A survey of results for sequencing problems with controllable processing times
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
Mathematics of Operations Research
Single machine scheduling to minimize total compression plus weighted flow cost is NP-hard
Information Processing Letters
Single machine scheduling with a variable common due date and resource-dependent processing times
Computers and Operations Research
Computers and Operations Research
Minimizing the total weighted flow time in a single machine with controllable processing times
Computers and Operations Research
A survey of scheduling with controllable processing times
Discrete Applied Mathematics
Some scheduling problems with sum-of-processing-times-based and job-position-based learning effects
Information Sciences: an International Journal
Some scheduling problems with deteriorating jobs and learning effects
Computers and Industrial Engineering
Single-machine and flowshop scheduling with a general learning effect model
Computers and Industrial Engineering
Some single-machine and m-machine flowshop scheduling problems with learning considerations
Information Sciences: an International Journal
Single-machine scheduling time-dependent jobs with resource-dependent ready times
Computers and Industrial Engineering
Single-machine group scheduling with resource allocation and learning effect
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
Two-machine flowshop scheduling with truncated learning to minimize the total completion time
Computers and Industrial Engineering
Uniform parallel-machine scheduling to minimize makespan with position-based learning curves
Computers and Industrial Engineering
Computers and Industrial Engineering
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We consider resource allocation scheduling with learning effect in which the processing time of a job is a function of its position in a sequence and its resource allocation. The objective is to find the optimal sequence of jobs and the optimal resource allocation separately. We concentrate on two goals separately, namely, minimizing a cost function containing makespan, total completion time, total absolute differences in completion times and total resource cost; minimizing a cost function containing makespan, total waiting time, total absolute differences in waiting times and total resource cost. We analyse the problem with two different processing time functions. For each combination of these, we provide a polynomial time algorithm to find the optimal job sequence and resource allocation.