Theodore W. Manikas and James T. Cain
While simulated annealing has been shown to produce good placement solutions, recent work in genetic algorithms has produced promising results. The purpose of this study is to determine which approach will result in better placement solutions.
D. Spinellis, C. Papadopoulos and J.M. Smith
We present a robust generalised queuing network algorithm as an evaluative procedure for optimising production line configurations using simulated annealing. We compare the results obtained with our algorithm to those of other studies and find some interesting similarities and design of production lines.
Diomidis Spinellis and Chrisoleon T. Papadopoulos
We describe a simulated annealing approach for solving the buffer allocation problem in reliable production lines. The problem entails the determination of near optimal buffer allocation plans in large production lines with the objective of maximising their average throughput.
Richard Frost
We present a simulated annealing library for the Intel Paragon other Unix-based systems. Our ensemble based, constant-speed approach is a true analog of annealing theory and compares with other heuristic optimization techniques. Three types of parallelism are exploited for task distribution.
W. Spears
Satisfiability (SAT) refers to the task of finding a truth assignment that makes an arbitrary boolean expression true. This paper compares a simulated annealing algorithm (SASAT) with GSAT a greedy algorithm for solving satisfiability problems.
Beate Bollig, Martin Löbbing and Ingo Wegener
The choice of a good variable ordering is crucial in applications of Ordered Binary Decision Diagrams (OBDDs). A simulated annealing approach with a new type of neighborhood is presented and analyzed. Better results as by known simulated annealing algorithms and heuristics are obtained. Some theoretical results underlining the experiments are stated.
Oliver C. Martin and Steve W. Otto
We introduce a meta-heuristic to combine simulated annealing with local search methods for CO problems. This new class of Markov chains leads to significantly more powerful optimization methods than either simulated annealing or local search. The main idea is to embed deterministic local search techniques into simulated annealing so that the chain explores only local optima.