Vidal R.V.V. Applied Simulated Annealing

Springer, 1993. — 361 p.In February 1992, I defended my doctoral thesis: Engineering Optimiza­tion - selected contributions (IMSOR, The Technical University of Den­mark, 1992, p. 92). This dissertation presents retrospectively my central contributions to the theoretical and applied aspects of optimization. When I had finished my thesis I became interested in editing a volume related to a new expanding area of applied optimization. I considered several approaches: simulated annealing, tabu search, genetic algorithms, neural networks, heuristics, expert systems, generalized multipliers, etc. Finally, I decided to edit a volume related to simulated annealing. My main three reasons for this choice were the following: (i) During the last four years my colleagues at IMSOR and I have car­ ried out several applied projects where simulated annealing was an essential. element in the problem-solving process. Most of the avail­ able reports and papers have been written in Danish. After a short review I was convinced that most of these works deserved to be pub­ lished for a wider audience. (ii) After the first reported applications of simulated annealing (1983- 1985), a tremendous amount of theoretical and applied work have been published within many different disciplines. Thus, I believe that simulated annealing is an approach that deserves to be in the curricula of, e.g. Engineering, Physics, Operations Research, Math­ ematical Programming, Economics, System Sciences, etc. (iii) A contact to an international network of well-known researchers showed that several individuals were willing to contribute to such a volume.Problem Independent Distributed Simulated Annealing and its Applications
On Simulating Thermodynamics
Solving the Quadratic Assignment Problem
A Computational Comparison of Simulated Annealing and Tabu Search Applied to the Quadratic Assignment Problem
School Timetables: A Case Study in Simulated Annealing
Using Simulated Annealing for Efficient Allocation of Students to Practical Classes
Timetabling by Simulated Annealing
Using Simulated annealing to solve concentrator location problems in telecommunication networks
Design of a Teleprocessing Communication Network Using Simulated Annealing
Location of Civil Defence Sirens
Solving the Afforestation Problem
Algorithms for Nesting Problems
Balanced Grouping through Simulated Annealing
Optimal Partition of an Interval — The Discrete Version
Simulated Annealing in Image Processing
Optimal Pallet Capacity For A FMS
Final Remarks