Hybrid Simulated Annealing Algorithm Research Articles

Hybrid simulated annealing and genetic approach for solving a multi-stage production planning with sequence-dependent setups in a closed-loop supply chain

This paper studies capacitated production planning problem with sequence-dependent setups in a closed-loop supply chain. A multi-stage, multi-period multi-product problem is considered and manufacturing and remanufacturing activities of each product are conducted consequently without additional setup. A mixed integer programming (MIP) model is provided to formulate the problem. In order to solve the model, MIP-based heuristics using rolling horizon are developed which contain non-permutation and permutation heuristics. Moreover, a hybrid simulated annealing (SA) algorithm is proposed in order to solve the problem using a genetic algorithm (GA) to prepare an appropriate initial solution. Taguchi method is used to adjust the parameters of the developed hybrid SA and GA. Finally, the efficiency of the hybrid algorithm versus MIP-based algorithms is demonstrated using computational results.

Minimising maximum tardiness in assembly flowshops with setup times

This paper addresses a two-stage assembly flowshop scheduling problem with the objective of minimising maximum tardiness where set-up times are considered as separate from processing times. The performance measure of maximum tardiness is important for some scheduling environments, and hence, it should be taken into account while making scheduling decisions for such environments. Given that the problem is strongly NP-hard, different algorithms have been proposed in the literature. The algorithm of Self-Adaptive Differential Evolution (SDE) performs as the best for the problem in the literature. We propose a new hybrid simulated annealing and insertion algorithm (SMI). The insertion step, in the SMI algorithm, strengthens the exploration step of the simulated annealing algorithm at the beginning and reinforces the exploitation step of the simulated annealing algorithm towards the end. Furthermore, we develop several dominance relations for the problem which are incorporated in the proposed SMI algorithm. We compare the performance of the proposed SMI algorithm with that of the best existing algorithm, SDE. The computational experiments indicate that the proposed SMI algorithm performs significantly better than the existing SDE algorithm. More specifically, under the same CPU time, the proposed SMI algorithm, on average, reduces the error of the best existing SDE algorithm over 90%, which indicates the superiority of the proposed SMI algorithm.

Assessing the Cost Impact of Multiple Transportation Modes to Enhance Sustainability in an Integrated, Two Stage, Automotive Supply Chain

As the automotive industry has been striving to enhance its efficiency, competitiveness, and sustainability, great focus is often placed on opportunities for improving its supply chain operations. We study the effect of introducing multiple modes of transportation in an industry-motivated production and transportation problem involving short-term automotive supply chain planning. We consider multiple, heterogeneous modes of transportation that offer a cost vs. delivery time option to the manufacturer. Having multiple modes of transportation in the system promotes supply chain sustainability. We present an integer linear programming model that captures the availability of multiple transportation modes. We then provide a solution approach based on a hybrid simulated annealing algorithm that we use to analyze the problem. Experimental results demonstrate the impact of additional transportation mode lead times compared to costs in the integrated supply chain.

Hybrid artificial immune system and simulated annealing algorithms for solving hybrid JIT flow shop with parallel batches and machine eligibility

Hybrid artificial immune system and simulated annealing algorithms for solving hybrid JIT flow shop with parallel batches and machine eligibility

A Hybrid Simulated Annealing and Back-Propagation Algorithm for Feed forward Neural Network to Detect Credit Card Fraud

Due to the ascent and fast development of E-commerce, utilization of credit cards for online buys has significantly expanded, and it brought about a blast in the credit card fraud. As credit card turns into the most prevalent method of installment for both online and also normal buy, cases of fraud associated with it are additionally rising. In actuality, false exchanges are scattered with veritable exchanges, and basic example for coordinating procedures is not frequently adequate to identify those frauds accurately. Usage of effective fraud recognition frameworks has in this manner gotten to be basic for all credit card distributing banks to decrease their losses. Many current systems based on artificial intelligence, Fuzzy logic, machine learning, data mining, sequence alignment, genetic programming, and so on have advanced in distinguishing different credit card fake transactions. A reasonable seeing on all these methodologies will absolutely lead to an efficient credit card fraud detection framework. This paper suggested an anomaly detection model based on a hybrid simulated annealing (SA) and back-propagation algorithm for feed-forward neural network (FFNN), which joined the significant global searching capability of SA with the precise local searching element of back-propagation FFNNs to improve theinitial weights of a neural network toward getting a better result for detection fraud. Index Terms: Artificial Neural Network, Back-propagation, Back-propagation Feed-forward Neural Network, Feed-forward Neural Network, Simulated Annealing, Simulated Annealing-back-propagation Feed-forward Neural Network

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.

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.

A multi-period scheduling of dynamic cellular manufacturing systems in the presence of cost uncertainty

Cell-load variation is considered as a major shortcoming in cellular manufacturing systems. It can cause long queues in front of machines and impose extra costs to the cellular layouts. In this paper the impact of inflation on cell-load variation in cellular manufacturing systems is examined. For this purpose, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim is finding the trade-off values between in-house manufacturing and using outsource services while system costs are not deterministic and may be varied from period to period by inflation. To solve the model, a hybrid genetic and simulated annealing algorithms is developed because of the high potential of outcomes to be trapped in the local optima. The results are then compared in LINGO® 12.0 software. In continue a Taguchi method (an L9 orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. Our findings show that the condition of dynamic costs affects the routing of materials in process and may induce machine-load variation that yield to cell-load diversity. An increase in changing costs causes the loading level of each cell to vary, which in turn results in the development of “complex dummy sub-cells.” To measure the level of cell-load variation a new mathematical index is developed. Then, a new method is proposed for minimizing cell-load variation in the mentioned condition by using control lines. The results indicate that the proposed method can significantly reduce the level of cell-load variation in CMS.

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.

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%.

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.

Hybrid brain storm optimisation and simulated annealing algorithm for continuous optimisation problems

Inspired by the brainstorming process of human beings, the brain storm optimisation algorithm, a new swarm intelligence algorithm, is proposed and has been applied in many fields in recent years. In this paper, a novel bio-inspired computation algorithm based on the brain storm optimisation algorithm and simulated annealing approach is proposed to solve continuous optimisation problems. The proposed algorithm integrates the simulated annealing process into the brain storm optimisation algorithm. The integrated part is in charge of creation of new individuals in later stages of evolution process, replacing the creation operator. The proposed algorithm is applied to solve 13 benchmark unconstrained continuous optimisation problems, and is compared with three state-of-the-art evolutionary algorithms: particle swarm optimisation, differential evolution, and brain storm optimisation algorithm. Experimental results show that the proposed algorithm produced a significant improvement over the brain storm optimisation algorithm and generally out performed the other three in terms of mean value, standard deviation, best fitness value ever found and convergence speed which can be seen from the evolution curve.

Architectural Level Crosstalk Minimization: A Tool

Design automation is one of the key requirements of today's growing ASIC/SOC design process. The era of high speed and high density designs has led the design engineers various challenges. As the technology is scaling, one of the major concerns of VLSI design is signal integrity. Crosstalk is the major cause of signal integrity that occurs due to coupling of charge between the conducting interconnects.Most of the CAD tools available in the market addresses this issue at the post layout level. But tackling crosstalk at the stage when the design is ready for fabrication may not be always feasible in terms of area and power overheads. This paper proposes a novel CAD tool which performs an optimization to crosstalk effect at architectural level using High Level Synthesis (HLS) procedures. The proposed work performs crosstalk aware simultaneous scheduling, binding and allocation of resources in the given data flow graph using hybrid genetic and simulated annealing algorithm (GASA). The data flow graph of an intended design is fed as an input to the tool and it generates corresponding crosstalk optimized verilog code as an output. The obtained design is synthesizable in any of the commercial tool. The work is tested on 3X3 matrix multiplier and experimental result shows that there is 65.07% improvement in crosstalk delay and hence the Signal Integrity. Hence this work suggests a technique for architectural level crosstalk optimization.

Evaluating impact of market changes on increasing cell-load variation in dynamic cellular manufacturing systems using a hybrid Tabu search and simulated annealing algorithms

In this paper, a new method is proposed for scheduling dynamic cellular manufacturing systems (D-CMS) in the presence of uncertain product demands. The aim of this method is to control the process of trading off between in-house manufacturing and outsourcing while product demands are uncertain and can be varied from period to period. To solve the proposed problem, a hybrid Tabu Search and Simulated Annealing are developed to overcome hardness of the proposed model and then results are compared with a Branch and Bound and Simulated Annealing algorithms. A Taguchi method (L_27 orthogonal optimization) is used to estimate parameters of the proposed method in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors. For evaluating the system imbalance in dynamic market demands, a new measuring index is developed. Our findings indicate that the uncertain condition of market demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. The results showed that the proposed hybrid method can provide solutions with better quality.

A new bi-level meta-heuristic approach for a single machine JIT-scheduling in the batch delivery system with controllable due dates

This paper considers pre-emption in single machine scheduling problem with batch delivery and release times in which due dates are controllable as realistic assumptions in the manufacturing environment. The objective is to minimise the lateness, holding, delivery and due date assignment costs. In this study set up time is independent and it follows of a function based on the number of loading on machine in each batch. We proposed a new solution methodology, as this problem is proven to be NP-hard, hence two meta-heuristics namely: bi-level double genetic algorithm (BDGA) and bi-level hybrid genetic and simulated annealing algorithms (BSGA) are employed for solving the problem. Taguchi method is applied to tune the parameters of proposed algorithms. The performance of the proposed algorithms are measured in terms of relative percent deviation (RPD) and computational time, the computational results reveal that statistically BDGA is better than BSGA based on RPD and computational time.

A Hybrid Simulated Annealing algorithm for Single Row Facility Layout Problem

A Hybrid Simulated Annealing algorithm for Single Row Facility Layout Problem

Estimation of shallow S-wave velocity structure and site response characteristics by microtremor array measurements in Tekirdag region, NW Turkey

In this study, we aimed to explore the S-wave velocity structure of shallow soils using microtremors in order to estimate site responses in Tekirdag and surrounding areas (NW Turkey). We collected microtremor array data at 44 sites in Tekirdag, Marmara Ereglisi, Corlu, and Muratlı. The phase velocities of Rayleigh waves were estimated from the microtremor data using a Spatial Autocorrelation method. Then, we applied a hybrid genetic simulated annealing algorithm to obtain a 1D S-wave velocity structure at each site. Comparison between the horizontal-to-vertical ratio of microtremors and computed ellipticities of the fundamental mode Rayleigh waves showed good agreement with validation models. The depth of the engineering bedrock changed from 20 to 50 m in the Tekirdag city center and along the coastline with a velocity range of 700–930 m/s, and it ranged between 10 and 65 m in Marmara Ereglisi. The average S-wave velocity of the engineering bedrock was 780 m/s in the region. We obtained average S-wave velocities in the upper 30 m to compare site amplifications. Empirical relationships between the AVs30, the site amplifications, and also average topographic slopes were established for use in future site effects microzonation studies in the region.