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Wu C.-C.,Feng Chia University | Wu W.-H.,Yuanpei University | Wu W.-H.,Kang Ning Junior College | Hsu P.-H.,Kang Ning Junior College | And 2 more authors.
Information Sciences | Year: 2014

Recently, machine scheduling problems with deteriorating jobs have received interestingly attention from the scheduling research community. Majority of the research assumed that the actual job processing time is an increasing function of its starting time. However, no job can remain undeteriorated indefinitely in real life situations. This paper considers a single-machine scheduling problem with a truncated linear deteriorating effect and ready times. By the truncated linear deteriorating effect, it means that the actual processing time of a job is a function of its starting time and a control parameter. The objective is to minimize the makespan. A mixed integer programming model and a branch-and-bound algorithm coupled with several dominance properties and two lower bounds are developed to search for the optimal solution. In addition, an ant colony and a Tabu search algorithm where each is refined by the three improvements are also proposed for a near-optimal solution, respectively. A computational experiment is then conducted to evaluate the impacts of the used parameters on the performances of the proposed algorithms. © 2013 Elsevier Inc. All rights reserved. Source


Yin Y.,East China Institute of Technology | Wu W.-H.,Yuanpei University | Wu W.-H.,Kang Ning Junior College | Wu C.-C.,Feng Chia University
Information Sciences | Year: 2014

This study considers an NP-hard problem of minimizing the total tardiness on a single machine with arbitrary release dates and position-dependent learning effects. A mixed-integer programming (MIP) model is first formulated to find the optimal solution for small-size problem instances. Some new dominance rules are then presented which provide a sufficient condition for finding local optimality. The branch-and-bound (B& B) strategy incorporating with several dominance properties and a lower bound is proposed to derive the optimal solution for medium- to-large-size problem instances, and four marriage-in-honey-bees optimization algorithms (MBO) are developed to derive near-optimal solutions for the problem. To show the effectiveness of the proposed algorithms, 3600 situations with 20 and 25 jobs, are randomly generated for experiments. © 2013 Elsevier Inc. All rights reserved. Source


Yin Y.,East China Institute of Technology | Wu C.-C.,Feng Chia University | Wu W.-H.,Yuanpei University | Hsu C.-J.,Nan Kai College | Wu W.-H.,Kang Ning Junior College
Applied Soft Computing Journal | Year: 2013

This paper addresses a two-agent scheduling problem on a single machine where the objective is to minimize the total weighted earliness cost of all jobs, while keeping the earliness cost of one agent below or at a fixed level Q. A mixed-integer programming (MIP) model is first formulated to find the optimal solution which is useful for small-size problem instances. To solve medium-to large-size problem instances, a branch-and-bound algorithm incorporating with several dominance properties and a lower bound is then provided to derive the optimal solution. A simulated annealing heuristic algorithm incorporating with a heuristic procedure is developed to derive the near-optimal solutions for the problem. A computational experiment is also conducted to evaluate the performance of the proposed algorithms. © 2012 Elsevier B.V. All rights reserved. Source


Cheng T.C.E.,Hong Kong Polytechnic University | Cheng S.-R.,Cheng Shiu University | Wu W.-H.,Kang Ning Junior College | Hsu P.-H.,Feng Chia University | Wu C.-C.,Feng Chia University
Computers and Industrial Engineering | Year: 2011

Scheduling with learning effects has received a lot of research attention lately. By learning effect, we mean that job processing times can be shortened through the repeated processing of similar tasks. On the other hand, different entities (agents) interact to perform their respective tasks, negotiating among one another for the usage of common resources over time. However, research in the multi-agent setting is relatively limited. Meanwhile, the actual processing time of a job under an uncontrolled learning effect will drop to zero precipitously as the number of jobs increases or a job with a long processing time exists. Motivated by these observations, we consider a two-agent scheduling problem in which the actual processing time of a job in a schedule is a function of the sum-of-processing-times-based learning and a control parameter of the learning function. The objective is to minimize the total weighted completion time of the jobs of the first agent with the restriction that no tardy job is allowed for the second agent. We develop a branch-and-bound and three simulated annealing algorithms to solve the problem. Computational results show that the proposed algorithms are efficient in producing near-optimal solutions. © 2010 Published by Elsevier Ltd. All rights reserved. Source


Yin Y.,Kunming University of Science and Technology | Yin Y.,East China Institute of Technology | Wu W.-H.,Kang Ning Junior College | Cheng T.C.E.,Feng Chia University | Wu C.-C.,Feng Chia University
International Journal of Computer Integrated Manufacturing | Year: 2015

In many real-life scheduling situations, the jobs deteriorate at a certain rate while waiting to be processed. This study introduces a new deterioration model where the actual processing time of a job depends not only on the starting time of the job but also on its scheduled position. The objective is to find the optimal schedule such that the makespan or total completion time is minimised. This study first shows that both problems are solvable in O(n log n) time. This study further shows that in both cases there exists an optimal schedule that is the shortest processing time, longest processing time, or V-shaped with respect to the job normal processing times, depending on the relationships between problem parameters. © 2014 Taylor & Francis. Source

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