The single machine early/tardy problem
Management Science
Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Selecting jobs for a heavily loaded shop with lateness penalties
Computers and Operations Research
Job selection in a heavily loaded shop
Computers and Operations Research
Multi-period job selection: planning work loads to maximize profit
Computers and Operations Research
An evolutionary algorithm approach to the share of choices problem in the product line design
Computers and Operations Research
Computers and Industrial Engineering
Order acceptance using genetic algorithms
Computers and Operations Research
Computers and Operations Research
A tabu search algorithm for order acceptance and scheduling
Computers and Operations Research
A survey on offline scheduling with rejection
Journal of Scheduling
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
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Over the past decade the strategic importance of order acceptance has been widely recognized in practice as well as academic research. This paper examines order acceptance decisions when capacity is limited, customers receive a discount for late delivery, but early delivery is neither penalized nor rewarded. We model a manufacturing facility that considers a pool of orders, and chooses for processing the subset that results in the highest profit. We present several solution methods, beginning with a straightforward application of an approach which separates sequencing and job acceptance. We then develop an optimal branch-and-bound procedure that uses a linear (integer) relaxation for bounding and performs the sequencing and job acceptance decisions jointly. We develop a variety of fast and high-quality heuristics based on this approach. For small problems, beam search runs almost 20 times faster than the benchmark, with a high degree of accuracy, and a branch-and-bound heuristic using Vogel's method for bounding is over 100 times faster with very high accuracy. For larger problems, a myopic heuristic based on the relaxation runs 2000 times faster than the beam-search benchmark, with comparable accuracy.