Cloud theory-based simulated annealing for a single-machine past sequence setup scheduling with scenario-dependent proce
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ORIGINAL ARTICLE
Cloud theory-based simulated annealing for a single-machine past sequence setup scheduling with scenario-dependent processing times Chin-Chia Wu1 · Win-Chin Lin1 Shuenn-Ren Cheng5
· Xin-Gong Zhang2,6 · Dan-Yu Bai3,6 · Yung-Wei Tsai1 · Tao Ren4 ·
Received: 17 July 2020 / Accepted: 4 September 2020 © The Author(s) 2020
Abstract Recently, the setup times or costs have become an important research topic. The main reason is huge economic savings can be achieved when setup times are explicitly included in scheduling decisions in various real-world industrial environments. On the other hand, many real systems commonly face various uncertainties, such as working environment changes, machine breakages, a worker becomes unstable, etc. In such an environment, job-processing times should not be fixed numbers. Motivated by these situations, this article introduces a single-machine scheduling problem with sequence-dependent setup times and scenario-dependent processing times where the objective function is the total completion time. The robust version of this problem without setup times has been shown to be NP hard. To tackle this problem, a lower bound and a dominance rule are derived in a branch-and-bound method for finding an optimal solution. As for determining approximate solutions, five neighborhood schemes are proposed and embedded in the cloud theory-based simulated annealing. Finally, the performances of all proposed algorithms are determined and compared. Keywords Machine scheduling · Setup times · Scenario-dependent processing times · Cloud theory-based simulated annealing
Introduction
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Win-Chin Lin [email protected] Chin-Chia Wu [email protected] Xin-Gong Zhang [email protected] Dan-Yu Bai [email protected] Yung-Wei Tsai [email protected] Tao Ren [email protected] Shuenn-Ren Cheng [email protected]
In most single-machine literature studies, researchers commonly assumed job-processing times or ready times or due dates are fixed numbers, and the setup times are even ignored. However, there are significant uncertainties in many real production systems. For example, machines may break down, job operators may lack ability, the working facility may be changed, i.e., random job arrivals, as well as several other external complex factors can lead to job cancellations or altered tool quality [12]. In light of these facts, job-processing times might not be assumed as fixed numbers. On the other hand, huge economic costs can be saved when setup times are effectively considered in scheduling decisions in various industrial environments [14].
1
Department of Statistics, Feng Chia University, No. 100, Wenhwa Road, Seatwen Dist., Taichung 40724, Taiwan
4
Software College, Northeastern University, Shenyang 110819, China
2
College of Mathematics Science, Chongqing Normal University, Chongqing, People’s Republic of China
5
Department of Business Administration, Cheng Shiu University, Kaohsiung, Taiwan
3
College of Management, Dalian Maritime University, Dalian, People’s Republic of Chi
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