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Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators' assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in smallsized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

Consider a lossy communication channel for unicast with zero-delay feedback. For this communication scenario, a simple retransmission scheme is optimum with respect to delay. An alternative approach is to use random linear coding in automatic repeat-request (ARQ) mode. We extend the work of Shrader and Ephremides in [1], by deriving an expression for the delay of random linear coding over a field of infinite size. Simulation results for various field sizes are also provided.

The search for optimal multicast subgraphs for network coding is considered. We assume unit link capacities and binary flow rates. In the first version of the problem, there is no constrained on the acyclicity of the subgraphs, whereas such constraints are imposed in the second version. These problems are known to be NP-hard. We provide heuristics to deal with both versions of the problem. The heuristics are based on well known optimization algorithms and they are therefore easy to implement.

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