Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Computers and Industrial Engineering
The learning effect: Getting to the core of the problem
Information Processing Letters
A new approach to the learning effect: Beyond the learning curve restrictions
Computers and Operations Research
Single-machine scheduling with sum-of-logarithm-processing-times-based learning considerations
Information Sciences: an International Journal
Some single-machine and m-machine flowshop scheduling problems with learning considerations
Information Sciences: an International Journal
Scheduling problems with deteriorating jobs and learning effects including proportional setup times
Computers and Industrial Engineering
Scheduling with job-dependent learning effects and multiple rate-modifying activities
Information Processing Letters
Computers and Operations Research
Proportionate flowshops with general position-dependent processing times
Information Processing Letters
Some single-machine scheduling problems with a truncation learning effect
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
The single-machine total weighted tardiness scheduling problem with position-based learning effects
Computers and Operations Research
Two-machine flowshop scheduling with truncated learning to minimize the total completion time
Computers and Industrial Engineering
Two-agent scheduling with learning consideration
Computers and Industrial Engineering
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
An Agent Based Approach to Patient Scheduling Using Experience Based Learning
International Journal of Agent Technologies and Systems
Several flow shop scheduling problems with truncated position-based learning effect
Computers and Operations Research
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Most of the papers devoted to scheduling problems with the learning effect concern the Wright's learning curve. On the other hand, the study about learning has pointed out that the learning curve in practice is very often an S-shaped function, which has not been considered in scheduling. Thus, in this paper, a single processor makespan minimization problem with an S-shaped learning model is investigated. We prove that this problem is strongly NP-hard even if the experience provided by each job is equal to its normal processing time. Therefore, to solve this problem, we prove some eliminating properties that are used to construct a branch and bound algorithm and some fast heuristic methods. Since the proposed algorithms are dedicated for the general case, i.e., where job processing times are arbitrary non-increasing experience dependent functions, their efficiency is verified numerically for the S-shaped model.