28,978 research outputs found
Bounded Decentralised Coordination over Multiple Objectives
We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents
Optimal Power Flow with Step-Voltage Regulators in Multi-Phase Distribution Networks
This paper develops a branch-flow based optimal power flow (OPF) problem for
multi-phase distribution networks that allows for tap selection of wye,
closed-delta, and open-delta step-voltage regulators (SVRs). SVRs are assumed
ideal and their taps are represented by continuous decision variables. To
tackle the non-linearity, the branch-flow semidefinite programming framework of
traditional OPF is expanded to accommodate SVR edges. Three types of
non-convexity are addressed: (a) rank-1 constraints on non-SVR edges, (b)
nonlinear equality constraints on SVR power flows and taps, and (c) trilinear
equalities on SVR voltages and taps. Leveraging a practical phase-separation
assumption on the SVR secondary voltage, novel McCormick relaxations are
provided for (c) and certain rank-1 constraints of (a), while dropping the
rest. A linear relaxation based on conservation of power is used in place of
(b). Numerical simulations on standard distribution test feeders corroborate
the merits of the proposed convex formulation.Comment: This manuscript has been submitted to IEEE Transactions on Power
System
Stock Management in Hospital Pharmacy using Chance-Constrained Model Predictive Control
One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals.Junta de AndalucÃa P12-TIC-240
A Distributed Asynchronous Method of Multipliers for Constrained Nonconvex Optimization
This paper presents a fully asynchronous and distributed approach for
tackling optimization problems in which both the objective function and the
constraints may be nonconvex. In the considered network setting each node is
active upon triggering of a local timer and has access only to a portion of the
objective function and to a subset of the constraints. In the proposed
technique, based on the method of multipliers, each node performs, when it
wakes up, either a descent step on a local augmented Lagrangian or an ascent
step on the local multiplier vector. Nodes realize when to switch from the
descent step to the ascent one through an asynchronous distributed logic-AND,
which detects when all the nodes have reached a predefined tolerance in the
minimization of the augmented Lagrangian. It is shown that the resulting
distributed algorithm is equivalent to a block coordinate descent for the
minimization of the global augmented Lagrangian. This allows one to extend the
properties of the centralized method of multipliers to the considered
distributed framework. Two application examples are presented to validate the
proposed approach: a distributed source localization problem and the parameter
estimation of a neural network.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0648
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