AN ARTIFICIAL IMMUNE SYSTEM FOR PUBLIC TRANSPORT REGULATION

International audienc

Optimising Electric Bus Departure Interval Considering Stochastic Traffic Conditions

Electric buses (EBs) have attracted more and more attention in recent years because of their energy-saving and pollution-free characteristics. However, very few studies have considered the impact of stochastic traffic conditions on their operations. This paper focuses on the departure interval optimisation of EBs which is a critical problem in the operations. We consider the stochastic traffic conditions in the operations and establish a departure interval optimisation model. The objective function aims at minimising passenger travel costs and enterprise operation costs, including waiting time costs, congestion costs, energy consumption costs and operational fixed costs. To solve this problem, a genetic algorithm (GA) based on fitness adjustment crossover and mutation rate is proposed. Based on the Harbin bus dataset, we find that improved GA performance is 4.481% higher, and it can solve the models more accurately and efficiently. Compared with the current situation, the optimisation model reduces passenger travel costs by 20.2% and helps improve passenger travel quality. Under stochastic traffic conditions, total cost change is small, but passenger travel costs increase significantly. This indicates the high impact degree of random traffic conditions on passenger travel. In addition, a sensitivity analysis is conducted to provide suggestions for improving the EBs operation and management

서울시 ‘따릉이’를 중심으로

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022.2. 황준석.더욱 합리적이고 인간적인 공공자전거 서비스를 제공하기 위해서 공공자전거가 시스템 더 효율적으로 운영되고 서비스 이용자 만족도를 높이도록 한 가지 자전거 재배치 경로 최적화 목적으로 공공자전거 재배치 최적화 모델을 제시하였다. 기존 재배치 모델들의 속도 느림, 정확도 낮음 등 한계점을 개선하기 위해서, 본 논문에서 GA와 ACO 알고리즘을 조합돼서 GAACO-BSP(a Genetic Hybrid Ant Colony Optimization Algorithm for Solving Bike-sharing Scheduling Problem) 알고리즘을 개발하였다. 그리고 성능 향상시키기 위하여 GA 수행횟수 제어 함수를 수립하여 두 알고리즘을 동적으로 연결하였다. 우선 GA가 스케줄링 가능한 초기해를 구하고, 그 다음으로 GA 수행횟수 제어 함수를 통해 최적 전환 시기를 파악해서 동적으로 ACO으로 전환한다. ACO가 GA에게서 초기화 필요한 페로몬을 얻고 최종 최적해를 찾는 것이다. 서울시 공공자전거 따릉이 사례로 결과를 검증하여, GAACO-BSP은 전통 단일 알고리즘보다 뛰어난 성능 우세로 대규모 자전거 시스템에 적용하고 더 짧은 시간 만에 재배치 거리를 더 많이 줄였다. 실험을 통해 GAACO-BSP가 실제 도시 공공자전거 시스템에서 적용할 수 있다는 것을 알 수 있다.To improve the service efficiency and customer satisfaction degree of public bicycle, a bike-sharing scheduling model is proposed, which aims to get the shortest length of the bicycle scheduling. To address the slow solution speed of the existing algorithms, which is not conducive to real-time scheduling optimization, this paper designed a Genetic Hybrid Ant Colony System Algorithm for Solving Bike-sharing Scheduling Problem (GAACS-BSP). Genetic algorithm was used to search initial feasible scheme， which was used to initialize pheromone distribution of ant colony algorithm. It solved problem of lack initial pheromone, to improve the efficiency of bike-sharing scheduling tasks. There also proposed a genetic algorithm control function to control the appropriate combination opportunity of the two algorithms. Finally, the results show that compared with GA or ACS, it is more suitable for solving the problem of large-scale bike-sharing scheduling tasks, which shortens the scheduling distance in a short period.제 1 장 서 론 1 1.1. 연구의 배경 1 1.2. 연구의 내용 2 제 2 장 선행 연구 3 2.1. 기존 공공자전거 재배치에 관한 연구 3 2.2. 기존 GA-ACO 융합 알고리즘 5 제 3 장 모델 구축 방법론 8 3.1. BSP 문제의 수학적 해석 8 3.2. BSP 해결을 위한 GAACO-BSP 11 3.2.1. 기본 생각 11 3.2.2. 전체 프레임워크 11 제 4 장 GAACO-BSP 알고리즘 13 4.1. GA 부분의 규칙 14 4.1.1. 인코딩 방식 및 초기화 14 4.1.2. 선택 15 4.1.3. 교차 및 변이 15 4.1.4. 정지 조건 및 전환 16 4.2. ACO 부분의 규칙 17 4.2.1. ACO 초기화 17 4.2.2. 경로 선택 규칙 18 4.2.3. Pheromone 농도 조절 18 4.3. 알고리즘 흐름도 20 제 5 장 실험 및 결과 21 5.1. 데이터 전처리 21 5.2. 지역센터(배송팀) 재구분 26 5.3. 재배치 전략방안 도출 29 5.3.1. 수요현황 분석 29 5.3.2. 재배치 최적화 방안 도출 32 제 6 장 결 론 38 참고 문헌 41석

A comprehensive survey on cultural algorithms

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Artificial immune system for static and dynamic production scheduling problems

Over many decades, a large number of complex optimization problems have brought researchers' attention to consider in-depth research on optimization. Production scheduling problem is one of the optimization problems that has been the focus of researchers since the 60s. The main problem in production scheduling is to allocate the machines to perform the tasks. Job Shop Scheduling Problem (JSSP) and Flexible Job Shop Scheduling Problem (FJSSP) are two of the areas in production scheduling problems for these machines. One of the main objectives in solving JSSP and FJSSP is to obtain the best solution with minimum total completion processing time. Thus, this thesis developed algorithms for single and hybrid methods to solve JSSP and FJSSP in static and dynamic environments. In a static environment, no change is needed for the produced solution but changes to the solution are needed. On the other hand, in a dynamic environment, there are many real time events such as random arrival of jobs or machine breakdown requiring solutions. To solve these problems for static and dynamic environments, the single and hybrid methods were introduced. Single method utilizes Artificial Immune System (AIS), whereas AIS and Variable Neighbourhood Descent (VND) are used in the hybrid method. Clonal Selection Principle (CSP) algorithm in the AIS was used in the proposed single and hybrid methods. In addition, to evaluate the significance of the proposed methods, experiments and One-Way ANOVA tests were conducted. The findings showed that the hybrid method was proven to give better performance compared to single method in producing optimized solution and reduced solution generating time. The main contribution of this thesis is the development of an algorithm used in the single and hybrid methods to solve JSSP and FJSSP in static and dynamic environment

implications to CRM and public policy

Thesis(Doctoral) --KDI School:Ph.D in Public Policy,2017With the advent of the Internet and Mobile Communications, the nature of communication has changed significantly over the past few decades .The promotion of technologies among the common people has been found to be an important element of public policy to reduce the digital divide. The rapid advancement of information technology (IT), automation systems and data communications systems leads to improvement of intelligent transport systems (ITS). ITS covers all branches of transportation and involves all dynamically interacting elements of transportation system, i.e. transport means, infrastructure, drivers and commuters. However, few researches have been carried out in the context of public sectors, especially that involving ITS. The purpose of this study is to investigate the justice dimensions that influence satisfaction and public confidence in the context of ITS and to explore implications to Citizen/Customer Relationship Management (CRM) and public policy. This study investigates the following research questions: i) Do levels of perceived justice (distributive, procedural and interactional) in ITS environment affect levels of satisfaction/dissatisfaction? ii) Do levels of satisfaction form ITS affect levels of public confidence? iii) Do levels of dissatisfaction form ITS affect levels of willingness to complain? iv) Do levels of dissatisfaction form ITS affect levels of complaining behavior? v) Do levels of complaining behavior in ITS environment affect levels of satisfaction with complaint handling when the complaints are resolved based on three dimensions (distributive, procedural and interactional)of justice? vi) Do levels of willingness to complain in ITS environment affect levels of public confidence? vii) Do levels of satisfaction with complaint handling in ITS environment affect levels of public confidence? The findings of this study imply that ITS users are more importantly perceive to equity and equality issues, or distributive justice. The employment of ITS should not be limited to the technical aspects of ITS, but should focus more attention on the subjective domain of justice. The results of this study also have important implications for public complaint handling in terms of increasing public satisfaction with ITS, which is crucial for CRM.Part I: Exploring Satisfaction/Dissatisfaction and Public Confidence in the ITS Environment; Implications to CRM and Public Policy Part II: ComparingSatisfaction/Dissatisfaction and Public Confidence in the ITS Environment in Public and Private Transportation Part III: Implementation Strategy of ITS in Developing CountriesdoctoralpublishedA. K. M. Anisur RAHMAN

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

A hybrid algorithm for the multi-depot vehicle scheduling problem arising in public transportation

In this article, a hybrid algorithm is proposed to solve the Vehicle Scheduling Problem with Multiple Depots. The proposed methodology uses a genetic algorithm, initialized with three specialized constructive procedures. The solution generated by this first approach is then refined by means of a Set Partitioning (SP) model, whose variables (columns) correspond to the current itineraries of the final population. The SP approach possibly improves the incumbent solution which is then provided as an initial point to a well-known MDVSP model. Both the SP and MDVSP models are solved with the help of a mixed integer programming (MIP) solver. The algorithm is tested in benchmark instances consisting of 2, 3 and 5 depots, and a service load ranging from 100 to 500. The results obtained showed that the proposed algorithm was capable of finding the optimal solution in most cases when considering a time limit of 500 seconds. The methodology is also applied to solve a real-life instance that arises in the transportation system in Colombia (2 depots and 719 services), resulting in a decrease of the required fleet size and a balanced allocation of services, thus reducing deadhead trips

The chemical reaction optimization approach to solving the environmentally sustainable network design problem

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