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.     

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.     

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。     

Constitutive modelling and numerical simulation of multivariant phase transformation in superelastic shape‐memory alloys

This work concerns the micromechanical constitutive modelling, algorithmic implementation and numerical simulation of polycrystalline superelastic alloys under multiaxial loading. The model is formulated in finite deformations and incorporates the effect of texture. The numerical implementation is based on the constrained minimization of the Helmholtz free energy with dissipation. Simulations are conducted for thin tubes of Nitinol under tension–torsion, as well as for a simplified model of a biomedical stent. Copyright © 2004 John Wiley & Sons, Ltd.     

基于非线性规划的指数自回归模型的辨识

讨论了指数自回归模型的辨识问题，证明了该模型最小二乘估计的目标函数的非凸性，并给出了使该函数为凸的条件，最后给出了辨识该模型的算法及该算法的收敛性，并以数值例子加以说明。     

Affine Dependence Classification for Communications Minimization

This paper introduces results on placement and communications minimization for systems of affine recurrence equations. We show how to classify the dependences according to the number and nature of communications they may result in. We give both communication-free conditions and conditions for an efficient use of broadcast or neighbor-to-neighbor communication primitives. Since the dependences of a problem can generally not be all communication-free, we finally introduce a heuristic to globally minimize the communications based on the classification of dependences.     

炼油厂废水的最小化

石油炼厂作为用水大户，在水资源逐渐短缺、环境污染日益严重的情况下，实施废水最小化改造具有重要的现实意义。笔者以石油炼厂整个水分配系统年度费用最小为目标函数，提出采用回用、再生回用和再生循环3种措施减少新鲜水用量和废水排放量。利用超结构建模求解所得优化的水分配系统不但要满足各用水过程的需求，而且使排放的废水达标。给出的实例证明，所提出的方法更全面地考虑了废水最小化的机会，且所得结果可行、有效。     

Energy minimization and design for testability

The problem of fault detection in general combinational circuits is NP-complete. The only previous result on identifying easily testable circuits is due to Fujiwara who gave a polynomial time algorithm for detecting any single stuck fault inK-bounded circuits. Such circuits may only contain logic blocks with no more thanK input lines and the blocks are so connected that there is no reconvergent fanout among them. We introduce a new class of combinational circuits called the (k, K)-circuits and present a polynomial time algorithm to detect any single or multiple stuck fault in such circuits. We represent the circuit as an undirected graphG with a vertex for each gate and an edge between a pair of vertices whenever the corresponding gates have a connection. For a (k, K)-circuit,G is a subgraph of ak-tree, which, by definition, cannot have a clique of size greater thank+1. Basically, this is a restriction on gate interconnections rather than on the function of gates comprising the circuit. The (k, K)-circuits are a generalization of Fujiwara'sK-bounded circuits. Using the bidirectional neural network model of the circuit and the energy function minimization formulation of the fault detection problem, we present a test generation algorithm for single and multiple faults in (k, K)-circuits. This polynomial time aggorithm minimizes the energy function by recursively eliminating the variables.