Optimization in driver’s scheduling for university shuttle bus using harmony search

This paper is about driver scheduling which is categorized as a difficult combinatorial problem. Driver scheduling is the process of assigning shift to the drivers based on hard and soft constraints. The proposed technique is Harmony Search, which is a recently developed population-based meta heuristic optimization algorithm. This study will be implemented to UTeM Shuttle Bus. The number of drivers involved is about 25. It is viewed that the objective function evaluated by Harmony Search is less than a manual solution. Therefore, the result produced for this project is quite promising since the objective function obtained is better than the real schedule which is done manually

Using harmony search for optimising university shuttle bus driver scheduling for better operational management

Managing human resource to achieve specific goal in an organisation is a crucial task. One of various aspects in managing human resource is preparing optimum scheduling to perform certain tasks. The main objective of this paper is to illustrate the preparation and the work of optimum scheduling for university shuttle bus driver using a recently develop meta-heuristic technique known as Harmony Search. A mathematical formulation for the university shuttle bus driver scheduling problem based on the requirement and the preference of the university is illustrated. The optimum schedule is generated using Harmony Search, an optimisation approach inspired by the processes in music improvisation with less mathematical computation. It can be seen that the result produced using harmony search approach to automate the optimum university shuttle bus driver scheduling is quite promising because it yield better value of objective function compared with the one being done manually. Automation of the optimum university bus driver scheduling certainly can enhanced the operational management processes. This work can be regarded as a multidisciplinary work which several domains such as computer science, mathematics, operational research and management are involved

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems

The Application of Bayesian Optimization and Classifier Systems in Nurse Scheduling

Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each persons assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems

Improved Squeaky Wheel Optimisation for Driver Scheduling

This paper presents a technique called Improved Squeaky Wheel Optimisation for driver scheduling problems. It improves the original Squeaky Wheel Optimisations effectiveness and execution speed by incorporating two additional steps of Selection and Mutation which implement evolution within a single solution. In the ISWO, a cycle of Analysis-Selection-Mutation-Prioritization-Construction continues until stopping conditions are reached. The Analysis step first computes the fitness of a current solution to identify troublesome components. The Selection step then discards these troublesome components probabilistically by using the fitness measure, and the Mutation step follows to further discard a small number of components at random. After the above steps, an input solution becomes partial and thus the resulting partial solution needs to be repaired. The repair is carried out by using the Prioritization step to first produce priorities that determine an order by which the following Construction step then schedules the remaining components. Therefore, the optimisation in the ISWO is achieved by solution disruption, iterative improvement and an iterative constructive repair process performed. Encouraging experimental results are reported

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

Literature Review Report on -“An Analytical Study on Working Conditions of Loco-Pilots (Railway Drivers) in India”

The work of Indian railways’ drivers is considered as extremely stressful. It is working in an environment over which they have no control whatsoever and is an atmosphere that wrecks their schedules, disrupts their home life, makes social activities and regular breaks very hard to plan. This paper deals with the working conditions of Indian railways’ drivers and the factors that lead to a fatigue and stress, causing high probability of accident. This review of literature deals with the working conditions of an Indian railways’ drivers which is having very high importance on their total wellbeing and hence their productivity and entire growth and safety of an Indian railway. Keywords: Railway driver, working conditions, fatigue and stress

Genetic algorithms for condition-based maintenance optimization under uncertainty

International audienceThis paper proposes and compares different techniques for maintenance optimization based on Genetic Algorithms (GA), when the parameters of the maintenance model are affected by uncertainty and the fitness values are represented by Cumulative Distribution Functions (CDFs). The main issues addressed to tackle this problem are the development of a method to rank the uncertain fitness values, and the definition of a novel Pareto dominance concept. The GA-based methods are applied to a practical case study concerning the setting of a condition-based maintenance policy on the degrading nozzles of a gas turbine operated in an energy production plant

Evolutionary squeaky wheel optimization: a new framework for analysis

Squeaky wheel optimization (SWO) is a relatively new metaheuristic that has been shown to be effective for many real-world problems. At each iteration SWO does a complete construction of a solution starting from the empty assignment. Although the construction uses information from previous iterations, the complete rebuilding does mean that SWO is generally effective at diversification but can suffer from a relatively weak intensification. Evolutionary SWO (ESWO) is a recent extension to SWO that is designed to improve the intensification by keeping the good components of solutions and only using SWO to reconstruct other poorer components of the solution. In such algorithms a standard challenge is to understand how the various parameters affect the search process. In order to support the future study of such issues, we propose a formal framework for the analysis of ESWO. The framework is based on Markov chains, and the main novelty arises because ESWO moves through the space of partial assignments. This makes it significantly different from the analyses used in local search (such as simulated annealing) which only move through complete assignments. Generally, the exact details of ESWO will depend on various heuristics; so we focus our approach on a case of ESWO that we call ESWO-II and that has probabilistic as opposed to heuristic selection and construction operators. For ESWO-II, we study a simple problem instance and explicitly compute the stationary distribution probability over the states of the search space. We find interesting properties of the distribution. In particular, we find that the probabilities of states generally, but not always, increase with their fitness. This nonmonotonocity is quite different from the monotonicity expected in algorithms such as simulated annealing