This paper shows how the simulated annealing (SA) algorithm provides a simple tool for solving fuzzy optimization problems. Often, the issue is not so much how to fuzzify or remove the conceptual imprecision, but which tools enable simple solutions for these intrinsically uncertain problems. A well-known linear programming example is used to discuss the suitability of the SA algorithm for solvi...

It is shown that the limit points of a stochastic approximation (SA) algorithm compose a connected set. Conditions are given to guarantee the uniqueness of the limit point for a given initial value. Examples are provided wherein of SA algorithm converges to a limit independent of initial values, but is unstable for the differential equation _ = ( ) with a nonnegative Lyapunov function. Finally,...

We describe a comparison between Simulated Annealing (SA), Dispatch Rules (DR), and a Coevolutionary Distributed Genetic Algorithm (DGA) solving a random sample of integrated planning and scheduling (IPS) problems. We found that for a wide range of optimization criteria the DGA consistently outperformed SA and DR. The DGA finds 8-9 unique high quality solutions per run, whereas the other techni...

The permutation flow shop scheduling problem (PFSP) is one of the most well known and well studied production scheduling problems with strong industrial background. Most scheduling problems are combinatorial optimization problems which are too difficult to be solved optimally, and hence heuristics are used to obtain good solutions in a reasonable time. The specific goal of this paper is to inve...

Multidisciplinary Design Optimization (MDO) is the most active fields in the current complex engineering system design. Forcing to the defects of traditional Collaborative Optimization, such as unable to convergence or falling into local optimum, we propose a Collaborate Optimization based on Simulated Annealing and Artificial Neural Networks, (SA-ANN-CO). The SA-ANN-CO algorithm inherit the pa...

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and lo...

Abstract—This paper aims to present non-population search algorithms called tabu search (TS), simulated annealing (SA) and variable neighborhood search (VNS) to minimize the total cost of capacitated MRP problem in multi-stage assembly flow shop with two alternative machines. There are three main steps for the algorithm. Firstly, an initial sequence of orders is constructed by a simple due date...

BACKGROUND The 6-minute walk test (6MWT) independently predicts congestive heart failure severity, death, and heart failure hospitalizations, but must be administered in clinic by qualified staff on a premeasured course. As part of the Health eHeart Study, we sought to develop and validate a self-administered 6MWT mobile application (SA-6MWTapp) for independent use at home by patients. METHOD...

We introduce four new general optimization algorithms based on thèdemon' algorithm from statistical physics and the simulated annealing (SA) optimization method. These algorithms use a computationally simpler acceptance function, but can use any SA annealing schedule or move generation function. Computation per trial is signiicantly reduced. The algorithms are tested on traveling salesman probl...

Make-to-order is a production strategy in which manufacturing starts only after a customer's order is received; in other words, it is a pull-type supply chain operation since manufacturing is carried out as soon as the demand is confirmed. This paper studies the order acceptance problem with weighted tardiness penalties in permutation flow shop scheduling with MTO production strategy, the objec...