Single machine past-sequence-dependent setup times scheduling with general position-dependent and time-dependent learning effects

Abstract

a b s t r a c tIn this paper we consider the single machine past-sequence-dependent (p-s-d) setup times scheduling problems with general position-dependent and time-dependent learning effects. By the general position-dependent and time-dependent learning effects, we mean that the actual processing time of a job is not only a function of the total normal processing times of the jobs already processed, but also a function of the job's scheduled position. The setup times are proportional to the length of the already processed jobs. We consider the following objective functions: the makespan, the total completion time, the sum of the hth (h P 0) power of job completion times, the total lateness, the total weighted completion time, the maximum lateness, the maximum tardiness and the number of tardy jobs. We show that the problems of makespan, the total completion time, the sum of the hth (h P 0) power of job completion times and the total lateness can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem, the maximum lateness minimization problem, maximum tardiness minimization problem and the number of tardy jobs minimization problem can be solved in polynomial time under certain conditions.

Using a branch-and-bound and a genetic algorithm for a single-machine total late work scheduling problem

“…Biskup (2008 further provided a comprehensive review of scheduling research with learning considerations. More recently, the readers can be referred to Cheng et al (2011), Yang et al (2010), Yin et al (2010), Yin and Wang (2011), Wang (2010), Yang (2011, 2012), Wang and Guo (2010), Wang and Li (2011), , 2011a, b, 2013, Wang et al (2010aWang et al ( , b, 2012Wang et al ( , 2013a, and Wu et al (2011), etc. On the other hand, it is commonly seen that time restrictions are usually modeled by due dates or deadlines and the quality of schedules is estimated with reference to these parameters (Sterna 2011). One of the performance measures involving due dates is the late work criterion.…”

A note on single-machine scheduling with general learning effect and past-sequence-dependent setup time

“…Wang et al [25] considered the Biskup [1] position-based learning effect model and provided the optimal solutions for some single-machine problems. Yin et al [26] and Wang and Li [27] considered both the position-based and sum-of-processing-time-based learning effect model, and showed some single-machine problems remain polynomially solvable. Moreover, Dutton and Thomas [28] pointed out that the learning rates show considerable variation within industries or firms after a study of more than 200 learning curves.…”

Cloud theory-based simulated annealing for a single-machine past sequence setup scheduling with scenario-dependent processing times

“…The situation occurs in high-tech manufacturing in which a batch of jobs consists of a group of electronic components mounted together on an integrated circuit (IC) board [14]. Following this approach, Wang [24][25][26] and Cheng et al [5] addressed some polynomial solutions to solve singlemachine scheduling models with past-sequence-dependent setup times and the exponential/or time-dependent learning effect. Wang and Li [27] examined several single-machine p-s-d setup times scheduling problems along with general position-dependent and time-dependent learning effects.…”