Aims and scope

Computational Management Science (CMS) is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models; computational statistics; analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms; dynamic models, such as dynamic programming and decision trees; new search tools and algorithms for global optimisation, modelling, learning and forecasting; models and tools of knowledge acquisition.

The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals.

Officially cited as: Comput Manag Sci

Special Issue on Stochastic Optimization: Methodological Advancements and Modern Applications
截稿日期: 2024-12-31

Overview:

We invite researchers to contribute to a collection in Computational Management Science focusing on Stochastic Optimization. The collection is connected to the 2nd Copenhagen School of Stochastic Programming (25th-28th June 2024, Copenhagen) and to the joint ECSO-CMS conference (4th-5th July 2024, Stockholm). This collection aims to showcase cutting-edge methodology as well as contemporary applications in the field.

Scope:

We welcome submissions that span a wide spectrum, encompassing both methodology and practical applications of stochastic optimization. Papers must be within the realm of Stochastic Programming, Distributionally Robust Optimization and Stochastic Programming with Decision-Dependent Uncertainty. Particularly, the special issue welcomes:

• Methodology papers. These papers must contribute to the state-of-the-art. Topics may include, for example, new solution algorithms or advancements in existing algorithms (with particular emphasis on exact methods), approximation methods (e.g. scenario generation methods), and bounding techniques. Submissions should provide theoretical insights, algorithmic details, and demonstrate the potential impact on practical problem-solving.

• Papers showcasing modern applications of stochastic optimization. These papers must illustrate how stochastic optimization contributes to contemporary management science scenarios. Submissions should emphasize novel and unexplored real-world problem-solving, highlighting the relevance and effectiveness of stochastic optimization in diverse domains. Of particular interest are papers illustrating modern applications of risk-averse stochastic optimization (e.g., the effect of introducing risk measures, time consistency issues).

Submissions are particularly encouraged from (but not restricted to) the participants to the Copenhagen School of Stochastic Programming and to the ECSO-CMS conference.

Submission Guidelines:

Authors are invited to submit original and unpublished research. Manuscripts should adhere to the journal's formatting guidelines. When submitting, authors should select the collection from a drop down menu. Detailed submission instructions can be found on the journal's webpage.

Guest Editors: Giovanni Pantuso, Trine K. Boomsma, Francesca Maggioni

Please direct all inquiries and submissions to the Guest editors [gp@math.ku.dk, trine@math.ku.dk, francesca.maggioni@unibg.it].