Two-stage stochastic programming with robust constraints for the logistics network post-disruption response strategy optimization

Abstract

Logistics networks (LNs) are essential for the transportation and distribution of goods or services from suppliers to consumers. However, LNs with complex structures are more vulnerable to disruptions due to natural disasters and accidents. To address the LN post-disruption response strategy optimization problem, this study proposes a novel two-stage stochastic programming model with robust delivery time constraints. The proposed model jointly optimizes the new-line-opening and rerouting decisions in the face of uncertain transport demands and transportation times. To enhance the robustness of the response strategy obtained, the conditional value at risk (CVaR) criterion is utilized to reduce the operational risk, and robust constraints based on the scenario-based uncertainty sets are proposed to guarantee the delivery time requirement. An equivalent tractable mixed-integer linear programming reformulation is further derived by linearizing the CVaR objective function and dualizing the infinite number of robust constraints into finite ones. A case study based on the practical operations of the JD LN is conducted to validate the practical significance of the proposed model. A comparison with the rerouting strategy and two benchmark models demonstrates the superiority of the proposed model in terms of operational cost, delivery time, and loading rate.

Graphical abstract

Keywords

logistics network design / post-disruption response strategy / two-stage stochastic programming / conditional value at risk / robust constraint

References

[1] Alkhaleel, B A Liao, H Sullivan, K M (2022a). Model and solution method for mean-risk cost-based post-disruption restoration of interdependent critical infrastructure networks. Computers & Operations Research, 144: 105812

[2] Alkhaleel, B A Liao, H Sullivan, K M (2022b). Risk and resilience-based optimal post-disruption restoration for critical infrastructures under uncertainty. European Journal of Operational Research, 296( 1): 174–202

[3] Almoghathawi, Y Barker, K Albert, L A (2019). Resilience-driven restoration model for interdependent infrastructure networks. Reliability Engineering & System Safety, 185: 12–23

[4] Cacchiani, V Huisman, D Kidd, M Kroon, L Toth, P Veelenturf, L Wagenaar, J (2014). An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Research Part B: Methodological, 63: 15–37

[5] Chen, C C Tsai, Y H Schonfeld, P (2016). Schedule coordination, delay propagation, and disruption resilience in intermodal logistics networks. Transportation Research Record: Journal of the Transportation Research Board, 2548( 1): 16–23

[6] Chen, D Sun, D Yin, Y Dhamotharan, L Kumar, A Guo, Y (2022). The resilience of logistics network against node failures. International Journal of Production Economics, 244: 108373

[7] Cheng, C Qi, M Zhang, Y Rousseau, L M (2018). A two-stage robust approach for the reliable logistics network design problem. Transportation Research Part B: Methodological, 111: 185–202

[8] Das, D Verma, P Tanksale, A N (2022). Designing a closed-loop supply chain for reusable packaging materials: A risk-averse two-stage stochastic programming model using CVaR. Computers & Industrial Engineering, 167: 108004

[9] Esmizadeh, Y Mellat Parast, M (2021). Logistics and supply chain network designs: Incorporating competitive priorities and disruption risk management perspectives. International Journal of Logistics Research and Applications, 24( 2): 174–197

[10] Fang, Y P Sansavini, G (2019). Optimum post-disruption restoration under uncertainty for enhancing critical infrastructure resilience. Reliability Engineering & System Safety, 185: 1–11

[11] Feng, B Ye, Q (2021). Operations management of smart logistics: A literature review and future research. Frontiers of Engineering Management, 8( 3): 344–355

[12] Gao, Y Feng, Z Zhang, S (2021). Managing supply chain resilience in the era of VUCA. Frontiers of Engineering Management, 8( 3): 465–470

[13] Huang, G Wang, J Chen, C Guo, C Zhu, B (2017). System resilience enhancement: Smart grid and beyond. Frontiers of Engineering Management, 4( 3): 271–282

[14] Ivanov, D (2019). Disruption tails and revival policies: A simulation analysis of supply chain design and production-ordering systems in the recovery and post-disruption periods. Computers & Industrial Engineering, 127: 558–570

[15] IvanovD (2021). Exiting the COVID-19 pandemic: After-shock risks and avoidance of disruption tails in supply chains. Annals of Operations Research, in press, doi:10.1007/s10479-021-04047-7

[16] JDLogistics Inc. (2021). 2021 Interim Report (in Chinese)

[17] Kasaei, M Salman, F S (2016). Arc routing problems to restore connectivity of a road network. Transportation Research Part E: Logistics and Transportation Review, 95: 177–206

[18] Khaled, A A Jin, M Clarke, D B Hoque, M A (2015). Train design and routing optimization for evaluating criticality of freight railroad infrastructures. Transportation Research Part B: Methodological, 71: 71–84

[19] Khalili, S M Jolai, F Torabi, S A (2017). Integrated production–distribution planning in two-echelon systems: A resilience view. International Journal of Production Research, 55( 4): 1040–1064

[20] Kulkarni, O Dahan, M Montreuil, B (2022). Resilient hyperconnected parcel delivery network design under disruption risks. International Journal of Production Economics, 251: 108499

[21] Liu, E Barker, K Chen, H (2022). A multi-modal evacuation-based response strategy for mitigating disruption in an intercity railway system. Reliability Engineering & System Safety, 223: 108515

[22] Manupati, V Schoenherr, T Ramkumar, M Panigrahi, S Sharma, Y Mishra, P (2022). Recovery strategies for a disrupted supply chain network: Leveraging blockchain technology in pre- and post-disruption scenarios. International Journal of Production Economics, 245: 108389

[23] Medal, H R Pohl, E A Rossetti, M D (2014). A multi-objective integrated facility location-hardening model: Analyzing the pre- and post-disruption tradeoff. European Journal of Operational Research, 237( 1): 257–270

[24] Mohammadi, M Tavakkoli-Moghaddam, R Siadat, A Dantan, J Y (2016). Design of a reliable logistics network with hub disruption under uncertainty. Applied Mathematical Modelling, 40( 9‒10): 5621–5642

[25] Moosavi, J Hosseini, S (2021). Simulation-based assessment of supply chain resilience with consideration of recovery strategies in the COVID-19 pandemic context. Computers & Industrial Engineering, 160: 107593

[26] Namdar, J Li, X Sawhney, R Pradhan, N (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56( 6): 2339–2360

[27] Ni, N Howell, B J Sharkey, T C (2018). Modeling the impact of unmet demand in supply chain resiliency planning. Omega, 81: 1–16

[28] Peng, P Snyder, L V Lim, A Liu, Z (2011). Reliable logistics networks design with facility disruptions. Transportation Research Part B: Methodological, 45( 8): 1190–1211

[29] People’s Daily (2022). The number of parcels in China in 2021 account for over half of the world’s total (in Chinese)

[30] Ponomarov, S Y Holcomb, M C (2009). Understanding the concept of supply chain resilience. International Journal of Logistics Management, 20( 1): 124–143

[31] Premkumar, P Gopinath, S Mateen, A (2021). Trends in third party logistics: The past, the present & the future. International Journal of Logistics Research and Applications, 24( 6): 551–580

[32] Tolooie, A Maity, M Sinha, A K (2020). A two-stage stochastic mixed-integer program for reliable supply chain network design under uncertain disruptions and demand. Computers & Industrial Engineering, 148: 106722

[33] Tuzun Aksu, D Ozdamar, L (2014). A mathematical model for post-disaster road restoration: Enabling accessibility and evacuation. Transportation Research Part E: Logistics and Transportation Review, 61: 56–67

[34] Wang, H Fang, Y P Zio, E (2022). Resilience-oriented optimal post-disruption reconfiguration for coupled traffic-power systems. Reliability Engineering & System Safety, 222: 108408

[35] Wang, X Herty, M Zhao, L (2016). Contingent rerouting for enhancing supply chain resilience from supplier behavior perspective. International Transactions in Operational Research, 23( 4): 775–796

[36] Wang, Y Peng, S Xu, M (2021). Emergency logistics network design based on space–time resource configuration. Knowledge-Based Systems, 223: 107041

[37] Wang, Y Shou, R Lee, L H Chew, E P (2017). A case study on sample average approximation method for stochastic supply chain network design problem. Frontiers of Engineering Management, 4( 3): 338–347

[38] Xu, M Ouyang, M Hong, L Mao, Z Xu, X (2022). Resilience-driven repair sequencing decision under uncertainty for critical infrastructure systems. Reliability Engineering & System Safety, 221: 108378

[39] Yin, J Wang, Y Tang, T Xun, J Su, S (2017). Metro train rescheduling by adding backup trains under disrupted scenarios. Frontiers of Engineering Management, 4( 4): 418–427

[40] Zhang, Y Han, L Zhuang, X (2022). Distributionally robust front distribution center inventory optimization with uncertain multi-item orders. Discrete and Continuous Dynamical Systems: Series S, 15( 7): 1777–1795

[41] Zou, Q Chen, S (2021). Resilience-based recovery scheduling of transportation network in mixed traffic environment: A deep-ensemble-assisted active learning approach. Reliability Engineering & System Safety, 215: 107800

[42] Zuo, M (2021). System reliability and system resilience. Frontiers of Engineering Management, 8( 4): 615–619

RIGHTS & PERMISSIONS

2023 Higher Education Press