Improved Meta-Heuristics for Solving Distributed Lot-Streaming Permutation Flow Shop Scheduling Problems

Pan, Yuxia; Gao, Kaizhou; Li, Zhiwu; Wu, Naiqi

doi:10.1109/tase.2022.3151648

Cited by 34 publications

(3 citation statements)

References 56 publications

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“…This indicator is to rank all algorithms. Friedman test [36] is a very powerful nonparametric statistical test, which is often used to compare the differences among algorithms [37], [38]. In this paper, the mean results of the applied algorithms are subjected to the Friedman test, and the Friedman mean rank (FMR) is recorded.…”

Section: Results and Comparisonsmentioning

confidence: 99%

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

Zhang

Huang

et al. 2024

IEEE Internet Things J.

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Internet of Things (IoT) devices have been widely deployed to build smart cities. How to efficiently collect data from large-scale IoT devices is a valuable and challenging research topic. Benefiting from agility, flexibility, and deployability, an unmanned aerial vehicle (UAV) has great potential to be an aerial base station. However, given the limited battery capacity, the flight time of a UAV is limited. This paper focuses on using multi-UAVs to execute long-distance data collection from largescale IoT devices. We design a multi-UAVs-assisted large-scale IoT data collection system. The core facilities of this system are the data center and charging stations, which are equipped with a limited number of charging piles to provide charging services for UAVs. To ensure the efficient operation of the system, the problem of deployment and flight planning of UAVs is formulated as a joint optimization problem. To solve the problem, a population-based optimization algorithm with a three-layer structure, namely EDDE-DPDE, is proposed. It includes two core components: elitedriven differential evolution (EDDE) and differential evolution with a dynamic population (DPDE), which are two variants of differential evolution. Thanks to ideas of reusing elite individuals and historical information, the proposed EDDE-DPDE shows an improvement of at least 11.11% compared with four powerful algorithms in terms of average travel time.

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Section: Results and Comparisonsmentioning

confidence: 99%

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

Zhang

Huang

et al. 2024

IEEE Internet Things J.

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show abstract

“…If a match is identified, then an alert message flash indicating duplication attempts. However, if a match is not found, it is identified as a new category and hence a new entry is added to the database [5].…”

Section: Category Managementmentioning

confidence: 99%

Market Maestro: A Smart E-commerce Application

Kalaivani B,

Bharathi B,

Kasu Manaswi

et al. 2024

Int Res J Adv Engg Mgt

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In an era marked by digital transformation, “Market Maestro” emerges as a pioneering e-commerce application, poised to redefine the very essence of shopping. The application transcends the commonplace, presenting a sophisticated shopping experience that seamlessly melds convenience, sustainability, and innovation. At the heart of the application lies an impeccable user interface, meticulously crafted to provide discerning shoppers with a comprehensive array of products, ranging from fresh produce to everyday essentials. The application is developed using Flask and SQLite which help in providing a better server-side logic. What truly distinguishes “Market Maestro” is its eco-conscious packaging options and strategic partnerships with local farmers and suppliers which embarks on a sustainable journey that harmonizes quality with environmental responsibility. The proposed application further prioritizes security, offering a robust and secure payment gateway rendering a seamless checkout and contactless endeavor. Real-time order tracking provides transparency ensuring customers remain informed about their delivery status. Furthermore, the application also encloses a recommendation feature that provides the users with the possible recipes that can be made from the list of ingredients as specified by the user. “Market Maestro” allows users into a new era of shopping, marked by intelligent design, environmental stewardship, and unwavering customer-centricity.

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“…Wu et al [12] focused on the robotic cell scheduling problem with batchprocessing machines, and a green schedule algorithm and a multi-objective differential evolution algorithm are proposed to optimize the makespan and energy consumption of the batch-processing machines simultaneously. Pan et al [13] executed five meta-heuristics are executed to solve the distributed batch flow alignment process shop scheduling problem. Qin et al [14] considered the limited waiting time between batch and discrete processors to develop a learning-based scheduling method through custom genetic programming.…”

Section: Low-carbon Manufacturing Scheduling Optimizationmentioning

confidence: 99%

A Low-Carbon Scheduling Method of Flexible Manufacturing and Crane Transportation Considering Multi-State Collaborative Configuration Based on Hybrid Differential Evolution

Liu,

Xu,

Pan

et al. 2023

Processes

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With increasingly stringent carbon policies, the development of traditional heavy industries with high carbon emissions has been greatly restricted. Manufacturing companies surveyed use multifunctional machining machines and variable speed cranes, as the lack of rational planning results in high energy wastage and low productivity. Reasonable scheduling optimization is an effective way to reduce carbon emissions, which motivates us to work on this research. To reduce the comprehensive energy consumption of the machining process and transportation process in an actual manufacturing environment, this paper addresses a new low-carbon scheduling problem of flexible manufacturing and crane transportation considering multi-state collaborative configuration (LSP-FM&CT-MCC). First, an integrated energy consumption model based on multi-state machining machines and cranes is established to optimize the overall energy efficiency of the production process. Then, a new hybrid differential evolution algorithm and firefly algorithm with collaborative state optimization strategy (DE-FA-CSOS) is proposed to solve the proposed MIP model. In DE-FA-CSOS, the differential evolution algorithm (DE) is used for global search, and the firefly algorithm (FA) is used for local search. The collaborative state optimization strategy (CSOS) is proposed to guide the search direction of the DE-FA algorithm, which greatly improves the performance of the hybrid algorithm. Finally, the practicality and superiority of the solution method are verified by examples. The results show that machining and transportation energy consumption is reduced by 25.17% and 34.52%, respectively. In the context of traditional optimization methods and manual scheduling modes facing failure, the method has a broad application background for manufacturing process optimization in such workshops, which is of guiding significance for promoting the low-carbon development of traditional heavy industry manufacturing.

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Improved Meta-Heuristics for Solving Distributed Lot-Streaming Permutation Flow Shop Scheduling Problems

Cited by 34 publications

References 56 publications

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

Joint Optimization of Deployment and Flight Planning of Multi-UAVs for Long-Distance Data Collection From Large-Scale IoT Devices

Market Maestro: A Smart E-commerce Application

A Low-Carbon Scheduling Method of Flexible Manufacturing and Crane Transportation Considering Multi-State Collaborative Configuration Based on Hybrid Differential Evolution

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