Comparison of heuristics for flowtime minimisation in permutation flowshops
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Tight Bounds for Permutation Flow Shop Scheduling
Mathematics of Operations Research
Some single-machine and m-machine flowshop scheduling problems with learning considerations
Information Sciences: an International Journal
Experience-based approach to scheduling problems with the learning effect
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computers and Industrial Engineering
Information Sciences: an International Journal
Several flow shop scheduling problems with truncated position-based learning effect
Computers and Operations Research
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This paper investigates flowshop scheduling problems with a general exponential learning effect, i.e., the actual processing time of a job is defined by an exponent function of the total weighted normal processing time of the already processed jobs and its position in a sequence, where the weight is a position-dependent weight. The objective is to minimize the makespan, the total (weighted) completion time, the total weighted discounted completion time, and the sum of the quadratic job completion times, respectively. Several simple heuristic algorithms are proposed in this paper by using the optimal schedules for the corresponding single machine problems. The tight worst-case bound of these heuristic algorithms is also given. Two well-known heuristics are also proposed for the flowshop scheduling with a general exponential learning effect.