Abstract-Combinatorial optimization problems are those problems that have a finite set of possible solutions. The best way to solve a combinatorial optimization problem is to check all the feasible solutions in the search space. However, checking all the feasible solutions is not always possible, especially when the search space is large. Thus, many meta-heuristic algorithms have been devised and modified to solve these problems. The meta-heuristic approaches are not guaranteed to find the optimal solution since they evaluate only a subset of the feasible solutions, but they try to explore different areas in the search space in a smart way to get a near-optimal solution in less cost and time. In this paper, we propose a new meta-heuristic algorithm that can be used for solving combinatorial optimization problems. The method introduced in this paper is named the Global Neighborhood Algorithm (GNA). The algorithm is principally based on a balance between both the global and local search. A set of random solutions are first generated from the global search space, and then the best solution will give the optimal value. After that, the algorithm will iterate, and in each iteration there will be two sets of generated solutions; one from the global search space and the other set of solutions will be generated from the neighborhood of the best solution. Throughout the paper, the algorithm will be delineated with examples. In the final phase of the research, the results of GNA will be discussed and compared with the results of Genetic Algorithm (GA) as an example of another optimization method.
In this paper, a stochastic model is developed to analyze the performance of a flexible manufacturing cell (FMC). The FMC considered in this paper consists of a single conveyor, a single robot, and one or more machine(s). The conveyor belt delivers the working part to the robot, which loads it onto the machine. A Markov chain model is constructed for one-machine and twomachine FMCs, after which the model is generalized to an FMC with n machines. Most importantly, the model provides an estimate of the overall machine utilization and production rate for the FMC under consideration and also illustrates the effect of different operational factors on machine utilization and production rate. The results indicated that the overall machine utilization increases with conveyor belt and robot delivery rates and decreases with machine rate, as expected. However, this decrease or the increase in the overall machine utilization is sharp at low levels of each parameter (e.g., conveyor belt delivery and robot loading), but it gradually stabilizes at higher levels of the parameters. Finally, the production rate increases sharply at low levels of each parameter and gradually stabilizes at higher levels.Index Terms-Flexible manufacturing cell (FMC), Markov chain, multiple machine, single machine.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.