A Primal-dual Steepest-edge Method for Even-flow Harvest Scheduling Problems

The even-flow harvest scheduling problem arises when the forestry agency has evolved into a rigid non-declining even-flow policy. In this paper, we investigate model formulation and solution strategies for the even-flow harvest scheduling problem. A multiple-objective linear programming problem is formulated for even-flow harvest scheduling problems with multiple-site classes and multiple periods. The aim of this problem is to simultaneously maximize a desired harvest-volume per hectare for each period of planning horizon and the total economic return. A block diagonal constraint structure, with many sets of network sub-problems and a set of coupling constraints, is identified in this linear programming problem. A longest path method for each of network sub-problems and a primal-dual steepest-edge algorithm for the entire problem are developed. The developed algorithm has been coded in Borland C++ and implemented on a personal computer. An illustrative example is used to display the detailed procedure for the developed algorithm and a real-world case study is used to show the trade-off between desired even-flow harvest volume policy and total economic return. Results show the potential benefits of this approach.     

Network dual steepest-edge methods for solving capacitated multicommodity network problems

This paper presents a two-phased network dual steepest-edge method for solving capacitated multicommodity network problems. In the first phase, an advanced starting solution in concert with a dual steepest-edge method is applied to solve each capacitated single-commodity network problem. At each iteration, either the primal infeasibility is improved or the dual objective value is inceased. In the second phase, the steepest-edge selection criterion is used to determine the leaving infeasible coupling constraint. By maintaining dual feasibility while improving the dual objective value, the number of infeasible coupling constraints is monotonically reduced to zero. The finite convergency property of this algorithm is shown. Finally, this algorithm is coded using Pascal language and tested in several problems. Results show this algorithm is promising.     

A new approach to solve the multi-product multi-period inventory lot sizing with supplier selection problem

This research work deals with the multi-product multi-period inventory lot sizing with supplier selection problem. Formerly, this kind of problem was formulated and solved using an exhaustive enumeration algorithm and a heuristic algorithm. In this paper, a new algorithm based on a reduce and optimize approach and a new valid inequality is proposed to solve the multi-product multi-period inventory lot sizing with supplier selection problem. Numerical experiments ratify the success of the proposed heuristic algorithm. For the set of 150 benchmark instances, including 75 small-sized instances, 30 medium-sized instances, and 45 large-sized instances, the algorithm always obtained better solutions compared with those previously published. Furthermore, according to the computational results, the developed heuristic algorithm outperforms the CPLEX MIP solver in both solution quality and computational time.     

A genetic algorithm approach for multi-product multi-period continuous review inventory models

This paper formulates an approach for multi-product multi-period (Q, r) inventory models that calculates the optimal order quantity and optimal reorder point under the constraints of shelf life, budget, storage capacity, and “extra number of products” promotions according to the ordered quantity. Detailed literature reviews conducted in both fields have uncovered no other study proposing such a multi-product (Q, r) policy that also has a multi-period aspect and which takes all the aforementioned constraints into consideration. A real case study of a pharmaceutical distributor in Turkey dealing with large quantities of perishable products, for whom the demand structure varies from product to product and shows deterministic and variable characteristics, is presented and an easily-applicable (Q, r) model for distributors operating in this manner proposed. First, the problem is modeled as an integer linear programming (ILP) model. Next, a genetic algorithm (GA) solution approach with an embedded local search is proposed to solve larger scale problems. The results indicate that the proposed approach yields high-quality solutions within reasonable computation times.     

Minimum risk criterion for uncertain production planning problems

This paper considers a new class of multi-product source and multi-period fuzzy random production planning problems with minimum risk and service levels where both the demands and the production costs are assumed to be uncertain and characterized as fuzzy random variables with known distributions. The proposed problems are formulated as a fuzzy random production planning (FRPP) model by maximizing the mean chance of the total costs less than a given allowable investment level. Because the exact value of the objective function for a given decision variable cannot be easily obtained, we adopt an approximation approach (AA) to evaluate the objective value and then discuss the convergence of the AA, including the convergence of the objective value, the convergence of the optimal solutions and the convergence of the optimal value. Since the approximating multi-product source multi-period FRPP model is neither linear nor convex, an approximation-based hybrid monkey algorithm (MA) which combines the AA, stochastic simulation (SS), neural network (NN) and MA is designed to solve the proposed model. Finally, numerical examples are provided to illustrate the effectiveness of the hybrid monkey algorithm.     

Inventory lot-sizing with supplier selection using hybrid intelligent algorithm

In supply chain management (SCM), multi-product and multi-period models are usually used to select the suppliers. In the real world of SCM, however, there are normally several echelons which need to be integrated into inventory management. This paper presents a hybrid intelligent algorithm, based on the push SCM, which uses a fuzzy neural network and a genetic algorithm to forecast the rate of demand, determine the material planning and select the optimal supplier. We test the proposed algorithm in a case study conducted in Iran.     

三层物流网络选址-路径优化及混合启发式算法研究*

为同时解决转运、分配、选址和车辆路径问题,在考虑车辆载重和行驶距离约束,配送中心处理能力约束的基础上,构建了一个多产品三层物流网络选址-路径模型,以总成本最小为目标,提出一种基于贪婪随机自适应搜索算法和里程节约算法的混合启发式算法,给出了该算法的步骤和伪代码。实验结果表明该算法具有可行性,并且与其他算法比较而言,算法具有高效性。     

基于交替方向乘子法的电力系统完全分布式动态经济调度方法

本文基于交替方向乘子法(alternating direction multiplier method,ADMM)提出了一种完全分布式的跨区域电力系统动态经济调度方法.其中的经济调度模型以整个系统的运行成本最小为目标,并满足各种系统运行约束.为了实现模型的分布式求解,本文利用交替方向乘子法将各区域之间的联系解耦,将整个系统的大型优化问题分解为各个区域内部的子优化问题,通过迭代求解每个区域的子问题即可得到整个系统的最优解.进一步地,本文算法取消了负责乘子更新的数据中心,实现了完全分布式的调度策略.同时,为了兼顾电力系统中时间断面之间的紧密联系,本文的经济调度模型采用了多时段优化方法.最后,本文对基于IEEE标准测试系统的3区域互联系统算例进行了分析,验证了本文的调度策略的有效性.     

Affinely adjustable robust optimization for a multi-period inventory problem with capital constraints and demand uncertainties

This study focuses on a multi-period inventory problem with capital constraints and demand uncertainties. The multi-period inventory problem is formulated as an optimization model with a joint chance constraint (JCC) requiring the purchase cost for each period not to exceed the available capital with a probability guarantee. To hedge against demand uncertainties, an affinely adjustable robust optimization approach is used to convert the developed model into a robust counterpart. By approximating the JCC under a budgeted uncertainty set to which the demands belong, the robust multi-period inventory model with the JCC is transformed into a linear programming model, which can be solved efficiently. Numerical studies are reported to illustrate the robustness, practicality, and effectiveness of the proposed model and the solution approach. The numerical results show that the proposed model and solution approach outperform the sample average approximation approach. Numerical studies are used further to analyze the impact of the budget coefficient and the upper bound parameter on the inventory costs and the realized capital constraint satisfaction rate. The proposed model and solution approach are further extended to the multi-product case.     

A primal-dual neural network for online resolving constrained kinematic redundancy in robot motion control.

This paper proposes a primal-dual neural network with a one-layer structure for online resolution of constrained kinematic redundancy in robot motion control. Unlike the Lagrangian network, the proposed neural network can handle physical constraints, such as joint limits and joint velocity limits. Compared with the existing primal-dual neural network, the proposed neural network has a low complexity for implementation. Compared with the existing dual neural network, the proposed neural network has no computation of matrix inversion. More importantly, the proposed neural network is theoretically proved to have not only a finite time convergence, but also an exponential convergence rate without any additional assumption. Simulation results show that the proposed neural network has a faster convergence rate than the dual neural network in effectively tracking for the motion control of kinematically redundant manipulators.     

基于消息传递的机载雷达组网航迹融合

机载雷达组网航迹融合需要解决目标跟踪、数据关联与航迹管理3个子问题, 然而这3个子问题相互耦合,采用开环序贯估计算法会导致性能下降. 本文提出了一种基于消息传递的机载雷达组网航迹融合方法, 该方法在联合优化框架下解决目标跟踪、数据关联与航迹管理3个子问题. 首先, 建立机载雷达组网航迹融合的联合概率密度函数, 并将其转换为因子图. 其次, 将因子图分解为置信传播区域与平均场近似区域. 目标运动状态的统计模型服从共轭指数模型, 因此采用平均场近似以获得简单的消息传递更新公式. 数据关联包含一对一约束, 因此采用置信传播. 目标存在状态同样采用置信传播, 以获得更好的近似结果. 最后, 可以通过闭环迭代框架近似估计后验分布, 从而有效处理目标跟踪、数据关联与航迹管理之间的耦合问题. 仿真结果表明, 所提算法的性能优于多假设跟踪算法和联合概率密度关联算法.     

Determination of aggregate preventive maintenance programs using production schedules

Scheduling preventive maintenance in a multi-period and multi-product situation has been dealt with in this paper. An aggergate preventive maintenance programme is determined based on the time of failure distribution, machine load in every period and varying cost of breakdown in different periods; using a steppingstone algorithm. Sample results from the computer programme written for solving this problem are given. This algorithm can be used to solve similar problems.     

基于数据网格环境的k近邻查询

提出一种在网格环境下的k近邻查询方法——GkNN．到目前为止,尚未有文献提出数据网格环境下的k近邻查询算法．当用户在查询节点提交一个查询向量和k,首先以一个较小的查询半径。在数据节点进行基于双重距离尺度的向量缩减,然后将缩减后的向量按照向量“打包”传输的方式发送到执行节点,在执行节点并行地对这些候选向量进行距离（求精）运算．最终将结果向量返回到查询节点．当返回的向量个数小于k时,扩大半径值,继续循环直到得到k个最近邻向量为止．理论分析和实验证明该方法在减少网络通信开销、增加I／O和CPU并行、降低-向应时间方面具有较好的性能,非常适合海量高维数据的查询．     

多跳多接口无线网络中的协作路由

协作通信可以利用空间分集效应抵抗无线信道衰弱而得到广泛关注。在多业务流多跳多接口无线协作网络中,研究联合路由选择和协作节点分配的最优化问题,将最大化最小业务流速率的联合优化问题建模为混合整数线性规划问题。针对这个问题提出一种基于分支定界的启发式算法JFRBB。JFRBB算法基于分支定界的思想是将原问题分解为多个子问题通过迭代获得最优解。仿真实验结果表明,JFRBB下的多接口协作网络获得的传输速率、聚合流量明显优于多接口无协作网络和单接口协作网络的性能。     

基于多线程评估的基因表达式编程算法

分析了基因表达式编程(GEP)算法的性能关键,指出提升的一个重要瓶颈是在个体评估阶段;结合多核CPU并行计算能力,提出了基于多线程评估的GEP算法(MTEGEP),并通过实验验证了MTEGEP的高效性：在双核CPU环境下MTEGEP运算速度是传统GEP的1.89倍,而在8核CPU环境下达到了6.48倍。实验结果表明该算法能有效提升GEP算法的性能。     

Low Power State Assignment Algorithm for FSMs Considering Peak Current Optimization

Finite state machine （FSM） plays a vital current which is drawn by state transitions can result in role in the sequential logic design. In an FSM, the high peak large voltage drop and electromigration which significantly affect circuit reliability. Several published papers show that the peak current can be reduced by post-optimization schemes or Boolean satisfiability （SAT）-based formulations. However, those methods of reducing the peak current either increase the overall power dissipation or are not efficient. This paper has proposed a low power state assignment algorithm with upper bound peak current constraints. First the peak current constraints are weighted into the objective function by Lagrangian relaxation technique with Lagrangian multipliers to penalize the violation. Second, Lagrangian sub-problems are solved by a genetic algorithm with Lagrangian multipliers updated by the subgradient optimization method. Finally, a heuristic algorithm determines the upper bound of the peak current, and achieves optimization between peak current and switching power. Experimental results of International Workshop on Logic and Synthesis （IWLS） 1993 benchmark suites show that the proposed method can achieve up to 45.27% reduction of peak current, 6.31% reduction of switching power, and significant reduction of run time compared with previously published results.     

Reliable Intermodal Freight Network Expansion with Demand Uncertainties and Network Disruptions

This paper develops a robust Mixed-Integer Linear Program (MILP) to assist railroad operators with intermodal network expansion decisions. Specifically, the objective of the model is to identify critical rail links to retrofit, locations to establish new terminals, and existing terminals to expand, where the intermodal freight network is subject to demand and supply uncertainties. Additional considerations by the model include a finite overall budget for investment, limited capacities on network links and at intermodal terminals, and time window constraints for shipments. A hybrid Genetic Algorithm (GA) is developed to solve the proposed MILP. It utilizes a column generation algorithm to solve the freight flow assignment problem and a multi-modal shortest path label-setting algorithm to solve the pricing sub-problems. An exact exhaustive enumeration method is used to validate the GA results. Experimental results indicate that the developed algorithm is capable of producing optimal solutions efficiently for small-sized intermodal freight networks. The impact of uncertainty on network configuration is discussed for a larger-sized case study.     

易腐品多周期闭环物流网络鲁棒优化模型

针对不同周期的易腐品需求与退货不确定性问题,构建了易腐品多周期闭环物流网络,并设计了对应的混合整数线性规划（MILP）模型,以实现最低系统总成本、最佳设施选址以及最优配送车辆运输路径的决策。为有效规避不确定参数的影响,采用基约束鲁棒方法,将模型中的部分清晰约束转换为鲁棒对应式。以上海市果蔬农产品企业为实例,通过遗传算法对模型进行求解。结果表明,相对单周期而言,多周期系统具有动态性、系统成本更低的优点,同时通过不确定预算参数的变化分析,验证了鲁棒模型的可行性与有效性,进而为不确定环境下构建多周期闭环物流网络及降低系统成本提供了借鉴。     

基于典型场景划分的多时段网损分摊算法

网损分摊是电力市场中的重要问题。传统的网损分摊算法研究往往针对单一时间断面下的静态分摊问题,不能满足实际系统连续运行分析计算的要求。为此,基于潮流跟踪算法,选取关键线损线路和强相关电厂两项指标,提出了相似网损分摊场景匹配标准。在此基础上,考虑实际运行需求提出了基于典型场景划分的多时段网损分摊算法,以解决多时段连续运行中的网损分摊问题;最后基于我国某省电网实际数据构造算例,验证了本方法的有效性。     

Distributed Resource Allocation via Accelerated Saddle Point Dynamics

In this paper, accelerated saddle point dynamics is proposed for distributed resource allocation over a multi-agent network, which enables a hyper-exponential convergence rate. Specifically, an inertial fast-slow dynamical system with vanishing damping is introduced, based on which the distributed saddle point algorithm is designed. The dual variables are updated in two time scales, i.e., the fast manifold and the slow manifold. In the fast manifold, the consensus of the Lagrangian multipliers and the tracking of the constraints are pursued by the consensus protocol. In the slow manifold, the updating of the Lagrangian multipliers is accelerated by inertial terms. Hyper-exponential stability is defined to characterize a faster convergence of our proposed algorithm in comparison with conventional primal-dual algorithms for distributed resource allocation. The simulation of the application in the energy dispatch problem verifies the result, which demonstrates the fast convergence of the proposed saddle point dynamics.