA. Pinha, D., De Queiroz, M.H., Cury, J. E R, (2011). Optimal scheduling of a repair shipyard based on Supervisory Control Theory. Proceedings of the IEEE Conference on Automation Science and Engineering (CASE), pp.39-44. PMI, (2013). A Guide to the Project Management Body of Knowledge, Fifth Edition, Project Management Institute. PMI KPMG, (2013). “Study on project schedule and cost overruns” http://www.pmi.org.in/downloads/PMI_KPMG_2013.pdf Primavera, (2015), https://docs.oracle.com/cd/E16688_01/Moving_From_P3_to_P6/MovingfromP3toP6.pdf Pritsker, A., Watters, L., Wolfe, P., (1969). Multi project scheduling with limited resources: A zero-one programming approach. Manage. Sci., Vol. 16, pp. 93–108. Ranjbar, M., Kianfar, F., (2010). Resource-constrained project scheduling problem with flexible work profiles: A genetic algorithm approach. Transaction E: Industrial Engineering, Vol.17, pp. 25–35. Pinha, Ahluwalia and Senna Rehm, M., Thiede, J., (2012). A survey of recent methods for solving project scheduling problems. Technische Universität Dresden, Fakultät Wirtschaftswissenschaften. Reichelt, K., Lyneis, J., (1999). The dynamics of project performance: benchmarking the drivers of cost and schedule overrun. European Management Journal, Vol. 17(2), pp. 135-150. Rieck, J., Zimmermann, J., Gather, T., (2012). Mixed-Integer Linear Programming for Resource Leveling Problems. European Journal of Operational Research, Vol. 221, pp. 27-37. Sabzehparvar, M., Seyed-Hosseini, S., (2008). A mathematical model for the multimode resource-constrained project scheduling problem with mode dependent time lags. Journal of Supercomputing, Vol. 44(3), pp. 257–273. Siu, M., Lu, M., AbouRizk, S., (2015). Zero-One Programming Approach to Determine Optimum Resource Supply under Time-Dependent Resource Constraints. Journal of Computing in Civil Engineering, 10.1061/ (ASCE) CP.1943-5487.0000498 , 04015028. Speranza, M. G, and C. Vercellis, (1993). Hierarchical models for multi-project planning and scheduling. European Journal of Operational Research, Vol. 64(2), pp. 312–325. Vanhoucke, M., (2013). Project Management with Dynamic Scheduling, Baseline Scheduling, Risk Analysis and Project Control. 2nd ed. 2013, XVIII, 318 p. 123 illus. Wongwai, N., Malaikrisanachalee, S., (2011). Augmented heuristic algorithm for multi-skilled resource scheduling. Automation in Construction, Vol. 20(4), pp. 429-445. Xu, J., Feng. C., (2014). Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project. The Scientific World Journal, Article ID 463692. Xue, H., Wei, S., Wang, Y., (2010). Resource-constrained multi-project scheduling based on ant colony neural network. Apperceiving Computing and Intelligence Analysis (ICACIA) International Conference, pp.179-182. Zhang, L., Sun, R., (2011). An improvement of resource-constrained multi-project scheduling model based on priority-rule based heuristics. Service Systems and Service Management (ICSSSM), 8th International Conference, pp.1-5.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2712 10.22034/2016.3.04 operations planning,scheduling & control A Scheduling Model for the Re-entrant Manufacturing System and Its Optimization by NSGA-II A Scheduling Model for the Re-entrant Manufacturing System and Its Optimization by NSGA-II Rabbani Masoud Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Famil Alamdar Safoura Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Famil Alamdar Parisa Department of Industrial Engineering, Amir Kabir University, Tehran, Iran 01 11 2016 3 3 1413 1428 20 01 2017 04 04 2017 Copyright © 2016, Kharazmi University. 2016 http://www.ijsom.com/article_2712.html

In this study, a two-objective mixed-integer linear programming model (MILP) for multi-product re-entrant flow shop scheduling problem has been designed. As a result, two objectives are considered. One of them is maximization of the production rate and the other is the minimization of processing time. The system has m stations and can process several products in a moment. The re-entrant flow shop scheduling problem is well known as NP-hard problem and its complexity has been discussed by several researchers. Given that NSGA-II algorithm is one of the strongest and most applicable algorithm in solving multi-objective optimization problems, it is used to solve this problem. To increase algorithm performance, Taguchi technique is used to design experiments for algorithm’s parameters. Numerical experiments are proposed to show the efficiency and effectiveness of the model. Finally, the results of NSGA-II are compared with SPEA2 algorithm (Strength Pareto Evolutionary Algorithm 2). The experimental results show that the proposed algorithm performs significantly better than the SPEA2.

Re-entrant manufacturing system Non-dominated sorting genetic algorithm (NSGA-II) Taguchi parameter setting
Belkaid, F., Yalaoui, F., & Sari, Z. (2016). An Efficient Approach for the Re-entrant Parallel Machines Scheduling Problem under Consumable Resources Constraints. International journal of Information Systems and Supply Chain Management, Vol. 9 (3), July, pp. 1-25. Chen, J-S., Pan, J.C-H., & Wu, C-K. (2008). Hybrid tabu search for re-entrant permutation flow-shop scheduling problem. Expert Systems with Applications, Vol. 34, pp. 1924–1930. Choi, S-W., & Kim, Y-D. (2008). Minimizing makespan on an m-machine re-entrant flowshop. Computers & Operations Research, Vol. 35, pp. 1684 – 1696. Choi, S-W., & Kim, Y-D. (2009). Minimizing total tardiness on a two-machine re-entrant flowshop. European Journal of Operational Research, Vol. 199, pp. 375–384. Choi, J-Y., & KO, S-S. (2009). Simulation-based two-phase genetic algorithm for the capacitated re-entrant line scheduling problem. Computers & Industrial Engineering, Vol. 57, pp. 660–666. Dong, M., He, F. (2012). A new continuous model for multiple re-entrant manufacturing systems. European Journal of Operational Research, Vol. 223, pp. 659–668. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA- . IEEE Trans. Evol. Comput, Vol. 6 (2), pp. 182–197. Fattahi, P., Tavakoli, N.B., Jalilvand-Nejad, A., & Jolai, F. (2010). A hybrid algorithm to solve the problem of re-entrant manufacturing system scheduling. CIRP Journal of manufacturing Science and Technology, Vol. 3, pp. 268-278. Cavory, G., Dupas, R., & Goncalves, G. (2005). A Genetic Approach to Solving the Problem of Cyclic Job Shop Scheduling with Linear Constraints. European Journal of Operational Research, Vol. 161, pp. 73–85. Hinze, R. (2015). A Lot Streaming Model for a Re-entrant Flow Shop Scheduling Problem with Missing Operations. Logistics Management, pp. 149-158. Hsu, T., Korbaa, O., Dupas, D., & Goncalves, G. (2008). Cyclic Scheduling for F.M.S.: Modeling and Evolutionary Solving Approach. European Journal of Operational Research, Vol. 191, pp. 464–484. Hwang, H., & Sun, J.U. (1998). Production sequencing problem with re-entrant work flows and sequence dependent setup times. Int. J. Prod. Res., Vol. 36, pp. 2435–2450. Jeong, B., & Kim, Y-D. (2014). Minimizing total tardiness in a two-machine re-entrant flowshop with sequence-dependent setup times. Accepted Manuscript to appear in: Computers & Operations Research. Jing, C., Tang, G., & Qian, X. (2008). Heuristic algorithms for two machine re-entrant flow shop. Theoretical Computer Science, Vol. 400, pp. 137–143. Jing, C., Huang, W., & Tang, G. (2011). Minimizing total completion time for re-entrant flow shop scheduling problems. Theoretical Computer Science, Vol. 412, pp. 6712-6719. Kia, H., Ghodsypour, S.H., & Davoudpour, H. (2017). New scheduling rules for a dynamic flexible flow line problem with sequence-dependent setup times. Journal of Industrial Engineering International, Vol. 13(1), pp. 1–10. Kim, S., Park, Y., & Jun, C-H. (2006). Performance evaluation of re-entrant manufacturing system with production loss using mean value analysis. Computers & Operations Research, Vol. 33, pp. 1308–1325. Kubale, M., & Nadolski, A. (2005). Chromatic Scheduling in a Cyclic Open Shop. European Journal of Operational Research, Vol. 164, pp. 585–591. Kumar, V.M., Murthy, A., & Chandrashekara, K. (2012). A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system. Journal of Industrial Engineering International, Vol. 8(1), pp. 3-15. Lee, C.K.M., & Lin, D. (2010). Hybrid genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs. IEEE International Conference on Industrial Engineering and Engineering Management, IEEE Computer Society, Macao, China, pp. 1240–1245. Lin D., Lee, C.K.M., & Ho, W. (2013). Multi-level genetic algorithm for the resource-constrained re-entrant scheduling problem in the flow shop. Engineering Applications of Artificial Intelligence, Vol. 26, pp. 1282-1290. Mirabi, M., Fatemi Ghomi, S.M.T., & Jolai, F. (2014). A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem. Journal of Industrial Engineering International,  Vol. 10(2), pp. 57-69. Rau, H., & Cho, K.H. (2009). Genetic algorithm modeling for the inspection allocation in re-entrant production systems. Expert Syst. Appl.,  Vol.  36, pp. 11287–11295. Teruo, M. (1990). The New Experimental Design, Taguchi’s Approach to Quality Engineering. ASI Press, First Editon, Printed In The United States Of American. Xu, J., Lin W-C., Wu, J., Cheng, S-R., & Wu, C-C. (2016).Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration. International Journal of Computational Intelligence Systems,  Vol.  9, pp. 1082-1100. Xu, J., Yin, Y., Cheng, T.C.E., Wu, C-C., & Gu, S. (2014). A memetic algorithm for the re-entrant permutation flowshop scheduling problem to minimize the makespan. Applied Soft Computing,  Vol.  24, pp. 277-283. Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. Technical Report 103, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Zurich, Switzerland.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2708 10.22034/2016.3.05 optimization in supply chain management Competitive Supply Chain Network Design Considering Marketing Strategies: A Hybrid Metaheuristic Algorithm Competitive Supply Chain Network Design Considering Marketing Strategies: A Hybrid Metaheuristic Algorithm Hasani Ali Akbar Industrial Engineering and Management Department, Shahrood University of Technology, Shahrood, Iran 01 11 2016 3 3 1429 1441 28 08 2016 15 03 2017 Copyright © 2016, Kharazmi University. 2016 http://www.ijsom.com/article_2708.html

In this paper, a comprehensive model is proposed to design a network for multi-period, multi-echelon, and multi-product inventory controlled the supply chain. Various marketing strategies and guerrilla marketing approaches are considered in the design process under the static competition condition. The goal of the proposed model is to efficiently respond to the customers’ demands in the presence of the pre-existing competitors and the price inelasticity of demands. The proposed optimization model considers multiple objectives that incorporate both market share and total profit of the considered supply chain network, simultaneously. To tackle the proposed multi-objective mixed-integer nonlinear programming model, an efficient hybrid meta-heuristic algorithm is developed that incorporates a Taguchi-based non-dominated sorting genetic algorithm-II and a particle swarm optimization. A variable neighborhood decomposition search is applied to enhance a local search process of the proposed hybrid solution algorithm. Computational results illustrate that the proposed model and solution algorithm are notably efficient in dealing with the competitive pressure by adopting the proper marketing strategies.

Supply Chain Management Marketing strategies Hybrid metaheuristic Non-dominated sorting genetic algorithm-II Particle swarm optimization Variable neighborhood decomposition search
Aboolian, R., Berman, O. & Krass, D. (2007). Competitive Facility Location and Design Problem. European Journal of Operational Research, Vol. 182, pp. 40-62. Bigat, C. E. (2012). Guerrilla Advertisement and Marketing. Procedia - Social and Behavioral Sciences, Vol. 51, pp. 1022–1029. Costantino, N., Dotoli, M., Falagario, M., Fanti, M. & Mangini, A. M. (2012). A Model for Supply Management of Agile Manufacturing Supply Chains. International Journal of Production Economics, Vol. 135, pp. 451–457. Eskandarpour, M., Nikbakhsh, E. & Zegordi, S. H. (2014). Variable Neighborhood Search for the Bi-objective Post-sales Network Design Problem: A Fitness Landscape Analysis Approach. Computers & Operations Research, Vol. 52, pp. 300–314. Eskandarpour, M., Zegordi, S. H. & Nikbakhsh, E. (2013). A Parallel Multi-objective Variable Neighborhood Search for the Sustainable Post-sales Network Design. International Journal of Production Economics, Vol. 145, pp. 117–131. Fahimnia, B., Farahani, R. Z. & Sarkis, J. (2013). Integrated Aggregate Supply Chain Planning Using Memetic Algorithm – A Performance Analysis Case Study. International Journal of Production Research, Vol. 51, pp. 5354–5373. Farahani, R. Z., Rezapour, S., Drezner, T. & Fallah, S. (2014). Competitive Supply Chain Network Design: An Overview of Classifications, Models, Solution Techniques, and Applications. Omega, Vol. 45, pp. 92–118. Fattahi, M., Mahootchi, M., Govindan, K. & Moattar Husseini, S. M. (2015). Dynamic Supply Chain Network Design with Capacity Planning and Multi-Period Pricing. Transportation Research Part E, Vol. 81, pp. 169–202. Goh, M., Lim, J. Y. S. & Meng, F. (2007). A Stochastic Model For Risk Management In Global Supply Chain Networks. European Journal of Operational Research, Vol. 182, pp. 164–173. Govindan, K., Jafarian, A. & Nourbakhsh, V. (2015). Bi-Objective Integrating Sustainable Order Allocation and Sustainable Supply Chain Network Strategic Design With Stochastic Demand Using A Novel Robust Hybrid Multi-Objective Metaheuristic. Computers & Operations Research, Vol. 62, pp. 112–130. Hasani, A. & Hosseini, S. M. H. (2015). A Comprehensive Robust Bi-objective Model and a Memetic Solution Algorithm for Designing Reverse Supply Chain Network under Uncertainty. Journal of Industrial Management Perspective, Vol. 16, pp. 9-32. Hasani, A. & Khosrojerdi, A. (2016). Robust Global Supply Chain Network Design under Disruption and Uncertainty Considering Resilience Strategies: A Parallel Memetic Algorithm for a Real-Life Case Study. Transportation Research Part E, 87. Hasani, A. & Zegordi, S. H. (2015). A Robust Competitive Global Supply Chain Network Design under Disruption: The Case of Medical Device Industry. International Journal of Industrial Engineering & Production Research, Vol. 26, pp. 63-84. Hasani, A., Zegordi, S. H. & NIKBAKHSH, E. (2012). Robust Closed-Loop Supply Chain Network Design For Perishable Goods in Agile Manufacturing Under Uncertainty. International Journal of Production Research, Vol. 50, pp. 4649–4669. Hasani, A., Zegordi, S. H. & Nikbakhsh, E. (2015). Robust Closed-Loop Global Supply Chain Network Design Under Uncertainty: The Case of The Medical Device Industry. International Journal of Production Research, Vol. 53, pp. 1596–1624. Levinson, J. C. (1998). Guerrilla Marketing: Secrets for Making Big Profits from Your Small Business, Houghton, Mifflin Books. Moghaddam, K. (2015). Fuzzy Multi-Objective Model for Supplier Selection and Order Allocation in Reverse Logistics Systems under Supply and Demand Uncertainty. Expert Systems with Applications, Vol. 42, pp. 6237–6254. Navrátilová, L. & Milichovský, F. (2015). Ways of Using Guerrilla Marketing in SMEs. Procedia. Social and Behavioral Sciences, Vol. 175, pp. 268–274. Paksoy, T. & Chang, C. T. (2010). Revised Multi-Choice Goal Programming for Multi-Period, Multi-Stage Inventory Controlled Supply Chain Model with Pop-up Stores in Guerrilla Marketing. Applied Mathematical Modeling, Vol. 34, pp. 3586–3598. Rezapour, S., Zanjirani Farahani, R. & Drezner, T. (2011). Strategic Design of Competing Supply Chain Networks for Inelastic Demand. Journal of the Operational Research Society, Vol. 62, pp. 1784- 1795. Thanh, P. N., Bostel, N. & Peton, O. (2008). A dynamic model for facility location in the design of complex supply chains. International Journal of Production Economics, Vol. 113, pp. 678–693. Wilhelm, W., Han, X. & Lee, C. (2013). Computational Comparison of Two Formulations for Dynamic Supply Chain Reconfiguration with Capacity Expansion and Contraction. Computer and Operation Research, Vol. 40, pp. 2340–2356.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2705 10.22034/2016.3.06 logistics, transportation, distribution, and materials Handling Joint Optimization of Star P-hub Median Problem and Seat Inventory Control Decisions Considering a Hybrid Routing Transportation System Joint Optimization of Star P-hub Median Problem and Seat Inventory Control Decisions Considering a Hybrid Routing Transportation System Tikani Hamid Department of Industrial Engineering, Yazd University, Yazd, Iran Honarvar Mahboobeh Department of Industrial Engineering, Yazd University, Yazd, Iran Zare Mehrjerdi Yahia Department of Industrial Engineering, Yazd University, Yazd, Iran 01 11 2016 3 3 1442 1465 22 07 2016 26 02 2017 Copyright © 2016, Kharazmi University. 2016 http://www.ijsom.com/article_2705.html

In this paper, we study the problem of integrated capacitated hub location problem and seat inventory control considering concept and techniques of revenue management. We consider an airline company maximizes its revenue by utilizing the best network topology and providing proper booking limits for all itineraries and fare classes. The transportation system arises in the form of a star/star network and includes both hub-stop and non-stop flights. This problem is formulated as a two-stage stochastic integer program with mixed-integer recourse. We solve various instances carried out from the Turkish network data set. Due to the NP-hardness of the problem, we propose a hybrid optimization method, consisting of an evolutionary algorithm based on genetic algorithm and exact solution. The quality of the solutions found by the proposed meta-heuristic is compared with the original version of GA and the mathematical programming model. The results obtained by the proposed model imply that integrating hub location and seat inventory control problem would help to increase the total revenue of airline companies. Also, in the case of serving non-stop flights, the model can provide more profit by employing less number of hubs.

Perishable products P-hub median Seat allocation Evolutionary algorithms Fare class segmentation Network revenue management
Adibi, A., & Razmi, J. (2015). 2-Stage stochastic programming approach for hub location problem under uncertainty: A case study of air network of Iran. Journal of Air Transport Management, Vol. 47, pp. 172-178. Alumur, S. A., Nickel, S., & Saldanha-da-Gama, F. (2012). Hub location under uncertainty. Transportation Research Part B: Methodological, Vol. 46(4), pp. 529-543. Belobaba, P. (1987). Air travel demand and airline seat inventory management. Cambridge, MA: Flight Transportation Laboratory, Massachusetts Institute of Technology,[1987]. Belobaba, P. P. (1989). OR practice—application of a probabilistic decision model to airline seat inventory control. Operations Research,  Vol. 37(2), pp. 183-197. Bertsimas, D., & De Boer, S. (2005). Simulation-based booking limits for airline revenue management. Operations Research,  Vol. 53(1), pp. 90-106. Birge, J. R., & Louveaux, F. (2011). Introduction to stochastic programming. Springer Science & Business Media. Brumelle, S. L., & McGill, J. I. (1993). Airline seat allocation with multiple nested fare classes. Operations Research,  Vol. 41(1), pp. 127-137. Campbell, J. F. (1994). Integer programming formulations of discrete hub location problems. European Journal of Operational Research,  Vol. 72(2), pp. 387-405. Campbell, J. F. (1996). Hub location and the p-hub median problem. Operations Research, 44(6), pp. 923-935. Chiang, W. C., Chen, J. C., & Xu, X. (2006). An overview of research on revenue management: current issues and future research. International Journal of Revenue Management, Vol. 1(1), pp. 97-128. Contreras, I., Cordeau, J. F., & Laporte, G. (2011). Stochastic uncapacitated hub location. European Journal of Operational Research, Vol. 212(3), pp. 518-528. Contreras, I., Fernández, E., & Marín, A. (2009). Tight bounds from a path based formulation for the tree of hub location problem. Computers & Operations Research, Vol. 36(12), pp. 3117-3127. Cook, G. N., & Goodwin, J. (2008). Airline Networks: A Comparison of Hub-and-Spoke and Point-to-Point SystemsAirline Networks: A Comparison of Hub-and-Spoke and Point-to-Point Systems. Journal of Aviation/Aerospace Education & Research, 17(2). Curry, R. E. (1990). Optimal airline seat allocation with fare classes nested by origins and destinations. transportation science, Vol. 24(3), pp. 193-204. De Boer, S. V., Freling, R., & Piersma, N. (2002). Mathematical programming for network revenue management revisited. European Journal of Operational Research, Vol. 137(1), pp. 72-92. Ernst, A. T., & Krishnamoorthy, M. (1996). Efficient algorithms for the uncapacitated single allocation p-hub median problem. Location science, Vol. 4(3), pp. 139-154. Feng, Y., & Xiao, B. (2001). A dynamic airline seat inventory control model and its optimal policy. Operations Research, Vol. 49(6), pp. 938-949. Glover, F., Glover, R., Lorenzo, J., & McMillan, C. (1982). The passenger-mix problem in the scheduled airlines. Interfaces, Vol. 12(3), pp. 73-80. Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. U Michigan Press. Jeng, C. Y. (1987). Routing strategies for an idealized airline network. Kara, B. Y., & Tansel, B. C. (2000). On the single-assignment p-hub center problem. European Journal of Operational Research, Vol. 125(3), pp. 648-655. Kratica, J., Milanović, M., Stanimirović, Z., & Tošić, D. (2011). An evolutionary-based approach for solving a capacitated hub location problem. Applied Soft Computing, Vol. 11(2), pp. 1858-1866. Kratica, J., Stanimirović, Z., Tošić, D., & Filipović, V. (2007). Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem. European Journal of Operational Research, Vol. 182(1), pp. 15-28. Labbé, M., & Yaman, H. (2004). Projecting the flow variables for hub location problems. Networks, Vol. 44(2), pp. 84-93. Labbé, M., & Yaman, H. (2008). Solving the hub location problem in a star–star network. Networks, Vol. 51(1), pp. 19-33. Labbé, M., Yaman, H., & Gourdin, E. (2005). A branch and cut algorithm for hub location problems with single assignment. Mathematical programming, Vol. 102(2), pp. 371-405. Lee, T. C., & Hersh, M. (1993). A model for dynamic airline seat inventory control with multiple seat bookings. Transportation Science, Vol. 27(3), pp. 252-265. Lin, C. C., Lin, J. Y., & Chen, Y. C. (2012). The capacitated p-hub median problem with integral constraints: An application to a Chinese air cargo network. Applied Mathematical Modelling, Vol. 36(6), pp. 2777-2787. Littlewood, K. (1972). Forecasting and Control of Passengers. In 12th AGIFORS Symposium Proceedings, Vol. 95, pp. 128. Marianov, V., & Serra, D. (2003). Location models for airline hubs behaving as M/D/c queues. Computers & Operations Research, Vol. 30(7), pp. 983-1003. Mayer, G., & Wagner, B. (2002). HubLocator: an exact solution method for the multiple allocation hub location problem. Computers & Operations Research, Vol. 29(6), pp. 715-739. McGill, J. I., & Van Ryzin, G. J. (1999). Revenue management: Research overview and prospects. Transportation science, Vol. 33(2), pp. 233-256. Mendes, J. J. D. M., Gonçalves, J. F., & Resende, M. G. (2009). A random key based genetic algorithm for the resource constrained project scheduling problem. Computers & Operations Research, Vol. 36(1), pp. 92-109. Mou, D., & Wang, X. (2014). Uncertain Programming for Network Revenue Management. Mathematical Problems in Engineering, 2014. O'kelly, M. E. (1987). A quadratic integer program for the location of interacting hub facilities. European Journal of Operational Research, Vol. 32(3), pp. 393-404. Robinson, L. W. (1995). Optimal and approximate control policies for airline booking with sequential nonmonotonic fare classes. Operations Research, Vol. 43(2), pp. 252-263. Sim, T., Lowe, T. J., & Thomas, B. W. (2009). The stochastic p-hub center problem with service-level constraints. Computers & Operations Research, Vol. 36(12), pp. 3166-3177. Taguchi G, Chowdhury S, Taguchi S. Robust engineering. McGraw-Hill Professional; 2000. Talluri, K. T., & Van Ryzin, G. J. (2004). The theory and practice of revenue management. Number 68 in International series in operations research and management science. Tan, P. Z., & Kara, B. Y. (2007). A hub covering model for cargo delivery systems. Networks, Vol. 49(1), pp. 28-39. Van Ryzin, G., & Vulcano, G. (2008). Simulation-based optimization of virtual nesting controls for network revenue management. Operations Research, Vol.  56(4), pp. 865-880. Wang, K. (1983). Optimum seat allocation for multi-leg flights with multiple fare types. In AGIFORS PROCEEDINGS. Wollmer, R. D. (1986). A hub-spoke seat management model. Unpublished Internal Report, Mc Donnell Douglas Corporation, Long Beach, CA. Wollmer, R. D. (1992). An airline seat management model for a single leg route when lower fare classes book first. Operations Research, Vol.  40(1), pp. 26-37. Yaman, H. (2008). Star p-hub median problem with modular arc capacities.Computers & Operations Research, Vol.  35(9), pp. 3009-3019. Yaman, H. (2011). Allocation strategies in hub networks. European Journal of Operational Research, Vol.  211(3), pp. 442-451. Yaman, H., & Elloumi, S. (2012). Star p-hub center problem and star p-hub median problem with bounded path lengths. Computers & Operations Research, Vol. 39(11), pp. 2725-2732. Yang, T. H. (2009). Stochastic air freight hub location and flight routes planning. Applied Mathematical Modelling, Vol.  33(12), pp. 4424-4430. Yang, T. H. (2010). A two-stage stochastic model for airline network design with uncertain demand. Transportmetrica, Vol. 6(3), pp. 187-213. Yoon, M. G., Lee, H. Y., & Song, Y. S. (2012). Linear approximation approach for a stochastic seat allocation problem with cancellation & refund policy in airlines. Journal of Air Transport Management, Vol. 23, pp. 41-46. Zade, A. E., Sadegheih, A., & Lotfi, M. M. (2014). A modified NSGA-II solution for a new multi-objective hub maximal covering problem under uncertain shipments. Journal of Industrial Engineering International, Vol. 10(4), pp.185-197.
IJSOM Kharazmi University International Journal of Supply and Operations Management 23831359 Kharazmi University 2713 10.22034/2016.3.07 performance measurement & productivity The Identifying, Evaluating and Prioritizing the Factors Affecting Customers’ Satisfaction with E-service Centers of Iran's Police The Identifying, Evaluating and Prioritizing the Factors Affecting Customers’ Satisfaction with E-service Centers of Iran's Police Ziaee Azimi Seyed Ali Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Saidi-Mehrabad Mohammad Faculty of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran 01 11 2016 3 3 1466 1485 25 07 2016 09 04 2017 Copyright © 2016, Kharazmi University. 2016 http://www.ijsom.com/article_2713.html

The present research is classified as an applied one employing a descriptive survey design to describe the status quo of the factors affecting customers’ satisfaction with the E-service centers of Iran’s police, known as 10 + police centers. The research population involves all the costumers of the 10+ police centers, among which 420 individuals were chosen through simple random sampling technique. Furthermore, 45 10 + police service centers were selected with probability proportional to size. After Determining the validity and reliability of the researcher-made questionnaire, it has been used to collect the required data. Then, a conceptual model was developed using the theoretical framework and background literature. After that, SPSS software was used to examine and make an analysis of the research hypothesises. The findings indicate that all the identified indices to the customers’ satisfaction with the 10 + police e- service centers (including trust and confidence, staff performance, system facility, environmental facility, basic amenity, providing sufficient notification, time and cost, easy access to the office) have an effect on the customers’ satisfaction. In the end, some practical suggestions were made for an improvement in the satisfaction level of the customers to the 10 + police e- service centers.

10 +police Measuring the customer satisfaction Improvement in quality of the services
Akhondzade, E., & Mohammadiani, Z., & Ahmadvand, M. (2015). "Citizen satisfaction evaluation of police+10 services with approach of citizen relationship management", Quarterly of order and security guards, Vol.7, No. 4(28), pp.147-172. Assariannejad, H., & Shirazi Roumenan, H. Summer (2011). "Police services quality assessment of police+10 through using SERVQUAL analysis model". Police management studies quarterly (PMSQ). Vol 6, No 2, pp.208 - 221. Cao, L. (2015). "Differentiating confidence in the police, trust in the police, and satisfaction with the police", policing: An International Journal of Police Strategies & Management, Vol. 38, No. 2, pp. 239-249. Donnely, M., & J. Kerr, N., & Rimmer, R., & M. Shiu, E. (2006). "Assessing the quality of police services using SERVQUAL", policing: An International Journal of Police Strategies & Management, Vol. 29, No. 1, pp.92-105. Ghasri, M., & Salehi, A. SPRING (2009). "Effect of privatization of police services in trend of disciplinary force practices by approach to police+10 agencies". Quarterly of order and security guards, Vol 2, No 1, pp.123-159. Haghighi nasab, M., & Abedin, B., & Janfeshan, Sh. (2009). "The success of the government in the provision of electronic services, communication services and 10 + police e-services centers". Journal of Managementt Futures Research. Vol. 2, No. 4, pp 131-153. Kavoosi, A., & Saghaei, A. (2013). "Methods of Measuring Customer Satisfaction", Third Edition, Tehran: Aimé. L.Dukes, R., & Portillos, E., & Miles, M. (2009). "Models of satisfaction with police service", policing: An International Journal of Police Strategies & Management, Vol.32, No.2, pp.297-318. Madan, M., & K. Nalla, M. (2015). "Exploring citizen satisfaction with police in India: The role of procedural justice, police performance, professionalism, and integrity", policing: An International Journal of police strategies & Management, Vol. 38, No. 1, pp.86-101. Oliver, R. L. (2010). Satisfaction: A behavioral perspective on the consumer. Second edition .New York: Irwin/McGraw-Hill. Pallant, j. (2004). SPSS survival Manual: A step by step guide to data analysis using SPSS. 4th - edition. Buckinghamshire: University of Buckinghamshire, UK. Schedler, K., & Summermatter, L. (2007). "Customer orientation in electronic government: Motives and effects". Government Information Quarterly, Vol. 24, pp. 291–311. Talaee Delshad, A. (2013). "Measurement and analysis of the level of customer satisfaction in hospitals". MSc Thesis, Industrial Engineering, Iran University of Science and Technology, pp. 12-13. Toepfer, A. (1999). "Customer satisfaction measure and increase", Second Edition, Luchterhand Publishing House, Germany, page 52. Zare, Z; Ghaemi, M. (2014). "Evaluating the quality of 10 + Police e-services centers in the Kordestan province ". Quarterly Police Organizational Development, Vol. 11, No. 48, pp. 81-103. Zeithaml,VA ,Mary Jo Bitner .(1996)."Service Marketing", McGraw Hill, Singapor, p.123.