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Cold chain logistics distribution orders have increased due to the impact of COVID-19. In view of the increasing difficulty of route optimization and the increase of carbon emissions in the process of cold chain logistics distribution, a mathematical model for route optimization of cold chain logistics distribution vehicles with minimum comprehensive cost is established by considering the cost of carbon emission intensity comprehensively in this paper. The main contributions of this paper are as follows: 1) An improved hybrid ant colony algorithm is proposed, which combined simulated annealing algorithm to get rid of the local optimal solution. 2) Chaotic mapping is introduced in pheromone update to accelerate convergence and improve search efficiency. The effectiveness of the proposed method in optimizing cold chain logistics distribution path and reducing costs is verified by simulation experiments and comparison with the existing classical algorithms. © 2023 The Author(s). Published by Oxford University Press.
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ABOUT ICMSOA2022Organized by Yaseen Academy, 2022 The 2nd International Conference on Modeling, Simulation, Optimization and Algorithm (ICMSOA 2022), which was planned to be held during 11-13 November, 2022 at Sanya, Hainan Province, China. Due to the travel restrictions caused by covid, the participants joined the conference online via Tencent Meeting at 12 November, 2022. The Conference looks for significant contributions to related fields of Modeling, Simulation, Optimization and Algorithm. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.CALL FOR PAPERSPlease make sure your submission is in related areas of the following general topics. The topics include, but are not limited to:Simulation modeling theory and technology, Computational modeling and simulation, System modeling and simulation, Device/VLSI modeling and simulation, Control theory and applications, Military Technology Simulation, Aerospace technology simulation, Information engineering simulation, Energy Engineering Simulation, Manufacturing Simulation, Intelligent engineering simulation, Building engineering simulation, Electromagnetic field simulation, Material engineering simulation, Visual simulation, Fluid mechanics engineering simulation, Manufacturing simulation technology, Simulation architecture, Simulation software platform and Intelligent Optimization Algorithm, Dynamic Programming, Ant Colony Optimization, Genetic Algorithm, Simulated Annealing Algorithm, Tabu Search Algorithm, Ant Colony System Algorithm, Hybrid Optimization Algorithm in other related areas.The conference was begun at 10:00am, ended at 17:30am, 12 November, 2022. There were 77 participants in total, 2 keynote speakers and 17 invited oral speakers, Assoc. Prof. Jinyang Xu from Shanghai Jiaotong Univeristy in China and Dr. Victor Koledov from Innowledgement GmbH in Germany delivered their keynote speeches, each speech cost about 50 minutes, including the questions&discussion time.On behalf of the conference organizing committee, we'd like to acknowledge the unstinting support from our colleagues at Yaseen Academy, all Technical Program Members, speakers, reviewers, and all the participants for their sincere support.Conference Organizing CommitteeICMSOA 2022List of Conference General Chair, Program Chair, Conference Committee Chair Members, International Technical Committee Members, International Reviewers are available in this Pdf.
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The outbreak of the COVID-19 epidemic has had a significant impact in increasing the number of emergency calls, which causes significant problems to emergency medical services centers (EMS) in many countries around the world, such as Saudi Arabia, which attracts a huge number of pilgrims during pilgrimage seasons. Among these issues, we address real-time ambulance dispatching and relocation problems (real-time ADRP). This paper proposes an improved MOEA/D algorithm using Simulated Annealing (G-MOEA/D-SA) to handle the real-time ADRP issue. The simulated annealing (SA) seeks to obtain optimal routes for ambulances to cover all emergency COVID-19 calls through the implementation of convergence indicator based dominance relation (CDR). To prevent the loss of good solutions once they are found in the G-MOEA/D-SA algorithm, we employ an external archive population to store the non-dominated solutions using the epsilon dominance relationship. Several experiments are conducted on real data collected from Saudi Arabia during the Covid-19 pandemic to compare our algorithm with three relevant state-of-art algorithms including MOEA/D, MOEA/D-M2M and NSGA-II. Statistical analysis of the comparative results obtained using ANOVA and Wilcoxon test demonstrate the merits and the outperformance of our G-MOEA/D-SA algorithm.
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Since early 2020, the COVID-19 outbreak has disrupted various supply chains including the on-demand food delivery sector. As a result, this service industry has witnessed a tremendous spike in demand that is affecting its delivery operations at the downstream level. Previous research studies have explored one-to-one and many-to-one solutions to the virtual food court delivery problem (VFCDP) to optimize on-demand food delivery services in different cities. However, research efforts have been limited to multiple restaurant orders from only one customer which does not apply to traditional systems where multiple customers request on-demand food delivery from multiple restaurants. This study rigorously analyses multiple restaurants to multiple customers (Many-to-many) food delivery simulation models in ideal weather conditions that are constrained with multiple key performance indicators (KPIs) such as delivery fleet utilization (the number of couriers utilized over the fleet size), average order delivery time, and fuel costs. This research also benchmarks the on-demand food delivery queueing methodologies using system dynamics and agent-based simulation modeling where three on-demand food delivery routing methodologies are simulated including First-in-First-Out (FIFO), Nearest, and Simulated Annealing using AnyLogic. The results suggest that the Many-to-many (Nearest) method outperforms other delivery routing methods which would have positive implications on optimizing existing food delivery systems and managerial decisions.
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The permutation flow shop scheduling problem (PFSSP) is well-applied in the industry, which is confirmed to be an NP-Hard optimization problem, and the objective is to find the minimum completion time (makespan). A modified coronavirus herd immunity optimizer (CHIO) with a modified solution update is suggested in this work. Meanwhile, the simulated annealing strategy is used on the updating herd immunity population to prevent trapping on local optima, and an adjusted state mechanism is involved to prevent fast state change/ convergence. Nine instances of different problem scales on the FPSSP dataset of Taillard were tested. The experimental results show that the proposed method can find the optimal solutions for the tested instances, with ARPDs no more than 0.1, indicating that the proposed method can effectively and stably solve the PFSSP. © 2022 IEEE.
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In order to improve the recognition rate and operational efficiency of the system, a method in which the image features compensation coefficients are optimized by using an improved simulated annealing algorithm is proposed. Firstly, eight computational factors with low computational complexity are given, which can be used to compensate image features. Secondly, the design flow of face recognition algorithm is presented. Thirdly, an improved simulated annealing algorithm is designed to solve the optimal combination of feature compensation coefficients in the face recognition system. Fourthly, the results of the feature compensation coefficients recommended by the improved simulated annealing algorithm are applied to the Efficient Face Recognition Algorithm (EFRA) in this paper, and verified on the simulation platform. Experiments show that the recognition rate can reach 100% when the training images are 6 in ORL. The proposed algorithm also performs well in MU_PIE dataset. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
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Digital growth during the Corona pandemic has generated massive data. The Hadoop in big data has to be more efficient in resource handling and job scheduling. This article proposes the improved job scheduler which is more efficient in fair job scheduling even with the heterogeneous resources. The faster job execution depends upon the localization too. The nearer the slots are, the faster is the execution. So, this article proposes a hybrid metaheuristic algorithm with fair scheduling and data locality as the two objectives in job scheduling. The dominant resource fairness policy in Hadoop YARN is updated by hybrid generalized particle swarm optimization and simulated annealing for minimum locality and maximum fairness in scheduling. The algorithm is tested on various workloads for heterogeneous resources.
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Under the background of COVID-19, the traditional ridesplitting scheduling mode is facing the potential risk of cross infection, which brings insecurity to travellers. In order to solve this contradiction, this paper first analyzes the matching constraints among travellers under the background of the epidemic, propose a ridesplitting strategy to separate low-risk and potential risk passengers. Considering the social benefits and service quality that the platform should take into account under the COVID-19, this paper develop a dual objective ridesplitting emergency management optimization scheduling model which minimize the waiting time on the basis of rated profit, and propose a combined simulated annealing algorithm for this model. The effectiveness of the model and algorithm is verified by a specific example. The results show that the model can converge to a stable value, and the calculation results can truly reflect the actual scene, which shows the effectiveness of the model and algorithm. Finally, through a simulation experiment, the role of the model in virus transmission is analyzed. The results show that the model can effectively inhibit the virus transmission speed caused by ridesplitting. (English) [ FROM AUTHOR]
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Traditional buses travel on fixed routes and areas, which cannot satisfy the flexible demands of athletes in the context of COVID-19 and the closed-loop traffic management policy during the 2022 Beijing Winter Olympic Games (BWOG). This study predicts the travel demands based on the characteristics of athletes' daily travel demands and then presents a flexible bus service scheduling model for cross-region scheduling among Beijing, Yanqing, and Zhangjiakou to provide high-level service. The flexible bus service is point-to-point and avoids unnecessary contact, which reduces the risk of spreading COVID-19 and ensures athletes' safety. In this study, the flexible bus scheduling model is established to optimize scheduling schemes, whose object is to minimize the cost of the system based on some realistic constraints. These constraints consider not only the preferred time windows of athletes' demand but also the vehicle's capacity, depot, minimum load factor, total demands, etc. In addition, a genetic-simulated annealing hybrid algorithm (GSAHA) is designed to solve the model based on the characteristics of the genetic algorithm (GA) and simulated annealing. To assess the feasibility and efficiency of the model and algorithm, a case study is conducted in the Beijing-Yanqing area. Furthermore, the travel time of the flexible bus is compared to that of the traditional bus, according to the results of the case study. Moreover, the sensitivity of the model and algorithm are analyzed. The experimental results show that the proposed model and algorithm can dispatch buses with superior flexibility and high-level services during the BWOG. [ FROM AUTHOR]
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The recent worldwide sanitary pandemic has made it clear that changes in user traffic patterns can create load balancing issues in networks (e.g., new peak hours of usage have been observed, especially in suburban residential areas). Such patterns need to be accommodated, often with reliable service quality. Although several studies have examined the user association and resource allocation (UA-RA) issue, there is still no optimal strategy to address such a problem with low complexity while reducing the time overhead. To this end, we propose Performance-Improved Reduced Search Space Simulated Annealing (PIRS(3)A), an algorithm for solving UA-RA problems in Heterogeneous Networks (HetNets). First, the UA-RA problem is formulated as a multiple 0/1 knapsack problem (MKP) with constraints on the maximum capacity of the base stations (BS) along with the transport block size (TBS) index. Second, the proposed PIRS(3)A is used to solve the formulated MKP. Simulation results show that PIRS(3)A outperforms existing schemes in terms of variability and Quality of Service (QoS), including throughput, packet loss ratio (PLR), delay, and jitter. Simulation results also show that PIRS3 A generates solutions that are very close to the optimal solution compared to the default simulated annealing (DSA) algorithm.
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Infectious diseases pose a severe threat to human health, especially the outbreak of COVID-19. After the infectious disease enters the stage of large-scale epidemics, vaccination is an effective way to control infectious diseases. However, when formulating a vaccination strategy, some restrictions still exist, such as insufficient vaccines or insufficient government funding to afford everyone's vaccination. Therefore, in this paper, we propose a vaccination optimization problem with the lowest total cost based on the susceptible-infected-recovered (SIR) model, which is called the Lowest Cost Of Vaccination Strategy (LCOVS) problem. We first establish a mathematical model of the LCOVS problem. Then we propose a practical Differential Evolution based Simulated Annealing (DESA) method to solve the mathematical optimization problem. We use the simulated annealing algorithm (SA) as a local optimizer for the results obtained by the differential evolution algorithm (DE) and optimized the mutation and crossover steps of DE. Finally, the experimental results on the six data sets demonstrate that our proposed DESA can achieve a more low-cost vaccination strategy than the baseline algorithms. IEEE
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Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency.
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This work proposes a new coordinated ambulance routing model suitable for implementation during the COVID-19 pandemic. This model is different from the existing model, where it was conducted uncoordinatedly, so that mismatch between supply and demand may occur. In general, high number of unserved requests and travel distance are unwanted. Therefore, this work proposes a model consisting of three steps: hospital-patient allocation, ambulance-patient dispatching, and ambulance pickup-delivery sequencing. The proposed model consists of two objectives: minimizing the number of unserved patients and minimizing total travel distance. It is developed by using cloud-theory-based simulated annealing. The simulation result shows that the proposed model outperforms the existing uncoordinated model in number of unserved patients, total travel distance, and average travel distance. It creates zero unserved patients if the total number of patients does not surpass the total number of slots in all hospitals. It produces 12 to 19 percent lower total travel distance and 27 to 29 percent lower average travel distance than the uncoordinated model. [ FROM AUTHOR] Copyright of Engineering Letters is the property of Newswood Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
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The COVID-19 pandemic calls for contactless deliveries. To prevent the further spread of the disease and ensure the timely delivery of supplies, this paper investigates a collaborative truck-drone routing problem for contactless parcel delivery (CRP-T&D), which allows multiple trucks and multiple drones to deliver parcels cooperatively in epidemic areas. We formulate a mixed-integer programming model that minimizes the delivery time, with the consideration of the energy consumption model of drones. To solve CRP-T&D, we develop an improved variable neighborhood descent (IVND) that combines the Metropolis acceptance criterion of Simulated Annealing (SA) and the tabu list of Tabu Search (TS). Meanwhile, the integration of K-means clustering and Nearest neighbor strategy is applied to generate the initial solution. To evaluate the performance of IVND, experiments are conducted by comparing IVND with VND, SA, TS, variants of VND, and large neighborhood search (LNS) on instances with different scales. Several critical factors are tested to verify the robustness of IVND. Moreover, the experimental results on a practical instance further demonstrate the superior performance of IVND. IEEE
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Taking Henan Province as the research object, this paper discusses the temporal and spatial distribution of COVID-19 and its spreading laws and characteristics. Through computer modeling and intelligent fitting, the Moran'I and Moran's I exponential distributions are obtained to describe the global space and local space density. Establish SEIRD model and use simulated annealing algorithm to predict its development trend. At the same time, taking into account the development of the epidemic and the infection rate under different conditions, as well as the local testing capabilities and testing costs, combined with mathematical expectations, design a reasonable virus testing program. © 2021 IEEE.
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The problem of patient admission scheduling (PAS) is a nondeterministic polynomial time (NP)-hard combinatorial optimization problem with numerous constraints. Researchers have divided the constraints of this problem into hard (i.e., feasible solution) and soft constraints (i.e., quality solution). The majority of research has dealt with PAS using integer linear programming (ILP) and single objective meta-heuristic searching-based approaches. ILP-based approaches carry high computational demand and the risk of non-feasibility for a large dataset. In a single objective optimization, there is a risk of local minima due to the non-convexity of the problem. In this article, we present the first pareto front-based optimization for PAS using set of meta-heuristic approaches. We selected four multi-objective optimization methods. Problem-specific operators were developed for each of them. Next, we compared them with single objective optimization approaches, namely, simulated annealing and particle swarm optimization. In addition, this article also deals with the dynamical aspect of this problem by comparing historical window-based decomposition with day decomposition, as has previously been proposed in the literature. An evaluation of the models proposed in the article and comparison with traditional models reveals the superiority of our proposed multi-objective optimization with window incorporation in terms of optimality.
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Classification accuracy achieved by a machine learning technique depends on the feature set used in the learning process. However, it is often found that all the features extracted by some means for a particular task do not contribute to the classification process. Feature selection (FS) is an imperative and challenging pre-processing technique that helps to discard the unnecessary and irrelevant features while reducing the computational time and space requirement and increasing the classification accuracy. Generalized Normal Distribution Optimizer (GNDO), a recently proposed meta-heuristic algorithm, can be used to solve any optimization problem. In this paper, a hybrid version of GNDO with Simulated Annealing (SA) called Binary Simulated Normal Distribution Optimizer (BSNDO) is proposed which uses SA as a local search to achieve higher classification accuracy. The proposed method is evaluated on 18 well-known UCI datasets and compared with its predecessor as well as some popular FS methods. Moreover, this method is tested on high dimensional microarray datasets to prove its worth in real-life datasets. On top of that, it is also applied to a COVID-19 dataset for classification purposes. The obtained results prove the usefulness of BSNDO as a FS method. The source code of this work is publicly available at https://github.com/ahmed-shameem/Feature_selection.
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RNA Design is an essential problem in bioinformatics to tailor RNA Sequences that guide our biology and medicine. While there are many RNA Design solutions in literature, the field has been limited by the high runtime to estimate how a candidate RNA sequence will fold, especially with “long sequences” over 750 “bases”;as these folding algorithms are called millions of times per RNA Design problem, this limits RNA Design to shorter sequences despite the prevalence of long sequences in nature;for example, computationally designing COVID's over 20000 bases RNA sequence is not feasible with current RNA Design algorithms. To address this issue, we are the first work to integrate LinearFold, a faster RNA prediction algorithm used by Baidu to analyze COVID with a higher efficiency, into RNA Design. We compare the runtime and solution quality of our Applied Research Lab's Simulated Annealing solution (SIMARD) with and without LinearFold to design sequences thousands of bases long that timeout after weeks of runtime with current algorithms. We also survey challenges in terms of solution quality and adjusting the cooling schedule parameters. This work is thus a first step into RNA Design for longer RNA sequences. © 2021 IEEE
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One critical problem in Indonesia's national joint courses program, initiated by the ministry of education and culture of Indonesia, is the lecturer-course assignment problem. Although the lecturer-course assignmentproblem has been studied widely as part of the education timetabling problem, no existing lecturer-courseassignment model suits this circumstance. The new cases in this program are as follows. First, this program isconducted online. Second, the participants are students and lecturers from different universities. Based on thisproblem, this work proposes a novel lecturer-course assignment model that suits this program. The lecturers'preferred courses and timeslots become hard constraints. The model has three objectives: (1) maximizing theeducational quality, (2) maximizing the lecturers' time preference, and (3) minimizing the number of unservedclasses. This model is developed by using integer linear programming and optimized by using cloud theory-basedsimulated annealing. This proposed model is then compared with the four previous lecturers-course assignmentmodels. The first model concerns about minimizing the number of unserved classes, while the second model focuseson maximizing the education quality. The maximum number of classes per course for every lecturer is considered inthe third model while balancing the lecturer’s load (teach, research, community service) is the feature of the fourthmodel. The research concludes that the proposed model is appropriate for lecture-course assignment in Indonesia’snational joint courses program compared to the previous models. Based on the simulation result, the proposed modelperforms moderately in education quality and several unserved classes. Meanwhile, the proposed model is the best inthe timeslot preference aspect by creating a 25% to 28% higher total timeslot score than other previous models © 2022, International Journal of Intelligent Engineering and Systems. All Rights Reserved.
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Favipiravir is one of the most commonly prescribed drugs in the treatment of COVID-19 in the early stages of the disease. In this work, the solubility of favipiravir was measured in supercritical CO2 at temperatures ranging from 308 to 338 K and pressures ranging from 12 to 30 MPa. The mole fraction solubility of favipiravir was in the range of 3.0 × 10-6 to 9.05 × 10-4. The solubility data were correlated with three types of methods including; (a) density-based models (Chrastil, Garlapati and Madras, Sparks et al., Sodeifian et al., K-J and Keshmiri et al.), (b) Equations of states SRK with quadratic mixing rules) and (c) expanded liquid theory (modified Wilson model). According to the results, modified Wilson and K-J models are generally capable of providing good correlation of solubility. Finally, the approximate values of total ( Δ H total ), vaporization ( Δ H vap ), and solvation ( Δ H sol ) enthalpies were computed.