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The machine loading problem of an FMS is well known for its complexity.
Therefore, numerous researchers have used meta-heuristic approaches for solving the machine loading problem.
[Tiwari and Vidyarthi (2000)] proposed a GA-based heuristic to solve the machine loading problem of a random-type FMS.
The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made.
It is clear from this research that the machine loading problem can be solved using a hybrid algorithm-based heuristic that can tackle the problem in a synergistic way.
In this regard, [Kumar and Shanker (2000), Tiwari and Vidyarthi (2000)], and [Swamkar and Tiwari (2004)] have addressed a machine loading problem having the bi-criterion objectives of minimizing system unbalance and maximizing the throughput using a hybrid algorithm based on Tabu search and simulated annealing (SA).
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To demonstrate the applicability of proposed algorithm over real-world problems, Machine-loading problem of flexible manufacturing system (FMS) is worked out and matched with the results present in literature.
Amongst pre-release decisions, machine loading is considered as one of the most vital production planning problems since operational effectiveness of FMS largely depends on it.
Loading problem, in particular, deals with allocation of jobs to various machines under technological constraints with the objective of meeting certain performance measures.
Machine loading and unloading operations utilize a robot to load and unload parts at a production machine.
They also proposed efficient machine loading heuristics.
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