How to cite this paper
Wang, X & Zhong, J. (2023). An integrated optimization for minimizing the operation cost of home delivery services in O2O retail.International Journal of Industrial Engineering Computations , 14(2), 341-360.
Refrences
Ahuja, R. K., Ergun, Ö., Orlin, J. B., & Punnen, A. P. (2002). A survey of very large-scale neighborhood search techniques. Discrete Applied Mathematics, 123(1), 75-102. doi:https://doi.org/10.1016/S0166-218X(01)00338-9
Álvarez, E., Ferrer, J.-C., Muñoz, J. C., & Henao, C. A. (2020). Efficient shift scheduling with multiple breaks for full-time employees: A retail industry case. Computers & Industrial Engineering, 150, 106884. doi:https://doi.org/10.1016/j.cie.2020.106884
Baker Kenneth, R. (1976). Workforce Allocation in Cyclical Scheduling Problems: A Survey. Journal of the Operational Research Society, 27(1), 155-167.
Bürgy, R., Michon-Lacaze, H., & Desaulniers, G. (2019). Employee scheduling with short demand perturbations and extensible shifts. Omega, 89, 177-192. doi:https://doi.org/10.1016/j.omega.2018.10.009
Chen, J., Fan, T., Gu, Q., & Pan, F. (2022). Emerging technology-based online scheduling for instant delivery in the O2O retail era. Electronic Commerce Research and Applications, 51, 101115. doi:https://doi.org/10.1016/j.elerap.2021.101115
Christiaens, J., & Vanden Berghe, G. (2020). Slack Induction by String Removals for Vehicle Routing Problems. Transportation Science, 54(2), 417-433. doi:10.1287/trsc.2019.0914
Dayarian, I., Crainic, T. G., Gendreau, M., & Rei, W. (2015). A branch-and-price approach for a multi-period vehicle routing problem. Computers & Operations Research, 55, 167-184. doi:https://doi.org/10.1016/j.cor.2014.06.004
Defraeye, M., & Van Nieuwenhuyse, I. (2016). Staffing and scheduling under nonstationary demand for service: A literature review. Omega, 58, 4-25. doi:https://doi.org/10.1016/j.omega.2015.04.002
Desaulniers, G., Pecin, D., & Contardo, C. (2019). Selective pricing in branch-price-and-cut algorithms for vehicle routing. EURO Journal on Transportation and Logistics, 8(2), 147-168. doi:https://doi.org/10.1007/s13676-017-0112-9
Elshaer, R., & Awad, H. (2020). A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Computers & Industrial Engineering, 140, 106242. doi:https://doi.org/10.1016/j.cie.2019.106242
Erhard, M., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2018). State of the art in physician scheduling. European Journal of Operational Research, 265(1), 1-18. doi:https://doi.org/10.1016/j.ejor.2017.06.037
Ernst, A. T., Jiang, H., Krishnamoorthy, M., & Sier, D. (2004). Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research, 153(1), 3-27. doi:https://doi.org/10.1016/S0377-2217(03)00095-X
Fang, W., Guan, Z., Yue, L., Zhang, Z., & Wang, H. (2022). Heterogeneous-vehicle distribution logistics planning for assembly line station materials with multiple time windows and multiple visits. International Journal of Industrial Engineering Computations, 13(4), 473-490. doi:10.5267/j.ijiec.2022.8.002
Fichera, S., Costa, A., & Cappadonna, F. A. (2017). Heterogeneous workers with learning ability assignment in a cellular manufacturing system. International Journal of Industrial Engineering Computations, 8(4), 427-440. doi:10.5267/j.ijiec.2017.3.005
Guastaroba, G., Côté, J. F., & Coelho, L. C. (2021). The Multi-Period Workforce Scheduling and Routing Problem. Omega (United Kingdom), 102. doi:10.1016/j.omega.2020.102302
He, B., Mirchandani, P., Shen, Q., & Yang, G. (2021). How should local Brick-and-Mortar retailers offer delivery service in a pandemic World? Self-building Vs. O2O platform. Transportation Research Part E: Logistics and Transportation Review, 154, 102457. doi:https://doi.org/10.1016/j.tre.2021.102457
Hema. (2021). www.freshhema.com.
Hendry, D. F., & Pretis, F. (2022). Analysing differences between scenarios. International Journal of Forecasting. doi:https://doi.org/10.1016/j.ijforecast.2022.02.004
Keskin, M., Laporte, G., & Çatay, B. (2019). Electric Vehicle Routing Problem with Time-Dependent Waiting Times at Recharging Stations. Computers and Operations Research, 107, 77-94. doi:10.1016/j.cor.2019.02.014
Lam, E., Desaulniers, G., & Stuckey, P. J. (2022). Branch-and-cut-and-price for the Electric Vehicle Routing Problem with Time Windows, Piecewise-Linear Recharging and Capacitated Recharging Stations. Computers & Operations Research, 105870. doi:https://doi.org/10.1016/j.cor.2022.105870
Larrain, H., Coelho, L. C., Archetti, C., & Speranza, M. G. (2019). Exact solution methods for the multi-period vehicle routing problem with due dates. Computers & Operations Research, 110, 148-158. doi:https://doi.org/10.1016/j.cor.2019.05.026
Lee, T.-S., & Loong, Y.-T. (2019). A review of scheduling problem and resolution methods in flexible flow shop. International Journal of Industrial Engineering Computations, 10(1), 67-88. doi:10.5267/j.ijiec.2018.4.001
Lespay, H., & Suchan, K. (2022). Territory Design for the Multi-Period Vehicle Routing Problem with Time Windows. Computers & Operations Research, 145, 105866. doi:https://doi.org/10.1016/j.cor.2022.105866
Liu, S., Sadowska, A., & De Schutter, B. (2022). A scenario-based distributed model predictive control approach for freeway networks. Transportation Research Part C: Emerging Technologies, 136, 103261. doi:https://doi.org/10.1016/j.trc.2021.103261
Mani, V., Kesavan, S., & Swaminathan, J. M. (2015). Estimating the Impact of Understaffing on Sales and Profitability in Retail Stores. Production and Operations Management, 24(2), 201-218. doi:https://doi.org/10.1111/poms.12237
Maria Gonzalez-Neira, E., Montoya-Torresc, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS, 8(4), 399-426. doi:10.5267/j.ijiec.2017.2.001
Mou, S., & Robb, D. J. (2019). Real-Time Labour Allocation in grocery stores: A simulation-based approach. Decision Support Systems, 124, 113095. doi:https://doi.org/10.1016/j.dss.2019.113095
Nagata, Y., & Bräysy, O. (2009). A powerful route minimization heuristic for the vehicle routing problem with time windows. Operations Research Letters, 37(5), 333-338. doi:10.1016/j.orl.2009.04.006
Nagata, Y., Bräysy, O., & Dullaert, W. (2010). A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Computers & Operations Research, 37(4), 724-737. doi:10.1016/j.cor.2009.06.022
Ojeda Rios, B. H., Xavier, E. C., Miyazawa, F. K., Amorim, P., Curcio, E., & Santos, M. J. (2021). Recent dynamic vehicle routing problems: A survey. Computers & Industrial Engineering, 160, 107604. doi:https://doi.org/10.1016/j.cie.2021.107604
Ojstersek, R., Brezocnik, M., & Buchmeister, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations, 11(3), 359-376. doi:10.5267/j.ijiec.2020.1.003
Pereira, D. L., Alves, J. C., & Moreira, M. C. d. O. (2020). A multiperiod workforce scheduling and routing problem with dependent tasks. Computers & Operations Research, 118, 104930. doi:https://doi.org/10.1016/j.cor.2020.104930
Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1-11. doi:https://doi.org/10.1016/j.ejor.2012.08.015
Pisinger, D., & Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers & Operations Research, 34(8), 2403-2435. doi:https://doi.org/10.1016/j.cor.2005.09.012
Punyakum, V., Sethanan, K., Nitisiri, K., Pitakaso, R., & Gen, M. (2022). Hybrid differential evolution and particle swarm optimization for Multi-visit and Multi-period workforce scheduling and routing problems. Computers and Electronics in Agriculture, 197, 106929. doi:https://doi.org/10.1016/j.compag.2022.106929
Qian, H., Guo, H., Sun, B., & Wang, Y. (2022). Integrated inventory and transportation management with stochastic demands: A scenario-based economic model predictive control approach. Expert Systems with Applications, 202, 117156. doi:https://doi.org/10.1016/j.eswa.2022.117156
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. doi:10.1287/trsc.1050.0135
Ropke, S., & Pisinger, D. (2006). A unified heuristic for a large class of Vehicle Routing Problems with Backhauls. European Journal of Operational Research, 171(3), 750-775. doi:https://doi.org/10.1016/j.ejor.2004.09.004
Shaw, P. (1998). Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. .Lecture Notes in Computer Science, 1520, 417–430.
Soeffker, N., Ulmer, M. W., & Mattfeld, D. C. (2022). Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. European Journal of Operational Research, 298(3), 801-820. doi:https://doi.org/10.1016/j.ejor.2021.07.014
Toth, N., & Kulcsar, G. (2021). New models and algorithms to solve integrated problems of production planning and control taking into account worker skills in flexible manufacturing systems. International Journal of Industrial Engineering Computations, 12(4), 381-400. doi:10.5267/j.ijiec.2021.5.004
Turkeš, R., Sörensen, K., & Hvattum, L. M. (2021). Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search. European Journal of Operational Research, 292(2), 423-442. doi:10.1016/j.ejor.2020.10.045
Wang, Y., Li, Q., Guan, X., Xu, M., Liu, Y., & Wang, H. (2021). Two-echelon collaborative multi-depot multi-period vehicle routing problem. Expert Systems with Applications, 167, 114201. doi:https://doi.org/10.1016/j.eswa.2020.114201
Windras Mara, S. T., Norcahyo, R., Jodiawan, P., Lusiantoro, L., & Rifai, A. P. (2022). A survey of adaptive large neighborhood search algorithms and applications. Computers & Operations Research, 146, 105903. doi:https://doi.org/10.1016/j.cor.2022.105903
Xu, L. N., Wang, Z. Y., Chen, X. D., & Lin, Z. W. (2022). Multi-Parking Lot and Shelter Heterogeneous Vehicle Routing Problem With Split Pickup Under Emergencies. Ieee Access, 10, 36073-36090. doi:10.1109/ACCESS.2022.3163715
Zamorano, E., & Stolletz, R. (2017). Branch-and-price approaches for the Multiperiod Technician Routing and Scheduling Problem. European Journal of Operational Research, 257(1), 55-68. doi:https://doi.org/10.1016/j.ejor.2016.06.058
Álvarez, E., Ferrer, J.-C., Muñoz, J. C., & Henao, C. A. (2020). Efficient shift scheduling with multiple breaks for full-time employees: A retail industry case. Computers & Industrial Engineering, 150, 106884. doi:https://doi.org/10.1016/j.cie.2020.106884
Baker Kenneth, R. (1976). Workforce Allocation in Cyclical Scheduling Problems: A Survey. Journal of the Operational Research Society, 27(1), 155-167.
Bürgy, R., Michon-Lacaze, H., & Desaulniers, G. (2019). Employee scheduling with short demand perturbations and extensible shifts. Omega, 89, 177-192. doi:https://doi.org/10.1016/j.omega.2018.10.009
Chen, J., Fan, T., Gu, Q., & Pan, F. (2022). Emerging technology-based online scheduling for instant delivery in the O2O retail era. Electronic Commerce Research and Applications, 51, 101115. doi:https://doi.org/10.1016/j.elerap.2021.101115
Christiaens, J., & Vanden Berghe, G. (2020). Slack Induction by String Removals for Vehicle Routing Problems. Transportation Science, 54(2), 417-433. doi:10.1287/trsc.2019.0914
Dayarian, I., Crainic, T. G., Gendreau, M., & Rei, W. (2015). A branch-and-price approach for a multi-period vehicle routing problem. Computers & Operations Research, 55, 167-184. doi:https://doi.org/10.1016/j.cor.2014.06.004
Defraeye, M., & Van Nieuwenhuyse, I. (2016). Staffing and scheduling under nonstationary demand for service: A literature review. Omega, 58, 4-25. doi:https://doi.org/10.1016/j.omega.2015.04.002
Desaulniers, G., Pecin, D., & Contardo, C. (2019). Selective pricing in branch-price-and-cut algorithms for vehicle routing. EURO Journal on Transportation and Logistics, 8(2), 147-168. doi:https://doi.org/10.1007/s13676-017-0112-9
Elshaer, R., & Awad, H. (2020). A taxonomic review of metaheuristic algorithms for solving the vehicle routing problem and its variants. Computers & Industrial Engineering, 140, 106242. doi:https://doi.org/10.1016/j.cie.2019.106242
Erhard, M., Schoenfelder, J., Fügener, A., & Brunner, J. O. (2018). State of the art in physician scheduling. European Journal of Operational Research, 265(1), 1-18. doi:https://doi.org/10.1016/j.ejor.2017.06.037
Ernst, A. T., Jiang, H., Krishnamoorthy, M., & Sier, D. (2004). Staff scheduling and rostering: A review of applications, methods and models. European Journal of Operational Research, 153(1), 3-27. doi:https://doi.org/10.1016/S0377-2217(03)00095-X
Fang, W., Guan, Z., Yue, L., Zhang, Z., & Wang, H. (2022). Heterogeneous-vehicle distribution logistics planning for assembly line station materials with multiple time windows and multiple visits. International Journal of Industrial Engineering Computations, 13(4), 473-490. doi:10.5267/j.ijiec.2022.8.002
Fichera, S., Costa, A., & Cappadonna, F. A. (2017). Heterogeneous workers with learning ability assignment in a cellular manufacturing system. International Journal of Industrial Engineering Computations, 8(4), 427-440. doi:10.5267/j.ijiec.2017.3.005
Guastaroba, G., Côté, J. F., & Coelho, L. C. (2021). The Multi-Period Workforce Scheduling and Routing Problem. Omega (United Kingdom), 102. doi:10.1016/j.omega.2020.102302
He, B., Mirchandani, P., Shen, Q., & Yang, G. (2021). How should local Brick-and-Mortar retailers offer delivery service in a pandemic World? Self-building Vs. O2O platform. Transportation Research Part E: Logistics and Transportation Review, 154, 102457. doi:https://doi.org/10.1016/j.tre.2021.102457
Hema. (2021). www.freshhema.com.
Hendry, D. F., & Pretis, F. (2022). Analysing differences between scenarios. International Journal of Forecasting. doi:https://doi.org/10.1016/j.ijforecast.2022.02.004
Keskin, M., Laporte, G., & Çatay, B. (2019). Electric Vehicle Routing Problem with Time-Dependent Waiting Times at Recharging Stations. Computers and Operations Research, 107, 77-94. doi:10.1016/j.cor.2019.02.014
Lam, E., Desaulniers, G., & Stuckey, P. J. (2022). Branch-and-cut-and-price for the Electric Vehicle Routing Problem with Time Windows, Piecewise-Linear Recharging and Capacitated Recharging Stations. Computers & Operations Research, 105870. doi:https://doi.org/10.1016/j.cor.2022.105870
Larrain, H., Coelho, L. C., Archetti, C., & Speranza, M. G. (2019). Exact solution methods for the multi-period vehicle routing problem with due dates. Computers & Operations Research, 110, 148-158. doi:https://doi.org/10.1016/j.cor.2019.05.026
Lee, T.-S., & Loong, Y.-T. (2019). A review of scheduling problem and resolution methods in flexible flow shop. International Journal of Industrial Engineering Computations, 10(1), 67-88. doi:10.5267/j.ijiec.2018.4.001
Lespay, H., & Suchan, K. (2022). Territory Design for the Multi-Period Vehicle Routing Problem with Time Windows. Computers & Operations Research, 145, 105866. doi:https://doi.org/10.1016/j.cor.2022.105866
Liu, S., Sadowska, A., & De Schutter, B. (2022). A scenario-based distributed model predictive control approach for freeway networks. Transportation Research Part C: Emerging Technologies, 136, 103261. doi:https://doi.org/10.1016/j.trc.2021.103261
Mani, V., Kesavan, S., & Swaminathan, J. M. (2015). Estimating the Impact of Understaffing on Sales and Profitability in Retail Stores. Production and Operations Management, 24(2), 201-218. doi:https://doi.org/10.1111/poms.12237
Maria Gonzalez-Neira, E., Montoya-Torresc, J. R., & Barrera, D. (2017). Flow-shop scheduling problem under uncertainties: Review and trends. INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING COMPUTATIONS, 8(4), 399-426. doi:10.5267/j.ijiec.2017.2.001
Mou, S., & Robb, D. J. (2019). Real-Time Labour Allocation in grocery stores: A simulation-based approach. Decision Support Systems, 124, 113095. doi:https://doi.org/10.1016/j.dss.2019.113095
Nagata, Y., & Bräysy, O. (2009). A powerful route minimization heuristic for the vehicle routing problem with time windows. Operations Research Letters, 37(5), 333-338. doi:10.1016/j.orl.2009.04.006
Nagata, Y., Bräysy, O., & Dullaert, W. (2010). A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows. Computers & Operations Research, 37(4), 724-737. doi:10.1016/j.cor.2009.06.022
Ojeda Rios, B. H., Xavier, E. C., Miyazawa, F. K., Amorim, P., Curcio, E., & Santos, M. J. (2021). Recent dynamic vehicle routing problems: A survey. Computers & Industrial Engineering, 160, 107604. doi:https://doi.org/10.1016/j.cie.2021.107604
Ojstersek, R., Brezocnik, M., & Buchmeister, B. (2020). Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations, 11(3), 359-376. doi:10.5267/j.ijiec.2020.1.003
Pereira, D. L., Alves, J. C., & Moreira, M. C. d. O. (2020). A multiperiod workforce scheduling and routing problem with dependent tasks. Computers & Operations Research, 118, 104930. doi:https://doi.org/10.1016/j.cor.2020.104930
Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1-11. doi:https://doi.org/10.1016/j.ejor.2012.08.015
Pisinger, D., & Ropke, S. (2007). A general heuristic for vehicle routing problems. Computers & Operations Research, 34(8), 2403-2435. doi:https://doi.org/10.1016/j.cor.2005.09.012
Punyakum, V., Sethanan, K., Nitisiri, K., Pitakaso, R., & Gen, M. (2022). Hybrid differential evolution and particle swarm optimization for Multi-visit and Multi-period workforce scheduling and routing problems. Computers and Electronics in Agriculture, 197, 106929. doi:https://doi.org/10.1016/j.compag.2022.106929
Qian, H., Guo, H., Sun, B., & Wang, Y. (2022). Integrated inventory and transportation management with stochastic demands: A scenario-based economic model predictive control approach. Expert Systems with Applications, 202, 117156. doi:https://doi.org/10.1016/j.eswa.2022.117156
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. doi:10.1287/trsc.1050.0135
Ropke, S., & Pisinger, D. (2006). A unified heuristic for a large class of Vehicle Routing Problems with Backhauls. European Journal of Operational Research, 171(3), 750-775. doi:https://doi.org/10.1016/j.ejor.2004.09.004
Shaw, P. (1998). Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. .Lecture Notes in Computer Science, 1520, 417–430.
Soeffker, N., Ulmer, M. W., & Mattfeld, D. C. (2022). Stochastic dynamic vehicle routing in the light of prescriptive analytics: A review. European Journal of Operational Research, 298(3), 801-820. doi:https://doi.org/10.1016/j.ejor.2021.07.014
Toth, N., & Kulcsar, G. (2021). New models and algorithms to solve integrated problems of production planning and control taking into account worker skills in flexible manufacturing systems. International Journal of Industrial Engineering Computations, 12(4), 381-400. doi:10.5267/j.ijiec.2021.5.004
Turkeš, R., Sörensen, K., & Hvattum, L. M. (2021). Meta-analysis of metaheuristics: Quantifying the effect of adaptiveness in adaptive large neighborhood search. European Journal of Operational Research, 292(2), 423-442. doi:10.1016/j.ejor.2020.10.045
Wang, Y., Li, Q., Guan, X., Xu, M., Liu, Y., & Wang, H. (2021). Two-echelon collaborative multi-depot multi-period vehicle routing problem. Expert Systems with Applications, 167, 114201. doi:https://doi.org/10.1016/j.eswa.2020.114201
Windras Mara, S. T., Norcahyo, R., Jodiawan, P., Lusiantoro, L., & Rifai, A. P. (2022). A survey of adaptive large neighborhood search algorithms and applications. Computers & Operations Research, 146, 105903. doi:https://doi.org/10.1016/j.cor.2022.105903
Xu, L. N., Wang, Z. Y., Chen, X. D., & Lin, Z. W. (2022). Multi-Parking Lot and Shelter Heterogeneous Vehicle Routing Problem With Split Pickup Under Emergencies. Ieee Access, 10, 36073-36090. doi:10.1109/ACCESS.2022.3163715
Zamorano, E., & Stolletz, R. (2017). Branch-and-price approaches for the Multiperiod Technician Routing and Scheduling Problem. European Journal of Operational Research, 257(1), 55-68. doi:https://doi.org/10.1016/j.ejor.2016.06.058