Multi-Reservoir Operation Planning Using Hybrid Genetic Algorithm and Linear Programming (GA-LP): An Alternative Stochastic Approach

Many models have been suggested to deal with the multi-reservoir operation planning stochastic optimization problem involving decisions on water releases from various reservoirs in different time periods of the year. A new approach using genetic algorithm (GA) and linear programming (LP) is proposed here to determine operational decisions for reservoirs of a hydro system throughout a planning period, with the possibility of considering a variety of equally likely hydrologic sequences representing inflows. This approach permits the evaluation of a reduced number of parameters by GA and operational variables by LP. The proposed algorithm is a stochastic approximation to the hydro system operation problem, with advantages such as simple implementation and the possibility of extracting useful parameters for future operational decisions. Implementation of the method is demonstrated through a small hypothetical hydrothermal system used in literature as an example for stochastic dual dynamic programming (SDDP) method of Pereira and Pinto (Pereira, M. V. F. and Pinto, L. M. V. G.: 1985, Water Res. Res. 21(6), 779–792). The proposed GA-LP approach performed equally well as compared to the SDDP method.     

幼儿营养粉的制备加工

根据最新国家标准GB10769-1997,应用线性规划法对以谷类、乳粉为主要原料的婴幼儿断奶期辅助食品进行配方设计与加工技术的探索,制备出了婴幼儿营养初级食品,尽可能使其营养成分符合国标要求,必需氨基酸配比合理,并添加了重要的生物活性物质。     

我国污水再生利用潜力的优化分析

为比较我国不同地区再生水利用量的差异和考察水价变化对再生水利用量的影响,建立了一个在技术、经济和资源约束下的区域污水再生利用潜力线性规划模型,对我国的再生水利用量进行了优化分析。结果显示,我国目前再生水利用最大可能规模为17.8×108m3/a,主要分布在东北、东南沿海和华北地区,其中北京、辽宁、广东和江苏的污水再生利用潜力约占我国再生水利用潜力的45%;合理的水价改革可有效刺激再生水利用量和利用结构的变化,进而为相关基础设施的发展带来新的机遇。     

拱坝封拱温度场多目标非线性规划方法研究

从拱坝温度荷载影响因素分析入手，对封拱温度场进行了优化，这是减小温度荷载不利影响的唯一有效措施。建立了拱坝封洪温度场多目标优化的数学模型，并利用该优化模型求解了位于南方典型气温条件下的某拱坝最优封拱温度场。     

结合浪费函数的多传感器管理算法

线性规划方法在多传感器管理中的应用已比较经典．利用效能函数和浪费函数的结合作为多传感器管理的目标函数．并将目标函数和约束条件写成规范的线性规划标准型．通过仿真分析说明了该方法的适用性．     

An efficient memetic algorithm for the multi-stage supply chain network problem

A supply chain is dynamic and involves the constant flow of information, production, services, and funds from suppliers to customers between different stages. In this paper, a memetic algorithm (MA, a hybrid genetic algorithm) is developed to find the strategy that can give the lowest cost of the physical distribution flow. The proposed MA is combined with the genetic algorithm (GA), a multi-greedy heuristic method (GH), three local search methods (LSMs): the pairwise exchange procedure (XP), the insert procedure (IP), and the remove procedure (RP), the Fibonacci number procedure, and the linear programming technique (LP) to improve the tradition genetic algorithm (GA). Preliminary computational experiments demonstrate the efficiency and performance of the proposed MA.     

The workload balancing problem at aircargo terminals

We consider a large air cargo handling facility composed of two identical cargo terminals. In order to improve the operational efficiency, the workload must be balanced between the terminals. Thus, we must assign each airline served by the facility to one of the terminals such that (ideally): (1) each terminal has the same total workload, and (2) the workload at each terminal is distributed evenly along the timeline. Complicating the problem is that cargo loads are difficult to predict (stochastic). We develop a stochastic mixed integer linear program model in which a weighted sum of the balance measures is minimized. We employ sample average approximation for the stochastic program and develop an accelerated Benders decomposition algorithm to reduce the computational time. The proposed model can also be applied to partially reassign the airlines for the operational schedule changes. The computational results show that a small number of reassignments are often sufficient to rebalance the workload. The simulation results based on data from a large international airport show that the proposed algorithms efficiently balance the workload and the cargo service time is consistently reduced.     

Sequence of linear programming for transmission of fine-scalable coded content in bandwidth-limited environments

In this paper, we propose an optimal peer assignment algorithm on peer-to-peer networks. This algorithm is designed to maximize the quality of transmitting fine-scalable coded content by exploiting the embedding property of scalable coding. To be more realistic, we assume that the requesting peer has a delay constraint to display the content within a certain delay bound, and it also has limited incoming bandwidth. We first use a simple example to illustrate the peer assignment problem, and then formulate this problem as a linear programming problem, followed by a nonlinear programming problem. To efficiently solve the second nonlinear problem, we transform it into a sequence of linear programming problems. Finally, we apply our proposed algorithm to both image and video transmissions in bandwidth-limited environments. Extensive experiments have been carried out to evaluate the complexity and performance of our approach by comparing it with both nonlinear formulation and two heuristic schemes. The results have verified the superior performance of our proposed algorithm.     

Symmetric approximate linear programming for factored MDPs with application to constrained problems

A weakness of classical Markov decision processes (MDPs) is that they scale very poorly due to the flat state-space representation. Factored MDPs address this representational problem by exploiting problem structure to specify the transition and reward functions of an MDP in a compact manner. However, in general, solutions to factored MDPs do not retain the structure and compactness of the problem representation, forcing approximate solutions, with approximate linear programming (ALP) emerging as a promising MDP-approximation technique. To date, most ALP work has focused on the primal-LP formulation, while the dual LP, which forms the basis for solving constrained Markov problems, has received much less attention. We show that a straightforward linear approximation of the dual optimization variables is problematic, because some of the required computations cannot be carried out efficiently. Nonetheless, we develop a composite approach that symmetrically approximates the primal and dual optimization variables (effectively approximating both the objective function and the feasible region of the LP), leading to a formulation that is computationally feasible and suitable for solving constrained MDPs. We empirically show that this new ALP formulation also performs well on unconstrained problems.      

Examples in the theory of convergence of the affine scaling method

Examples of homogeneous linear programming problems are investigated. The objective function of such a problem is not bounded below on a feasible set. A starting point is presented such that the affine scaling method generates a sequence of vectors that converges to zero. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 1, pp. 178–179, January–February 2006.