@misc{bub,
	author = {{Bobaco LTD.}},
    title = {{Hakkimizda}},
    year = "2022",
    howpublished = "\url{https://www.thebobaco.com.tr/Hakkimizda} [Online; accessed 10-september-2023]"
}

@article{F81,
  abstract = {One of the most computationally useful ideas of the 1970s is the observation that many hard integer programming problems can be viewed as easy problems complicated by a relatively small set of side constraints. Dualizing the side constraints produces a Lagrangian problem that is easy to solve and whose optimal value is a lower bound (for minimization problems) on the optimal value of the original problem. The Lagrangian problem can thus be used in place of a linear programming relaxation to provide bounds in a branch and bound algorithm. This approach has led to dramatically improved algorithms for a number of important problems in the areas of routing, location, scheduling, assignment and set covering. This paper is a review of Lagrangian relaxation based on what has been learned in the last decade.},
  added-at = {2011-08-17T16:08:47.000+0200},
  author = {Fisher, Marshall L.},
  biburl = {https://www.bibsonomy.org/bibtex/20270f0e1533f59095910bb05893f5dc4/pcbouman},
  interhash = {c5b237a75259493db147487666ec985d},
  intrahash = {0270f0e1533f59095910bb05893f5dc4},
  journal = {Management Science},
  keywords = {article lagrangean lagrangian relaxation},
  language = {English},
  number = 1,
  timestamp = {2011-08-18T09:10:27.000+0200},
  title = {{The Lagrangian Relaxation Method for Solving Integer Programming Problems}},
  url = {http://www.jstor.org/stable/30046157},
  volume = 27,
  year = 1981
}

@article{KLOOS2020203,
author = {Konstantin Kloos and Richard Pibernik},
title = {Allocation planning under service-level contracts},
journaltitle = {European Journal of Operational Research},
year = 2020,
month = 1,
volume = {280},
number = {1},
doi = {10.1016/j.ejor.2019.07.018},
url = {https://www.sciencedirect.com/science/article/pii/S037722171930582X/},
urldate = {Dec. 10, 2023}
}

