Network planning and design

About: Network planning and design is a research topic. Over the lifetime, 12393 publications have been published within this topic receiving 229776 citations. The topic is also known as: network design.

Design and optimization of district energy systems

Abstract: The reduction of CO2 emissions is a challenge for the coming decade, especially with the implementation of the Kyoto protocol. Since energy services (mainly heating and cooling of buildings) contribute to over 40% of the final energy consumption in a country like Switzerland, it is essential to find ways to improve the efficiency of energy conversion technologies. This can be done by combining these energy conversion technologies into polygeneration systems for instance. However, to ensure that polygeneration systems operate as often as possible at or near their optimal load, it is meaningful to implement systems that meet the requirements of more than just one building, in order to take advantage of the various load profiles of the buildings by compensating the fluctuations and having therefore a smoother operation. Besides, because these systems are complex and defacto difficult to operate, there are usually not justified in an individual building where no continuous professional control can be guaranteed. It is much more advantageous to implement them in a small plant that serves several buildings, and that is managed by an energy service company. This means that a network needs to be designed, that optimally connects the buildings and the energy conversion technologies together. A new methodology to design and optimize district energy systems is therefore being developed. The method (see figure) is based on the combination of an evolutionary algorithm, a network design and optimization algorithm, and several thermo- economic models for the energy conversion technologies. The first step is to select a district for which an energy system has to be developed or modified. The available renewable energy sources existing near or in the district, and thus the possible energy conversion technologies, are identified. Besides, all the relevant information regarding the district have to be structured: the geographical coordinates of the buildings, the load profiles of the buildings and finally the constraints (legal regulations, topology, existing networks,…). Once this information structuring phase is completed, the method for the design of the network and the energy conversion technologies can be applied, resulting in a number of different configurations. The costs and CO2-emissions are computed for each configuration on a Pareto-curve and the results compared. In this presentation, we present the first results of the implementation of this method.

A simulation-based multi-objective genetic algorithm (SMOGA) procedure for BOT network design problem

Abstract: Solving optimization problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary algorithms, particularly genetic algorithms, have shown to be effective in solving this type of complex problems. In this paper, we develop a simulation-based multi-objective genetic algorithm (SMOGA) procedure to solve the build-operate-transfer (BOT) network design problem with multiple objectives under demand uncertainty. The SMOGA procedure integrates stochastic simulation, a traffic assignment algorithm, a distance-based method, and a genetic algorithm (GA) to solve a multi-objective BOT network design problem formulated as a stochastic bi-level mathematical program. To demonstrate the feasibility of SMOGA procedure, we solve two mean-variance models for determining the optimal toll and capacity in a BOT roadway project subject to demand uncertainty. Using the inter-city expressway in the Pearl River Delta Region of South China as a case study, numerical results show that the SMOGA procedure is robust in generating ‘good’ non-dominated solutions with respect to a number of parameters used in the GA, and performs better than the weighted-sum method in terms of the quality of non-dominated solutions.

Polyhedral and Computational Investigations for Designing Communication Networks with High Survivability Requirements

Abstract: We consider the important practical and theoretical problem of designing a low-cost communications network which can survive failures of certain network components. Our initial interest in this area was motivated by the need to design certain “two-connected” survivable topologies for fiber optic communication networks of interest to the regional telephone companies. In this paper, we describe some polyhedral results for network design problems with higher connectivity requirements. We also report on some preliminary computational results for a cutting plane algorithm for various real-world and random problems with high connectivity requirements, which shows promise for providing good solutions to these difficult problems.

The Hub Line Location Problem

Abstract: This paper presents the hub line location problem in which the location of a set of hub facilities connected by means of a path or line is considered. Potential applications arise in the design of public transportation and rapid transit systems, where network design costs greatly dominate routing costs and thus full interconnection of hub facilities is unrealistic. Given that service time is the predominant objective in these applications, the problem considers the minimization of the total weighted travel time between origin/destination nodes while taking into account the time spent to access and exit the hub line. An exact algorithm based on a Benders decomposition of a strong path-based formulation is proposed. The standard decomposition method is enhanced through the incorporation of several features such as a multicut strategy, an efficient algorithm to solve the subproblem and to obtain stronger optimality cuts, and a Benders branch-and-cut scheme that requires the solution of only one master problem. Computational results obtained on benchmark instances with up to 100 nodes confirm the efficiency of the proposed algorithm, which is considerably faster and able to solve larger instances than a general purpose solver.