@example{DBLP:journals/cacm/Knuth74,
  author    = {Donald E. Knuth},
  title     = {{Computer Programming as an Art}},
  journal   = {Commun. {ACM}},
  volume    = {17},
  number    = {12},
  pages     = {667--673},
  year      = {1974},
  doi       = {10.1145/361604.361612},
  timestamp = {Tue, 07 Jun 2011 16:50:57 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/cacm/Knuth74},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{SocialEventPlanning,
  author={Bikakis, Nikos and Kalogeraki, Vana and Gunopulos, Dimitrios},
  booktitle={2018 IEEE 34th International Conference on Data Engineering (ICDE)}, 
  title={Social Event Scheduling}, 
  year={2018},
  volume={},
  number={},
  pages={1272-1275},
  abstract={A major challenge for social event organizers (e.g., event planning and marketing companies, venues) is attracting the maximum number of participants, since it has great impact on the success of the event, and, consequently, the expected gains (e.g., revenue, artist/brand publicity). In this paper, we introduce the Social Event Scheduling (SES) problem, which schedules a set of social events considering user preferences and behavior, events' spatiotemporal conflicts, and competing events, in order to maximize the overall number of attendees. We show that SES is strongly NP-hard, even in highly restricted instances. To cope with the hardness of the SES problem we design a greedy approximation algorithm. Finally, we evaluate our method experimentally using a dataset from the Meetup event-based social network.},
  keywords={Schedules;Scheduling;Companies;Social network services;Conferences;Social Event Planning;Attendance Maximization;Social Event Arrangement;Event Organizers;Event Participants;Social Event Mining;Assignment Problems;Event based Social Networks},
  doi={10.1109/ICDE.2018.00128},
  ISSN={2375-026X},
  month={April},}

@article{distributedOptimizationOfCalendarEvents,
  author={Boodhoo, Steffan and Hosein, Patrick},
  booktitle={2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA)}, 
  title={On the distributed optimization of calendar events}, 
  year={2017},
  volume={},
  number={},
  pages={79-84},
  abstract={Many organizations use scheduling software to assist in the organizing of events. One popular example is Microsoft Outlook. Such software typically requires event participants to vote on time slot allocations or to negotiate with one another on time slot allocations, until event attendees are appropriately satisfied. The process of scheduling events into time slots can be automated by requiring users to apply soft constraints on their availability for different time slots. In a soft constraint system, users assign values to time-slots, which denote the relative importance of each event. The problem can then be formalized as an optimization problem where the objective is to increase the efficiency in the scheduling of events versus availability in participants' schedules. Since knowledge of all events is not known initially, the optimal placement of events can change over time. Therefore, there is a need for on-demand re-optimization of schedules. This can be a timely process as the scheduler has to account for the best placement of all events across all users' time slots. One method which deals with problems that require a high volume of processing is to split and distribute the problem across machines. However, since the optimal solution depends on a maximization of all events across all time-slots, an event may need to be `aware' of other events' placements, as this can affect which slot is best. This paper presents an efficient way of distributing users, and by extension, events to distribute work while keeping relevant events together.},
  keywords={Schedules;Scheduling;Patents;Optimization;Medical services;Organizations;Software;Event Scheduling;Electronic Calendar;Optimization},
  doi={10.1109/IWCIA.2017.8203565},
  ISSN={},
  month={Nov},}

@article{EventSchedulingWithSoftConstraints,
   author={Hosein, Patrick and Boodhoo, Steffan},
  booktitle={2016 IEEE International Conference on Knowledge Engineering and Applications (ICKEA)}, 
  title={Event scheduling with soft constraints and on-demand re-optimization}, 
  year={2016},
  volume={},
  number={},
  pages={62-66},
  keywords={Decision Support System;Electronic Calendar;Optimization;Event Scheduler},
  doi={10.1109/ICKEA.2016.7802993}
}

@article{
JobShopSchedulingWithDeadlines,
    author = {Egon Balas, Giuseppe Langcia, Paolo Serafini, Alkiviadis Vazacopoulos},
    title = {Job Shop Scheduling With Deadlines},
    journal = {Journal of Combinatorial Optimization},
    year = {(1998)},
    month = {December},
    doi = {https://doi.org/10.1023/A:1009750409895}
}

@Inbook{Karp1972,
author="Karp, Richard M.",
editor="Miller, Raymond E.
and Thatcher, James W.
and Bohlinger, Jean D.",
title="Reducibility among Combinatorial Problems",
bookTitle="Complexity of Computer Computations: Proceedings of a symposium on the Complexity of Computer Computations, held March 20--22, 1972, at the IBM Thomas J. Watson Research Center, Yorktown Heights, New York, and sponsored by the Office of Naval Research, Mathematics Program, IBM World Trade Corporation, and the IBM Research Mathematical Sciences Department",
year="1972",
publisher="Springer US",
address="Boston, MA",
pages="85--103",
abstract="A large class of computational problems involve the determination of properties of graphs, digraphs, integers, arrays of integers, finite families of finite sets, boolean formulas and elements of other countable domains. Through simple encodings from such domains into the set of words over a finite alphabet these problems can be converted into language recognition problems, and we can inquire into their computational complexity. It is reasonable to consider such a problem satisfactorily solved when an algorithm for its solution is found which terminates within a number of steps bounded by a polynomial in the length of the input. We show that a large number of classic unsolved problems of covering, matching, packing, routing, assignment and sequencing are equivalent, in the sense that either each of them possesses a polynomial-bounded algorithm or none of them does.",
isbn="978-1-4684-2001-2",
doi="10.1007/978-1-4684-2001-2_9",
url="https://doi.org/10.1007/978-1-4684-2001-2_9"
}