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Single-Machine and Parallel-Machine Parallel-Batching Scheduling Considering Deteriorating Jobs, Various Group, and Time-Dependent Setup Time
Volume 29, Issue 2 (2018), pp. 281–301
Pub. online: 1 January 2018      Type: Research Article      Open accessOpen Access
Received
1 May 2017
Accepted
1 February 2018
Published
1 January 2018

Abstract

This paper studies a set of novel integrated scheduling problems by taking into account the combinatorial features of various groups, parallel-batching, deteriorating jobs, and time-dependent setup time simultaneously under the settings of both single-machine and parallel-machine, and the objective of the studied problems is to minimize the makespan. In order to solve the single-machine scheduling problem, we first investigate the structural properties on jobs sequencing, jobs batching, and batches sequencing for the optimal solution, and then develop a scheduling rule. Moreover, for solving the parallel-machine scheduling problem, we exploit the optimal structural properties and batching rule, and propose a novel hybrid AIS-VNS algorithm incorporating Artificial Immune System algorithm (AIS) and Variable Neighbourhood Search (VNS). Extensive computational experiments are conducted to evaluate the performance of the proposed AIS-VNS algorithm, and comparison results show that the proposed algorithm performs quite well in terms of both efficiency and solution quality.

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Biographies

Liao Baoyu

B. Liao is currently working on his PhD degree at the School of Management, Hefei University of Technology. He obtained his master’s degrees from Hefei University of Technology in 2015. His research interests include supply chain scheduling and metaheuristics.

Pei Jun
feiyijun.ufl@gmail.com

J. Pei is currently an associate professor at the School of Management, Hefei University of Technology, Hefei, China. He is also the associate editor for Journal of Global Optimization (2018.1-Present), Optimization Letters (2016.8-Present), Energy Systems (2015.12-Present), Computational Social Networks (2016.7-Present) and a guest editor for Journal of Combinatorial Optimization. His research interests include supply chain scheduling, artificial intelligence, and information systems.

Yang Shanlin

S. Yang is a professor at School of Management, Hefei University of Technology, Hefei, China. He is a member of Chinese Academy of Engineering. He servers as the director of National & Local Joint Engineering Research Center for Intelligent Decision and Information Systems and the director of Key Laboratory of Process Optimization and Intelligent Decision-making. His research interests include decision science and technology, optimization theory and method, management information system.

Pardalos Panos M.

P.M. Pardalos serves as a distinguished professor of industrial and systems engineering at the University of Florida, Gainesville, FL, USA. He is also the director of the Center for Applied Optimization. Dr. Pardalos is a world leading expert in global and combinatorial optimization. His recent research interests include network design problems, optimization in telecommunications, e-commerce, data mining, biomedical applications, and massive computing.

Lu Shaojun

S. Lu is currently working on his PhD degree at the School of Management, Hefei University of Technology. He obtained his BS degrees from Hefei University of Technology in 2015. His research interests include supply chain scheduling and metaheuristics.


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Keywords
scheduling parallel-batching group scheduling deterioration time-dependent setup time

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