How to cite this paper
Wu, X., Hu, D & Gao, T. (2025). Green pickup and delivery problem with private drivers for crowd-shipping distribution considering traffic congestion.International Journal of Industrial Engineering Computations , 16(1), 129-146.
Refrences
Archetti, C., Savelsbergh, M., & Speranza, M. G. (2016). The vehicle routing problem with occasional drivers. European Journal of Operational Research, 254, 472-480. https://doi.org/10.1016/j.ejor.2016.03.049
Archetti, C., Guerriero. F., & Macrina, G. (2021). The online vehicle routing problem with occasional drivers. Computers & Operations Research, 127, 105144. https://doi.org/10.1016/j.cor.2020.105144
Barth, M., Younglove, T., & Scora, G. (2005). Development of a heavy-duty diesel modal emissions and fuel consumption model. Tech. rep. UC Berkeley: Research report California Partners for Advanced Transit and Highways (PATH).
Bektas, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45, 1232-1250. https://doi.org/10.1016/j.trb.2011.02.004
Bortolini, M., Calabrese, F., & Galizia, F. G. (2022). Crowd logistics: a survey of successful applications and implementation potential in northern Italy. Sustainability, 14(24), 16881. https://doi.org/10.3390/su142416881
Boysen, N., Emde, S., & Schwerdfeger, S. (2022). Crowdshipping by employees of distribution centers: Optimization approaches for matching supply and demand. European Journal of Operational Research, 296(2), 539-556. https://doi.org/10.1016/j.ejor.2021.04.002
Chen, J., Liao, W., & Yu, C. (2021). Route optimization for cold chain logistics of front warehouses based on traffic congestion and carbon emission. Computers & Industrial Engineering, 161, 107663. https://doi.org/10.1016/j.cie.2021.107663
Cirovic, G., Pamucar, D., & Bozanic, D. (2014). Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy mode. Expert Systems with Applications, 41(9), 4249-4258. https://doi.org/10.1016/j.eswa.2014.01.005
Dahle, L., Andersson, H., Christiansen, M., & Speranza, M. G. (2019). The pickup and delivery problem with time windows and occasional drivers. Computers & Operations Research, 109, 122-133. https://doi.org/10.1016/j.cor.2019.04.023
Ghaderi, H., Tsai, P., Zhang, L., & Moayedikia, A. (2021). An integrated crowdshipping framework for green last mile delivery. Sustainable Cities and Society, 78, 103552. https://doi.org/10.1016/j.scs.2021.103552
Cheng, C., Yang, P., Qi, M., & Rousseau, L. M. (2016). Modeling a green inventory routing problem with a heterogeneous fleet. Transportation Research Part E: Logistics and Transportation Review, 97, 97-112. https://doi.org/10.1016/j.tre.2016.11.001
EMEP. (2013). Road vehicle tyre and brake wear, road surface wear. https://www.eea.europa.eu/publications/emep-eea-guidebook-2013/part-b-sectoral-guidance-chapters/1-eSperanzanergy/1-a-combustion/1-a-3-b-road-tyre/view
EMEP. (2019). Road transport. https://www.eea.europa.eu/publications/emep-eea-guidebook-2019/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion/1-a-3-b-i/view
Grigoratos, T., Mamakos, A., Arndt, M., Lugovyy, D., Anderson, A., Hafenmayer, C., Moisio, M., Vanhanen, J., Frazee, R., Agudelo, C., & Giechaskiel, B. (2023). Characterization of particle number setups for measuring brake particle emissions and comparison with exhaust setups. Atmosphere, 14(1), 103. https://doi.org/10.3390/atmos14010103
Hla, Y. A. A., Othman, M., & Saleh, Y. (2019). Optimising an eco-friendly vehicle routing problem model using regular and occasional drivers integrated with driver behaviour control. Journal of Cleaner Production, 234, 984-1001. https://doi.org/10.1016/j.jclepro.2019.06.156
Ichoua, G. M., & Potvin, J. Y. (2003). Vehicle dispatching with time-dependent travel times. European Journal of Operational Research, 144(2), 379-396. https://doi.org/10.1016/S0377-2217(02)00147-9
Kalayci, C. B., & Kaya, C. (2016). An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery. Expert Systems with Applications, 956(66), 163-175. https://doi.org/10.1016/j.eswa.2016.09.017
Kara, I., Kara, B. Y., & Yetis, M. K. (2007). Energy minimizing vehicle routing problem. In: International Conference on Combinatorial Optimization and Applications Springer, pp. 62-71.
Kitjacharoenchai, P., Min, B. C., & Lee, S. (2020). Two echelon vehicle routing problem with drones in last mile delivery. International Journal of Production Economics, 225, 107598. https://doi.org/10.1016/j.ijpe.2019.107598
Le, T. V., Stathopoulos, A., Woensel, T. V., & Ukkusuri, S. V. (2019). Supply, demand, operations, and management of crowd-shipping services: A review and empirical evidence. Transportation Research Part C: Emerging Technologies, 103, 83-103. https://doi.org/10.1016/j.trc.2019.03.023
Liu, Y., Chen, H., Li, Y., Gao, J., Dave, K., Chen, J., Li, T., & Tu, R. (2022). Exhaust and non-exhaust emissions from conventional and electric vehicles: A comparison of monetary impact values. Journal of Cleaner Production, 331, 129965. https://doi.org/10.1016/j.jclepro.2021.129965
Lou, P., Zhou, Z., & Zeng, Y. (2024). Vehicle routing problem with time windows and carbon emissions: a case study in logistics distribution. Environmental Science and Pollution Research, 31(11), 16177-16187. https://doi.org/10.1007/s11356-024-31927-9
Ma, B., Hu, D., Chen, X., Wang, Y., & Wu, X. (2021). The vehicle routing problem with speed optimization for shared autonomous electric vehicles service. Computers & Industrial Engineering, 161(8), 107614. https://doi.org/10.1016/j.cie.2021.107614
Macrina, G., Pugliese, L. D. P., & Guerriero, F. (2020). Crowd-shipping: a new efficient and eco-friendly delivery strategycrowdsourced delivery—a dynamic pickup and delivery problem with ad hoc drivers. Procedia Manufacturing, 42, 483-487. https://doi.org/10.1016/j.promfg.2020.02.048
Masmoudi, M. A., Hosny, M., Demir, E., Genikomsakis, K. N., & Cheikhrouhou, N. (2018). The dial-a-ride problem with electric vehicles and battery swapping stations. Transportation Research Part E: Logistics and Transportation Review, 118, 392-420. https://doi.org/10.1016/j.tre.2018.08.005
Meyer, M., & Dallmann, T. (2022). Air quality and health impacts of diesel truck emissions in New York City and policy implications. https://www.trueinitiative.org/media/792240/true-nyc-report-fv.pdf
Michiels, H., Mayeres, I., Panis, I. L., Nocker, L. D., Deutsch, F., & Lefebvre, W. (2012). PM2.5 and NOx from traffic: Human health impacts, external costs and policy implications from the Belgian perspective. Transportation Research Part D: Transport and Environment, 17(8), 569-577. https://doi.org/10.1016/j.trd.2012.07.001
Niu, Y., Yang, Z., Chen, P., & Xiao, J. (2018). Optimizing the green open vehicle routing problem with time windows by minimizing comprehensive routing cost. Journal of Cleaner Production, 171, 962-971. https://doi.org/10.1016/j.jclepro.2017.10.001
Peng, S., Park, W., Eltoukhy, A. E., & Xu, M. (2024). Outsourcing service price for crowd-shipping based on on-demand mobility services. Transportation Research Part E: Logistics and Transportation Review, 193, 103451. https://doi.org/10.1016/j.tre.2024.103451
Pisinger, D., & Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers & Operations Research, 34(8), 2403-2435. https://doi.org/10.1016/j.cor.2005.09.012
Poonthalir, G., & Nadarajan, R. (2018). A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP). Expert Systems with Applications, 100, 131-144. https://doi.org/10.1016/j.eswa.2018.01.052
Pralet, C. (2023). Iterated maximum large neighborhood search for the traveling salesman problem with time windows and its time-dependent version. Computers & Operations Research, 150, 106078. https://doi.org/10.1016/j.cor.2022.106078
Punel, A., & Stathopoulos, A. (2017). Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Research Part E: Logistics Transportation Review, 105, 18-38. https://doi.org/10.1016/j.tre.2017.06.007
Rafael, A., Roel, G., & Wouter, D. (2023). Strategic multi-echelon and cross-modal CO2 emissions calculation in parcel distribution networks: first step toward a common language. Transportation Research Record, 2677(6), 620-630. 10.1177/03611981221149431
Rexeis, M., & Hausberger, S. (2009). Trend of vehicle emission levels until 2020- Prognosis based on current vehicle measurements and future emission legislation. Atmospheric Environment, 43, 4689-4698. https://doi.org/10.1016/j.atmosenv.2008.09.034
Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455-472. https://doi.org/10.1016/j.cor.2016.01.018
Scerri, M. M., Weinbruch, S., Delmaire, G., Mercieca, N., Nolle, M., Prati, P., & Massabò, D. (2023). Exhaust and non-exhaust contributions from road transport to PM10 at a Southern European traffic site. Environmental Pollution, 316, 120569. https://doi.org/10.1016/j.envpol.2022.120569
Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. International conference on principles and practice of constraint programming, Springer, Berlin, Heidelberg, pp. 417-431, 10.1007/3-540-49481-2_30
Shi, Y., Zhou, Y., Ye, W., & Zhao, Q. Q. (2020). A relative robust optimization for a vehicle routing problem with time-window and synchronized visits considering greenhouse gas emissions. Journal of Cleaner Production, 275, 124112. https://doi.org/10.1016/j.jclepro.2020.124112
Su, E., Qin, H., Li, J., & Pan, K. (2023). An exact algorithm for the pickup and delivery problem with crowdsourced bids and transshipment. Transportation Research Part B: Methodological, 177, 102831. https://doi.org/10.1016/j.trb.2023.102831
Tao, Y., Zhou, H., & Lai, X. (2023). The pickup and delivery problem with multiple depots and dynamic occasional drivers in crowdshipping delivery. Computers & Industrial Engineering, 182, 109440. https://doi.org/10.1016/j.cie.2023.109440
Timmers, V. R. J. H., & Achten, P. A. J. (2016). Non-exhaust PM emissions from electric vehicles. Atmospheric Environment, 134, 10-17. https://doi.org/10.1016/j.atmosenv.2016.03.017
Wolfinger, D. (2020). A large neighborhood search for the pickup and delivery problem with time windows, split loads and transshipments. Computers & Operations Research, 126, 105110. https://doi.org/10.1016/j.cor.2020.105110
Wu, F., & Dong, M. (2023). Eco-routing problem for the delivery of perishable products. Computers & Operations Research, 154, 106198. https://doi.org/10.1016/j.cor.2023.106198
Yao, K., Yang, B., & Zhu, X. (2019). Low-carbon vehicle routing problem based on realtime traffic conditions. Computer Engineering and Applications, 55(03), 231-237. http://cea.ceaj.org/EN/Y2019/V55/I3/231
Zhan, X., Szeto, W., & Wang, Y. (2023). The ride-hailing sharing problem with parcel transportation. Transportation Research Part E: Logistics Transportation Review, 172, 1033073. https://doi.org/10.1016/j.tre.2023.103073
Archetti, C., Guerriero. F., & Macrina, G. (2021). The online vehicle routing problem with occasional drivers. Computers & Operations Research, 127, 105144. https://doi.org/10.1016/j.cor.2020.105144
Barth, M., Younglove, T., & Scora, G. (2005). Development of a heavy-duty diesel modal emissions and fuel consumption model. Tech. rep. UC Berkeley: Research report California Partners for Advanced Transit and Highways (PATH).
Bektas, T., & Laporte, G. (2011). The pollution-routing problem. Transportation Research Part B: Methodological, 45, 1232-1250. https://doi.org/10.1016/j.trb.2011.02.004
Bortolini, M., Calabrese, F., & Galizia, F. G. (2022). Crowd logistics: a survey of successful applications and implementation potential in northern Italy. Sustainability, 14(24), 16881. https://doi.org/10.3390/su142416881
Boysen, N., Emde, S., & Schwerdfeger, S. (2022). Crowdshipping by employees of distribution centers: Optimization approaches for matching supply and demand. European Journal of Operational Research, 296(2), 539-556. https://doi.org/10.1016/j.ejor.2021.04.002
Chen, J., Liao, W., & Yu, C. (2021). Route optimization for cold chain logistics of front warehouses based on traffic congestion and carbon emission. Computers & Industrial Engineering, 161, 107663. https://doi.org/10.1016/j.cie.2021.107663
Cirovic, G., Pamucar, D., & Bozanic, D. (2014). Green logistic vehicle routing problem: Routing light delivery vehicles in urban areas using a neuro-fuzzy mode. Expert Systems with Applications, 41(9), 4249-4258. https://doi.org/10.1016/j.eswa.2014.01.005
Dahle, L., Andersson, H., Christiansen, M., & Speranza, M. G. (2019). The pickup and delivery problem with time windows and occasional drivers. Computers & Operations Research, 109, 122-133. https://doi.org/10.1016/j.cor.2019.04.023
Ghaderi, H., Tsai, P., Zhang, L., & Moayedikia, A. (2021). An integrated crowdshipping framework for green last mile delivery. Sustainable Cities and Society, 78, 103552. https://doi.org/10.1016/j.scs.2021.103552
Cheng, C., Yang, P., Qi, M., & Rousseau, L. M. (2016). Modeling a green inventory routing problem with a heterogeneous fleet. Transportation Research Part E: Logistics and Transportation Review, 97, 97-112. https://doi.org/10.1016/j.tre.2016.11.001
EMEP. (2013). Road vehicle tyre and brake wear, road surface wear. https://www.eea.europa.eu/publications/emep-eea-guidebook-2013/part-b-sectoral-guidance-chapters/1-eSperanzanergy/1-a-combustion/1-a-3-b-road-tyre/view
EMEP. (2019). Road transport. https://www.eea.europa.eu/publications/emep-eea-guidebook-2019/part-b-sectoral-guidance-chapters/1-energy/1-a-combustion/1-a-3-b-i/view
Grigoratos, T., Mamakos, A., Arndt, M., Lugovyy, D., Anderson, A., Hafenmayer, C., Moisio, M., Vanhanen, J., Frazee, R., Agudelo, C., & Giechaskiel, B. (2023). Characterization of particle number setups for measuring brake particle emissions and comparison with exhaust setups. Atmosphere, 14(1), 103. https://doi.org/10.3390/atmos14010103
Hla, Y. A. A., Othman, M., & Saleh, Y. (2019). Optimising an eco-friendly vehicle routing problem model using regular and occasional drivers integrated with driver behaviour control. Journal of Cleaner Production, 234, 984-1001. https://doi.org/10.1016/j.jclepro.2019.06.156
Ichoua, G. M., & Potvin, J. Y. (2003). Vehicle dispatching with time-dependent travel times. European Journal of Operational Research, 144(2), 379-396. https://doi.org/10.1016/S0377-2217(02)00147-9
Kalayci, C. B., & Kaya, C. (2016). An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery. Expert Systems with Applications, 956(66), 163-175. https://doi.org/10.1016/j.eswa.2016.09.017
Kara, I., Kara, B. Y., & Yetis, M. K. (2007). Energy minimizing vehicle routing problem. In: International Conference on Combinatorial Optimization and Applications Springer, pp. 62-71.
Kitjacharoenchai, P., Min, B. C., & Lee, S. (2020). Two echelon vehicle routing problem with drones in last mile delivery. International Journal of Production Economics, 225, 107598. https://doi.org/10.1016/j.ijpe.2019.107598
Le, T. V., Stathopoulos, A., Woensel, T. V., & Ukkusuri, S. V. (2019). Supply, demand, operations, and management of crowd-shipping services: A review and empirical evidence. Transportation Research Part C: Emerging Technologies, 103, 83-103. https://doi.org/10.1016/j.trc.2019.03.023
Liu, Y., Chen, H., Li, Y., Gao, J., Dave, K., Chen, J., Li, T., & Tu, R. (2022). Exhaust and non-exhaust emissions from conventional and electric vehicles: A comparison of monetary impact values. Journal of Cleaner Production, 331, 129965. https://doi.org/10.1016/j.jclepro.2021.129965
Lou, P., Zhou, Z., & Zeng, Y. (2024). Vehicle routing problem with time windows and carbon emissions: a case study in logistics distribution. Environmental Science and Pollution Research, 31(11), 16177-16187. https://doi.org/10.1007/s11356-024-31927-9
Ma, B., Hu, D., Chen, X., Wang, Y., & Wu, X. (2021). The vehicle routing problem with speed optimization for shared autonomous electric vehicles service. Computers & Industrial Engineering, 161(8), 107614. https://doi.org/10.1016/j.cie.2021.107614
Macrina, G., Pugliese, L. D. P., & Guerriero, F. (2020). Crowd-shipping: a new efficient and eco-friendly delivery strategycrowdsourced delivery—a dynamic pickup and delivery problem with ad hoc drivers. Procedia Manufacturing, 42, 483-487. https://doi.org/10.1016/j.promfg.2020.02.048
Masmoudi, M. A., Hosny, M., Demir, E., Genikomsakis, K. N., & Cheikhrouhou, N. (2018). The dial-a-ride problem with electric vehicles and battery swapping stations. Transportation Research Part E: Logistics and Transportation Review, 118, 392-420. https://doi.org/10.1016/j.tre.2018.08.005
Meyer, M., & Dallmann, T. (2022). Air quality and health impacts of diesel truck emissions in New York City and policy implications. https://www.trueinitiative.org/media/792240/true-nyc-report-fv.pdf
Michiels, H., Mayeres, I., Panis, I. L., Nocker, L. D., Deutsch, F., & Lefebvre, W. (2012). PM2.5 and NOx from traffic: Human health impacts, external costs and policy implications from the Belgian perspective. Transportation Research Part D: Transport and Environment, 17(8), 569-577. https://doi.org/10.1016/j.trd.2012.07.001
Niu, Y., Yang, Z., Chen, P., & Xiao, J. (2018). Optimizing the green open vehicle routing problem with time windows by minimizing comprehensive routing cost. Journal of Cleaner Production, 171, 962-971. https://doi.org/10.1016/j.jclepro.2017.10.001
Peng, S., Park, W., Eltoukhy, A. E., & Xu, M. (2024). Outsourcing service price for crowd-shipping based on on-demand mobility services. Transportation Research Part E: Logistics and Transportation Review, 193, 103451. https://doi.org/10.1016/j.tre.2024.103451
Pisinger, D., & Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers & Operations Research, 34(8), 2403-2435. https://doi.org/10.1016/j.cor.2005.09.012
Poonthalir, G., & Nadarajan, R. (2018). A Fuel Efficient Green Vehicle Routing Problem with varying speed constraint (F-GVRP). Expert Systems with Applications, 100, 131-144. https://doi.org/10.1016/j.eswa.2018.01.052
Pralet, C. (2023). Iterated maximum large neighborhood search for the traveling salesman problem with time windows and its time-dependent version. Computers & Operations Research, 150, 106078. https://doi.org/10.1016/j.cor.2022.106078
Punel, A., & Stathopoulos, A. (2017). Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Research Part E: Logistics Transportation Review, 105, 18-38. https://doi.org/10.1016/j.tre.2017.06.007
Rafael, A., Roel, G., & Wouter, D. (2023). Strategic multi-echelon and cross-modal CO2 emissions calculation in parcel distribution networks: first step toward a common language. Transportation Research Record, 2677(6), 620-630. 10.1177/03611981221149431
Rexeis, M., & Hausberger, S. (2009). Trend of vehicle emission levels until 2020- Prognosis based on current vehicle measurements and future emission legislation. Atmospheric Environment, 43, 4689-4698. https://doi.org/10.1016/j.atmosenv.2008.09.034
Ropke, S., & Pisinger, D. (2006). An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science, 40(4), 455-472. https://doi.org/10.1016/j.cor.2016.01.018
Scerri, M. M., Weinbruch, S., Delmaire, G., Mercieca, N., Nolle, M., Prati, P., & Massabò, D. (2023). Exhaust and non-exhaust contributions from road transport to PM10 at a Southern European traffic site. Environmental Pollution, 316, 120569. https://doi.org/10.1016/j.envpol.2022.120569
Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problems. International conference on principles and practice of constraint programming, Springer, Berlin, Heidelberg, pp. 417-431, 10.1007/3-540-49481-2_30
Shi, Y., Zhou, Y., Ye, W., & Zhao, Q. Q. (2020). A relative robust optimization for a vehicle routing problem with time-window and synchronized visits considering greenhouse gas emissions. Journal of Cleaner Production, 275, 124112. https://doi.org/10.1016/j.jclepro.2020.124112
Su, E., Qin, H., Li, J., & Pan, K. (2023). An exact algorithm for the pickup and delivery problem with crowdsourced bids and transshipment. Transportation Research Part B: Methodological, 177, 102831. https://doi.org/10.1016/j.trb.2023.102831
Tao, Y., Zhou, H., & Lai, X. (2023). The pickup and delivery problem with multiple depots and dynamic occasional drivers in crowdshipping delivery. Computers & Industrial Engineering, 182, 109440. https://doi.org/10.1016/j.cie.2023.109440
Timmers, V. R. J. H., & Achten, P. A. J. (2016). Non-exhaust PM emissions from electric vehicles. Atmospheric Environment, 134, 10-17. https://doi.org/10.1016/j.atmosenv.2016.03.017
Wolfinger, D. (2020). A large neighborhood search for the pickup and delivery problem with time windows, split loads and transshipments. Computers & Operations Research, 126, 105110. https://doi.org/10.1016/j.cor.2020.105110
Wu, F., & Dong, M. (2023). Eco-routing problem for the delivery of perishable products. Computers & Operations Research, 154, 106198. https://doi.org/10.1016/j.cor.2023.106198
Yao, K., Yang, B., & Zhu, X. (2019). Low-carbon vehicle routing problem based on realtime traffic conditions. Computer Engineering and Applications, 55(03), 231-237. http://cea.ceaj.org/EN/Y2019/V55/I3/231
Zhan, X., Szeto, W., & Wang, Y. (2023). The ride-hailing sharing problem with parcel transportation. Transportation Research Part E: Logistics Transportation Review, 172, 1033073. https://doi.org/10.1016/j.tre.2023.103073