62 research outputs found

    E-Commerce Logistics System Based on Discrete Dynamic Modeling Analysis

    Get PDF
    The development of green logistics in a low-carbon environment is an important way for logistics companies to reduce operating costs, and it is also a strategic measure to strengthen the construction of ecological civilization. This study improves the bat algorithm for single-delivery express delivery and multi-delivery, determines the optimal target and seeks the optimal solution, establishes the optimal logistics distribution scheme combined with corporate profits, and finally, compares it with other algorithms to verify the feasibility of the model. In the same experimental environment, it is proven that the performance of the built model is about 20% higher than that of other methods, and the planned path is the most reasonable. In the future application of e-commerce logistics system, it is a more efficient, reasonable, and perfect discrete logistics model

    ๊ฐœ๋ฏธ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•œ ๋“œ๋ก ์˜ ์ œ์„ค ๊ฒฝ๋กœ ์ตœ์ ํ™”

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ฑด์„คํ™˜๊ฒฝ๊ณตํ•™๋ถ€, 2022.2. ๊น€๋™๊ทœ.Drones can overcome the limitation of ground vehicles by replacing the congestion time and allowing rapid service. For sudden snowfall with climate change, a quickly deployed drone can be a flexible alternative considering the deadhead route and the labor costs. The goal of this study is to optimize a drone arc routing problem (D-ARP), servicing the required roads for snow removal. A D-ARP creates computational burden especially in large network. The D-ARP has a large search space due to its exponentially increased candidate route, arc direction decision, and continuous arc space. To reduce the search space, we developed the auxiliary transformation method in ACO algorithm and adopted the random walk method. The contribution of the work is introducing a new problem and optimization approach of D-ARP in snow removal operation and reduce its search space. The optimization results confirmed that the drone travels shorter distance compared to the truck with a reduction of 5% to 22%. Furthermore, even under the length constraint model, the drone shows 4% reduction compared to the truck. The result of the test sets demonstrated that the adopted heuristic algorithm performs well in the large size networks in reasonable time. Based on the results, introducing a drone in snow removal is expected to save the operation cost in practical terms.๋“œ๋ก ์€ ํ˜ผ์žก์‹œ๊ฐ„๋Œ€๋ฅผ ๋Œ€์ฒดํ•˜๊ณ  ๋น ๋ฅธ ์„œ๋น„์Šค๋ฅผ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•จ์œผ๋กœ์จ ์ง€์ƒ์ฐจ๋Ÿ‰์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•  ์ˆ˜ ์žˆ๋‹ค. ์ตœ๊ทผ ๊ธฐํ›„๋ณ€ํ™”์— ๋”ฐ๋ฅธ ๊ฐ‘์ž‘์Šค๋Ÿฐ ๊ฐ•์„ค์˜ ๊ฒฝ์šฐ์—, ๋“œ๋ก ๊ณผ ๊ฐ™์ด ๋น ๋ฅด๊ฒŒ ํˆฌ์ž…ํ•  ์ˆ˜ ์žˆ๋Š” ์„œ๋น„์Šค๋Š” ์šดํ–‰ ๊ฒฝ๋กœ์™€ ๋…ธ๋™๋น„์šฉ์„ ๊ณ ๋ คํ–ˆ์„ ๋•Œ๋„ ์œ ์—ฐํ•œ ์šด์˜ ์˜ต์…˜์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋ชฉ์ ์€ ๋“œ๋ก  ์•„ํฌ ๋ผ์šฐํŒ…(D-ARP)์„ ์ตœ์ ํ™”ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด๋Š” ์ œ์„ค์— ํ•„์š”ํ•œ ๋„๋กœ๋ฅผ ์„œ๋น„์Šคํ•˜๋Š” ๊ฒฝ๋กœ๋ฅผ ํƒ์ƒ‰ํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋“œ๋ก  ์•„ํฌ ๋ผ์šฐํŒ…์€ ํŠนํžˆ ํฐ ๋„คํŠธ์›Œํฌ์—์„œ ์ปดํ“จํ„ฐ ๋ถ€ํ•˜๋ฅผ ์ƒ์„ฑํ•œ๋‹ค. ๋‹ค์‹œ ๋งํ•ดD-ARP๋Š” ํฐ ๊ฒ€์ƒ‰๊ณต๊ฐ„์„ ํ•„์š”๋กœ ํ•˜๋ฉฐ, ์ด๋Š” ๊ธฐํ•˜๊ธ‰์ˆ˜์ ์œผ๋กœ ์ฆ๊ฐ€ํ•˜๋Š” ํ›„๋ณด ๊ฒฝ๋กœ ๋ฐ ํ˜ธ์˜ ๋ฐฉํ–ฅ ๊ฒฐ์ • ๊ทธ๋ฆฌ๊ณ  ์—ฐ์†์ ์ธ ํ˜ธ์˜ ๊ณต๊ฐ„์œผ๋กœ๋ถ€ํ„ฐ ๊ธฐ์ธํ•œ๋‹ค. ๊ฒ€์ƒ‰๊ณต๊ฐ„์„ ์ค„์ด๊ธฐ ์œ„ํ•ด, ์šฐ๋ฆฌ๋Š” ๊ฐœ๋ฏธ์•Œ๊ณ ๋ฆฌ์ฆ˜์— ๋ณด์กฐ๋ณ€ํ™˜๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜๋Š” ๋ฐฉ์•ˆ์„ ๋„์ž…ํ•˜์˜€์œผ๋ฉฐ ๋˜ํ•œ ๋žœ๋ค์›Œํฌ ๊ธฐ๋ฒ•์„ ์ฑ„ํƒํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ธฐ์—ฌ๋Š” ์ œ์„ค ์šด์˜์— ์žˆ์–ด D-ARP๋ผ๋Š” ์ƒˆ๋กœ์šด ๋ฌธ์ œ๋ฅผ ์„ค์ •ํ•˜๊ณ  ์ตœ์ ํ™” ์ ‘๊ทผ๋ฒ•์„ ๋„์ž…ํ•˜์˜€์œผ๋ฉฐ ๊ฒ€์ƒ‰๊ณต๊ฐ„์„ ์ตœ์†Œํ™”ํ•œ ๊ฒƒ์ด๋‹ค. ์ตœ์ ํ™” ๊ฒฐ๊ณผ, ๋“œ๋ก ์€ ์ง€์ƒํŠธ๋Ÿญ์— ๋น„ํ•ด ์•ฝ 5% ~ 22%์˜ ๊ฒฝ๋กœ ๋น„์šฉ ๊ฐ์†Œ๋ฅผ ๋ณด์˜€๋‹ค. ๋‚˜์•„๊ฐ€ ๊ธธ์ด ์ œ์•ฝ ๋ชจ๋ธ์—์„œ๋„ ๋“œ๋ก ์€ 4%์˜ ๋น„์šฉ ๊ฐ์†Œ๋ฅผ ๋ณด์˜€๋‹ค. ๋˜ํ•œ ์‹คํ—˜๊ฒฐ๊ณผ๋Š” ์ ์šฉํ•œ ํœด๋ฆฌ์Šคํ‹ฑ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ํฐ ๋„คํŠธ์›Œํฌ์—์„œ๋„ ํ•ฉ๋ฆฌ์  ์‹œ๊ฐ„ ๋‚ด์— ์ตœ์ ํ•ด๋ฅผ ์ฐพ์Œ์„ ์ž…์ฆํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ, ๋“œ๋ก ์„ ์ œ์„ค์— ๋„์ž…ํ•˜๋Š” ๊ฒƒ์€ ๋ฏธ๋ž˜์— ์ œ์„ค ์šด์˜ ๋น„์šฉ์„ ์‹ค์งˆ์ ์œผ๋กœ ๊ฐ์†Œ์‹œํ‚ฌ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.Chapter 1. Introduction 4 1.1. Study Background 4 1.2. Purpose of Research 6 Chapter 2. Literature Review 7 2.1. Drone Arc Routing problem 7 2.2. Snow Removal Routing Problem 8 2.3. The Classic ARPs and Algorithms 9 2.4. Large Search Space and Arc direction 11 Chapter 3. Method 13 3.1. Problem Statement 13 3.2. Formulation 16 Chapter 4. Algorithm 17 4.1. Overview 17 4.2. Auxilary Transformation Method 18 4.3. Ant Colony Optimization (ACO) 20 4.4. Post Process for Arc Direction Decision 23 4.5. Length Constraint and Random Walk 24 Chapter 5. Results 27 5.1. Application in Toy Network 27 5.2. Application in Real-world Networks 29 5.3. Application of the Refill Constraint in Seoul 31 Chapter 6. Conclusion 34 References 35 Acknowledgment 40์„

    Holistic, data-driven, service and supply chain optimisation: linked optimisation.

    Get PDF
    The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

    Evolutionary Computation 2020

    Get PDF
    Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

    Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

    Full text link
    In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure

    A Survey on Mobile Charging Techniques in Wireless Rechargeable Sensor Networks

    Get PDF
    The recent breakthrough in wireless power transfer (WPT) technology has empowered wireless rechargeable sensor networks (WRSNs) by facilitating stable and continuous energy supply to sensors through mobile chargers (MCs). A plethora of studies have been carried out over the last decade in this regard. However, no comprehensive survey exists to compile the state-of-the-art literature and provide insight into future research directions. To fill this gap, we put forward a detailed survey on mobile charging techniques (MCTs) in WRSNs. In particular, we first describe the network model, various WPT techniques with empirical models, system design issues and performance metrics concerning the MCTs. Next, we introduce an exhaustive taxonomy of the MCTs based on various design attributes and then review the literature by categorizing it into periodic and on-demand charging techniques. In addition, we compare the state-of-the-art MCTs in terms of objectives, constraints, solution approaches, charging options, design issues, performance metrics, evaluation methods, and limitations. Finally, we highlight some potential directions for future research

    Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

    Get PDF
    More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy
    • โ€ฆ
    corecore