Hybrid Simulated Annealing Research Articles

Port-IO: an integrative mobile cloud platform for real-time inter-terminal truck routing optimization

Besides the transport of containers between transshipment areas, an increasing amount of containers needs to be moved to value-added logistics and auxiliary service areas leading to additional container flows within a seaport. Both real-time information exchange and optimization are necessary to efficiently coordinate actors and container movements being involved in respective inter-terminal transport (ITT). However, there is no decision support system facilitating real-time planning and management of ITT taking advantage of modern information technologies and optimization algorithms. In this paper, we formulate the inter-terminal truck routing problem as a novel optimization problem and propose two greedy heuristics and two hybrid simulated annealing algorithms. The computational experiments, conducted using real locations from the Port of Hamburg (Germany), are evaluated extensively. They indicate that the proposed hybrid simulated annealing algorithms are able to report feasible and improved routes within seconds. The optimization component is embedded into a scalable cloud platform that integrates both real-time data from truck drivers using a mobile app and current traffic data. As such, the proposed mobile cloud platform realizes the vision of a decision support system facilitating real-time communication and context-aware ITT planning for reducing costs and the carbon footprint.

Coping with Complexity When Predicting Surface Roughness in Milling Processes: Hybrid Incremental Model with Optimal Parametrization

The complexity of machining processes relies on the inherent physical mechanisms governing these processes including nonlinear, emergent, and time-variant behavior. The measurement of surface roughness is a critical step done offline by expensive quality control procedures. The surface roughness prediction using an online efficient computational method is a difficult task due to the complexity of machining processes. The paradigm of hybrid incremental modeling makes it possible to address the complexity and nonlinear behavior of machining processes. Parametrization of models is, however, one bottleneck for full deployment of solutions, and the optimal setting of model parameters becomes an essential task. This paper presents a method based on simulated annealing for optimal parameters tuning of the hybrid incremental model. The hybrid incremental modeling plus simulated annealing is applied for predicting the surface roughness in milling processes. Two comparative studies to assess the accuracy and overall quality of the proposed strategy are carried out. The first comparative demonstrates that the proposed strategy is more accurate than theoretical, energy-based, and Taguchi models for predicting surface roughness. The second study also corroborates that hybrid incremental model plus simulated annealing is better than a Bayesian network and a multilayer perceptron for correctly predicting the surface roughness.

Electricity Demand Projection Using a Path-Coefficient Analysis and BAG-SA Approach: A Case Study of China

Path-coefficient analysis is utilized to investigate the direct and indirect effects of economic growth, population growth, urbanization rate, industrialization level, and carbon intensity on electricity demand of China. To improve the projection accuracy of electricity demand, this study proposes a hybrid bat algorithm, Gaussian perturbations, and simulated annealing (BAG-SA) optimization method. The proposed BAG-SA algorithm not only inherits the simplicity and efficiency of the standard BA with a capability of searching for global optimality but also enhances local search ability and speeds up the global convergence rate. The BAG-SA algorithm is employed to optimize the coefficients of the multiple linear and quadratic forms of electricity demand estimation model. Results indicate that the proposed algorithm has higher precision and reliability than the coefficients optimized by other single-optimization methods, such as genetic algorithm, particle swarm optimization algorithm, or bat algorithm. And the quadratic form of BAG-SA electricity demand estimation model has better fitting ability compared with the multiple linear form of the model. Therefore, the quadratic form of the model is applied to estimate electricity demand of China from 2016 to 2030. The findings of this study demonstrate that China’s electricity demand will reach 14925200 million KWh in 2030.

Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.

Solving a discrete congested multi-objective location problem by hybrid simulated annealing with customers' perspective

Solving a discrete congested multi-objective location problem by hybrid simulated annealing with customers' perspective

Development of CAMD based on the hybrid gene algorithm and simulated annealing algorithm and the application on solvent selection

AbstractIn this paper, an evolutionary approach, improved CAMD based on the hybrid gene algorithm and simulated annealing algorithm (GASA), is developed. The new approach combines the feature of GA and SA, avoiding the problem of prematurity. With a new category strategy of candidate groups from the Mod UNIFAC group database adopted, a repair operator is introduced to guarantee the integrity of randomly generated molecules and thus the search of straight chain alkane as well as cyclane solutions can be performed together. The properties of molecules are obtained by the group contribution method. An example of extractive solvent selection for a methanol‐methyl acetate system has been illustrated in detail to further explain the method. The stability as well as other properties of the molecules which the authors think important is considered in the fitness function. Results of the example show that the fitness ranking values are better than those in the literature. The CAMD method in this paper can be used in practical chemical processes.

Autonomous mobile robot navigation in cluttered environment using hybrid Takagi-Sugeno fuzzy model and simulated annealing algorithm controller

Purpose This paper aims to design a Takagi–Sugeno fuzzy model with a simulated annealing hybrid algorithm (fuzzy-SAA) that was implemented for mobile robot navigation and obstacle avoidance in a cluttered environment. Design/methodology/approach The SAA is used to optimize the output parameters of the fuzzy controller. The ultrasonic range finder sensor and sharp infrared range sensor are used for calculating the different obstacle distances, such as front, right and left obstacle distance, for selecting the suitable steering angle control command in the environment. Findings The simulation and experimental results show the proposed method is feasible and valid for a wheeled mobile robot moving in a cluttered environment. Originality/value The developed fuzzy-SAA hybrid algorithm provides better results (in terms of navigation path length and time) as compared to previous works, which verifies the effectiveness and efficiency of the developed hybrid algorithm.

Solving a discrete congested multi-objective location problem by hybrid simulated annealing with customer’s perspective

Inthe current competitive market, obtaining a greater share of the market requires consideration of the customers’ preferences and meticulous demands. Thisstudy addresses this issue with a queuing modelthat uses multi-objective set covering constraints. It considers facilities as potential locations with the objective of covering all customers with a minimum number of facilities. Themodel is designed based on the assumption that customers can meet their needs from a singlefacility. It also considers three objective functions, namely minimizing the total number of the assigned server, minimizing the total transportation and facility deploymentcostsand maximizing the quality of service from the customers’ point of view. The main constraintis that every center shouldhave less than b numbers of people in line with a probability of at least α, upon the arrival of a new customer.The feasibility of the approach is demonstrated by several examples which aredesigned and optimized byaproposed hybrid simulated annealing (SA)algorithm to evaluate the model’svalidity. Finally, the study compares the performance of the proposed algorithm with that of variable neighborhood search (VNS)algorithm and concludes that it can arrive at an optimal solution in much less time than the VNS algorithm.

Lagrangean relaxation and hybrid simulated annealing tabu search procedure for a two-echelon capacitated facility location problem with plant size selection

In this paper, we study a two-echelon capacitated facility location problem with plant size selection (TECFLP-PSS). Given a set of potential sites for plants, each of which is associated with several possible sizes and corresponding unit production costs, a set of potential sites for capacitated depots and a set of customers with demands, the TECFLP-PSS aims to optimise the plant locations and sizes, the depot locations and the product flows from the opened plants to the opened depots and then to the end customers under single-source constraints. The objective is to satisfy all customers’ demands with a minimum total cost of facility opening, production and transportation. We develop a mixed integer programming model and propose a Lagrangean relaxation approach combined with new valid inequalities and core problem to achieve tight lower and upper bounds for this problem. We then improve the upper bound with a hybrid simulated annealing tabu search procedure. Computational experiments on benchmarks and randomly generated instances are conducted to validate the effectiveness and efficiency of the proposed method.

Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing – tabu search algorithm to solve the symmetrical traveling salesman problem

This paper applies a hybrid simulated annealing – tabu search algorithm to solve the Traveling Salesman Problem (TSP). Fully considering the characteristics of the hybrid algorithm, we develop a dynamic neighborhood structure for the hybrid algorithm to improve search efficiency by reducing the randomness of the conventional 2-opt neighborhood. A circle-directed mutation is developed to achieve this dynamic neighborhood structure. Furthermore, we propose adaptive parameters that can be automatically adjusted by the algorithm based on context specific examples. This negates the need to frequently readjust algorithm parameters. We employ benchmarks obtained from TSPLIB (a library of sample instances for the TSP) to test our algorithm, and find that the proposed algorithm can obtain satisfactory solutions within a reasonable amount of time. The experimental results demonstrate that the proposed hybrid algorithm can overcome the disadvantages of traditional simulated annealing and tabu search methods. The results also show that the dynamic neighborhood structure is more efficient and accurate than the classical 2-opt. Also, adaptive parameters are appropriate for almost all of the numerical examples tested in this paper. Finally, the experimental results are compared with those of other algorithms, to demonstrate the improved accuracy and efficiency of the proposed algorithm.

A multi-period scheduling method for trading-off between skilled-workers allocation and outsource service usage in dynamic CMS

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in a dual resource constrained environment. The aim of this method is to find best production strategy of in-house manufacturing using worker assignment (both temporary and skilled workers) and outsourcing, while part demands are uncertain and can be varied periodically. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to use in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; a hybrid Tabu Search and Simulated Annealing algorithms and a hybrid Ant Colony Optimization and Simulated Annealing algorithms. A Taguchi method (L27 orthogonal optimisation) is used to estimate parameters of the proposed method in order to solve experiments derived from the literature. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain market demands affects the part allocating which may induce workstation-load variations that yield to cell-load variation accordingly. To solve this problem, two methods are offered. The results show that promoting staff and using freezing technique are promising ways to reduce system imbalance while confronting with the mentioned condition. The outcomes also show the superiority of the proposed hybrid method in providing solutions with better quality.

A multi-layer perceptron for scheduling cellular manufacturing systems in the presence of unreliable machines and uncertain cost

In this paper, a new method is proposed for short-term period scheduling of dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to find best production strategy of in-house manufacturing and outsourcing in small and medium scale cellular manufacturing companies. For this purpose, a multi-period scheduling model has been proposed which is flexible enough to be used in real industries. To solve the proposed problem, a number of metaheuristics are developed including Branch and Bound; Simulated Annealing algorithms; Fuzzy Art Control; Ant Colony Optimization and a hybrid Multi-layer Perceptron and Simulated Annealing algorithms. Our findings indicate that the uncertain condition of system costs affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed method can significantly reduce cell load variation while finding the best trading off values between in-house manufacturing and outsourcing.

Prediction of pyrite oxidation in a coal washing waste pile using a hybrid method, coupling artificial neural networks and simulated annealing (ANN/SA)

This paper presents a novel hybrid method coupling artificial neural network (ANN) and simulated annealing (SA), called ANN/SA to predict the fraction of pyrite remaining and therefore the pyrite oxidation rate in the wastes at different depths of a coal washing pile in the Alborz Markazi Coalfield, in northeast Iran. Waste depth, oxygen mole fraction and initial pyrite content in the waste particles were used as inputs to the network. The output of the network was the amount of pyrite content remaining. An ANN/SA model with Levenberg-Marquardt algorithm and a 3-4-3-1 arrangement showed a great capability. The network was used to predict the pyrite content remaining at two trenches E and F over the study waste pile once it was trained with the field-measured data. Simulated results obtained by the ANN/SA model were very closer to the experimental data compared to the outputs of simple ANN and multivariable least squares regression methods. The correlation coefficient (R) value, by the ANN/SA model, was 0.999 for training set, and in testing stage it was 0.998 and 0.99957 for trench E and trench F respectively which shows the model prediction was quite satisfactory. The performance of the model on the training and testing data, mean squared error (MSE) and mean absolute percent error (MAPE), indicate that it has both good predictive ability and generalisation performance.

Numerical optimization of vehicle noises in multi-peak frequency points based on hybrid genetic algorithm and simulated annealing

The finite element model of Body in White was built, and the corresponding modes were computed in this paper. These computational modes were then compared with experimental results. The small errors showed that the accuracy of the finite element model can satisfy the computational requirements. Based on the verified finite element model, acoustic cavities in the vehicle were extracted to build a boundary element model. Sound pressure levels at all passengers in the vehicle were then computed, compared and analyzed. Results indicated that the sound pressure curve had 6 peak noises. Using the characteristic frequency weight coefficient and field point weight coefficient, the body panels which made large acoustic contributions to the comprehensive sound field under multi-characteristic frequencies were determined. Finally, the improved genetic algorithm based on simulated annealing was used to optimize the key body panels, and peak noises at researched field points after the optimization were further computed. The computational results were compared with those of the original structure, which presented that the noise was improved at most frequency points in the spectrum and peak noises were suppressed obviously.

Lifetime Improvement in Wireless Sensor Networks using Hybrid Differential Evolution and Simulated Annealing (DESA)

Abstract The major concerns in Wireless Sensor Networks (WSN) are energy efficiency as they utilize small sized batteries, which can neither be replaced nor be recharged. Hence, the energy must be optimally utilized in such battery operated networks. One of the traditional approaches to improve the energy efficiency is through clustering. In this paper, a hybrid differential evolution and simulated annealing (DESA) algorithm for clustering and choice of cluster heads is proposed. As cluster heads are usually overloaded with high number of sensor nodes, it tends to rapid death of nodes due to improper election of cluster heads. Hence, this paper aimed at prolonging the network lifetime of the network by preventing earlier death of cluster heads. The proposed DESA reduces the number of dead nodes than Low Energy Adaptive Clustering Hierarchy (LEACH) by 70%, Harmony Search Algorithm (HSA) by 50%, modified HSA by 40% and differential evolution by 60%.

Worker assignment and production planning with learning and forgetting in manufacturing cells by hybrid bacteria foraging algorithm

We consider a joint decision model of worker assignment and production planning in a dynamic cellular manufacturing system of fiber connector manufacturing industry. On one hand, due to the learning and forgetting effects of workers, the production rate of each workstation will often change. Thus, the bottleneck workstation may transfer to another one in the next period. It is worthwhile to reassign multi-skilled workers such that the production rate of bottleneck workstation may increase. On the other hand, because of the limited production capacity and variety of orders, late delivery or production in advance often occurs at each period. The parts with operational sequence need to be dispatched to the desirable cells for processing. The objective is to minimize backorder cost and holding cost of inventory. To solve this complicated problem, we propose an efficient hybrid bacteria foraging algorithm (HBFA) with elaborately designed solution representation and bacteria evolution operators. A two-phase based heuristic is embedded in the HBFA to generate a high quality initial solution for further search. We tested our algorithm using randomly generated instances by comparing with the original bacteria foraging algorithm (OBFA), discrete bacteria foraging algorithm (DBFA), hybrid genetic algorithm (HGA) and hybrid simulated annealing (HSA). Our results show that the proposed HBFA has better performance than the four compared algorithms with the same running time.

Optimization of static and dynamic travel range of electrostatically driven microbeams using particle swarm optimization

This paper examines the enhancement of static and dynamic travel range of electrostatically driven microbeams using shape optimization approach. Continuous functions of width and thickness are used for optimizing the geometry of both cantilever and fixed–fixed microbeams. Rayleigh–Ritz energy method is employed to compute the static and dynamic pull-in parameters. Particle swarm optimization and hybrid simulated annealing are used for shape optimization of microbeams. Constraints on design variables are imposed using penalty approach. Enhanced pull-in parameters obtained for variable geometry microbeams have been validated using 3-D finite element analysis. Optimized shapes of microbeams show significant improvement in static and dynamic travel range. Pull-in displacement is increased up to 54.92% for cantilever microbeam and 40.79% for fixed–fixed microbeam with hybrid simulated annealing. Effectiveness of particle swarm optimization is brought out through representative test cases. The convergence of the particle swarm optimization is approximately five times faster as compared to the hybrid simulated annealing, while maintaining the same level of accuracy.

Subset selection in multiple linear regression: An improved Tabu search

This paper proposes an improved tabu search method for subset selection in multiple linear regression models. Variable selection is a vital combinatorial optimization problem in multivariate statistics. The selection of the optimal subset of variables is necessary in order to reliably construct a multiple linear regression model. Its applications widely range from machine learning, timeseries prediction, and multi-class classification to noise detection. Since this problem has NP-complete nature, it becomes more difficult to find the optimal solution as the number of variables increases. Two typical metaheuristic methods have been developed to tackle the problem: the tabu search algorithm and hybrid genetic and simulated annealing algorithm. However, these two methods have shortcomings. The tabu search method requires a large amount of computing time, and the hybrid algorithm produces a less accurate solution. To overcome the shortcomings of these methods, we propose an improved tabu search algorithm to reduce moves of the neighborhood and to adopt an effective move search strategy. To evaluate the performance of the proposed method, comparative studies are performed on small literature data sets and on large simulation data sets. Computational results show that the proposed method outperforms two metaheuristic methods in terms of the computing time and solution quality.

NC process reuse-oriented flexible process planning optimization approach for prismatic parts

NC process reuse is a new strategy to generate the process scheme for parts with higher efficiency. However, the existing process planning approaches based on NC process reuse are few and far from the requirements of actual machining. To address this urgent industrial need, this paper proposes a novel NC process reuse-oriented flexible process planning optimization approach for prismatic parts with the objective of minimizing total manufacturing cost. Firstly, the NC process candidate inheritance mechanisms are explored to generate the NC process candidates for query subparts efficiently and automatically. Secondly, the NC process for each query subpart is selected using the roulette wheel approach in terms of the reusability. Then, through the analysis of feature attributes, the precedence constraint feature-driven characteristic is revealed to determine the precedence between operations. Moreover, the precedence and distances between operations are formulated in an operation precedence graph (OPG). Finally, a hybrid ant colony algorithm (ACA) and simulated annealing (SA) approach based on OPG is presented to achieve the global optimal NC process scheme for the part. In the experiments, a prototype system based on CATIA is developed and a prismatic part is conducted to illustrate the application effectiveness of the proposed approach. The experimental results show that our approach can achieve highly satisfactory results and outperform other approaches.

Hybridization of simulated annealing with genetic algorithm for cell formation problem

The grouping of parts and machines into manufacturing cells with the objective to improve grouping efficacy is the prime focus of cell formation problem. This paper presents the hybridization of simulated annealing (SA) with genetic algorithm (GA), where exploration power of GA, to broaden the search space, is combined with the intensification power of SA. A certain percentage of best solution in each generation of GA is sent, for further intensification and trajectory search, to SA. In intensification phase (with SA), Boltzmann probability distribution functions are used to allow a downhill move, with variable temperature. Starting temperature is chosen to be a small value with rapid cooling rate to keep search in a narrow region. If same solution is repeated for a certain number of generations, then shake-off is given to escape local maxima. During this shake-off, the SA parameters are relaxed to broaden the search for best neighbourhood solution. The effectiveness of proposed hybrid heuristic algorithm is evaluated through 35 benchmark problems from the literature. The GA with intensification through SA along with shake-off performed well in terms of solution quality, i.e. produced 24 best results with overall mean of 66.20. The overall comparative study shows that the proposed approach not only achieves the best solutions consistently, with minimum computational time, but also improves the results of two problems (problems 29 and 33), reporting it for the first time.