A Risk-averse Inventory-based Supply Chain Protection Problem with Adapted Stochastic Measures under Intentional Facility Disruptions: Decomposition and Hybrid Algorithms
Subject Areas : Executive Management
Sajjad
Jalali
1
(Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University)
Mehdi
Seifbarghy
2
(Faculty of Engineering, Alzahra University, Tehran, Iran)
Seyed Taghi
Akhavan Niaki
3
(Sharif University of Technology)
Keywords: Inventory-based protection problem, Tri-level Stackelberg game, Mean-risk formulation, Value of stochastic solution, Decomposition-based heuristic algorithm,
Abstract :
Owing to rising intentional events, supply chain disruptions have been considered by setting up a game between two players, namely, a designer and an interdictor contesting on minimizing and maximizing total cost, respectively. The previous studies have found the equilibrium solution by taking transportation, penalty and restoration cost into account. To contribute further, we examine how incorporation of inventory cost influences the players’ strategies. Assuming risk-averse feature of the designer and fully optimizing property of the interdictor with limited budget, the conditional-value-at-risk is employed to be involved in total cost. Using special order sets of type two and duality role, the linearized tri-level problem is solved by column-and-constraint generation and benders decomposition algorithms in terms of small-sized instances. In terms of larger-sized instances, we also contribute to prior studies by hybridizing corresponding algorithms with bio-geography based optimization method. Another non-trivial extension of our work is to define adapted stochastic measures based on the proposed mean-risk tri-level formulation. Borrowing instances from prior papers, the computational results indicate the managerial insights on players’ decisions, the model’s efficiency and performance of the algorithms.
Akbari-Jafarabadi, M., Tavakkoli-Moghaddam, R., Mahmoodjanloo, M., Rahimi, Y., 2017. A tri-level r-interdiction median model for a facility location problem under imminent attack. Comput. Ind. Eng. 114(C), 151-165.
An, Y., Zeng, B., Zhang, Y., Zhao, L., 2014. Reliable p-median facility location problem: two-stage robust models and algorithms. Transportation Research Part B: Methodological 64(0), 54-72.
Berrebi, C., Ostwald, J., 2013. Exploiting the Chaos: Terrorist Target Choice Following Natural Disasters. Southern Economic Journal 79(4), 793-811.
Bricha, N., Nourelfath, M., 2013. Critical supply network protection against intentional attacks: A game-theoretical model. Reliability Engineering & System Safety 119, 1-10.
Bricha, N., Nourelfath, M., 2014. Extra-capacity versus protection for supply networks under attack. Reliability Engineering & System Safety 131, 185-196.
Chen, Q., Li, X., Ouyang, Y., 2011. Joint inventory-location problem under the risk of probabilistic facility disruptions. Transportation Research Part B: Methodological 45(7), 991-1003.
Diabat, A., Theodorou, E., 2015. A location–inventory supply chain problem: Reformulation and piecewise linearization. Computers & Industrial Engineering 90, 381-389.
Du, B., Zhou, H., Leus, R., 2020. A two-stage robust model for a reliable p-center facility location problem. Applied Mathematical Modelling 77, 99-114.
Fan, Y., Feng, Y., Shou, Y., 2020. A risk-averse and buyer-led supply chain under option contract: CVaR minimization and channel coordination. International Journal of Production Economics 219, 66-81.
Gan, H.-S., Safaei, A.S., 2016. Monitoring process variability: a hybrid Taguchi loss and multiobjective genetic algorithm approach. Journal of Optimization in Industrial Engineering 9(20), 1-8.
Ghavamifar, A., Makui, A., Taleizadeh, A.A., 2018. Designing a resilient competitive supply chain network under disruption risks: A real-world application. Transportation Research Part E: Logistics and Transportation Review 115, 87-109.
Heijnen, P., Schoonbeek, L., 2019. Rent-seeking with uncertain discriminatory power. European Journal of Political Economy 56, 103-114.
Jalali, S., Seifbarghy, M., Niaki, S.T.A., 2018. A risk-averse location-protection problem under intentional facility disruptions: A modified hybrid decomposition algorithm. Transportation Research Part E: Logistics and Transportation Review 114, 196-219.
Jalali, S., Seifbarghy, M., Sadeghi, J., Ahmadi, S., 2016. Optimizing a bi-objective reliable facility location problem with adapted stochastic measures using tuned-parameter multi-objective algorithms. Knowledge-Based Systems 95, 45-57.
Karamyar, F., Sadeghi, J., Yazdi, M.M., 2018. A Benders decomposition for the location-allocation and scheduling model in a healthcare system regarding robust optimization. Neural Computing and Applications 29(10), 873-886.
Khanduzi, R., Sangaiah, A.K., 2019. A fast genetic algorithm for a critical protection problem in biomedical supply chain networks. Applied Soft Computing 75, 162-179.
Kim, J.R., Lee, J.U., Jo, J.-B., 2009. Hierarchical spanning tree network design with Nash genetic algorithm. Computers & Industrial Engineering 56(3), 1040-1052.
Konak, A., Kulturel-Konak, S., Snyder, L.V., 2015. A Game-Theoretic Genetic Algorithm for the reliable server assignment problem under attacks. Computers & Industrial Engineering 85, 73-85.
Liberatore, F., Scaparra, M.P., Daskin, M.S., 2012. Hedging against disruptions with ripple effects in location analysis. Omega 40(1), 21-30.
Lin, B., Liu, S., Lin, R., Wang, J., Sun, M., Wang, X., Liu, C., Wu, J., Xiao, J., 2019. The location-allocation model for multi-classification-yard location problem. Transportation Research Part E: Logistics and Transportation Review 122, 283-308.
Mahmoodjanloo, M., Parvasi, S.P., Ramezanian, R., 2016. A tri-level covering fortification model for facility protection against disturbance in r-interdiction median problem. Computers & Industrial Engineering 102, 219-232.
Noyan, N., 2012. Risk-averse two-stage stochastic programming with an application to disaster management. Computers & Operations Research 39(3), 541-559.
Paul, J.A., Bagchi, A., 2018. Does Terrorism Increase after a Natural Disaster? An Analysis based upon Property Damage. Defence and Peace Economics 29(4), 407-439.
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.
Rawls, C.G., Turnquist, M.A., 2010. Pre-positioning of emergency supplies for disaster response. Transportation Research Part B: Methodological 44(4), 521-534.
Rizvi, S.A., Wajahat, A., 2019. Integration of grey-based Taguchi technique in optimization of parameters process during the turning operation of 16MnCr5 steel. IUST 30(3), 245-254.
Sadeghi, J., Niaki, S.T.A., Malekian, M.R., Wang, Y., 2018. A Lagrangian Relaxation for a Fuzzy Random EPQ Problem with Shortages and Redundancy Allocation: Two Tuned Meta-heuristics. International Journal of Fuzzy Systems 20(2), 515-533.
Salehi, A., Masoumi, B., 2019. Participative Biogeography-Based Optimization. Journal of Optimization in Industrial Engineering 12(1), 79-91.
Scaparra, M.P., Church, R.L., 2008. A bilevel mixed-integer program for critical infrastructure protection planning. Computers & Operations Research 35(6), 1905-1923.
Setak, M., Feizizadeh, F., Tikani, H., Ardakani, E.S., 2019. A bi-level stochastic optimization model for reliable supply chain in competitive environments: Hybridizing exact method and genetic algorithm. Applied Mathematical Modelling 75, 310-332.
Shen, B., Li, Q., 2016. Market disruptions in supply chains: a review of operational models. International Transactions in Operational Research 24(4), 697-711.
Tang, C.S., 2006. Robust strategies for mitigating supply chain disruptions. International Journal of Logistics Research and Applications 9(1), 33-45.
Yolmeh, A., Baykal-Gürsoy, M., 2017. A robust approach to infrastructure security games. Computers & Industrial Engineering 110, 515-526.
Zeng, B., Zhao, L., 2013. Solving two-stage robust optimization problems using a column-and-constraint generation method. Operations Research Letters 41(5), 457-461.
Zhang, Y., Snyder, L.V., Qi, M., Miao, L., 2016. A heterogeneous reliable location model with risk pooling under supply disruptions. Transportation Research Part B: Methodological 83, 151-178.
Zheng, X., Yin, M., Zhang, Y., 2019. Integrated optimization of location, inventory and routing in supply chain network design. Transportation Research Part B: Methodological 121, 1-20.