A Simulated Annealing Algorithm for the Vehicle Routing Problem With Parcel Lockers

Yu, Vincent F.; Susanto, Hadi; Jodiawan, Panca; Wei, Ho-Tsai; Lin, Shih-Wei; Huang, Yu-Tsung

doi:10.1109/access.2022.3152062

Cited by 33 publications

(23 citation statements)

References 42 publications

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“…en, taking the Fourier inverse transform of (20), its solution can be obtained using the following formula:…”

Section: Simulation Results and Analysis Of Heat Transfer Characteris...mentioning

confidence: 99%

“…It is the outer temperature of the dummy skin that is used to nd the optimal thickness of layer II and IV nodes by searching for pheromone updates, but since the solution generated by each ant is randomly generated by probability, this may make the pheromone update also randomly and not necessarily get the optimal thickness, thus falling into a local optimal solution. Based on this, the SA [20] algorithm is proposed in this paper to optimize the range of ant search solutions in the ACO algorithm, which is an inspirational algorithm [21][22][23], and its principle originates from the thermodynamic problem of heating a solid to a certain temperature and then slowly cooling it down to a stable state, i.e., the more ordered solid crystal goes through the process of a disordered liquid state and then to a more ordered state. e interval probability of acceptance of the optimal solution can be obtained using the Metropolis sampling process to update the pheromone.…”

Section: Study On the Optimization Model Of The Thickness Of Protecti...mentioning

confidence: 99%

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Study on Heat Transfer Characteristics and Intelligent Optimization of Protective Clothing Materials in front of the Blast Furnace

Duan

et al. 2022

Computational Intelligence and Neuroscience

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Special protective clothing should have a certain thermal insulation and protective effect in the production in front of the blast furnace. In order to study the design of special protective clothing for blast furnace foreman, its temperature distribution and internal thickness structure are deeply analyzed. First, based on the existing experimental data, combined with heat transfer and the Fourier law theory, a one-dimensional unsteady heat conduction equation is established. Using the explicit finite difference solution algorithm, the distribution law of the temperature of each fabric layer with time is obtained and the error analysis is performed. The error result is 0.42877%, which shows that the numerical solution is stable and convergent. Then, through the intelligent optimization model of a simulated annealing ant colony (SA-ACO), compared with the single ant colony algorithm (ACO) and a simulated annealing algorithm (SA), it avoids falling into the local optimal solution, reversely solves the optimal thickness of two layers of protective clothing in front of the blast furnace, improves the convergence speed of optimization, and verifies the reliability of the model with a sensitivity test. Finally, this paper actually solves the problem of the protective clothing design and mathematical intelligent model in front of the blast furnace and combines it with numerical simulation software to realize the numerical simulation of temperature distribution and thickness optimization of special protective clothing in front of the blast furnace, so as to provide theoretical guidance for enterprise blast furnace production and development of special protective clothing.

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“…en, taking the Fourier inverse transform of (20), its solution can be obtained using the following formula:…”

Section: Simulation Results and Analysis Of Heat Transfer Characteris...mentioning

confidence: 99%

Section: Study On the Optimization Model Of The Thickness Of Protecti...mentioning

confidence: 99%

Study on Heat Transfer Characteristics and Intelligent Optimization of Protective Clothing Materials in front of the Blast Furnace

Duan

et al. 2022

Computational Intelligence and Neuroscience

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“…e other is the heuristic intelligent algorithm, such as genetic algorithm [8][9][10], simulated annealing algorithm [11][12][13], ant colony algorithm [14][15][16], and particle swarm algorithm [17][18][19].…”

Section: Hungarian Algorithm For Model Solutionmentioning

confidence: 99%

Optimization of Digital Management Path for Human Resource Performance Evaluation Based on Multiobjective Decision-Making Mathematical Model

Liu

Yong

2022

Mobile Information Systems

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Talent is an indispensable and important factor for the development and growth of an enterprise. Therefore, human resources have become an important resource of enterprises, and performance appraisal in human resource management has also become one of the important contents of modern enterprise management. Without a set of scientific and standardized performance appraisal systems, it will make it difficult for the daily human resource performance management of enterprises to achieve the expected goals. This study analyzes the actual needs, business processes, and existing problems of an enterprise in performance management and uses the multiobjective decision-making mathematical model to obtain an optimization plan for human resources performance assessment. Finally, a digital management system for enterprise performance appraisal is designed. The main work of this study is as follows: (1) based on the work characteristics of a certain enterprise, the key indicators of enterprise performance evaluation are analyzed; (2) a multiobjective decision-making mathematical model is constructed for the optimization of human resources performance evaluation; (3) the Hungarian algorithm is used to solve the construction The mathematical model of multiobjective decision-making can obtain the optimal work arrangement. This study combines performance appraisal with practical work and uses the proposed multiobjective decision-making mathematical model to obtain the optimization results of human resources, guide practical work, and improve the efficiency of enterprise operation.

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“…Constraints (15) to (20) describe the bus's ability to dispatch passengers within the time window that passengers expect; the bus cannot be earlier than the time expected by the passengers nor later than the time expected by the passengers. Constraints (21) and (23) ensure that passengers do not spend too much time waiting for the bus and pass fewer intermediate stations during a trip. Constraint (22) captures the fow balance, i.e., during the pickup process, the vehicle k must leave after entering the reserved station to maintain fow conservation.…”

Section: Model Descriptionmentioning

confidence: 99%

“…But the coverage rate of trips of their genetic algorithms is slow. Additionally, Yu et al [21] developed a simulated annealing (SA) algorithm to deal with vehicle routing problems with time windows, and Candido and de Souza [22] developed an algorithm based on simulated annealing and local search that uses a collection of packing heuristics to address the loading constraints. Tey also proposed three new heuristics.…”

Section: Introductionmentioning

confidence: 99%

Flexible Scheduling Model of Bus Services between Venues of the Beijing Winter Olympic Games

Zhang

Huang

Jiang

et al. 2022

Journal of Advanced Transportation

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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.

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A Simulated Annealing Algorithm for the Vehicle Routing Problem With Parcel Lockers

Abstract: https://www.google.co.id/intl/en/about/products?tab=rhDate of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 33 publications

References 42 publications

Study on Heat Transfer Characteristics and Intelligent Optimization of Protective Clothing Materials in front of the Blast Furnace

Study on Heat Transfer Characteristics and Intelligent Optimization of Protective Clothing Materials in front of the Blast Furnace

Optimization of Digital Management Path for Human Resource Performance Evaluation Based on Multiobjective Decision-Making Mathematical Model

Flexible Scheduling Model of Bus Services between Venues of the Beijing Winter Olympic Games

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