||Games, such as Chess, Eight Queens, and Tiles Puzzle, have traditionally been used as popular benchmarks for evaluating different problem solving strategies. Such benchmarks usually impose specific constraints that are considered important for a technique to address and to solve. These specific problems and constraints facilitate a fair and tangible comparison of different techniques. Many of the game benchmarks (e.g. Eight Queens and Tiles Puzzle) are suitable for evaluating static scheduling techniques. In such benchmarks, the scheduling phase and the execution phase (i.e. when the schedule is executed to play the game) are disjoint. The scheduling technique can be executed to compute a complete schedule prior to the execution of any moves to play the game. Due to recent interests in on-line problem solving techniques, there is a need for benchmarks which can evaluate the performance trade-off of dynamic scheduling techniques. Many modem video and computer games can be suitable candidates for dynamic scheduling benchmarks, since they require on-line problem solving. These benchmarks and their system testbeds should be chosen and implemented such that they can accurately reveal important performance trade-off of dynamic scheduling techniques. In this thesis, we introduce a dynamic scheduling benchmark and its system testbed. This benchmark is based on an extended version of the Ten-is computer game. The rules and semantics of the game were modified to lend themselves well to evaluation of discrete problem solving and optimization techniques. The system testbed is implemented in a distributed and asynchronous fashion, on a network of workstations, to reveal performance trade-off between scheduling time, schedule quality, and problem constraints. We provide the results of a set of experiments which evaluate the benchmark and its system, and which evaluate the performance trade-off of a set of scheduling techniques under different benchmark constraints. The results of the experiments reveal that our algorithms are capable of adjusting their scheduling effort automatically to perform as well as the best of the other candidate algorithms under different conditions.