Bus network design is an important problem in public transportation. A main step to this design is determining the number of required terminals and their locations. This is a special type of facility location problem, which is a time-consuming, large scale, combinatorial problem. In a previous attempt by the authors, this problem had been solved by GAMS, based on a branch and bound algorithm. ...

This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minim...

In this paper, a model is presented to locate ambulances, considering backup facility (to increase reliability) and the restriction of ambulance capacity. This model is designed for emergencies. In this model the covered demand for each demand point depends on the number of coverage times and the amount of demand. The demand amount and ambulance coverage radius are considered...

The Graph k-Colorability Problem (GCP) is a well known NP-hard problem which consist in finding the k minimum number of colors to paint the vertices of a graph in such a way that any two vertices joined by an edge have always different colors. Many years ago, Simulated Annealing (SA) was used for graph coloring task obtaining good results; however SA is not a complete algorithm and it not alway...

It is a long time that the Simulated Annealing (SA) procedure is introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules start...

It is a long time that the Simulated Annealing (SA) procedure is introduced as a model-free optimization for solving NP-hard problems. Improvements from the standard SA in the recent decade mostly concentrate on combining its original algorithm with some heuristic methods. These modifications are rarely happened to the initial condition selection methods from which the annealing schedules start...

This paper addresses the Vehicle Routing Problem with Delivery Time Cost. This problem aims to find a set of routes of minimal total costs including the travelling cost and delivery time cost, starting and ending at the depot, in such a way that each customer is visited by one vehicle given the capacity of the vehicle to satisfy a specific demand. In this research, a hybrid metaheuristic approa...

This work deals with using a method of artificial intelligence, mainly the generic probabilistic meta-algorithm can be used in such a difficult task which is analyzed and control of dynamical systems. Simulated annealing (SA) is used in this investigation. The adaptive control system was used in simulations with optimization by Simulated Annealing and the results are presented in graphs. Use of...

This report proposes a double simulated annealing (DSA) for functional magnetic resonance image (fMRI) analysis. The first simulated annealing (SA) is used to disconnect the brain from the skull. The second SA is applied to locate the activation area of the fMRIs. The performance evaluation of this approach includes receiver-operating characteristic (ROC) analysis, similarity analysis, and comp...

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...