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Shenyang, China

Shenyang Aerospace University , formerly known as Shenyang Institute of Aeronautical Engineering, is a comprehensive research university in Shenyang, the capital of Liaoning province in Northeast China. It educates students for supporting the military and civil aviation industries of China. Wikipedia.


Wang J.-B.,Shenyang Aerospace University | Wang J.-J.,Dalian University of Technology
Computers and Operations Research | Year: 2014

This paper investigates flowshop scheduling problems with a general exponential learning effect, i.e., the actual processing time of a job is defined by an exponent function of the total weighted normal processing time of the already processed jobs and its position in a sequence, where the weight is a position-dependent weight. The objective is to minimize the makespan, the total (weighted) completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times, respectively. Several simple heuristic algorithms are proposed in this paper by using the optimal schedules for the corresponding single machine problems. The tight worst-case bound of these heuristic algorithms is also given. Two well-known heuristics are also proposed for the flowshop scheduling with a general exponential learning effect. © 2013 Published by Elsevier Ltd. Source


Wang J.-B.,Shenyang Aerospace University | Wang J.-J.,Dalian University of Technology
Information Sciences | Year: 2014

In this paper we investigate a single machine scheduling problem with time dependent processing times and group technology (GT) assumption. By time dependent processing times and group technology assumption, we mean that the group setup times and job processing times are both increasing functions of their starting times, i.e., group setup times and job processing times are both described by function which is proportional to a linear function of time. We attempt to minimize the makespan with ready times of the jobs. We show that the problem can be solved in polynomial time when start time dependent processing times and group technology are considered simultaneously. © 2014 Elsevier Inc. All rights reserved. Source


Wang D.,Shenyang Aerospace University
WIT Transactions on Information and Communication Technologies | Year: 2014

This note deals with a scheduling problem jobs with group technology, deteriorating jobs, and learning effect on a single machine. The setup time of a group is a function of its resource allocation, and the processing time of a job is a function of its starting time and its position in a sequence. We show that some results are incorrect in recent paper [1] by counter-examples, and we also provide corrected results. © 2014 WIT Press. Source


Wang J.-B.,Shenyang Aerospace University | Wang M.-Z.,Dalian University of Technology
Computers and Operations Research | Year: 2013

In this paper, a three-machine permutation flow shop scheduling problem with time-dependent processing times is considered. By the time-dependent processing times we mean that the jobs processing time is an increasing function of its starting time. The objective is to find a sequence that minimizes the makespan. This problem is well known to be NP-hard. Several dominance properties and a lower bound are derived to speed up the elimination process of a branch-and-bound algorithm. Moreover, two heuristic algorithms are proposed to overcome the inefficiency of the branch-and-bound algorithm. Computational experiments on randomly generated problems are conducted to evaluate the branch-and-bound algorithm and heuristic algorithm. Computational results show that the proposed heuristic algorithm M-NEH perform effectively and efficiently. © 2012 Elsevier Ltd. All rights reserved. Source


Liu Y.-J.,Shenyang Aerospace University | Sun D.,National University of Singapore | Toh K.-C.,National University of Singapore
Mathematical Programming | Year: 2012

The nuclear norm minimization problem is to find a matrix with the minimum nuclear norm subject to linear and second order cone constraints. Such a problem often arises from the convex relaxation of a rank minimization problem with noisy data, and arises in many fields of engineering and science. In this paper, we study inexact proximal point algorithms in the primal, dual and primal-dual forms for solving the nuclear norm minimization with linear equality and second order cone constraints. We design efficient implementations of these algorithms and present comprehensive convergence results. In particular, we investigate the performance of our proposed algorithms in which the inner sub-problems are approximately solved by the gradient projection method or the accelerated proximal gradient method. Our numerical results for solving randomly generated matrix completion problems and real matrix completion problems show that our algorithms perform favorably in comparison to several recently proposed state-of-the-art algorithms. Interestingly, our proposed algorithms are connected with other algorithms that have been studied in the literature. © 2011 Springer and Mathematical Optimization Society. Source

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