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The linear move and exchange move optimization (LEG) is an algorithm based on a simulated annealing algorithm (SA), a relatively recent algorithm for solving hard combinatorial optimization problems. The LEO algorithm was successfully applied to a facility layout problem, a scheduling problem and a line balancing problem. We try to apply the LEO algorithm to the problem of optimizing a manufacturing simulation model, based on a steelworks plant. This paper also demonstrates the effectiveness and versatility of this algorithm. We compare the search effort of this algorithm with a genetic algorithm (GA) implementation of the same problem.

We present a parallel algorithm for finding the maximum flow in 0-1-networks. Our design is for a mesh-connected processor array and is based on the observation that an augmenting path in a 0-1-network can be found in time proportional to the number of vertices in the given network, rather than to the number of edges. Let integer n be the number of vertices in a 0-1-network. The time complexity of the parallel algorithm is O (n2+e), where e can be any small positive real number.

All previously known efftcient maximum-flow algorithms work by finding augmenting paths, either one path at a time (as in the original Ford and Fulkerson algorithm) or all shortest-length augmenting paths at once (using the layered network approach of Dinic). An alternative method based on the preflow concept of Karzanov is introduced. A preflow is like a flow, except that the total amount flowing into a vertex is allowed to exceed the total amount flowing out. The method maintains a preflow in the original network and pushes local flow excess toward the sink along what are estimated to be shortest paths. The algorithm and its analysis are simple and intuitive, yet the algorithm runs as fast as any other known method on dense. graphs, achieving an O(n)) time bound on an n-vertex graph. By incorporating the dynamic tree data structure of Sleator and Tarjan, we obtain a version of the algorithm running in O(nm log(n'/m)) time on an n-vertex, m-edge graph. This is as fast as any known method for any graph density and faster on graphs of moderate density. The algorithm also admits efticient distributed and parallel implementations. A parallel implementation running in O(n'log n) time using n processors and O(m) space is obtained. This time bound matches that of the Shiloach-Vishkin

A new algorithm for global flow analysis on reducible graphs is presented. The algorithm is shown to treat a very general class of function spaces. For a graph of e edges, the algorithm has a worst-case time bound of O ( e log e ) function operations. It is also shown that in programming terms, the number of operations is proportional to e plus the number of exits from program loops. Consequently a restriction to one-entry one-exit control structures guarantees linearity. The algorithm can be extended to yet larger classes of function spaces and graphs by relaxing the time bound. Examples are given of code improvement problems which can be solved using the algorithm.