Efficient solutions to a class of generalized time-dependent
combinatorial optimization problems As a branch of operations
research, combinatorial optimization plays important role in
obtaining efficient solutions to NP-hard problems common in
science, engineering, and management domains. In 2001, Michael
L. Gargano and William Edelson described a model of time-
dependent combinatorial optimization problems, and outlined
several important applications of the model in terms of the
genetic algorithm meta-heuristic. The model assumes a set of
unit-time tasks to be completed sequentially, a set of workers
bidding on completing the tasks with time-dependent costs, and
seeks an optimized ordering for completing the tasks with an
optimized assignment of the tasks to the workers. The model can
be subdivided into sub-models depending on whether a worker can
bid on multiple tasks, or whether a worker can be assigned to
completing more than one task. ^ In 2002, Joseph DeCicco used
genetic algorithm to solve a sub-model o