SavedAdd your journal

Journals / An International Journal of Optimization and Control: Theories & Applications (IJOCTA) / 2020 / Cilt: 10 - Sayı: 1

A multi-start iterated tabu search algorithm for the multi-resource agent bottleneck generalized assignment problem

Gülçin BEKTUR
Pages
37–46
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results of the proposed algorithm are compared by the resultsof the tabu search (TS) algorithm and mixed integer linear programming (MILP)model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a multi-resource agent bottleneck generalized assignment problem(MRBGAP) is addressed.
📊
Key Findings & Data:Consequently, a multi-startiterated tabu search (MITS) algorithm has been proposed for the solution of largescaleproblems.
💡
Conclusion & Impact:The results of the proposed algorithm are compared by the resultsof the tabu search (TS) algorithm and mixed integer linear programming (MILP)model.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

In this study, a multi-resource agent bottleneck generalized assignment problem(MRBGAP) is addressed. In the bottleneck generalized assignment problem(BGAP), more than one job can be assigned to an agent, and the objective function is to minimize the maximum load over all agents. In this problem, multipleresources are considered and the capacity of the agents is dependent on theseresources and it has minimum two indices. In addition, agent qualifications aretaken into account. In other words, not every job can be assignable to every agent.The problem is defined by considering the problem of assigning jobs to employeesin a firm. BGAP has been shown to be NP- hard. Consequently, a multi-startiterated tabu search (MITS) algorithm has been proposed for the solution of largescaleproblems. The results of the proposed algorithm are compared by the resultsof the tabu search (TS) algorithm and mixed integer linear programming (MILP)model.

🔖

Cite This Article

BEKTUR, G. (2022). A multi-start iterated tabu search algorithm for the multi-resource agent bottleneck generalized assignment problem. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 10 - Sayı: 1, 37–46. https://ajindex.com/articles/440560

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. Pentico, D.W. (2007). Assignment problems: A golden anniversary survey. European Journal of Operational Research, 176, 774- 793
  2. Mazzola, J. B., & Wilcox, S.P. (2001). Heuristics for the multi- resource generalized assignment problem. Naval Research Logistics, 48, 468- 483
  3. Karsu, Ö., & Azizoğlu, M. (2012). The multiresource agent bottleneck generalised assignment problem. International Journal of Production Research, 5(2), 309- 324
  4. Ross, G. T. (1975). A branch and bound algorithm for the generalized assignment problem. Mathematical Programming, 8, 91- 103
  5. Posta, M., Ferland, J.A., & Michelon, P. (2012). An exact method with variable fixing for solving the generalized assignment problem. Computational Optimization and Applications, 52, 629- 644
  6. Avella, P., Boccia, M., & Vasilyev, I. (2010). A computational study of exact knapsack separation for the generalized assignment problem. Computational Optimization and Applications, 45, 543- 555
  7. Savelsbergh, M. (1997). A branch- and- pricealgorithm for the generalized assignment problem. Operations Research, 831- 841
  8. Pigatti, A., Poggi, M., & Uchoa, E. (2005). Stabilized branch- and- cut- and- price for the generalized assignment problem. Electronic Notes in Discrete Mathematics, 19, 389- 395
  9. Öncan, T., Şuvak, Z., Akyüz, M. H., & Altınel, İ. K. (2019). Assignment problem with conflicts. Computers and Operations Research (In press)
  10. Wu, W., Iori, M., Martello, S., & Yagiura, M. (2018). Exact and heuristic algorithms for the interval min- max regret generalized assignment problem. Computers and Industrial Engineering, 125, 98- 110
  11. Öncan, T. (2007). A survey of the generalized assignment problem and its applications. Information Systems and Operational Research, 45 (3), 123- 141
  12. Higgins, A. J. (2001). A dynamic tabu search for large- scale generalised assignment problems. Computers and Operations Research, 28, 1039- 1048
  13. Diaz, J. A., & Fernandez, E. (2001). A tabu search heuristic for the generalized assignment problem. European Journal of Operational Research, 132, 22- 38
  14. Osman, İ. (1995). Heuristics for the generalized assignment problem: simulated annealing and tabu search approaches. Operations Research- Spektrum, 17, 211- 225
  15. Chu, P. L., & Beasley, J. E. (1997). A genetic algorithm for the generalized assignment problem. Computers and Operations Research, 24 (1), 17- 23
  16. Ozbakır, L., Baykasoğlu, A., & Tapkan, P. (2010). Bees algorit