Call for papers: Advances in Stochastic Optimization
Stochastic optimization involves mathematical methods for optimal decision making when important model parameters are random. Its importance is demonstrated by a wide diversity of applications, spanning, e.g., energy, health, transportation and logistics, business analytics, finance, education, agriculture, public sector analytics, supply chain management, and the internet. Further applications arise in laboratory settings to help with drug discovery or materials science, design of computer simulations, field experimentation and implementation, covering strategic, tactical and real-time problems.
The application settings are so broad that multiple disciplines have evolved to respond to the different problem characteristics and research questions. Fields have developed with names such as stochastic programming, dynamic programming (including Markov decision processes, approximate/adaptive dynamic programming, and reinforcement learning), stochastic control, stochastic search, robust optimization, online computation, and stochastic equilibrium. Just as important are fields that evolved around learning unknown functions, including global optimization, ranking and selection, and the multi-armed bandit problem. Of increasing importance is the close relationship between stochastic optimization and machine learning, and the importance of careful modeling of stochastic processes, which is creating bridges to the field of uncertainty quantification.
The aim of this special issue is to collect high quality papers spanning all the different flavors of stochastic optimization, so that different communities can learn from each other. The major acceptance criterion for a submission will be the quality and originality of the contribution. However, the special issue will strive to feature a balanced representation of the different communities and problem domains. Applications are highly welcome, but each paper is expected to contain some novel methodological/mathematical content.
Manuscript Preparation and Submission
Submitted manuscripts will need to conform to the usual high standard requirements of EJOR and will be peer reviewed in the same manner as any other submission. Prospective authors are asked to follow the EJOR guide for authors and submit their full papers via the EES (http://ees.elsevier.com/ejor), choosing \”Stochastic Optimization\” as the “Article type” from the pull-down menu.
Before the submission of a full paper, prospective authors may submit an extended abstract – i.e., a 3-5 page abstract (single-spaced) – to the guest editors summarizing the focus of the proposed paper. Submission of the extended abstracts will ensure a quick feedback to the authors on the suitability of the paper for the special issue, and will help to insure a balanced issue. Acceptance of an abstract is not a guarantee of acceptance of the paper, which will be refereed and expected to meet the publishing standards of EJOR.
Extended abstract submission due: October 31, 2017 (not mandatory, but recommended)
Full paper submission due: January 31, 2018
Planned final decision: December 31, 2018
Planned on-line publication: February 2019
Special Issue Guest Editors
Raimund Kovacevic, Vienna University of Technology,
Carlo Meloni, Politecnico di Bari,
Dario Pacciarelli [Managing Guest Editor], Roma Tre University,
Warren B. Powell, Princeton University,