A novel two-stage Lagrangian decomposition approach for refinery production scheduling with operational transitions in mode switching

To address large scale industrial processes,a novel Lagrangian scheme is proposed to decompose a refinery scheduling problem with operational transitions in mode switching into a production subproblem and a blending and delivery subproblem.To accelerate the convergence of Lagrange multipliers,some auxiliary constraints are added in the blending and delivery subproblem.A speed-up scheme is presented to increase the efficiency for solving the production subproblem.An initialization scheme of Lagrange multipliers and a heuristic algorithm to find feasible solutions are designed.Computational results on three cases with different lengths of time horizons and different numbers of orders show that the proposed Lagrangian scheme is effective and efficient.

Optimal joint relay selection and resource allocation with QoS constraints in multiuser OFDM-based cellular networks

This paper investigates the relay selection and resource allocation problem in multiuser orthogonal frequency division multiplexing （OFDM） based cooperative cellular networks, in which user nodes could relay information for each other using the decode-and-forward （DF） protocol to achieve spatial diversity gain. Specifically, the paper proposes an optimal joint relay selection and resource allocation （0RSRA） algorithm whose objective is to maximize system total achievable data rate with the constraints of each user＇ s individual quality of service （QoS） requirement and transmission power. Due to being a mixed binary integer programming （MBIP） problem, a novel two-level Lagrangian dual-primal decomposition and subgradient projection approach is proposed to not only select the appropriate cooperative relay nodes, but also allocate subcarries and power optimally. Simulation re- suits demonstrate that our proposed scheme can efficiently enhance overall system data rate and guarantee each user＇ s QoS requirement. Meanwhile, the fairness among users can be improved dramatically.

Resource Allocation for Physical Layer Security in Heterogeneous Network with Hidden Eavesdropper

The tremendous performance gain of heterogeneous networks(Het Nets) is at the cost of complicated resource allocation. Considering information security, the resource allocation for Het Nets becomes much more challenging and this is the focus of this paper. In this paper, the eavesdropper is hidden from the macro base stations. To relax the unpractical assumption on the channel state information on eavesdropper, a localization based algorithm is first given. Then a joint resource allocation algorithm is proposed in our work, which simultaneously considers physical layer security, cross-tier interference and joint optimization of power and subcarriers under fairness requirements. It is revealed in our work that the considered optimization problem can be efficiently solved relying on convex optimization theory and the Lagrangian dual decomposition method is exploited to solve the considered problem effectively. Moreover, in each iteration the closed-form optimal resource allocation solutions can be obtained based on the Karush-Kuhn-Tucker(KKT) conditions. Finally, the simulation results are given to show the performance advantages of the proposed algorithm.

Recent Advances in Mathematical Programming with Semi-continuous Variables and Cardinality Constraint

Mathematical programming problems with semi-continuous variables and cardinality constraint have many applications,including production planning,portfolio selection,compressed sensing and subset selection in regression.This class of problems can be modeled as mixed-integer programs with special structures and are in general NP-hard.In the past few years,based on new reformulations,approximation and relaxation techniques,promising exact and approximate methods have been developed.We survey in this paper these recent developments for this challenging class of mathematical programming problems.

QoS-guaranteed multi-relay selection and power allocation optimization in cooperative systems

This paper investigates the relay selection and power allocation problem in multi-user based cooperative networks,where intermediate relay nodes help source forward information to destination using decode-and-forward （DF） relaying protocol. Specifically,we propose a novel multi-relay nodes selection strategy taking both instantaneous channel state information （I-CSI） and residual energy into consideration,by which ＇emergence＇ diversity gain can be achieved and the imbalance of resource utilization can be overcome. Besides,using Largangian dual-primal decomposition and subgradient projection approach,an optimal power allocation algorithm at source and cooperative relay nodes is presented with the constraints of each user＇s individual quality of service （QoS） requirements and system total transmit power. Theoretical analysis and simulation results demonstrate that the proposed scheme can significantly improve energy efficiency,while guaranteeing a good balance between achievable data rate and average network lifetime with relatively low implementation complexity.