TY - JOUR
T1 - Single-machine scheduling with autonomous and induced learning to minimize total weighted number of tardy jobs
AU - Chen, Ke
AU - Cheng, T. C.E.
AU - Huang, Hailiang
AU - Ji, Min
AU - Yao, Danli
N1 - Funding Information:
We are thankful to three anonymous referees for their helpful comments and suggestions on earlier versions of our paper. Chen was supported by the Soft Science Research Project of Shanghai under grant number 22692198700. Cheng was supported in part by The Hong Kong Polytechnic University under the Fung Yiu King - Wing Hang Bank Endowed Professorship in Business Administration. Huang was supported by the National Natural Science Foundation of China under grant number 72271151 . Ji was supported in part by the National Natural Science Foundation of China under grant number 11971434 , Zhejiang Provincial Natural Science Foundation of China under grant number LY21G010002 , and the Contemporary Business and Trade Research Center of Zhejiang Gongshang University, which is a key Research Institute of Social Sciences and Humanities of the Ministry of Education of China.
Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/8/16
Y1 - 2023/8/16
N2 - As commonly observed in practice, firms can accelerate job processing by adding additional resources to the manufacturing system while striving to sustain on-time delivery. In this paper we consider due date assignment and scheduling with a learning effect that can be invoked by proactive investment at the beginning, known as induced learning in the literature. The objective is to find the optimal decisions on due date assignment, job sequencing, and level of induced learning to minimize a cost function consisting of the total weighted number of tardy jobs, due date penalty, and investment cost. We find a novel property of the optimal solution that although the due date penalty may be larger, the number of on-time jobs will not decrease with the increasing induced learning effect, which is a breakthrough to solve the problem. Exploiting the property, we present a polynomial-time algorithm that generates an approximation solution with a gap less than Lϵ compared with the optimal result, where ϵ can tend to be infinitesimal, and L is a constant.
AB - As commonly observed in practice, firms can accelerate job processing by adding additional resources to the manufacturing system while striving to sustain on-time delivery. In this paper we consider due date assignment and scheduling with a learning effect that can be invoked by proactive investment at the beginning, known as induced learning in the literature. The objective is to find the optimal decisions on due date assignment, job sequencing, and level of induced learning to minimize a cost function consisting of the total weighted number of tardy jobs, due date penalty, and investment cost. We find a novel property of the optimal solution that although the due date penalty may be larger, the number of on-time jobs will not decrease with the increasing induced learning effect, which is a breakthrough to solve the problem. Exploiting the property, we present a polynomial-time algorithm that generates an approximation solution with a gap less than Lϵ compared with the optimal result, where ϵ can tend to be infinitesimal, and L is a constant.
KW - Dynamic programming
KW - Induced learning effect
KW - Scheduling
KW - Tardy jobs
UR - http://www.scopus.com/inward/record.url?scp=85147368833&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2023.01.028
DO - 10.1016/j.ejor.2023.01.028
M3 - Journal article
AN - SCOPUS:85147368833
SN - 0377-2217
VL - 309
SP - 24
EP - 34
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -