ORIGINAL_ARTICLE
An Integrated Neural Networks and MCMC Model to Predicting Bank’s Efficiency
In the banking industry, there is intense competition between banks to attract resources and facilities. With the development of new services, bank managers try to improve their services and attract more customer deposits by differentiating between competitors' services. This research uses a two-stage TOPSIS method with the combination of neural network model and Monte Carlo simulation trading method to analyze and compare bank productivity forecasts with the 4 efficiency criteria of the banking industry. TOPSIS was first used in two steps to rate the efficiency of banks and then a model was created for banking performance with clear forecasting ability. Secondly, an MCMC sampling method and ANN training was presented. Integrated neural networks and MCMCs were used which are consistent with TOPSIS results. The simulation effect of the selected variables was predicted and their effect on performance was observed. The proposed method was used successfully for predicting performance and ranking banks based on the relative importance of performance criteria expressed by considering the performance levels in the TOPSIS method. Then, the artificial neural network was modeled using the results obtained from the TOPSIS method, an effective model for appropriate prediction of bank performance. Based on the results of the proposed model and the level of importance of performance measures, cost and revenue structure were considered to be the main causes of inefficiency
https://aie.ut.ac.ir/article_81137_ee6fdff70c9f4076650f404fcbb70821.pdf
2020-01-01
1
14
10.22059/jieng.2021.312818.1743
Forecast
TOPSIS
neural networks
Monte Carlo
Efficiency
Farideh
Sobhanifard
f.sobhanifard@gmail.com
1
Industrial Engineering Department, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran
LEAD_AUTHOR
Mohammad Reza
Shahraki
mr.shahraki@eng.usb.ac.ir
2
Industrial Engineering Department, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran
AUTHOR
[1] Matousek, R., Rughoo, A., Sarantis, N., Assaf, G.A., )2014(. Bank performance and convergence during the financial crisis: evidence from the ‘old’ EuropeanUnion and Eurozone. J. Bank. Finance,
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[3] Sufian, F., Kamarudin, F., Noor, N.H.H.M., )2014(. Revenue efficiency and returns to scale in Islamic Banks: empirical evidence from Malaysia. J. Econ. Coop.Dev. 35 (1), 47–80
3
[4] Harvey, A., & Kattuman, P. (2020). Time series models based on growth curves with applications to forecasting coronavirus. Harvard Data Science Review.
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[5] Lampe, H.W., Hilgers, D., )2014(. Trajectories of efficiency measurement: a bibliometric analysis of DEA and SFA. Eur. J. Oper. Res. (in press).
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[6] Maghyereh, A.I., Awartani, B., (2012). Financial integration of GCC banking markets: a non-parametric bootstrap DEA estimation approach. Res. Int. Bus.Finance 26 (2), 181–195.
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[7] Bilbao-Terol, A., Arenas- Parra, M., Canal ˜ Fernandes, V., 2014. Using Topsis for assessing the sustainability of government bond funds. Omega 49, 1–17.
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[8] San, O.T., Theng, L.Y., Heng, T.B., )2011(. A comparison on efficiency of domestic and foreign banks in Malaysia: a DEA approach. Bus. Manag. Dyn. 1 (4),33–49.
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[9] Geisser, S., )1993(. Predictive Inference: an Introduction. Chapman & Hall, New York.
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[10] Azad, A.S.M.S., Yasushi, S., Fang, V., Ahsan, A., (2014). Impact of policy changes on the efficiency and returns-to-scale of Japanese financial institutions: an evaluation. Res. Int. Bus. Finance 32, 159–171.
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[11] Kuhn, M., Johnson, K., )2013(. Applied Predictive Modeling. Springer, New York.
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[12] Chen, M.-C.,) 2007(. Ranking discovered rules from data mining with multiple criteria by data envelopment analysis. Expert Syst. Appl. 33 (4), 1110e1116.
12
[13] Wanke, P., Azad, M.D.A., Barros, C.P., )2016(. Predicting effciency in Malaysian Islamic bank: A two-stage TOPSIS and neural networks approach. Business and Finance. 36, 485–498.
13
[14] Huang, I.B.; Keisler, J.; Linkov, I. (2011). "Multi-criteria decision analysis in environmental science: ten years of applications and trends". Science of the Total Environment 409: 3578–3594.
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[15] Maschio, C., Schiozer, D.J., (2014).Bayesian history matching using artificial neural network and Markov Chain Monte Carlo. Journal of Petroleum Science and Engineering. Caixa Postal 6122, 13.083-970.
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[17] Freund, J. E. (1992). Mathematical Statistics: Prentice- Hall.
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[18] Sufian, F., Mohamad, A., Muhamed-Zulkhibri, A.M., )2008(. The efficiency of Islamic Banks: empirical evidence from the MENA and Asian countries Islamicbanking sectors. Middle East Bus. Econ. Rev. 20 (1), 1–19.
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[19] Simar, L.,Wilson,P.W., (1998). Sensitivity analysis of efficiency scores: how to bootstrap in non parametric frontier models. Manag.Sci. 44(1), 49–61.
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[20] Xu, C., He, H.S., Hu, Y., Chang, et al., (2005). Latin hypercube sampling and geostatistical modeling of spatial uncertainty in a spatially explicit forest landscape model simulation. Ecol. Model. 185, 255–269.
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[21] Osei-Bryson, K.-M., Ngwenyama, O., )2014(. Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking. Springer Series, New York.
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[22] Chen, Y.-S., Cheng, C.-H., )2013(. Hybrid models based on rough set classifiers for setting credit rating decision rules in the global banking industry. Knowl. Based Syst. 39, 224e239. http://dx.doi.org/10.1016/j.knosys.2012.11.004.
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23
ORIGINAL_ARTICLE
Timetabling of Metro Trains in a Dynamic Demand Situation Considering the Capacity of Trains and Stations on Peak and Off-Peak Times
This paper aims to propose a mathematical model in order to minimize total waiting time of passengers in metro systems. The main contribution of this paper is considering the capacity of trains and stations, as well as the assumption of a constant interval for travelling between two successive stations. To reach this aim, the sum of dwell time and travel time are assumed constant. The dwell time is considered a function of number of passengers who can board the train. To show the effectiveness of the proposed model, a numerical example is studied. The parameters of the metro system are considered according to Tehran Urban and Suburban Railway Operation Co. The results show that an increase in the capacity of trains and the number of trains separately leads to the reduction of total waiting time. Furthermore, the best amount of Headway in order to minimize the waiting time is six minutes.
https://aie.ut.ac.ir/article_81138_8a5d4ec4d6193ba986e9c89ff2cc85a0.pdf
2020-01-01
15
23
10.22059/jieng.2021.317846.1749
Dynamic Demand
Mathematical model
Metro Timetabling
Scheduling
waiting time
Mohammad Mahdi
Nasiri
mmnasiri@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
LEAD_AUTHOR
Mohammad
Zenoozadeh
zenoozadeh@ut.ac.ir
2
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
AUTHOR
[1] Aliabadi L, Yazdanparast R, Nasiri MM (2018) An inventory model for non-instantaneous deteriorating items with credit period and carbon emission sensitive demand: a signomial geometric programming approach. International Journal of Management Science and Engineering Management:1-13 doi:10.1080/17509653.2018.1504331
1
[2] Bababeik M, Nasiri MM, Khademi N, Chen A (2017) Vulnerability evaluation of freight railway networks using a heuristic routing and scheduling optimization model. Transportation:1-28
2
[3] Wong K, Ho T (2004) Dynamic coast control of train movement with genetic algorithm. International journal of systems science 35:835-846
3
[4] Higgins A, Kozan E, Ferreira L (1997) Heuristic techniques for single line train scheduling. Journal of Heuristics 3:43-62
4
[5] Brännlund U, Lindberg PO, Nou A, Nilsson J-E (1998) Railway timetabling using Lagrangian relaxation. Transportation science 32:358-369
5
[6] Caprara A, Monaci M, Toth P, Guida PL (2006) A Lagrangian heuristic algorithm for a real-world train timetabling problem. Discrete applied mathematics 154:738-753
6
[7] Caprara A, Kroon L, Monaci M, Peeters M, Toth P (2007) Passenger railway optimization. Handbooks in operations research and management science 14:129-187
7
[8] Kroon L et al. (2009) The new Dutch timetable: The OR revolution. Interfaces 39:6-17
8
[9] Zhou X, Zhong M (2007) Single-track train timetabling with guaranteed optimality: Branch-and-bound algorithms with enhanced lower bounds. Transportation Research Part B: Methodological 41:320-341
9
[10] Mu S, Dessouky M (2011) Scheduling freight trains traveling on complex networks. Transportation Research Part B: Methodological 45:1103-1123
10
[11] Barrena E, Canca D, Coelho LC, Laporte G (2014) Exact formulations and algorithm for the train timetabling problem with dynamic demand. Comput Oper Res 44:66-74
11
[12] Xu X, Li K, Yang L, Ye J (2014) Balanced train timetabling on a single-line railway with optimized velocity. Appl Math Modell 38:894-909
12
[13] Sun L, Jin JG, Lee D-H, Axhausen KW, Erath A (2014) Demand-driven timetable design for metro services. Transportation Research Part C: Emerging Technologies 46:284-299
13
[14] Yin J, Chen D, Li L (2014) Intelligent train operation algorithms for subway by expert system and reinforcement learning. IEEE Transactions on Intelligent Transportation Systems 15:2561-2571
14
[15] Jamili A, Aghaee MP (2015) Robust stop-skipping patterns in urban railway operations under traffic alteration situation. Transportation Research Part C: Emerging Technologies 61:63-74
15
[16] Yaghini M, Ghofrani F, Karimi M, Esmi-Zadeh M (2016) Concurrent Locomotive Assignment and Freight Train Scheduling. International Journal of Industrial Engineering & Production Research 27:321-335
16
[17] Hassannayebi E, Zegordi SH, Yaghini M, Amin-Naseri MR (2017) Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals. Transportation Planning and Technology 40:278-304
17
[18] Qi J, Yang L, Gao Y, Di Z (2018) Service-oriented train timetabling problem with consideration of women-only passenger cars. Comput Ind Eng
18
[19] Kamandanipour K, Nasiri MM, Konur D, Yakhchali SH (2020) Stochastic data-driven optimization for multi-class dynamic pricing and capacity allocation in the passenger railroad transportation. Expert Systems with Applications 158:113568
19
[20] Yang L, Yao Y, Shi H, Shang P (2020) Dynamic passenger demand-oriented train scheduling optimization considering flexible short-turning strategy. Journal of the Operational Research Society:1-19 doi:10.1080/01605682.2020.1806745
20
[21] Gong C, Shi J, Wang Y, Zhou H, Yang L, Chen D, Pan H (2021) Train timetabling with dynamic and random passenger demand: A stochastic optimization method. Transportation Research Part C: Emerging Technologies 123:102963 doi:https://doi.org/10.1016/j.trc.2021.102963
21
ORIGINAL_ARTICLE
Multi-Objective Optimization of Nurse Scheduling Problem by Modeling Teamwork and Decision Making Style
This study presents a multi-objective nurse scheduling model by considering and integrating teamwork and decision making styles in order to maximize job satisfaction. To achieve high job satisfaction, teamwork which minimizes incompatibility among team members is considered. Teamwork has sustainable impact on job satisfaction in healthcare. In this study, a new mathematical model is proposed for scheduling nurses based on teamwork. First, nursing teams are generated by considering decision making styles. Then, each team is assigned to work shifts in the planning horizon. The unique multi-objective mathematical model considers the inconsistency of nurses’ decision making styles, reliability of teams, allocation costs and penalty of violating soft constraints as the objective functions. A real case study is considered to show the applicability of the proposed model. Finally, the proposed multi objective model is solved using goal programming method. Sensitivity analysis shows the robustness of the proposed mathematical programming model and solution methodology.
https://aie.ut.ac.ir/article_81139_34e552856c9663d7a4602050e38c7920.pdf
2020-01-01
25
40
10.22059/jieng.2021.317869.1750
Nurse Scheduling
Team working
Decision Making Style
reliability
Mohammad
Sheikhalishahi
m.alishahi@ut.ac.ir
1
Department of Industrial Engineering and Center of Excellence for Intelligent-Based Experimental Mechanic, College of Engineering, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Hasan
Gharoun
hassan.gharoun@yahoo.com
2
Department of Industrial Engineering and Center of Excellence for Intelligent-Based Experimental Mechanic, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Seyed Mohammad Reza
Goldansaz
smr.goldansaz@gmail.com
3
Department of Industrial Engineering and Center of Excellence for Intelligent-Based Experimental Mechanic, College of Engineering, University of Tehran, Tehran, Iran
AUTHOR
Kalisch, B. J., and Lee, K. H. (2013). Variations of nursing teamwork by hospital, patient unit, and staff characteristics. Applied Nursing Research, 26(1), 2-9.
1
[2] Hall, P., and Weaver, L. (2001). Interdisciplinary education and teamwork: a long and winding road. Medical education, 35(9), 867-875.
2
[3] Driver, M. J., Brousseau, K. R., and Hunsaker, P. L. (1998). The dynamic decision maker: Five decision styles for executive and business success: iUniverse.
3
[4] Azadeh, A., Rezaei-Malek, M., Evazabadian, F., and Sheikhalishahi, M. (2015). Improved design of CMS by considering operators decision-making styles. International Journal of Production Research, 53(11), 3276-3287. doi:10.1080/00207543.2014.975860
4
[5] Simmons, D., and Sherwood, G. (2010). Neonatal Intensive Care Unit and Emergency Department Nurses' Descriptions of Working Together: Building Team Relationships to Improve Safety. Critical Care Nursing Clinics of North America, 22(2), 253-260. doi: http://dx.doi.org/10.1016/j.ccell.2010.03.007
5
[6] Apker, J., Propp, K. M., Ford, W. S. Z., and Hofmeister, N. (2006). Collaboration, credibility, compassion, and coordination: professional nurse communication skill sets in health care team interactions. Journal of Professional Nursing, 22(3), 180-189.
6
[7] Utriainen, K., and KyngÄS, H. (2009). Hospital nurses' job satisfaction: a literature review. Journal of Nursing Management, 17(8), 1002-1010. doi:10.1111/j.1365-2834.2009.01028.x
7
[8] Kalisch, B. J., Lee, H., and Rochman, M. (2010). Nursing staff teamwork and job satisfaction. Journal of nursing management, 18(8), 938-947.
8
[9] Mahon, M. M., and Nicotera, A. M. (2011). Nursing and conflict communication: avoidance as preferred strategy. Nursing Administration Quarterly, 35(2), 152-163.
9
[10] Brunetto, Y., Farr-Wharton, R., and Shacklock, K. (2011). Supervisor-nurse relationships, teamwork, role ambiguity and well-being: Public versus private sector nurses. Asia Pacific Journal of Human Resources, 49(2), 143-164.
10
[11] Burtscher, M. J., and Manser, T. (2012). Team mental models and their potential to improve teamwork and safety: A review and implications for future research in healthcare. Safety science, 50(5), 1344-1354.
11
[12] Poghosyan, L., Boyd, D., and Knutson, A. R. (2014). Nurse Practitioner Role, Independent Practice, and Teamwork in Primary Care. The Journal for Nurse Practitioners, 10(7), 472-479.
12
[13] Dietz, A. S., Pronovost, P. J., Mendez-Tellez, P. A., Wyskiel, R., Marsteller, J. A., Thompson, D. A., and Rosen, M. A. (2014). A systematic review of teamwork in the intensive care unit: What do we know about teamwork, team tasks, and improvement strategies? Journal of critical care, 29(6), 908-914.
13
[14] Ferland, J., Berrada, I., Nabli, I., Ahiod, B., Michelon, P., Gascon, V., and Gagné, É. (2001). Generalized Assignment Type Goal Programming Problem: Application to Nurse Scheduling. Journal of Heuristics, 7(4), 391-413. doi:10.1023/A:1011392328632
14
[15] Azaiez, M. N., and Sharif, S. S. A. (2005). A 0-1 goal programming model for nurse scheduling. Comput. Oper. Res., 32(3), 491-507. doi:10.1016/s0305-0548(03)00249-1
15
[16] Topaloglu, S. (2006). A multi-objective programming model for scheduling emergency medicine residents. Computers and Industrial Engineering, 51(3), 375-388.
16
[17] Maenhout, B., and Vanhoucke, M. (2010). Branching strategies in a branch-and-price approach for a multiple objective nurse scheduling problem. Journal of Scheduling, 13(1), 77-93.
17
[18] Topaloglu, S., and Selim, H. (2010). Nurse scheduling using fuzzy modeling approach. Fuzzy Sets and Systems, 161(11), 1543-1563.
18
[19] Yilmaz, E. (2012). A mathematical programming model for scheduling of nurses’ labor shifts. Journal of Medical Systems, 36(2), 491-496.
19
[20] Nelsey, L., and Brownie, S. (2012). Effective leadership, teamwork and mentoring–Essential elements in promoting generational cohesion in the nursing workforce and retaining nurses. Collegian, 19(4), 197-202.
20
[21] M’Hallah, R., and Alkhabbaz, A. (2013). Scheduling of nurses: a case study of a Kuwaiti health care unit. Operations Research for Health Care, 2(1), 1-19.
21
[22] Wright, P. D., and Mahar, S. (2013). Centralized nurse scheduling to simultaneously improve schedule cost and nurse satisfaction. Omega, 41(6), 1042-1052. doi:http://dx.doi.org/10.1016/j.omega.2012.08.004
22
[23] Güler, M. G., İdi, K., and Güler, E. Y. (2013). A goal programming model for scheduling residents in an anesthesia and reanimation department. Expert Systems with Applications, 40(6), 2117-2126.
23
[24] Güler, M. G. (2013). A hierarchical goal programming model for scheduling the outpatient clinics. Expert Systems with Applications, 40(12), 4906-4914.
24
[25] Meskens, N., Duvivier, D., and Hanset, A. (2013). Multi-objective operating room scheduling considering desiderata of the surgical team. Decision Support Systems, 55(2), 650-659.
25
[26] Wong, T., Xu, M., and Chin, K. (2014). A two-stage heuristic approach for nurse scheduling problem: A case study in an emergency department. Computers and Operations Research, 51, 99-110.
26
[27] Legrain, A., Bouarab, H., and Lahrichi, N. (2015). The nurse scheduling problem in real-life. Journal of Medical Systems, 39(1), 1-11.
27
[28] Jafari, H., and Salmasi, N. (2015). Maximizing the nurses’ preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm. Journal of Industrial Engineering International, 11(3), 439-458.
28
[29] Hamid, M., Nasiri, M.M. Frank Werner., Sheikhahmadi, F. and Zhalechian, M. (2019). Operating room scheduling by considering the decision-making styles of surgical team members: a comprehensive approach, Computers and Operations Research, 108: 166-181.
29
[30] Cetin, E., and Sarucan, A. (2015, 27-29 May 2015). Nurse scheduling using binary fuzzy goal programming. Paper presented at the Modeling, Simulation, and Applied Optimization (ICMSAO), 2015 6th International Conference on.
30
ORIGINAL_ARTICLE
A Two Stage Recourse Stochastic Mathematical Model for the Tramp Ship Routing with Time Windows Problem
Nowadays, the majority of international trade in goods is carried by sea, and especially by ships deployed in the industrial and tramp segments. This paper addresses routing the tramp ships and determining the schedules including the arrival times to the ports, berthing times at the ports, and the departure times in an operational planning level. In the operational planning level, the weather can be almost exactly forecasted, however in some routes some uncertainties may remain. In this paper, the voyaging times between some of the ports are considered to be uncertain. To that end, a two stage stochastic mathematical model is proposed. In order to find near to optimum solutions in a limited amount of time, a new hybrid heuristic algorithm is proposed to solve large-size examples. Moreover, a case study is defined and tested with the presented model. The computational results show that this mathematical model is promising and can represent acceptable solutions. Specifically, the value of the stochastic solution, VSS, is computed, and the results show that using two stage stochastic with recourse improve 1.1% of the objective value.
https://aie.ut.ac.ir/article_81140_87dd89b660e077f8bc253689b225a3e9.pdf
2020-01-01
41
52
10.22059/jieng.2021.318000.1751
Scheduling
Uncertainty
Hybrid Heuristic Algorithm
Amin
Jamili
a_jamili@ut.ac.ir
1
School of Industrial Engineering, College of Engineering, University of Tehran, Iran
LEAD_AUTHOR
[1] Toth P. and D. Vigo, “The Vehicle Routing Problem”, Philadelphia: Society for Industrial and Applied Mathematics, 2002.
1
[2] Berbeglia, G. Cordeau, G.-F., Gribkovskaia I., and Laporte G., “Static Pickup and Delivery Problems: A Classification Scheme and Survey, TOP, 2007; 15: 1-31.
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[3] Grandinetti, L., Guerriero, F., Pezzella, F., Pisacane, O., The Multi-Objective Multi-Vehicle Pickup and Delivery Problem with Time Windows, Social and Behavioral Sciences, 2014; 111: 203 – 212.
3
[4] Wassan N. A., and Nagy, G., Vehicle Routing Problem with Deliveries and Pickups: Modelling Issues and Meta-heuristics Solution Approaches, International Journal of Transportation, 2014; 2 (1) 95-110.
4
[5] Ronen, D., Cargo ship routing and scheduling: Survey of models and problems, European Journal of Operational Research, 1983; 12: 119-126.
5
[6] Christiansen, M., Fagerholt, K., Nygreen, B., Ronen, D., Ship routing and scheduling in the new millennium, European Journal of Operational Research, 2013; 228 (3): 467-483.
6
[7] Kjeldsen K. H., Routing and Scheduling in Liner Shipping, PhD Thesis, Dep. of Economics and Business, AARHUS University, Denmark, 2012.
7
[8] Cho, S. C., and Perakis, A. N., An Improved Formulation for Bulk Cargo Ship Scheduling with a Single Loading Port. Maritime Policy and Management, 2001; 28 (4): 339-345.
8
[9] Fagerholt, K., Christiansen, M., A combined ship scheduling and allocation problem. Journal of the Operational Research Society, 2000; 51: 834-842.
9
[10] Fagerholt, K., Ship Scheduling with Soft Time Windows: An Optimization Based Approach. European Journal of Operational Research, 2001; 131: 559-571.
10
[11] Liu H.-Y., Chen, C.-Y., An Optimization Model for the Tramp Ship Routing Problem, Storage Management Solutions, 2013; 3: 189-204.
11
[12] Romero, G., Dur´an, G., Marenco, J., Weintraub, A., An approach for efficient ship routing, International Transactions In Operational Research, 00: 1–28; 2013.
12
[13] De, A., Krishna Reddy Mamanduru, V., , Gunaskaran, A., Subramanian, N., Kumar Tiwari, M., Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization, Computers & Industrial Engineering, 2016; 96: 201-215
13
[14] Dithmer P., Reinhardt L., Kontovas C. A., The Liner Shipping Routing and Scheduling Problem Under Environmental Considerations: The Case of Emissions Control Areas, International Conference on Computational Logistics, ICCL 2017; 336-350
14
[15] Fan, H., Yu, J., and Liu, X., "Tramp Ship Routing and Scheduling with Speed Optimization Considering Carbon Emissions " sustainability, 2019; 11: 1-19.
15
[16] Kim H-J, Son D-H, Yang W., Kim J-G, Liner Ship Routing with Speed and Fleet Size Optimization: KSCE Journal of Civil Engineering, 2019; 23 (3),1341–1350
16
[17] Homsi, G., Martinelli, R., Vidal, T., and Fagerholt, K., "Industrial and tramp ship routing problems: Closing the gap for real-scale instances," European Journal of Operational Research, 2020; 283(3), 972-990.
17
[18] Dumas, Y., Desrosiers, J., E. Gelinas, Solomon, M.-M., An Optimal Algorithm for the Traveling Salesman Problem with Time Windows, Operations Research, 1995; 43(2):367-371.
18
[19] Eglese, R.W., simulated annealing: A Tool for Operational Research, European Journal of Operational Research, 1990; 46, 271-281.
19
[20] Kennedy, J., Eberhart, R.C., Particle swarm optimization. In: Proc. of the IEEE International Conference on Neural Networks, IEEE service Center, Piscataway, N.J., 1995; 4, 1942-1948.
20
[21] Alhamed A., Alkharashi S., Dong J., Metaheuristic algorithm for ship routing and scheduling problems with time window: Cogent Business & Management ; 2019, 6(1), 1-16
21
ORIGINAL_ARTICLE
A Multi-Visit Heterogeneous Drone Routing Model Considering Recharging Decision in Disaster
The complex nature of disasters has required communities and governments to implement plans to reduce the disturbing effects of these disasters. With the breakdown and destruction of road infrastructure in times of disaster, the need to use an Unmanned Aerial Vehicle (UAV) fleet under the concept of humanitarian logistics has become increasingly essential. Therefore, we present a Multi-Visit Drone Routing Problem in this paper. The relief goods are delivered to the disaster-affected areas by using heterogeneous drones. We use a linear approximation function to calculate energy consumption. We formulated the proposed bi-objective Mixed Integer Linear Programming (MILP) model by a compromise programming method. To validate the proposed model and to show the model’s efficiency, we generate several test problems with the data extracted by experts. The computational results show the satisfactory performance of the model for the delivery of relief items to the damaged nodes by humanitarian drones in the shortest possible time.
https://aie.ut.ac.ir/article_81141_0fe1d002a8890e8db2fa11ebca14ae3a.pdf
2020-01-01
53
73
10.22059/jieng.2021.320964.1756
natural disaster
Humanitarian Logistics
Heterogeneous drones
Linear approximation function
Energy consumption
Mehdi
Changizi
mchangizi@email.kntu.ac.ir
1
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
AUTHOR
Donya
Rahmani
drahmani@kntu.ac.ir
2
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
LEAD_AUTHOR
Reza
Rmezanian
ramezanian@kntu.ac.ir
3
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran.
AUTHOR
[1] Center for Research on the Epidemiology of Disasters (CRED). EM-DAT: The International Disaster Database. Brussels, Belgium: Ecole de Sante Publique, Universite Catholique de Louvain
1
[2] Tricoire, F., Graf, A., and Gutjahr, W. J. (2012). The bi-objective stochastic covering tour problem. Computers and operations research, 39(7), 1582-1592.
2
[3] Abounacer, R., Rekik, M., and Renaud, J. (2014). An exact solution approach for multi-objective location–transportation problem for disaster response. Computers and Operations Research, 41, 83-93.
3
[4] Rabta, B., Wankmüller, C., and Reiner, G. (2018). A drone fleet model for last-mile distribution in disaster relief operations. International Journal of Disaster Risk Reduction, 28, 107-112.
4
[5] Kouadio, I. K., Aljunid, S., Kamigaki, T., Hammad, K., and Oshitani, H. (2012). Infectious diseases following natural disasters: prevention and control measures. Expert review of anti-infective therapy, 10(1), 95-104.
5
[6] Hirschinger, M. (2016). No vehicle means no aid–A paradigm change for the humanitarian logistics business model. In Essays on Supply Chain Management in Emerging Markets (pp. 43-64). Springer Gabler, Wiesbaden.
6
[7] Sudbury, A. W., and Hutchinson, E. B. (2016). A cost analysis of amazon prime air (drone delivery). Journal for Economic Educators, 16(1), 1-12.
7
[8] Glaser, A. (2018). Watch Amazon’s Prime Air make its first public US drone delivery (2017).
8
[9] Vincent, J. (2017). Google’s Project Wing has successfully tested its air traffic control system for drones.
9
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ORIGINAL_ARTICLE
A Comparison of Solution Methods for the Multi-Objective Closed Loop Supply Chains
Increased pressure on natural resources, rising production costs, and multiple disposal challenges resulted in a growing global demand for integrated closed sustainable supply chain networks. In this paper, a bi-objective mixed integer linear programming model is developed to minimize the overall cost and maximize the use of eco-friendly materials and clean technology. The paper evaluates the exact, heuristic, and metaheuristic methods in solving the proposed model in both small and large sizes. The sensitivity analysis was conducted on LP-metric method as it outperformed the other two exact methods in solving the small size problems. The evaluation of LP-metric, modified ε-constraint, and TH as the exact methods, and Lagrange relaxation algorithm as the heuristic method in terms of solution value and CPU time revealed the inability of exact methods in solving the large size problems. The best combination of effective parameters for meta-heuristic algorithms were determined using the Taguchi method. The evaluation of MOPSO, NSGA-II, SPEA-II, and MOEA/D as the metaheuristic methods by means of Number of Pareto Solutions (NPS), Mean Ideal Distance (MID), The Spread of Non-dominance Solutions (SNS), and CPU Time revealed the performance of these methods in solving the proposed model in a large size. The implementation of VIKOR technique identified the SPEA-II as the best method among the meta-heuristic methods. This study provides a holistic view regarding the importance of selecting an appropriate solution methodology based on the problem dimension to ensure obtaining the optimum and accurate solution within the reasonable processing time.
https://aie.ut.ac.ir/article_81143_4f5ad99bbbe3b40ccb51d3183bc0f8aa.pdf
2020-01-01
75
98
10.22059/jieng.2021.321634.1758
Closed-Loop Supply Chain (CLSC)
Exact methods
Lagrange Relaxation Algorithm
Heuristic and Meta-Heuristic Aalgorithms
VIKOR Technique
Omid
Abdolazimi
omid15215@yahoo.com
1
Department of Economy, Kharazmi University, Tehran, Iran.
AUTHOR
Mitra
Salehi Esfandarani
mslhsfnd@memphis.edu
2
Department of Civil Engineering, University of Memphis, Memphis, TN, USA
AUTHOR
Maryam
Salehi
mssfndrn@memphis.edu
3
Department of Civil Engineering, University of Memphis, Memphis, TN, USA.
AUTHOR
Davood
Shishebori
shishebori@yazd.ac.ir
4
Department of Industrial Engineering, Yazd University, Yazd, Iran
LEAD_AUTHOR
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