Optimization Of Temporal Networks Under Uncertainty
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Optimization of Temporal Networks under Uncertainty
Author: Wolfram Wiesemann
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-01-04
Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises. This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters.
Optimization of Temporal Networks under Uncertainty
Author: Wolfram Wiesemann
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-01-05
Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes. Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises. This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters.
Distributed Computing and Intelligent Technology
This book constitutes the proceedings of the 18th International Conference on Distributed Computing and Intelligent Technology, ICDCIT 2022, held in Bhubaneswar, India, in January 20212. The 11 full papers presented together with 4 short papers were carefully reviewed and selected from 50 submissions. There are also 4 invited papers included. The papers were organized in topical sections named: invited papers, distributed computing and intelligent technology.