Earliness–tardiness minimization on scheduling a batch processing machine with non-identical job sizes

This paper considers the scheduling problem of minimizing earliness–tardiness (E/T) on a single batch processing machine with a common due date. The problem is extended to the environment of non-identical job sizes. First, a mathematical model is formulated, which is tested effectively under IBM ILOG CPLEX using the constraint programming solver. Then several optimal properties are given to schedule batches effectively, and by introducing the concept of ARB (Attribute Ratio of Batch), it is proven that the ARB of each batch should be made as small as possible in order to minimize the objective, designed as the heuristic information for assigning jobs into batches. Based on these properties, a heuristic algorithm MARB (Minimum Attribute Ratio of Batch) for batch forming is proposed, and a hybrid genetic algorithm is developed for the problem under study by combining GA (genetic algorithm) with MARB. Experimental results demonstrate that the proposed algorithm outperforms other algorithms in the literature, both for small and large problem instances.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

Design and validation of robotic bionic eye with multiple flexible ropes parallel mechanism inspired by oculomotor law

Producing a stable and agile bionic eye for visual image acquisition in robotics is a challenging task. In this paper, we design a bionic eye with mirror-symmetric distribution and cross-connection of flexible ropes. This mechanism is based on oculomotor law and the physiological structure of the extraocular muscles (EOMs). Specifically, the basic structural parameters are determined by Listing’s law, and the unique connection of the flexible ropes can realize the functions of the recti and oblique muscles. Furthermore, to mimic the trochlea structure, a pulley mechanism is constructed to permit the free movement of the flexible ropes. Through simulation and physical experiments, it is demonstrated that the bionic eye mechanism can move with agility under the structural parameters. The experimental results indicate that the proposed bionic eye mechanism has a superior motion accuracy of 2.798 mm, which is 6.7% of the maximum motion distance, and the repeatable accuracy of the mechanism can up to 0.210 mm.     

Modelling consumer choice through the random regret minimization model: An application in the food domain

In line with findings on post-purchase food-choice regret, one can expect that pre-purchase anticipated regret with respect to forgone (non-chosen) alternatives has an impact on consumer food choices, especially when the choice is considered to be important. The traditional Random Utility Maximization (RUM) models for discrete choices may not fully capture this impact. This study investigates the usefulness and potential in the food domain of a discrete choice model that follows the regret minimization principle, the Random Regret Minimization (RRM) model, as an alternative and complement to existing RUM models. The two models are applied to consumer stated choices of cheese in a choice experiment. The study also investigates whether and to what extent a number of personality traits determine whether particular consumers rather choose according to utility-maximization, or regret-minimization principles. Results show that at the aggregate level the two models have a similar goodness of fit to the data and prediction ability. Still, each of them shows better fit for particular subgroups of consumers, based on personality traits. Hence, the present study reveals a potential for the RRM model applications in the food domain, and adds to the empirical literature supporting previous findings on the RRM model found in other contexts. Further research is needed to explore in which situations and for which consumer segments the RRM model is the most useful model.     

结构振动试验转接器的设计及安装问题

一些产品及其相关部件在作振动试验时，由于产品的外形或特殊要求不能直接安装在振动台面上，需要通过特别设计的转接器（夹具）来进行连接，这种夹具实际上是一种附加结构，它作为台面与试件之间的中介，如设置不当将影响振动试验结果的可靠性。本文通过对某事例的分析与研究，从试验频带、允许误差、跟随条件以及安装共振频率等方面阐明与讨论了相关夹具的特性、设计原则与安装等问题。     

交通平衡分配下的公路桥收费策略

假定一个城市被一条河流分为两部分（如我国的武汉，重庆等市），河上至少架设了两座公路桥梁，本文研究在交通平衡状态下，怎样制定车辆过桥梁的收费水平，使获得的总收入最大，乘客的总行驶时间最少，对多车种问题，研究了等价的数学模型和算法。还对一个含两座桥梁与三种车型的简单算例，进行了计算与分析。     

2Mbit/s高速信令链路在移动通信七号信令网中的应用

通过对2Mbit/s高速信令链路原理、特点的介绍，阐明了该技术在现阶段使用的实际意义，并给出了2Mbit/s高速信令链路在移动通信七号信令网中的应用。     

可靠组播中的差错检测与恢复技术

在可靠组播的实现中要解决的关键问题之一是如何进行快速有效的差错检测和恢复，主要有重传和纠错两类机制。文章介绍了这两类机制的研究现状，从模型特点、网络负荷、适用悄况等不同角度进行分析，讨沦了本地重传及其范围策略。