PON and WiMAX Convergence Network Planning Based on Mixed Integer Programming Model

This article analyzes the characteristics of PON and WiMAX convergence network planning.Based on user coverage ratio,WiMAX channel allocation,cell radius,carrier-to-noise ratio threshold,and bandwidth constraint,we propose a mixed integer programming model solved by a Branch-Band and Heuristic Search method.Finally,the simulation result is given and analyzed.The planning method based on a mixed integer programming model can save 20 percentage of the overall planning cost,compared with the greedy algorithm.The relationship between the convergence network planning cost and frequency usage is also analyzed.The optimized planning result with the lowest cost can be acquired through the best frequency usage.

Particle Swarm Optimization and Its Application in Transmission Network Expansion Planning

The author introduced particle swarm optimization as a new method for power transmission network expansion planning. A new discrete method for particle swarm optimization was developed,which is suitable for power transmission network expansion planning, and requires less computer s memory.The optimization fitness function construction, parameter selection, convergence judgement, and their characters were analyzod.Numerical simulation demonstrated the effectiveness and correctness or the method. This paper provides an academic and practical basis of particle swarm optimization in application of transmission network expansion planning for further investigation.

Multi-period mine planning with multi-process routes

This paper attempts to optimize optimal capacities, block routing and mine sequencing problems in a mining system. The solution approach is based on a heuristics and the mixed integer programming (MIP). Unlike previous sequential solution approaches, the problems are herein solved at the same time. Furthermore, the proposed approach guarantees practical solutions because it considers ore material distribution within orebody. The paper has two main contributions: (a) the proposed approach generates production rates in a manner that the capacities are satisfied; (b) the proposed approach does not use pre-defined marginal cut-off grades. Thus, idle capacity problem is eliminated and different scheduling combinations are allowed. To see the performance of the approach proposed, a case study is carried out using a gold data. The schedule generated shows that the approach can determine optimal production rates, block destination and sequencing effectively.

Modeling and optimization methods of integrated production planning for steel plate mill with flexible customization

With diversified requirements and varying manufacturing environments, the optimal production planning for a steel mill becomes more flexible and complicated. The flexibility provides operators with auxiliary requirements through an implementable integrated production planning. In this paper, a mixed-integer nonlinear programming(MINLP) model is proposed for the optimal planning that incorporates various manufacturing constraints and flexibility in a steel plate mill. Furthermore, two solution strategies are developed to overcome the weakness in solving the MINLP problem directly. The first one is to transform the original MINLP formulation to an approximate mixed integer linear programming using a classic linearization method. The second one is to decompose the original model using a branch-and-bound based iterative method. Computational experiments on various instances are presented in terms of the effectiveness and applicability. The result shows that the second method performs better in computational efforts and solution accuracy.

PRECESION： progressive recovery and restoration planning of interdependent services in enterprise data centers

The primary focus of this paper is to design a progressive restoration plan for an enterprise data center environment following a partial or full disruption. Repairing and restoring disrupted components in an enterprise data center requires a significant amount of time and human effort. Following a major disruption, the recovery process involves multiple stages, and during each stage, the partially recovered infrastructures can provide limited services to users at some degraded service level. However, how fast and efficiently an enterprise infrastructure can be recovered de- pends on how the recovery mechanism restores the disrupted components, considering the inter-dependencies between services, along with the limitations of expert human operators. The entire problem turns out to be NP- hard and rather complex, and we devise an efficient meta-heuristic to solve the problem. By considering some real-world examples, we show that the proposed meta-heuristic provides very accurate results, and still runs 600-2800 times faster than the optimal solution obtained from a general purpose mathematical solver [1].