Abstract
Standard methodologies for redesigning physical networks rely on Geographic Information Systems (GIS), which strongly depend on local demographic specifications. The absence of a universal definition of demography makes its use for cross-border purposes much more difficult. This paper presents a Decision Making Model (DMM) for redesigning networks that works without geographical constraints. There are multiple advantages of this approach: on one hand, it can be used in any country of the world; on the other hand, the absence of geographical constraints widens the application scope of our approach, meaning that it can be successfully implemented either in physical (ATM networks) or non-physical networks such as in group decision making, social networks, e-commerce, e-governance and all fields in which user groups make decisions collectively. Case studies involving both types of situations are conducted in order to illustrate the methodology. The model has been designed under a data reduction strategy in order to improve application performance.
Highlights
Network redesigning is a dynamic problem that arises in several fields
This paper presents a decision-making model that can be used in order to design the future network structure of either physical or non-physical networks
This works on the basis that it allows the evaluation of the future performance of the network in different scenarios, thereby anticipating future needs
Summary
Network redesigning is a dynamic problem that arises in several fields. The wide range of reasons for redesigning includes the need to adapt to changing requirements (new regulatory scenarios, for instance) or to enhance the sector’s capacities [1,2,3]. Redesigning processes go much further than adapting to changes as they involve several factors. At their core, all these variables may be considered to be within two main groups: (i) identifying operational shortfalls and (ii) projecting future performance in order to anticipate future needs. The latter concern could be interpreted as re-distributing nodes in the network (adding, removing and merging nodes) and assessing the results for each possibility. This paper is related to the latter concept: an approach is developed to model the evaluation of the future performance of a network in different scenarios in order to design the future network structure
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