Evolutionary Ruin And Stochastic Recreate: A Case Study On The Exam Timetabling Problem

This paper presents a new class of intelligent systems, called Evolutionary Ruin and Stochastic Recreate, that can learn and adapt to the changing enviroment. It improves the original Ruin and Recreate principle’s performance by incorporating an Evolutionary Ruin step which implements evolution within a single solution. In the proposed approach, a cycle of Solution Decomposition, Evolutionary Ruin and Stochastic Recreate continues until stopping conditions are reached. The Solution Decomposition step first uses some domain knowledge to break a solution down into its components and assign a score to each. The Evolutionary Ruin step then applies two operators (namely Selection and Mutation) to destroy a certain fraction of the entire solution. 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 Stochastic Recreate step to reintroduce the removed items in a specific way (somewhat stochastic in order to have a better chance to jump out of the local optima), and then ask the underlying improvement heuristic whether this move will be accepted. These three steps are executed in sequence until a specific stopping condition is reached. Therefore, optimisation is achieved by solution disruption, iterative improvement and a stochastic constructive repair process performed within. Encouraging experimental results on exam timetabling problems are reported

Genetic optimization of fuzzy membership functions for cloud resource provisioning

The successful usage of fuzzy systems can be seen in many application domains owing to their capabilities to model complex systems by exploiting knowledge of domain experts. Their accuracy and performance are, however, primarily dependent on the design of its membership functions and control rules. The commonly employed technique to design membership functions is to exploit the knowledge of domain experts. However, in certain application domains, the knowledge of domain experts are limited and therefore, cannot be relied upon. Alternatively, optimization techniques such as genetic algorithms are utilized to optimize the various design parameters of fuzzy systems. In this paper, we report a case study of optimizing the membership functions of a fuzzy system using genetic algorithm, which is an important part of our recently developed cloud elasticity framework. This work aims to improve the overall performance of the framework. Results obtained from this research work demonstrate performance improvement in comparison with our previous experimental settings

Modelling travel time distribution and its influence over stochastic vehicle scheduling

Due to the paucity of well-established modelling approaches or well-accepted travel time distributions, the existing travel time models are often assumed to follow certain popular distributions, such as normal or lognormal, which may lead to results deviating from actual ones. This paper proposes a modelling approach for travel times using distribution fitting methods based on the data collected by Automatic Vehicle Location (AVL) systems. By this proposed approach, a compound travel time model can be built, which consists of the best distribution models for the travel times in each period of a day. Applying to stochastic vehicle scheduling, the influence of different travel time models is further studied. Results show that the compound model can fit more precisely to the actual travel times under various traffic situations, whilst the on-time performance of resulting vehicle schedules can be improved. The research findings have also potential benefit for the other research based on travel time models in public transport including timetabling, service planning and reliability measurement

Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems

Search with evolutionary ruin and stochastic rebuild: a theoretic framework and a case study on exam timetabling

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle by embedding an additional phase of Evolutionary Ruin to mimic the survival-of-the-fittest mechanism within single solutions. This method executes a cycle of Solution Decomposition, Evolutionary Ruin, Stochastic Recreate and Solution Acceptance until a certain stopping condition is met. The Solution Decomposition phase first uses some problem-specific knowledge to decompose a complete solution into its components and assigns a score to each component. The Evolutionary Ruin phase then employs two evolutionary operators (namely Selection and Mutation) to destroy a certain fraction of the solution, and the next Stochastic Recreate phase repairs the “broken” solution. Last, the Solution Acceptance phase selects a specific strategy to determine the probability of accepting the newly generated solution. Hence, optimisation is achieved by an iterative process of component evaluation, solution disruption and stochastic constructive repair. From the state transitions point of view, this paper presents a probabilistic model and implements a Markov chain analysis on some theoretical properties of the approach. Unlike the theoretical work on genetic algorithm and simulated annealing which are based on state transitions within the space of complete assignments, our model is based on state transitions within the space of partial assignments. The exam timetabling problems are used to test the performance in solving real-world hard problems

Tabu search for bus and train driver scheduling with time windows

The bus and train driver scheduling problem involves assigning bus or train work to drivers in such a way that all the bus or train work is covered and the number of drivers and duty costs are minimised. This is complicated by the fact that there are many restrictions on the duty generation. The generate-and-select approach is at present the most successful for bus and train driver scheduling. It involves generating a set of legal potential driver duties from which a minimal and most efficient subset is selected. Filtering rules are often applied so that the set of potential duties generated would not be prohibitively large. Moreover, windows of relief opportunities (WROs), which provide ranges of opportunities for relieving drivers, are beyond the capability of being handled by the existing systems. The usual practice is to consider one, sometimes two, discrete times within each time window. Optimality of solution is therefore compromised. The research presented in this thesis focuses on solving the driver scheduling problem with WROs using a constructive approach, which builds and refines a single schedule iteratively. Filtering rules are unnecessary under the approach. The 2-opt heuristic approach is first investigated, during which the potential of constructive heuristics is explored. Based on the experience, the Tabu Search meta-heuristic approach is then investigated. Multi-neighbourhoods and an appropriate memory scheme, which are essential elements of Tabu Search are designed and tailored for the driver scheduling problem with WROs. Alternative designs have been tested and compared with best known solutions drawn from real-life data sets. The tabu search approach is very fast, can handle WROs, and has achieved results comparable to those based on mathematical programming approaches. Taking advantage of WROs, it can improve best known solutions obtained by the existing systems. Consequently, it could be incorporated into existing systems to improve the solution by taking advantage of WROs

Setting Scheduled Trip Time Based on AVL Data

The scheduled trip time (ST), as an essential parameter, greatly affects the compilation and on-time probability of a vehicle schedule in public transit services. Unfortunately, the significance of ST has not been generally recognized in China. The values of STs are usually set manually based on experiences, which are normally hard to reflect the real-world situation. To set proper trip times, a novel automatic approach based on the AVL data is proposed. The basic process is as follows. First, running time samples are abstracted from a large set of AVL data, based on which the homogeneous running time (HRT) bands are then decided. Meanwhile, the knowledge base of running time distribution is established. Then, the ST parameters corresponding to each HRT band are generated based on a waiting time model. Finally, a simulation system is developed to test the schedules compiled based on a given set of STs, which may be revised further according to the simulation results. Experiments on the bus line 4 of Haikou of China show that setting the STs by the proposed approach brings high on-time probability to vehicle schedules

Traffic Accident Severity Prediction Based on Random Forest

The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO). In the proposed model, BO-RF, RF is adopted as a basic predictive model and BO is used to tune the parameters of RF. Experimental results show that BO-RF achieves higher accuracy than conventional algorithms. Moreover, BO-RF provides interpretable results by relative importance and a partial dependence plot. We can identify important influential factors for traffic accident severity by relative importance. Further, we can investigate how the influential factors affect traffic accident severity by the partial dependence plot. These results provide insights to mitigate the severity of traffic accident consequences and contribute to the sustainable development of transportation

An estimation of distribution algorithm for public transport driver scheduling

Public transport driver scheduling is a process of selecting a set of duties for the drivers of vehicles to form a number of legal driver shifts. The problem usually has two objectives which are minimising both the total number of shifts and the total shift cost, while taking into account some constraints related to labour and company rules. A commonly used approach is firstly to generate a large set of feasible shifts by domain-specific heuristics, and then to select a subset to form the final schedule by an integer programming method. This paper presents an estimation of distribution algorithm (EDA) to deal with the subset selection problem which is NP-hard. To obtain a candidate schedules, the EDA applies a number of rules, with each rule corresponding to a particular way of selecting a shift. Computational results from some real-world instances of drive scheduling demonstrate the availability of this approach

A Probabilistic Model for Vehicle Scheduling Based on Stochastic Trip Times

Vehicle scheduling plays a profound role in public transit planning. Traditional approaches for the Vehicle Scheduling Problem (VSP) are based on a set of predetermined trips in a given timetable. Each trip contains a departure point/time and an arrival point/time whilst the trip time (i.e. the time duration of a trip) is fixed. Based on fixed durations, the resulting schedule is hard to comply with in practice due to the variability of traffic and driving conditions. To enhance the robustness of the schedule to be compiled, the VSP based on stochastic trip times instead of fixed ones is studied. The trip times follow the probability distributions obtained from the data captured by Automatic Vehicle Locating (AVL) systems. A network flow model featuring the stochastic trips is devised to better represent this problem, meanwhile the compatibility of any pair of trips is redefined based on trip time distributions instead of fixed values as traditionally done. A novel probabilistic model of the VSP is proposed with the objectives of minimizing the total cost and maximizing the on-time performance. Experiments show that the probabilistic model may lead to more robust schedules without increasing fleet size