118,030 research outputs found
Dynamic Robust Transmission Expansion Planning
Recent breakthroughs in Transmission Network Expansion Planning (TNEP) have
demonstrated that the use of robust optimization, as opposed to stochastic
programming methods, renders the expansion planning problem considering
uncertainties computationally tractable for real systems. However, there is
still a yet unresolved and challenging problem as regards the resolution of the
dynamic TNEP problem (DTNEP), which considers the year-by-year representation
of uncertainties and investment decisions in an integrated way. This problem
has been considered to be a highly complex and computationally intractable
problem, and most research related to this topic focuses on very small case
studies or used heuristic methods and has lead most studies about TNEP in the
technical literature to take a wide spectrum of simplifying assumptions. In
this paper an adaptive robust transmission network expansion planning
formulation is proposed for keeping the full dynamic complexity of the problem.
The method overcomes the problem size limitations and computational
intractability associated with dynamic TNEP for realistic cases. Numerical
results from an illustrative example and the IEEE 118-bus system are presented
and discussed, demonstrating the benefits of this dynamic TNEP approach with
respect to classical methods.Comment: 10 pages, 2 figures. This article has been accepted for publication
in a future issue of this journal, but has not been fully edited. Content may
change prior to final publication. Citation information: DOI
10.1109/TPWRS.2016.2629266, IEEE Transactions on Power Systems 201
Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain
In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method
Modelling network travel time reliability under stochastic demand
A technique is proposed for estimating the probability distribution of total network travel time, in the light of normal day-to-day variations in the travel demand matrix over a road traffic network. A solution method is proposed, based on a single run of a standard traffic assignment model, which operates in two stages. In stage one, moments of the total travel time distribution are computed by an analytic method, based on the multivariate moments of the link flow vector. In stage two, a flexible family of density functions is fitted to these moments. It is discussed how the resulting distribution may in practice be used to characterise unreliability. Illustrative numerical tests are reported on a simple network, where the method is seen to provide a means for identifying sensitive or vulnerable links, and for examining the impact on network reliability of changes to link capacities. Computational considerations for large networks, and directions for further research, are discussed
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