Original Simulated Annealing Algorithm Research Articles

Mathematical models and simulated annealing algorithms for the robotic assembly line balancing problem

PurposeRobots 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.Design/methodology/approachFour 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.FindingsThe 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.Originality/valueFour 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.

A new heuristic memory-based simulated annealing approach applied to mine production scheduling problem

Mine production scheduling serves to maximise the net present value of a mine by solving three interconnected sub-problems: a) extraction sequence of mining blocks; b) ore-waste discrimination; c) production rates. Even though potential of scheduling is well-recognised, some issues have not been resolved: 1) the sub-problems given above are solved in a sequential fashion rather than simultaneously that leads to sub-optimality; 2) the number of decision variables and constraints can easily be over millions. In this paper, a notion of memory is introduced into simulated annealing (SA) to solve such a large problem efficiently. A new heuristic memory is added to SA such that better search results are obtained faster. At each iteration, the heuristic and the objective function are recorded. If their correlation is high, heuristic is also incorporated into the objective function. A case study demonstrated the proposed method performs faster than the original simulated annealing algorithm.

An Efficient Algorithm For Solving A Stochastic Location-routing Problem

The purpose of this paper is to determine the location of facilities and routes of vehicles, in which the facilities and routes are available within probability interval (0, 1). Hence, this study is coherent the stochastic aspects of the location problem and the vehicle routing problem (VRP). The location problem is solved by optimization software. Because of the computational complexity of the stochastic vehicle routing problem (SVRP), it is solved by a meta-heuristic algorithm based on simulated annealing (SA). This hybrid algorithm uses genetic operators in order to improve the quality of the obtained solutions. Our proposed hybrid SA is more efficient than the original SA algorithm. The associated results are compared with the results obtained by SA and optimization software.

A Novel Combinatorial Approach to Discrete Fracture Network Modeling in Heterogeneous Media

Fractured reservoirs contain about 85 and 90 percent of oil and gas resources respectively in Iran. A comprehensive study and investigation of fractures as the main factor affecting fluid flow or perhaps barrier seems necessary for reservoir development studies. High degrees of heterogeneity and sparseness of data have incapacitated conventional deterministic methods in fracture network modeling. Recently, simulated annealing (SA) has been applied to generate stochastic realizations of spatially correlated fracture networks by assuming that the elastic energy of fractures follows Boltzmann distribution. Although SA honors local variability, the objective function of geometrical fracture modeling is defined for homogeneous conditions. In this study, after the introduction of SA and the derivation of the energy function, a novel technique is presented to adjust the model with highly heterogeneous data for a fractured field from the southwest of Iran. To this end, the regular object-based model is combined with a grid-based technique to cover the heterogeneity of reservoir properties. The original SA algorithm is also modified by being constrained in different directions and weighting the energy function to make it appropriate for heterogeneous conditions. The simulation results of the presented approach are in good agreement with the observed field data.

Simulated annealing optimization algorithm for power systems quality analysis

This article presents a new application of Simulated Annealing (SA) optimization algorithm for harmonics and frequency evaluation, for power system quality analysis and frequency relaying. An objective function based on the sum of the squares of the error is to be minimized to identify the amplitude and phase angle of each harmoic component as well as the fundamental frequency of the voltage signal. The problem formulated is a highly nonlinear optimization problem and does not need any approximation such as Taylor's series expansion. The proposed algorithm uses the digitized samples of the voltage signal at the place where the power quality and frequency relaying are to be implemented. The proposed algorithm has an adaptive cooling schedule and a variable discretization to enhance the speed and convergence of the original SA algorithm. The proposed algorithm is tested on simulated data. Effects of different parameters on the estimated parameters are studied. It is shown that the proposed algorithm is able to identify harmonics spectrum in the signal.

Simulated annealing for maximum a posteriori parameter estimation of hidden Markov models

Hidden Markov models are mixture models in which the populations from one observation to the next are selected according to an unobserved finite state-space Markov chain. Given a realization of the observation process, our aim is to estimate both the parameters of the Markov chain and of the mixture model in a Bayesian framework. We present an original simulated annealing algorithm which, in the same way as the EM (expectation-maximization) algorithm, relies on data augmentation, and is based on stochastic simulation of the hidden Markov chain. This algorithm is shown to converge toward the set of maximum a posteriori (MAP) parameters under suitable regularity conditions.

A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization

We present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems. Like the original simulated annealing algorithm, our method has the hill climbing feature, so it can find global optimal solutions to discrete stochastic optimization problems with many local solutions. However, our method differs from the original simulated annealing algorithm in that it uses a constant (rather than decreasing) temperature. We consider two approaches for estimating the optimal solution. The first approach uses the number of visits the algorithm makes to the different states (divided by a normalizer) to estimate the optimal solution. The second approach uses the state that has the best average estimated objective function value as estimate of the optimal solution. We show that both variants of our method are guaranteed to converge almost surely to the set of global optimal solutions, and discuss how our work applies in the discrete deterministic optimization setting. We also show how both variants can be applied for solving discrete optimization problems when the objective function values are estimated using either transient or steady-state simulation. Finally, we include some encouraging numerical results documenting the behavior of the two variants of our algorithm when applied for solving two versions of a particular discrete stochastic optimization problem, and compare their performance with that of other variants of the simulated annealing algorithm designed for solving discrete stochastic optimization problems.

A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization

We present a modification of the simulated annealing algorithm designed for solving discrete stochastic optimization problems. Like the original simulated annealing algorithm, our method has the hi...