Journal•ISSN: 1619-697X
Computational Management Science
Springer Science+Business Media
About: Computational Management Science is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Stochastic programming & Computer science. It has an ISSN identifier of 1619-697X. Over the lifetime, 462 publications have been published receiving 11157 citations.
Topics: Stochastic programming, Computer science, Portfolio, Optimization problem, Portfolio optimization
Papers published on a yearly basis
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TL;DR: This paper presented to the 8th International Meeting of the Institute of Management Sciences, Brussels, August 23-26, 1961 presents a meta-analyses of the determinants of infectious disease in eight operation rooms of the immune system and its consequences.
Abstract: Paper presented to the 8th International Meeting of the Institute of Management Sciences, Brussels, August 23-26, 1961.
1,750 citations
TL;DR: In this first part of a two-part article, the principal characteristics of the TIMES model and of its global incarnation as ETSAP-TIAM are presented and discussed.
Abstract: In this first part of a two-part article, the principal characteristics of the TIMES model and of its global incarnation as ETSAP-TIAM are presented and discussed. TIMES was conceived as a descendent of the MARKAL and EFOM paradigms, to which several new features were added to extend its functionalities and its applicability to the exploration of energy systems and the analysis of energy and environmental policies. The article stresses the technological nature of the model and its economic foundation and properties. The article stays at the conceptual and practical level, while a companion article is devoted to the more detailed formulation of TIMES equations. Special sections are devoted to the description of four optional features of TIMES: lumpy investments, endogenous technology learning, stochastic programming, and the climate module. The article ends with a brief description of recent applications of the ETSAP-TIAM model.
502 citations
TL;DR: In Pang and Fukushima, a sequential penalty approach was presented for a quasi-variational inequality (QVI) with particular application to the generalized Nash game, but numerical results due to an inverted sign in the penalty term in the example and some missing terms in the derivatives of the firms’ Lagrangian functions are incorrect.
Abstract: In Pang and Fukushima (Comput Manage Sci 2:21–56, 2005), a sequential penalty approach was presented for a quasi-variational inequality (QVI) with particular application to the generalized Nash game. To test the computational performance of the penalty method, numerical results were reported with an example from a multi-leader-follower game in an electric power market. However, due to an inverted sign in the penalty term in the example and some missing terms in the derivatives of the firms’ Lagrangian functions, the reported numerical results in Pang and Fukushima (Comput Manage Sci 2:21–56, 2005) are incorrect. Since the numerical examples of this kind are scarce in the literature and this particular example may be useful in the future research, we report the corrected results.
424 citations
TL;DR: The necessity and efficacy of the techniques is empirically assessed on a two-stage stochastic network flow problem with integer variables in both stages and algorithmic innovations in the context of a broad class of scenario-based resource allocation problem.
Abstract: Numerous planning problems can be formulated as multi-stage stochastic programs and many possess key discrete (integer) decision variables in one or more of the stages. Progressive hedging (PH) is a scenario-based decomposition technique that can be leveraged to solve such problems. Originally devised for problems possessing only continuous variables, PH has been successfully applied as a heuristic to solve multi-stage stochastic programs with integer variables. However, a variety of critical issues arise in practice when implementing PH for the discrete case, especially in the context of very difficult or large-scale mixed-integer problems. Failure to address these issues properly results in either non-convergence of the heuristic or unacceptably long run-times. We investigate these issues and describe algorithmic innovations in the context of a broad class of scenario-based resource allocation problem in which decision variables represent resources available at a cost and constraints enforce the need for sufficient combinations of resources. The necessity and efficacy of our techniques is empirically assessed on a two-stage stochastic network flow problem with integer variables in both stages.
300 citations
TL;DR: This article is a companion to “ETSAP-TIAM: the TIMes integrated assessment model” and contains three sections, presenting respectively: the simplified formulation of the TIMES Linear Program, the details of the computation of the supply demand equilibrium, and the Endogenous Technology Learning Formulation.
Abstract: This article is a companion to “ETSAP-TIAM: the TIMES integrated assessment model. part I: model structure”. It contains three sections, presenting respectively: the simplified formulation of the TIMES Linear Program (Sect. 1), the details of the computation of the supply demand equilibrium (Sect. 2), and the Endogenous Technology Learning Formulation (Sect. 3). The full details of these three formulations are available in the complete TIMES documentation at www.etsap/org/documentation.
197 citations