Abstract
With the acceleration of urbanization, there is an urgent need to build more transportation facilities to alleviate travel pressure. However, during the construction of a subway station, a large amount of muck is generated and must be transported to a treatment center. In response to transportation policies, this paper establishes a regional and time-limited transportation model for muck trucks based on their departure time points. The model aims to dispatch the least number of vehicles and complete all transportation tasks as quickly as possible, taking into account constraints such as restricted travel time. This paper uses the NSGA-II algorithm with multi-segment encoding to solve this problem, and numerical experiments are conducted to analyze the performance of the proposed method. The results indicate that the improved algorithm has better convergence and distribution than the standard NSGA-II. The study also validates the effectiveness of the proposed method through a real-world example of muck transportation at subway stations in a specific city. The collaborative scheduling schemes developed through the methods proposed in this paper have effectively avoided the travel restriction period, providing managers with multiple decision-making options.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
Data will be made available upon reasonable request.
Code availability
Coding was a standard code using MATLAB software.
References
Gao W, Zhang H, Ren Q et al (2023) A low-carbon approach to recycling engineering muck to produce non-sintering lightweight aggregates: Physical properties, microstructure, reaction mechanism, and life cycle assessment. J Clean Prod 385:135650. https://doi.org/10.1016/j.jclepro.2022.135650
Xia D, Zheng L, Cai X et al (2022) Urban Customized Bus Design for Private Car Commuters. IEEE Internet Things J 9:21723–21735. https://doi.org/10.1109/JIOT.2022.3181591
Albalate D, Fageda X (2021) On the relationship between congestion and road safety in cities. Transp Policy 105:145–152. https://doi.org/10.1016/j.tranpol.2021.03.011
Geneletti D, Cortinovis C, Zardo L (2022) Simulating crowding of urban green areas to manage access during lockdowns. Landsc Urban Plan 219:104319. https://doi.org/10.1016/j.landurbplan.2021.104319
Arnold F, Sörensen K (2019) What makes a VRP solution good? The generation of problem-specific knowledge for heuristics. Comput Oper Res 106:280–288. https://doi.org/10.1016/j.cor.2018.02.007
Elshaer R, Awad H (2020) A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Comput Ind Eng 140:106242. https://doi.org/10.1016/j.cie.2019.106242
Chen J, Xu W, Zhang R (2023) Optimization of chemical synthesis with heuristic algorithms. Phys Chem Chem Phys 25:4323–4331. https://doi.org/10.1039/D2CP03970B
Mishra A, Goel L (2023) Metaheuristic Algorithms in Smart Farming: An Analytical Survey. IETE Tech Rev. 1–20. https://doi.org/10.1080/02564602.2023.2219226
Ma H, Zhang Y, Sun S et al (2023) A comprehensive survey on NSGA-II for multi-objective optimization and applications. Artif Intell Rev. https://doi.org/10.1007/s10462-023-10526-z
Lin C-C, Liu W-Y, Peng Y-C, Lee T-K (2023) Altruistic production and distribution planning in the multilayer dual-channel supply chain: Using an improved NSGA-II with lion pride algorithm. Comput Ind Eng 176:108884. https://doi.org/10.1016/j.cie.2022.108884
Ji B, Huang H, Yu SS (2023) An Enhanced NSGA-II for Solving Berth Allocation and Quay Crane Assignment Problem With Stochastic Arrival Times. IEEE Trans Intell Transport Syst 24:459–473. https://doi.org/10.1109/TITS.2022.3213834
Latpate R, Kurade SS (2022) Multi-Objective Multi-Index Transportation Model for Crude Oil Using Fuzzy NSGA-II. IEEE Trans Intell Transport Syst 23:1347–1356. https://doi.org/10.1109/TITS.2020.3024693
Bao Y, Wang Y, Zhao L, Zhang A (2022) Optimization Production Scheduling of Underground Backfilling Mining Based on NSGA-II. Mining Metal Explor 39:1521–1536. https://doi.org/10.1007/s42461-022-00606-z
Li X, Li C, Li P et al (2021) Structural Design and Optimization of the Crossbeam of a Computer Numerical Controlled Milling-Machine Tool Using Sensitivity Theory and NSGA-II Algorithm. Int J Precis Eng Manuf 22:287–300. https://doi.org/10.1007/s12541-020-00435-4
Guo Q, Wang N, Su B, Zhang M (2020) Bi-Objective Vehicle Routing for Muck Transportation in Urban Road Networks. IEEE Access 8:114219–114227. https://doi.org/10.1109/ACCESS.2020.3002276
Xu S, He H, Yang M et al (2023) To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19. Stoch Environ Res Risk Assess 37:1479–1495. https://doi.org/10.1007/s00477-022-02351-7
Liu Z, Li R, Wang XC, Shang P (2020) Noncompliance behavior against vehicle restriction policy: A case study of Langfang, China. Trans Res Part A Policy Pract 132:1020–1033. https://doi.org/10.1016/j.tra.2020.01.005
Sun C, Xu S, Yang M, Gong X (2022) Urban traffic regulation and air pollution: A case study of urban motor vehicle restriction policy. Energ Policy 163:112819. https://doi.org/10.1016/j.enpol.2022.112819
Chen Z, Ye X, Li B, Jia S (2023) Effect of Driving-Restriction Policies Based on System Dynamics, the Back Propagation Neural Network, and Gray System Theory. Arab J Sci Eng 48:7109–7125. https://doi.org/10.1007/s13369-022-07405-0
Chen Z, Zan Z, Jia S (2022) Effect of urban traffic-restriction policy on improving air quality based on system dynamics and a non-homogeneous discrete grey model. Clean Techn Environ Policy 24:2365–2384. https://doi.org/10.1007/s10098-022-02319-9
Sun D, Ding X (2019) Spatiotemporal evolution of ridesourcing markets under the new restriction policy: A case study in Shanghai. Trans Res Part A Policy Pract 130:227–239. https://doi.org/10.1016/j.tra.2019.09.052
Wei X, Yu W, Wang W et al (2020) Optimization and Comparative Analysis of Traffic Restriction Policy by Jointly Considering Carpool Exemptions. Sustainability 12:7734. https://doi.org/10.3390/su12187734
Zhao Y, Han X, Xu X (2022) Traffic Flow Prediction Model Based on the Combination of Improved Gated Recurrent Unit and Graph Convolutional Network. Front Bioeng Biotechnol 10:804454. https://doi.org/10.3389/fbioe.2022.804454
Zhao J, Zhang J, Sun L et al (2018) Characterization of PM2.5-bound nitrated and oxygenated polycyclic aromatic hydrocarbons in ambient air of Langfang during periods with and without traffic restriction. Atmos Res 213:302–308. https://doi.org/10.1016/j.atmosres.2018.06.015
Qin Z, Liang Y, Yang C et al (2023) Externalities from restrictions: Examining the short-run effects of urban core-focused driving restriction policies on air quality. Transp Res Part D: Transp Environ 119:103723. https://doi.org/10.1016/j.trd.2023.103723
Macea LF, Márquez L, Soto JJ (2023) How do the affective and symbolic factors of private car driving influence car users’ travel behavior in a car restriction policy scenario? Transp Policy 140:100–113. https://doi.org/10.1016/j.tranpol.2023.07.001
Ben Ticha H, Absi N, Feillet D, Quilliot A (2019) Multigraph modeling and adaptive large neighborhood search for the vehicle routing problem with time windows. Comput Oper Res 104:113–126. https://doi.org/10.1016/j.cor.2018.11.001
Zhang W, Yang D, Zhang G, Gen M (2020) Hybrid multiobjective evolutionary algorithm with fast sampling strategy-based global search and route sequence difference-based local search for VRPTW. Expert Syst Appl 145:113151. https://doi.org/10.1016/j.eswa.2019.113151
Feng B, Wei L (2023) An improved multi-directional local search algorithm for vehicle routing problem with time windows and route balance. Appl Intell 53:11786–11798. https://doi.org/10.1007/s10489-022-04061-7
Niu Y, Kong D, Wen R et al (2021) An improved learnable evolution model for solving multi-objective vehicle routing problem with stochastic demand. Knowl-Based Syst 230:107378. https://doi.org/10.1016/j.knosys.2021.107378
Dong W, Zhou K, Qi H et al (2018) A tissue P system based evolutionary algorithm for multi-objective VRPTW. Swarm Evol Comput 39:310–322. https://doi.org/10.1016/j.swevo.2017.11.001
Cai X, Jiang L, Guo S et al (2022) TLHSA and SACA: two heuristic algorithms for two variant VRP models. J Comb Optim 44:2996–3022. https://doi.org/10.1007/s10878-021-00831-0
Gutierrez A, Dieulle L, Labadie N, Velasco N (2018) A multi-population algorithm to solve the VRP with stochastic service and travel times. Comput Ind Eng 125:144–156. https://doi.org/10.1016/j.cie.2018.07.042
Harbaoui Dridi I, Ben Alaïa E, Borne P, Bouchriha H (2020) Optimisation of the multi-depots pick-up and delivery problems with time windows and multi-vehicles using PSO algorithm. Int J Prod Res 58:4201–4214. https://doi.org/10.1080/00207543.2019.1650975
Lesch V, König M, Kounev S et al (2022) Tackling the rich vehicle routing problem with nature-inspired algorithms. Appl Intell 52:9476–9500. https://doi.org/10.1007/s10489-021-03035-5
Asefi H, Shahparvari S, Chhetri P, Lim S (2019) Variable fleet size and mix VRP with fleet heterogeneity in Integrated Solid Waste Management. J Clean Prod 230:1376–1395. https://doi.org/10.1016/j.jclepro.2019.04.250
Das S, Lee S-H, Kumar P et al (2019) Solid waste management: Scope and the challenge of sustainability. J Clean Prod 228:658–678. https://doi.org/10.1016/j.jclepro.2019.04.323
Adeniran AE, Nubi AT, Adelopo AO (2017) Solid waste generation and characterization in the University of Lagos for a sustainable waste management. Waste Manage 67:3–10. https://doi.org/10.1016/j.wasman.2017.05.002
Deus RM, Bezerra BS, Battistelle RAG (2019) Solid waste indicators and their implications for management practice. Int J Environ Sci Technol 16:1129–1144. https://doi.org/10.1007/s13762-018-2163-3
Akbarpour N, Salehi-Amiri A, Hajiaghaei-Keshteli M, Oliva D (2021) An innovative waste management system in a smart city under stochastic optimization using vehicle routing problem. Soft Comput 25:6707–6727. https://doi.org/10.1007/s00500-021-05669-6
Hina SM, Szmerekovsky J, Lee E et al (2020) Effective municipal solid waste collection using geospatial information systems for transportation: A case study of two metropolitan cities in Pakistan. Res Transp Econ 84:100950. https://doi.org/10.1016/j.retrec.2020.100950
Shi Y, Lv L, Hu F, Han Q (2020) A Heuristic Solution Method for Multi-Depot Vehicle Routing-Based Waste Collection Problems. Appl Sci 10:2403. https://doi.org/10.3390/app10072403
Claveria JB, Hernandez S, Anderson JC, Jessup EL (2019) Understanding truck driver behavior with respect to cell phone use and vehicle operation. Transp Res F: Traffic Psychol Behav 65:389–401. https://doi.org/10.1016/j.trf.2019.07.010
Kudo T, Belzer MH (2019) The association between truck driver compensation and safety performance. Saf Sci 120:447–455. https://doi.org/10.1016/j.ssci.2019.07.026
Madhusudhanan AK, Na X, Boies A, Cebon D (2020) Modelling and evaluation of a biomethane truck for transport performance and cost. Transp Res Part D: Transp Environ 87:102530. https://doi.org/10.1016/j.trd.2020.102530
Wang Z-Y, Lu C (2021) An integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing. J Manuf Syst 61:27–44. https://doi.org/10.1016/j.jmsy.2021.08.003
Yilmaz OF, Oztaysi B, Durmusoglu MB, Oner SC (2017) Determination of Material Handling Equipment for Lean In-Plant Logistics Using Fuzzy Analytical Network Process Considering Risk Attitudes of the Experts. International Journal of Industrial Engineering: Theory, Appl Pract 24(1). https://doi.org/10.23055/ijietap.2017.24.1.2890
Kilic HS, Durmusoglu MB, Baskak M (2012) Classification and modeling for in-plant milk-run distribution systems. Int J Adv Manuf Technol 62:1135–1146. https://doi.org/10.1007/s00170-011-3875-4
Kim KW, Gen M, Yamazaki G (2003) Hybrid genetic algorithm with fuzzy logic for resource-constrained project scheduling. Appl Soft Comput 2:174–188. https://doi.org/10.1016/S1568-4946(02)00065-0
Paquete L, Schulze B, Stiglmayr M, Lourenço AC (2022) Computing representations using hypervolume scalarizations. Comput Oper Res 137:105349. https://doi.org/10.1016/j.cor.2021.105349
Ishibuchi H, Imada R, Setoguchi Y, Nojima Y (2018) How to Specify a Reference Point in Hypervolume Calculation for Fair Performance Comparison. Evol Comput 26:411–440. https://doi.org/10.1162/evco_a_00226
Mahmud MSA, Abidin MSZ, Mohamed Z et al (2019) Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment. Comput Electron Agric 157:488–499. https://doi.org/10.1016/j.compag.2019.01.016
Acknowledgements
The authors are very thankful to an anonymous reviewer who provided feedback that helped to improve the quality, accuracy and presentation of this study.
Funding
Not applicable.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there are no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, D., Liu, Z., Chen, L. et al. Solving the cooperative scheduling problem of muck transport under time-segment restriction in an entire region. Appl Intell 54, 317–333 (2024). https://doi.org/10.1007/s10489-023-05189-w
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10489-023-05189-w