Main Article Content
This research focuses on the third-party logistics (3PL) management in sustainable reverse logistics industry that involves fuel consumption and emission concerns based on the comprehensive modal emission model (CMEM) in transportation operations on either deliver finished products to customers or pick-up malfunctioned/expired products or perform both operations for recycling or waste management at the depot. We formulated a novel mixed-integer linear programming (MILP) model for an extension of the green vehicle routing problem with mixed and simultaneous pickup and delivery problem, time windows, and road types (G-VRPMSPDTW-RT) that yields optimal solutions and proposed a self-adaptive learning particle swarm optimization (SAL-PSO) to improve the quality of solutions in large problems. Our work aims to minimize total transportation costs, including fuel consumption costs and driver costs. The validation of SAL-PSO was conducted by the comparison of the optimal solutions obtained from CPLEX and the best solutions obtained from the standard and proposed meta-heuristics. The relative improvement (RI) between the standard PSO and the SAL-PSO in the G-VRPMSPDTW-RT was 0.15-7.31%. The SAL-PSO outperformed the standard PSO by the average of 3.25%.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
 Saowanee R, Pornsawan R, Yuwawan J. The effects of COVID-19 towards a new trend of transportation under the next normal [Internet]. Bangok, Thailand; 2020 [cited 2020 Oct 7]. Available from: https://www.bot.or.th/Thai/ResearchAndPublications/DocLib_/Article_14Apr2020.pdf.
 Council O of the NE and SD. Thailand’s logistics report 2018 [Internet]. 2019 [cited 2020 Oct 7]. Available from: https://www.nesdb.go.th/ewt_dl_link.php?nid=9359.
 Energy forecast and information technology center. Energy situation January-June 2020 [Internet]. Bangkok, Thailand; 2020. [cited 2020 Oct 7]. Available from: http://www.eppo.go.th/index.php/th/component/k2/item/download/20313_2995209d28edea089f8d992494177bb3.
 Faria MV, Duarte GO, Varella RA, Farias TL, Baptista PC. How do road grade, road type and driving aggressiveness impact vehicle fuel consumption? Assessing potential fuel savings in Lisbon, Portugal. Transp Res Transp Environ. 2019;72:148-61.
 Min H. The multiple vehicle routing problem with simultaneous delivery and pick-up points. Transp Res A Gen. 1989;23(5):377-86.
 Nagy G, Salhi S. Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries. Eur J Oper Res. 2005;162(1):126-41.
 Lin C, Choy KL, Ho GTS, Ng TW. A genetic algorithm-based optimization model for supporting green transportation operations. Expert Syst Appl. 2014;41(7):3284-96.
 Osaba E, Yang XS, Diaz F, Onieva E, Masegosa AD, Perallos A. A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy. Soft Comput. 2017;21(18):5295-308.
 Lin C, Choy KL, Ho GTS, Chung SH, Lam HY. Survey of green vehicle routing problem: past and future trends. Expert Syst Appl. 2014;41(4):1118-38.
 Bloemhof-Ruwaard JM, Van Beek P, Hordijk L, Van Wassenhove LN. Interactions between operational research and environmental management. Eur J Oper Res. 1995;85(2):229-43.
 Sbihi A, Eglese RW. Combinatorial optimization and green logistics. 4OR-Q J Oper Res. 2007;5(2):99-116.
 Kuo Y. Using simulated annealing to minimize fuel consumption for the time-dependent vehicle routing problem. Comput Ind Eng. 2010;59(1):157-65.
 Bektaş T, Laporte G. The pollution-routing problem. Transp Res B Meth. 2011;45(8):1232-50.
 Erdogan S, Miller-Hooks E. A green vehicle routing problem. Transp Res E Logist Transp Rev. 2012;48(1):100-14.
 Xiao Y, Zhao Q, Kaku I, Xu Y. Development of a fuel consumption optimization model for the capacitated vehicle routing problem. Comput Oper Res. 2012;39(7):1419-31.
 Franceschetti A, Honhon D, Van Woensel T, Bektaş T, Laporte G. The time-dependent pollution-routing problem. Transp Res B Meth. 2013;56:265-93.
 Demir E, Van Woensel T. Mathematical modeling of CO2e emissions in one-to-one pickup and delivery problems. IEEE International conference on industrial engineering and engineering management; 2013 Dec 10-13; Bangkok, Thailand. New York: IEEE; 2013. p. 63-7.
 Xiao Y, Konak A. The heterogeneous green vehicle routing and scheduling problem with time-varying traffic congestion. Transp Res E Logist Transp Rev. 2016;88:146-66.
 Xiao Y, Konak A. A genetic algorithm with exact dynamic programming for the green vehicle routing & scheduling problem. J Clean Prod. 2017;167:1450-63.
 Poonthalir G, Nadarajan R. A fuel efficient green vehicle routing problem with varying speed constraint (F-GVRP). Expert Syst Appl. 2018;100:131-44.
 Soysal M, Çimen M, Demir E. On the mathematical modeling of green one-to-one pickup and delivery problem with road segmentation. J Clean Prod. 2018;174:1664-78.
 Tuntitippawan N, Asawarungsaengkul K. An artificial bee colony algorithm with local search for vehicle routing problem with backhauls and time windows. KKU Eng J. 2016;43(S3):404-8.
 Sethanan K, Jamrus T. Hybrid differential evolution algorithm and genetic operator for multi-trip vehicle routing problem with backhauls and heterogeneous fleet in the beverage logistics industry. Comput Ind Eng. 2020;146:106571.
 Majidi S, Hosseini-Motlagh SM, Ignatius J. Adaptive large neighborhood search heuristic for pollution-routing problem with simultaneous pickup and delivery. Soft Comput. 2018;22(9):2851-65.
 Kachitvichyanukul V. Comparison of three evolutionary algorithms: GA, PSO, and DE. Ind Eng Manag Syst. 2012;11(3):215-23.
 Kar AK. Bio inspired computing-a review of algorithms and scope of applications. Expert Syst Appl. 2016;59:20-32.
 Ai TJ, Kachitvichyanukul V. A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery. Comput Oper Res. 2009;36(5):1693-702.
 Goksal FP, Karaoglan I, Altiparmak F. A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Comput Ind Eng. 2013;65(1):39-53.
 Kachitvichyanukul V, Sombuntham P, Kunnapapdeelert S. Two solution representations for solving multi-depot vehicle routing problem with multiple pickup and delivery requests via PSO. Comput Ind Eng. 2015;89:125-36.
 Norouzi N, Sadegh-Amalnick M, Tavakkoli-Moghaddam R. Modified particle swarm optimization in a time-dependent vehicle routing problem: minimizing fuel consumption. Optim Lett. 2017;11:121-34.
 Li Y, Lim MK, Tseng ML. A green vehicle routing model based on modified particle swarm optimization for cold chain logistics. Ind Manag Data Syst. 2019;119(3):473-94.
 Zhan Z, Zhang J. Adaptive particle swarm optimization. International conference on ant colony optimization and swarm intelligence; 2008 Sep 22-24; Brussels, Belgium. Berlin: Springer; 2008. p. 227-34.
 Wang Y, Li B, Weise T, Wang J, Yuan B, Tian Q. Self-adaptive learning based particle swarm optimization. Inform Sci. 2011;181(20):4515-38.
 Xu G. An adaptive parameter tuning of particle swarm optimization algorithm. Appl Math Comput. 2013;219(9):4560-9.
 Pornsing C, Sodhi MS, Lamond BF. Novel self-adaptive particle swarm optimization methods. Soft Comput. 2016;20(9):3579-93.
 Marinakis Y, Marinaki M, Migdalas A. A multi-adaptive particle swarm optimization for the vehicle routing problem with time windows. Inform Sci. 2019;481:311-29.
 Demir E, Bektaş T, Laporte G. An adaptive large neighborhood search heuristic for the pollution-routing problem. Eur J Oper Res. 2012;223(2):346-59.
 Barth M, Younglove T, Scora G. Development of a heavy-duty diesel modal emissions and fuel consumption model. California PATH. 2005:1-124.
 Barth M, Boriboonsomsin K. Energy and emissions impacts of a freeway-based dynamic eco-driving system. Transp Res D Transp Environ. 2009;14(6):400-10.
 Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN'95 - international conference on neural networks; 1995 Nov 27-Dec 1; Perth, Australia. New York: IEEE; 1995. p. 1942-8.
 Shi Y, Eberhart RC. Parameter selection in particle swarm optimization. International conference on evolutionary programming; 1998 Mar 25-27; San Diego, USA. Berlin: Springer; 1998. p. 591-600.
 Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput. 2004;8(3):240-55.
 Tian D, Shi Z. MPSO: modified particle swarm optimization and its applications. Swarm Evol Comput. 2018;41:49-68.