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The purpose of this paper is to create a flight route optimization for all flights that aims to minimize the total cost consists of fuel cost, ground delay cost and air delay cost over the fixed route and free route airspaces.

Efficient usage of current available airspace capacity becomes more and more important with the increasing flight demands. The efficient capacity usage of an airspace is generally in contradiction to optimum flight efficiency of a single flight. It can only be achieved with the holistic approach that focusing all flights over mixed airspaces and their routes instead of single flight route optimization for a single airspace. In the scope of this paper, optimization methods were developed to find the best route planning for all flights considering the benefits of all flights not only a single flight. This paper is searching for an optimization to reduce the total cost for all flights in mixed airspaces. With the developed optimization models, the determination of conflict-free optimum routes and delay amounts was achieved with airway capacity and separation minimum constraints in mixed airspaces. The mathematical model and the simulated annealing method were developed for these purposes.

The total cost values for flights were minimized by both developed mathematical model and simulated annealing algorithm. With the mathematical model, a reduction in total route length of 4.13% and a reduction in fuel consumption of 3.95% was achieved in a mixed airspace. The optimization algorithm with simulated annealing has also 3.11% flight distance saving and 3.03% fuel consumption enhancement.

Although the wind condition can change the fuel consumption and flight durations, the paper does not include the wind condition effects. If the wind condition effect is considered, the shortest route may not always cause the least fuel consumption especially under the head wind condition.

The results of this paper show that a flight route optimization as a holistic approach considering the all flight demand information enhances the fuel consumption and flight duration. Because of this reason, the developed optimization model can be effectively used to minimize the fuel consumption and reduce the exhaust emissions of aircraft.

This paper develops the mathematical model and simulated annealing algorithm for the optimization of flight route over the mixed airspaces that compose of fixed and free route airspaces. Each model offers the best available and conflict-free route plan and if necessary required delay amounts for each demanded flight under the airspace capacity, airspace route structure and used separation minimum for each airspace.

The purpose of this paper is to present an application of the simulated annealing algorithm to the redundant system reliability optimization. Its main aim is to analyze and compare this optimization method performance with those of similar application.

The methods that were used to compare results are the genetic algorithm, the Lagrange Multipliers, and the evolution strategy. A hybrid algorithm composed by simulated annealing and genetic algorithm was developed in order to achieve the general applicability of the methods. The hybrid algorithm also tries to exploit the positive aspects of each method.

The results presented by the simulated annealing and the hybrid algorithm are significant, and validate the methods as a robust tool for parameter optimization in mechanical projects development.

The main objective is to propose a method for redundancy optimization in mechanical systems, which are not as large as electric and electronic systems, but involves high costs associated to redundancy and requires a high level of safety standards like: automotive and aerospace systems.

This paper aims to present a new model for solving the integrated production planning and scheduling. Usually, the two decision levels are treated sequentially because of their complexity. Scheduling depends on the lot sizes calculated at the tactical level and ignoring scheduling constraints generates unrealistic and inconsistent decisions. Therefore, integrating more detail scheduling constraint in production planning is important for managing efficiently operations. Therefore, an integrated model was developed, and two evolutionary optimization approaches were suggested for solving it, namely, genetic algorithm (GA) and the hybridization of simulated annealing (SA) with GA HSAGA. The proposed algorithms have some parameters that must be adjusted using Taguchi method. Therefore, to evaluate the proposed algorithm, the authors compared the results given by GA and the hybridization. The SA-based local search is embedded into a GA search mechanism to move the GA away from being closed within local optima. The analysis shows that the combination of simulated annealing with GA gives better solutions and minimizes the total production costs.

The paper opted for an approached resolution method particularly GA and simulated annealing. The study represents a comparison between the results found using GA and the hybridization of simulated annealing and GA. A total of 45 instances were studied to evaluate job-shop problems of different sizes.

The results illustrate that for 36 instances of 45, the hybridization of simulated annealing and GA HSAGA has provided best production costs. The efficiency demonstrated by HSAGA approach is related to the combination between the exploration ability of GA and the capacity to escape local optimum of simulated annealing.

This study provides a new resolution approach to the integration of planning and scheduling while considering a new operational constrain. The model suggested aims to control the available capacity of the resources and guaranties that the resources to be consumed do not exceed the real availability to avoid the blocking that results from the unavailability of resources. Furthermore, to solve the MILP model, a GA is proposed and then it is combined to simulated annealing.

Accurate presentation of the rock microstructure is critical to the grain-scale analysis of rock deformation and failure in numerical modelling. 3D granite microstructure modelling has only been used in limited studies with the mineral pattern often remaining poorly constructed. In this study, the authors developed a new approach for generating 2D and 3D granite microstructure models from a 2D image by combining a heterogeneous material reconstruction method (simulated annealing method) with Voronoi tessellation.

More specifically, the stochastic information in the 2D image is first extracted using the two-point correlation function (TPCF). Then an initial 2D or 3D Voronoi diagram with a random distribution of the minerals is generated and optimised using a simulated annealing method until the corresponding TPCF is consistent with that in the 2D image. The generated microstructure model accurately inherits the stochastic information (e.g. volume fraction and mineral pattern) from the 2D image. Lastly, the authors compared the topological characteristics and mechanical properties of the 2D and 3D reconstructed microstructure models with the model obtained by direct mapping from the 2D image of a real rock sample.

The good agreements between the mapped and reconstructed models indicate the accuracy of the reconstructed microstructure models on topological characteristics and mechanical properties.

The newly developed reconstruction method successfully transfers the mineral pattern from a granite sample into the 2D and 3D Voronoi-based microstructure models ready for use in grain-scale modelling.

Robots are used in assembly lines because of their higher flexibility and lower costs. The purpose of this paper is to develop mathematical models and simulated annealing algorithms to solve the robotic assembly line balancing (RALB-II) to minimize the cycle time.

Four mixed-integer linear programming models are developed and encoded in CPLEX solver to find optimal solutions for small-sized problem instances. Two simulated annealing algorithms, original simulated annealing algorithm and restarted simulated annealing (RSA) algorithm, are proposed to tackle large-sized problems. The restart mechanism in the RSA methodology replaces the incumbent temperature with a new temperature. In addition, the proposed methods use iterative mechanisms for updating cycle time and a new objective to select the solution with fewer critical workstations.

The comparative study among the tested algorithms and other methods adapted verifies the effectiveness of the proposed methods. The results obtained by these algorithms on the benchmark instances show that 23 new upper bounds out of 32 tested cases are achieved. The RSA algorithm ranks first among the algorithms in the number of updated upper bounds.

Four models are developed for RALBP-II and their performance is evaluated for the first time. An RSA algorithm is developed to solve RALBP-II, where the restart mechanism is developed to replace the incumbent temperature with a new temperature. The proposed methods also use iterative mechanisms and a new objective to select the solution with fewer critical workstations.

The purpose of this paper is to provide some heuristic and meta heuristic tools to aid airline companies in flight planning while taking into account maintenance planning. The objective is to maximize aircraft utilization before the maintenance interventions and to smooth the long‐term flight load of the aircraft so that maintenance checks are regular for all the fleet.

The proposed methods, based on operational research solution technics, build a flight planning for an airline company. The simulation of the proposed methods is observed over 40 weeks and evaluated with different problem instances and maintenance policies.

The longest path based method with increasing priority and the simulated annealing are shown to have the best aircraft utilization results.

Further research could propose some methods which build simultaneous maintenance and flight planning.

The economic value and legal considerations of maintenance activities in the airline industry show the importance of maximizing aircraft utilization. The proposed methods are compared to decide the best maintenance policy. These methods are simple and efficient.

This paper provides a connection between an industrial problem of aircraft maximization under maintenance constraints and operational research. Simple but efficient methods are evaluated in terms of two criteria: aircraft maximization and flight load smoothing.

Considers, without loss of generality, a simple linear problem, where in a certain domain the magnetic field, generated by infinitely long conductors, whose locations as well as the currents are unknown, has to meet a certain figure. The problem is solved by applying hierarchical simulated annealing, which iteratively reduces the dimension of the search space to save computational cost. A Gauss‐Newton scheme, making use of analytical Jacobians, preceding a sequential quadratic program (SQP), will be applied as a second approach to tackle this severely ill‐posed problem. The results of these two techniques will be analyzed and discussed and some comments on future work will be given.

Considers the daily production‐scheduling problem in the “make‐to‐order” apparel‐manufacturing industry and presents a solution procedure for the problem based on the simulated annealing technique. The development is aimed at the quick generation of a feasible solution and the improvement on the solution.

Factors affecting convergence of the simulated annealing method are investigated using an actual model. The convergence characteristics of various optimization methods are examined using the contour line of objective function. Two kinds of combination methods with the simulated annealing method and the Rosenbrock’s method are investigated.

This research aims to develop an effective and efficient algorithm for solving the curve fitting problem arising in automated manufacturing systems.

This paper takes curve fitting as an optimization problem of a set of data points. Expressing the data as a function will be very effective to the data analysis and application. This paper will develop the stochastic optimization method to apply to curve fitting. The proposed method is a combination optimization method based on pattern search (PS) and simulated annealing algorithm (SA).

The proposed method is used to solve a nonlinear optimization problem and then to implement it to solve three circular arc‐fitting problems of curve fitting. Based on the analysis performed in the experimental study, the proposed algorithm has been found to be suitable for curve fitting.

Curve fitting is one of the basic form errors encountered in circular features. The proposed algorithm is tested and implemented by using nonlinear problem and circular data to determine the circular parameters.

The developed machine vision‐based approach can be an online tool for measurement of circular components in automated manufacturing systems.