Didattica

Mathematical Optimisation


Modalità d'esame

  1. L'esame può essere preparato singolarmente o da un gruppo composto al massimo da due persone.
  2. L'esame consiste di due parti
    1. Implementazione di un modello (o di una serie di modelli) di programmazione lineare (mista-) intera.
      1. Il linguaggio di programmazione è a scelta dello studente/gruppo. Si consiglia Gurobi per python, ma altre scelte sono certamente possibili.
      2. La formulazione del modello è disponibile su un articolo (paper) pubblicato in una rivista scientifica (journal).
      3. Il paper è a scelta dello studente/gruppo previa conferma da parte del docente.
      4. Il paper deve essere stato pubblicato non prima dell'anno 2020 in uno dei seguenti journals:
    2. Una volta inviato il codice al docente via mail, lo studente/gruppo deve preparare una presentazione in PowerPoint (o software simile) del lavoro svolto.
      1. La presentazione verrà effettuata in presenza (a meno di cambiamenti delle normative COVID) in una data da concordarsi con il docente.
      2. L'esposizione della presentazione deve coinvolgere tutti i membri del gruppo in maniera equa.
  3. Non c'è limite di tempo da quando si riceve l'approvazione del paper scelto a quando si invia la mail con l'implementazione.
  4. Nel caso (frequente) in cui dal paper non sia possibile ricavare i dati di input del modello implementato, sarà cura dello studente/gruppo creare un dataset opportuno.
  5. Sarà valutata l'analisi della scalabilità del modello implementato, cioè come si comporta da un punto di vista computazionale il modello all'aumentare delle dimensioni dell'istanza.
  6. Saranno valutati eventuali migliorie e/o estensioni del modello presente nel paper scelto.
  7. All'esposizione seguiranno domande del docente inerenti o legate al progetto.
  8. Non è possibile scegliere un paper che sia stato già assegnato (vedi di seguito).

Papers già assegnati

  1. Al-Shihabi, S., & AlDurgam, M. M. (2020). The contractor time–cost–credit trade-off problem: integer programming model, heuristic solution, and business insights. International Transactions in Operational Research, 27(6), 2841-2877.
  2. Hochbaum, D. S., Rao, X., & Sauppe, J. (2022). Network flow methods for the minimum covariate imbalance problem. European Journal of Operational Research, 300(3), 827-836.
  3. Zhang, S., Hua, L., & Yu, B. (2022). Peak-easing strategies for urban subway operations in the context of COVID-19 epidemic. Transportation Research Part E: Logistics and Transportation Review, 102724.
  4. Hughes, M. S., Lunday, B. J., Weir, J. D., & Hopkinson, K. M. (2021). The multiple shortest path problem with path deconfliction. European Journal of Operational Research, 292(3), 818-829.
  5. Brekkå, I., Randøy, S., Fagerholt, K., Thun, K., & Vadseth, S. T. (2022). The Fish Feed Production Routing Problem. Computers & Operations Research, 144, 105806.
  6. Fontaine, P. (2022). The vehicle routing problem with load-dependent travel times for cargo bicycles. European Journal of Operational Research, 300(3), 1005-1016.
  7. Ramos, N., de Rezende, P. J., & de Souza, C. C. (2022). Optimal area polygonization problems: Exact solutions through geometric duality. Computers & Operations Research, 105842.
  8. Xie, L., Thieme, N., Krenzler, R., & Li, H. (2021). Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems. European Journal of Operational Research, 288(1), 80-97.
  9. Kabadurmus, O., Kazançoglu, Y., Yüksel, D., & Pala, M. Ö. (2022). A circular food supply chain network model to reduce food waste. Annals of Operations Research, 1-31.
  10. Jamili, N., van den Berg, P. L., & de Koster, R. (2022). Quantifying the impact of sharing resources in a collaborative warehouse. European Journal of Operational Research, 302(2), 518-519.
  11. Xu, C., Chang, W., & Liu, W. (2022). Data-driven decision model based on local two-stage weighted ensemble learning. Annals of Operations Research, 1-34.
  12. dos Santos, A. G., Viana, A., & Pedroso, J. P. (2022). 2-echelon lastmile delivery with lockers and occasional couriers. Transportation Research Part E: Logistics and Transportation Review, 162, 102714.
  13. Li, X., Huang, Y. H., Fang, S. C., & Zhang, Y. (2020). An alternative efficient representation for the project portfolio selection problem. European Journal of Operational Research, 281(1), 100-113.
  14. Pottel, S., & Goel, A. (2022). Scheduling activities with time-dependent durations and resource consumptions. European Journal of Operational Research, 301(2), 445-457.
  15. Sun, Y., Wang, S., Shen, Y., Li, X., Ernst, A. T., & Kirley, M. (2022). Boosting ant colony optimization via solution prediction and machine learning. Computers & Operations Research, 143, 105769.
  16. Mancini, S., Triki, C., & Piya, S. (2022). Optimal selection of touristic packages based on user preferences during sports mega-events. European Journal of Operational Research, 302(3), 819-830.
  17. Pina-Pardo, J. C., Silva, D. F., & Smith, A. E. (2021). The traveling salesman problem with release dates and drone resupply. Computers & Operations Research, 129, 105170.
  18. Gandra, V. S., Çalik, H., Toffolo, T. A., Carvalho, M. A. M., & Berghe, G. V. (2022). The vessel swap-body routing problem. European Journal of Operational Research, 303(1), 354-369.
  19. Alonso, M. T., Martinez-Sykora, A., Alvarez-Valdes, R., & Parreño, F. (2022). The pallet-loading vehicle routing problem with stability constraints. European Journal of Operational Research, 302(3), 860-873.
  20. Bhaya, A., & Kaszkurewicz, E. (2022). The generalized cash balance problem: optimization-based one step ahead optimal control. International Transactions in Operational Research.
  21. Alozie, G. U., Arulselvan, A., Akartunal?, K., & Pasiliao Jr, E. L. (2021). Efficient methods for the distance-based critical node detection problem in complex networks. Computers & Operations Research, 131, 105254.
  22. Luo, Y., Golden, B., Poikonen, S., Wasil, E., & Zhang, R. (2023). The paired mail carrier problem. European Journal of Operational Research, 308(2), 801-817.
  23. Salama, M. R., & Srinivas, S. (2022). Collaborative truck multi-drone routing and scheduling problem: Package delivery with flexible launch and recovery sites. Transportation Research Part E: Logistics and Transportation Review, 164, 102788.
  24. Poikonen, S., & Golden, B. (2020). Multi-visit drone routing problem. Computers & Operations Research, 113, 104802.
  25. Bacci, T., Mattia, S., & Ventura, P. (2020). A branch-and-cut algorithm for the restricted block relocation problem. European Journal of Operational Research, 287(2), 452-459.
  26. Silva, A., Coelho, L. C., Darvish, M., & Renaud, J. (2022). A cutting plane method and a parallel algorithm for packing rectangles in a circular container. European Journal of Operational Research, 303(1), 114-128.
  27. Zheng, J., Hong, Y., Xu, W., Li, W., & Chen, Y. (2022). An effective iterated two-stage heuristic algorithm for the multiple Traveling Salesmen Problem. Computers & Operations Research, 143, 105772.
  28. Salehipour, A. (2022). An Optimization Method for Characterizing Two Groups of Data. International Transactions in Operational Research.
  29. Ahmadian, M. M., & Salehipour, A. (2022). Heuristics for flights arrival scheduling at airports. International Transactions in Operational Research, 29(4), 2316-2345.
  30. Akhlaghi, V. E., & Campbell, A. M. (2022). The two-echelon island fuel distribution problem. European Journal of Operational Research, 302(3), 999-1017.
  31. Tönissen, D. D., & Schlicher, L. (2021). Using 3D-printing in disaster response: The two-stage stochastic 3D-printing knapsack problem. Computers & Operations Research, 133, 105356.
  32. Mancini, S., Gansterer, M., & Hartl, R. F. (2021). The collaborative consistent vehicle routing problem with workload balance. European Journal of Operational Research, 293(3), 955-965.
  33. Krutein, K. F., & Goodchild, A. (2022). The isolated community evacuation problem with mixed integer programming. Transportation Research Part E: Logistics and Transportation Review, 161, 102710.
  34. Che, Y., Hu, K., Zhang, Z., & Lim, A. (2021). Machine scheduling with orientation selection and two-dimensional packing for additive manufacturing. Computers & Operations Research, 130, 105245.
  35. Xu, M., Yan, X., & Yin, Y. (2022). Truck routing and platooning optimization considering drivers’ mandatory breaks. Transportation Research Part C: Emerging Technologies, 143, 103809.
  36. Demeulemeester, T., Goossens, D., Hermans, B., & Leus, R. (2023). A pessimist’s approach to one-sided matching. European Journal of Operational Research, 305(3), 1087-1099.
  37. Karsu, Ö., & Solyalı, O. (2023). A new formulation and an effective matheuristic for the airport gate assignment problem. Computers & Operations Research, 151, 106073.
  38. Morgenroth, C., Boysen, N., Schwerdfeger, S., & Weidinger, F. (2021). Scheduling taxi services for a team of car relocators. Computers & Operations Research, 128, 105188.
  39. Kyriakakis, N. A., Marinaki, M., Matsatsinis, N., & Marinakis, Y. (2022). A cumulative unmanned aerial vehicle routing problem approach for humanitarian coverage path planning. European Journal of Operational Research, 300(3), 992-1004.
  40. Bazirha, M., Kadrani, A., & Benmansour, R. (2023). Stochastic home health care routing and scheduling problem with multiple synchronized services. Annals of Operations Research, 320(2), 573-601.
  41. Zhang, Y., Lin, W. H., Huang, M., & Hu, X. (2021). Multi-warehouse package consolidation for split orders in online retailing. European Journal of Operational Research, 289(3), 1040-1055.
  42. Luo, Y., Golden, B., Poikonen, S., & Zhang, R. (2022). A fresh look at the Traveling Salesman Problem with a Center. Computers & Operations Research, 143, 105748.
  43. Witteman, M., Deng, Q., & Santos, B. F. (2021). A bin packing approach to solve the aircraft maintenance task allocation problem. European Journal of Operational Research, 294(1), 365-376.
  44. Xu, Y., Wandelt, S., & Sun, X. (2021). Airline integrated robust scheduling with a variable neighborhood search based heuristic. Transportation Research Part B: Methodological, 149, 181-203.
  45. Dell'Amico, M., Montemanni, R., & Novellani, S. (2023) Pickup and Delivery with Lockers. Transportation Research Part C: Emerging Technologies, 148, 104022.
  46. Kurowski, K., Pecyna, T., Slysz, M., Różycki, R., Waligóra, G., & Węglarz, J. (2023). Application of quantum approximate optimization algorithm to job shop scheduling problem. European Journal of Operational Research, 310(2), 518-528.
  47. Yin, J., D’Ariano, A., Wang, Y., Yang, L., & Tang, T. (2021). Timetable coordination in a rail transit network with time-dependent passenger demand. European Journal of Operational Research, 295(1), 183-202.
  48. Baller, R., Fontaine, P., Minner, S., & Lai, Z. (2022). Optimizing automotive inbound logistics: A mixed-integer linear programming approach. Transportation Research Part E: Logistics and Transportation Review, 163, 102734.
  49. Liu, Q., Lin, X., Li, M., Li, L., & He, F. (2023). Coordinated lane-changing scheduling of multilane CAV platoons in heterogeneous scenarios. Transportation Research Part C: Emerging Technologies, 147, 103992.
  50. Shiri, D., Akbari, V., & Tozan, H. (2023). Online optimisation for ambulance routing in disaster response with partial or no information on victim conditions. Computers & Operations Research, 106314.
  51. Monaci, M., Pike-Burke, C., & Santini, A. (2022). Exact algorithms for the 0–1 Time-bomb Knapsack Problem. Computers & Operations Research, 145, 105848.
  52. De, A., Gorton, M., Hubbard, C., & Aditjandra, P. (2022). Optimization model for sustainable food supply chains: An application to Norwegian salmon. Transportation Research Part E: Logistics and Transportation Review, 161, 102723.
  53. Araújo, E. J., Darvish, M., & Renaud, J. (2023). The road train optimization problem with load assignment. Computers & Operations Research, 153, 106184.
  54. Mendes, A. B., & e Alvelos, F. P. (2023). Iterated local search for the placement of wildland fire suppression resources. European Journal of Operational Research, 304(3), 887-900.
  55. Cazzaro, D., Koza, D. F., & Pisinger, D. (2023). Combined layout and cable optimization of offshore wind farms. European Journal of Operational Research, 311(1), 301-315.
  56. Dong, Z. L., Ribeiro, C. C., Xu, F., Zamora, A., Ma, Y., & Jing, K. (2023). Dynamic scheduling of e-sports tournaments. Transportation Research Part E: Logistics and Transportation Review, 169, 102988.
  57. Olmez, O. B., Gultekin, C., Balcik, B., Ekici, A., & Özener, O. Ö. (2022). A variable neighborhood search based matheuristic for a waste cooking oil collection network design problem. European Journal of Operational Research, 302(1), 187-202.
  58. Zhang, H., Yao, S., Liu, Q., Leng, J., & Wei, L. (2023). Exact approaches for the unconstrained two-dimensional cutting problem with defects. Computers & Operations Research, 106407.
  59. Kloster, K., Moeini, M., Vigo, D., & Wendt, O. (2023). The multiple traveling salesman problem in presence of drone-and robot-supported packet stations. European Journal of Operational Research, 305(2), 630-643.
  60. Biró, P., & Gyetvai, M. (2023). Online voluntary mentoring: Optimising the assignment of students and mentors. European Journal of Operational Research, 307(1), 392-405.
  61. Tran, T. H., Nguyen, T. B. T., Le, H. S. T., & Phung, D. C. (2024). Formulation and solution technique for agricultural waste collection and transport network design. European Journal of Operational Research, 313(3), 1152-1169.
  62. Caselli, G., Delorme, M., Iori, M., & Magni, C. A. (2024). Exact algorithms for a parallel machine scheduling problem with workforce and contiguity constraints. Computers & Operations Research, 163, 106484.
  63. Lyu, Z., & Yu, A. J. (2023). The pickup and delivery problem with transshipments: Critical review of two existing models and a new formulation. European Journal of Operational Research, 305(1), 260-270.
  64. Bigler, T., Kammermann, M., & Baumann, P. (2023). A matheuristic for a customer assignment problem in direct marketing. European Journal of Operational Research, 304(2), 689-708.


Informazioni aggiornate al: 25.3.2024 alle ore 18:57