Explaining the Approach of Traffic Modeling to Vehicle Routing Issues Based on the Paradigm of Green Transportation (Case Study: ZAMZAM Co)

Document Type : Research Paper

Authors

1 Associate Prof. in Industrial Management, University of Tehran, Tehran, Iran

2 Prof. in Industrial Management, University of Tehran, Tehran, Iran

3 Ph.D. Candidate in Management, University of Tehran, Tehran, Iran

Abstract

The purpose of this paper is to explain the most appropriate approaches for traffic modeling in vehicle routing problem based on green transportation paradigm. There are four approaches in literature for modeling of traffic including:  simple, discrete, continuous, and random. Based on the qualitative meta-analysis method, 67 sources of green transportation were examined descriptively (in terms of using the above-mentioned approaches based on descriptive statistics) and evaluating (assessing the strengths and weaknesses of the approaches), which resulted in It was better to use a continuous approach. Regarding the existence of different patterns of modeling in the continuous approach, in order to achieve the appropriate model, Zamzam's distribution network was used based on Tehran Pars sales data on 21 August 2016. The results showed that existing patterns were inappropriate and that a proper pattern for the Zamzam distribution network should be developed. The developed pattern consists of two indicators: 1) the definition of the virtual node; and 2) the calculation of the average speed, taking into account multiple traffic conditions. This pattern corrects the weaknesses of previous patterns based on continuous approach.

Keywords

References

Alinaghian, M. & Zamani, M. (2016). Three new heuristic algorithms for the fleet size and mix green vehicle routing problem. Journal of Industrial and Systems Engineering 9(2), 88-101.
Alinaghian, M. & Naderipour, M. (2016). A novel comprehensive macroscopic model for time-dependent vehicle routing problem with multi-alternative graph to reduce fuel consumption: A case study. Computers & Industrial Engineering, 99, 210-222.
Ando, N. & Taniguchi, E. (2006). Travel time reliability in vehicle routing and scheduling with time windows. Networks and Spatial Economics, 6(3), 71-82.
Behnke, M. & Kirschstein, T. (2017). The impact of path selection on GHG emissions in city logistics. Transportation Research, 106, 320-336.
Bektas, T. & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45(8), 1232-1250.
Bjorklund, M. (2011). Influence from the business environment on environmental purchasing — Drivers and hinders of purchasing green transportation services. Journal of Purchasing & Supply Management, 17(1), 11-22.
Chen, P. & Nie, Y. (2015). Stochastic optimal path problem with relays. Transportation Research, 59, 48-65.
Çimen, M. & Soysal, M. (2017). Time-dependent green vehicle routing problem with stochastic vehicle speeds: An approximate dynamic programming algorithm. Transportation Research, 54, 82-98.
Demir, E., Bektas, T. & Laporte, G. (2012). An adaptive large neighborhood search heuristic for the pollution-routing problem. European Journal of Operational Research, 223(2), 346-359.
Demir, E., Bektas, T, & Laporte, G. (2013). The bi-objective Pollution-Routing Problem. European Journal of Operational Research, 232(3), 464-478.
Demir, E., Bektaş, T. & Laporte, G. (2014). A review of recent research on green road freight transportation. European Journal of Operational Research, 237(3), 775-793.
Demir, E. (2012). Models and Algorithms for the Pollution-Routing Problem and Its Variations. PhD thesis, University of Southampton, Southampton.
El-Berishy, N.M. & Scholz-Reiter, B. (2016). Development and implementation of a green logistics-oriented framework for batch process industries: two case studies. Logistics Research, 9, 1-10.
Faulin, J., Juan, A., Lera, F. & Grasman, S. (2011). Solving the Capacitated Vehicle Routing Problem with Environmental Criteria Based on Real Estimations in Road Transportation: A Case Study. Procedia-Social and Behavioral Sciences, 20, 323-334.
Figliozzi, M.A. (2010). Vehicle routing problem for emissions minimization. Transportation Research Record: Journal of the Transportation Research Board, 2197(1), 1-7.
Figliozzi, M.A. (2011). The impacts of congestion on time-definitive urban freight distribution networks CO2 emission levels: Results from a case study in Portland, Oregon. Transportation Research Part C: Emerging Technologies, 19(5), 766-778.
Franceschetti, A., Honhon, D., Woensel, T V., Bektas, T. & Laporte, G. (2013). The time-dependent pollution routing problem. Transportation Research Methodological, 56, 265-293.
Gaur, D.R., Mudgal, A. & Singh, R.R. (2013). Routing vehicles to minimize fuel consumption. Operations Research Letters, 41(6), 576-580.
Goodchild, A. (2011). Cost, emissions, and customer service trade-off analysis in pickup and delivery systems (Civil & Environmental Engineering, Trans). Washington: University of Washington.
Hao, X. (2010). A fuel consumption objective ofVRP and the genetic algorithm. Paper presented at the IEEE Conference, Henan, China.
Hassanzadeh, A. & Rasti-Barzoki, M. (2017). Minimizing total resource consumption and total tardiness penalty in a resource allocation supply chain scheduling and vehicle routing problem. Applied Soft Computing, 58, 307-323.
Huang, Y., Shi, C., Zhao, L. & Woensel, T. V. (2012). A study on carbon reduction in the vehicle routing problem with simultaneous pickups and deliveries. Paper presented at the Service Operations and Logistics, and Informatics (SOLI), 2012 IEEE International Conference.
Huang, Y., Zhao, L., Woensel, T. V. & Gross, J.P. (2017). Time-dependent vehicle routing problem with path flexibility. Transportation Research, 95, 169-195.
Ichoua, S., Gendreau, M. & Potvin, J.Y. (2003). Vehicle dispatching with time-dependent travel times. European Journal of Operational Research, 144(2), 379–396.
Jabali, O., Woensel, T. V. & de Kok, A.G. (2012). Analysis of travel times and CO2 emissions in time-dependent vehicle routing. Production and Operations Management, 21(6), 1060-1074.
Jemai, J., Zekri, M. & Mellouli, K. (2012). An NSGA-II algorithm for the green vehicle routing problem. Paper presented at the Evolutionary Computation in Combinatorial Optimization: 12th European Conference, Malaga, Spain.
Kara, I., Kara, B.Y. & Yetis, MK. (2007). Energy Minimizing Vehicle Routing Problem. International Conference on Combinatorial Optimization and Applications, PP. 62-71.
Koç, Ç., Bektaş, T., Jabali, O., Laporte, G. (2014). The fleet size and mix pollution-routing problem. Transportation Research Part B: Methodological, 70, 239-254.
Koç, Ç., Bektaş, T., Jabali, O., Laporte, G. (2016). The impact of depot location, fleet composition and routing on emissions in city logistics. Transportation Research Part B, 84, 81-102.
Kopfer, H.W, Schönberger, J. & Kopfer, H. (2013). Reducing greenhouse gas emissions of a heterogeneous vehicle fleet. Flexible Services and Manufacturing Journal, 26(1-2), 221-248.
Kuo, Y. & Wang, C.C. (2011). Optimizing the VRP by minimizing fuel consumption. Management of Environmental Quality, 22(4), 440-450.
Kuo, Y. (2010). Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Computers & Industrial Engineering, 59(1), 157-165.
Kwon, Y.J, Choi, Y.J. & Lee, D.H. (2013). Heterogeneous fixed fleet vehicle routing considering carbon emission. Transportation Research Part D: Transport and Environment, 23, 81-89.
Leggieri, V. & Haouari, M. (2017). A practical solution approach for the green vehicle routing problem. Transportation Research Part E, 104, 97-112.
Li, J. (2012). Vehicle routing problem with time windows for reducing fuel consumption. Journal of Computers, 7(12), 3020-3027.
Li, Q., Nie, Y., Vallamsundar, S., Lin, J. & Homem-de-Mello, T. (2016). Finding Efficient and Environmentally Friendly Paths for Risk-Averse Freight Carriers. Netw Spat Econ, 16(1), 255- 275.
Lin, C., Choy, K.L., Ho, G.T.S., Chung, S.H. & Lam, H.Y. (2014). Survey of Green Vehicle Routing Problem: Past and future trends. Expert Systems with Applications, 41(4), 1118–1138.
Maden, W., Eglese, R.W. & Black, D. (2010). Vehicle routing and scheduling with time-varying data: A case study. Journal of the Operational Research Society, 61(3), 515-522.
Malandraki, C. & Daskin, M.S. (1992). Time dependent vehicle routing problems: formulations, properties and heuristic algorithms. Transportation Science, 26(3), 185-200.
Morovati-Sharifabadi, A. & Bavarkob, M. (2016). Solving of vehicle routing problem with cross docking by scatter search algorithm. Journal of industrial management, 8(1), 75-96. (in Persian)
Naderipour, M. & Alinaghian, M. (2016). Measurement, evaluation and minimization of CO 2 , NO x , and CO emissions in the open time dependent vehicle routing problem. Measurement, 90, 443-452.
Oberscheider, M., Zazgornik, J., Henriksen, C.B., Gronalt, M. & Hirsch, P. (2013). Minimizing driving times and greenhouse gas emissions in timber transport with a near-exact solution approach. Scandinavian Journal of Forest Research, 28(5), 493-506.
Omidvar, A. & Tavakkoli-Moghaddam, R. (2012). Sustainable vehicle routing: Strategies for congestion management and refueling scheduling. Paper presented at the Energy Conference and Exhibition (ENERGYCON), 2012 IEEE International, Florence, Italy.
Omidvar, A. (2013). Models of sustainable transport vehicle routing problem with regard to reducing pollution and fuel consumption. MSc thesis, University of Tehran, Tehran. (in Persian)
Paksoy, T. & Özceylan, E. (2013). Environmentally conscious optimization of supply chain networks. Forthcoming in Journal of the Operational Research Society, 65(6), 855-872.
Palmer, A. (2007). The development of an integrated routing and carbon dioxide emissions model for goods vehicles. PhD thesis, Cranfield University, Bedford.
Pan, S., Ballot, E. & Fontane, F. (2010). The reduction of greenhouse gas emissions from freight transport by pooling supply chains. International Journal of Production Economics, 143(1), 86-94.
Peiying, Y., Jiafu, T. & Yang, Y.U. (2013). Based on low carbon emissions cost model and algorithm for vehicle routing and scheduling in picking up and delivering customers to airport service. Paper presented at the 25th Chinese Control and Decision Conference (CCDC). The IEEE International Conference, Guiyang, China.
Pitera, K., Sandoval, F. & Goodchild, A. (2011). Evaluation of Emmissions Reduction in Urban Pickup Systems. Transportation Research Record: Journal of the Transportation Research Board, 2224, 8-16.
Pradenas, L., Oportus, B. & Parada, V. (2013). Mitigation of greenhouse gas emissions in vehicle routing problems with backhauling. Expert Systems with Applications, 40(8), 2985-2991.
Psaraftis, H.N. & Tsitsiklis, J.N. (1993). Dynamic shortest paths in acyclic networks with Markovian arc costs. Operations Research, 41(1), 91-101.
Qian, J. (2012). Fuel emission optimization in vehicle routing problems with time-varying speeds. PhD thesis, Lancaster University, Lancaster, United Kingdom.
Qian, J. & Eglese, R. (2016). Fuel emissions optimization in vehicle routing problems with time-varying speeds. European Journal of Operational Research, 248, 840-848.
Raeesi, R. (2012). A Novel Optimization Method for Capacitated Vehicle Routing Problem with a Green Transportation Approach, MSc thesis, Persian Gulf University, Bushehr. (in Persian)
Rahmani, S. (2013). Providing a transport model to determine the optimal route due to economic and environmental parameters and the maximum capacity of the fleet, MSc thesis, University of Tehran, Tehran. (in Persian)
Ramos, T.R.P., Gomes, M.I. & Barbosa- póvoa, A.P. (2012). Minimizing CO 2 emissions in a recyclable waste collection system with multiple depots. Paper presented at the EUROMA/POMS Joint Conference, Amsterdam, The Netherlands.
Rao, W. & Jin, C. (2012). A model of vehicle routing problem minimizing energy consumption in urban environment. Paper presented at the 2012 Asian conference of management science and Applications.
Saberi, M. & Verbas, I. Ö. (2012). Continuous approximation model for the vehicle routing problem for emissions minimization at the strategic level. Journal of Transportation Engineering, 138(11), 1368-1376.
Salimifard, K.H., Ghasemieh, R. & Raghibzadeh, A. (2013). A novel mathemathical model for time dependence vehicle routing problem. Paper presented at conference of management and acounting: 1st conference, Natanz, Iran.
(in Persian)
Satak, M., Habibi, M., Karimi, H. & Abedzadeh, M. (2013). Modelling and Solving Time Dependent Vehicle Routing Problem with Semi Soft Time Windows in Multi graphs. Transportation Research Journal (TRJ), 10(3), 263-280.
(in Persian)
Shen, Y., Potvin, J..Y., Rousseau, J.M. & Roy, S. (1995). A computer assistant for vehicle dispatching with learning capabilities Annals of Operations Research, 61(1), 189-211.
Soysal, M. & Imen, M. C. (2017). A Simulation Based Restricted Dynamic Programming Approach for the Green Time Dependent Vehicle Routing Problem. Computers and Operations Research, 88, 297-305.
Soysal, M., Bloemhof-Ruwaard, J. M. & Bektaş, T. (2015). The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations. International Journal of Production Economics, 164, 366-378.
Suzuki, Y. (2011). A new truck-routing approach for reducing fuel consumption and pollutants emission. Transportation Research Part D: Transport and Environment, 16(1), 73-77.
Tajik, N., Tavakkoli-Moghaddam, R., Vahdani, B. & Mousavi, S.M. (2014). A robust optimization approach for pollution routing problem with pickup and delivery under uncertainty. Journal of Manufacturing Systems, 33(2), 277-286.
Tajik, N. (2013). Multi-objective optimization of the routing green transport modeling under uncertainty. MSc thesis, University of Tehran, Tehran.
(in Persian)
Tavakoli, R. (2012). Solving the Transportation routing problem with time-dependent speed and travel time window. MSc thesis, University of Tehran, Tehran. (in Persian)
Toro, E. M., Franco, J. F., Echeverri, M. G. & Guimarães, F. G. (2017). A Multi-Objective Model for the Green Capacitated Location-Routing Problem Considering Environmental Impact. Computers & Industrial Engineering, 110, 114-125.
Treitl, S., Nolz, P.C. & Jammernegg, W. (2014). Incorporating environmental aspects in an inventory routing problem. Flexible Services and Manufacturing Journal, 26(1-2), 143-169.
Ubeda, S., Arcelus, F.J. & Faulin, J. (2011). Green logistics at Eroski: A case study. International Journal of Production Economics, 131(1), 44-51.
Urquhart, N., Hart, E. & Scott, C. (2010). Building low CO 2 solutions to the vehicle routing problem with Time Windows using an evolutionary algorithm. Paper presented at the Evolutionary Computation (CEC), 2010 IEEE Congress, Barcelona, Spain.
Urquhart, N., Scott, C. & Hart, E. (2010). Using an Evolutionary Algorithm to Discover Low CO2 Tours within a Travelling Salesman Problem. Applications of Evolutionary Computation, 6025, 421-430.
Van Woensel, T., Kerbache, L., Peremans, H. & Vandaele, N. (2007). A Queueing Framework for Routing Problems with Time-dependent Travel Times. Journal of Mathematical Modelling and Algorithms, 6(1), 151-173.
Wolf, F. (1986). Meta-analysis: Quantitative methods for research synthesis. Beverly Hills CA: Sage.
Xiao, Y., Zhao, Q., Kaku, I. & Xu, Y. (2012). Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Computers & Operations Research, 39(7), 1419-1431.
Xiao, Y. & Konak, A. (2015). A simulating annealing algorithm to solve the green vehicle routing & scheduling problem with hierarchical objectives and weighted tardiness. Applied Soft Computing, 34, 372-388.
Xiao, Y. & Konak, A. (2016). The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transportation Research Part E, 88, 146-166.
Yong, P. & Xiaofeng, W. (2009). Research on a vehicle routing schedule to reduce fuel consumption. Paper presented at the Measuring Technology and Mechatronics Automation, Shanghai, China.
Zhang, J., Zhao, Y., Xue, W. & Li, J. (2015). Vehicle routing problem with fuel consumption and carbon emission. International Journal of Production Economics, 170, 234-242.
Industrial Management Journal
Volume 9, Issue 2
2017
Pages 217-244

Files

Share

How to cite

Statistics