Hybrid simulated annealing and genetic algorithm for optimizing arterial signal timings under oversaturated traffic conditions

The implementation of system‐wide signal optimization models requires efficient solution algorithms that can quickly generate optimal or near‐optimal signal timings. This paper presents a hybrid algorithm based on simulated annealing (SA) and a genetic algorithm (GA) for arterial signal timing optimization. A decoding scheme is proposed that exploits our prior expectations about efficient solutions, namely, that the optimal green time distribution should reflect the proportion of the critical lane volumes of each phase. An SA algorithm, a GA algorithm and a hybrid SA‐GA algorithm are developed here using the proposed decoding scheme. These algorithms can be adapted to a wide range of signal optimization models and are especially suitable for those optimizing phase sequences with oversaturated intersections. To comparatively evaluate the performance of the proposed algorithms, we apply them to a signal optimization model for oversaturated arterial intersections based on an enhanced cell transmission model. The numerical results indicate that the SA‐GA algorithm outperforms both SA and GA in terms of solution quality and convergence rate. Copyright © 2014 John Wiley & Sons, Ltd.     

A math-heuristic algorithm for the integrated air service recovery

A sophisticated flight schedule might be easily disrupted due to adverse weather, aircraft mechanical failures, crew absences, etc. Airlines incur huge costs stemming from such flight schedule disruptions in addition to the serious inconveniences experienced by passengers. Therefore, an efficient recovery solution that simultaneously decreases an airline's recovery cost while simultaneously mitigating passenger dissatisfaction is of great importance to the airline industry. In this paper, we study the integrated airline service recovery problem in which the aircraft and passenger schedule recovery problems are simultaneously addressed, with the objective of minimizing aircraft recovery and operating costs, passenger itinerary delay cost, and passenger itinerary cancellation cost.Recognizing the inherent difficulty in modeling the integrated airline service recovery problem within a single formulation (due to its huge solution space and quick response requirement), we propose a three-stage sequential math-heuristic framework to efficiently solve this problem, wherein the flight schedules and aircraft rotations are recovered in the first stage, Then, a flight rescheduling problem and passenger schedule recovery problems are iteratively solved in the next two stages. Time-space network flow representations, along with mixed-integer programming formulations, and algorithms that take advantages of the underlying problem structures, are proposed for each of three stages. This algorithm was tested on realistic data provided by the ROADEF 2009 challenge and the computational results reveal that our algorithm generated the best solution in nearly 72% of the test instances, and a near-optimal solution was achieved in the remaining instances within an acceptable timeframe. Furthermore, we also ran additional computational runs to explore the underlying characteristics of the proposed algorithm, and the recorded insights can serve as a useful guide during practical implementations of this algorithm.     

Scheduling highway work zones with genetic algorithm considering the impact of traffic diversion

Highway work zones caused excessive delay to road users. To reduce user and maintenance costs, work zones shall be designed and scheduled accordingly. An analytical model is developed to jointly optimize work zone lengths and schedule as well as diverted traffic volume for highway maintenance projects, considering time‐varying demand, variable maintenance cost, and various production rates of maintenance crew. With a genetic algorithm, an iterative procedure is developed to search for the optimal solution. A numerical example is illustrated, in which various traffic mitigation plans for a highway maintenance project are evaluated. A sensitivity analysis is conducted, and results indicate the threshold volumes for various conditions (e.g., maintenance crews and capacity of the work zone) at which diverting traffic is desirable. This study demonstrates an effective approach to search for the optimal work zone schedule, which is also applicable to evaluate the effectiveness of traffic diversion plans for a pre‐planned work zone schedule. Copyright © 2012 John Wiley & Sons, Ltd.     

A column generation algorithm for the bus driver scheduling problem

Bus driver scheduling aims to find the minimum number of bus drivers to cover a published timetable of a bus company. When scheduling bus drivers, contractual working rules must be enforced, thus complicating the problem. In this research, we develop a column generation algorithm that decomposes this complicated problem into a master problem and a series of pricing subproblems. The master problem selects optimal duties from a set of known feasible duties, and the pricing subproblem augments the feasible duty set to improve the solution obtained in the master problem. The proposed algorithm is empirically applied to the realistic problems of several bus companies. The numerical results show that the proposed column generation algorithm can solve real‐world problems and obtain bus driver schedules that are better than those developed and used by the bus companies. Copyright © 2016 John Wiley & Sons, Ltd.     

Vehicle sharing system with fleet sizing and multi-transportation modes under allowable shortages: a hybrid metaheuristic approach

In this paper, a vehicle sharing system with multi-transportation modes and allowable shortage is presented. This model aims to minimize the system's total cost by using optimum locations and number of stations, routes, transportation modes, station capacities for different modes and time between stations balancing. Because of the model's complexity, currently available proprietary software is not able to solve the model in a reasonable computational time, so a hybrid algorithm based on a genetic algorithm (GA) and particle swarm optimization is presented. The results confirm its efficiency compared with the classic GA and exact solution methods. Moreover, a sensitivity analysis shows the applicability of the proposed algorithm.     

道路客运小件快运车辆路径和调度研究

制约小件运输发展的瓶颈之一是站到站运输与客户门到门需求之间的矛盾,目前道路客运企业正大力发展门到门的取送货业务,但缺少一套切实可行的取送货运输组织调度方案,基于此,文章建立了考虑客户时效性需求的车辆路径调度数学模型并设计了一种改进的遗传算法进行求解。通过实例证明该算法可以求得满意解,为道路客运企业发展小件快运的"最后一公里"取送货服务提供运输组织调度方案参考。     

基于混合遗传算法的供水管道漏损安全预测

为解决供水行业中供水管道漏损安全性问题,建立了供水管道漏损安全预测模型,并引入遗传算法对其优化进行漏损预测,同时,针对传统的遗传算法优化该模型的过程中出现的缺陷,提出了混合遗传算法对其求解.模拟实例的预测结果表明：该模型预测方法准确可靠,具有较高的实用性。     

Constraint handling methods in pavement maintenance programming

The problem of pavement maintenance management at the network level is one of maintaining as high a level of serviceability as possible for a pavement network system through reactive and proactive repair actions, whilst optimising the use of available resources. This problem has traditionally been solved using techniques like mathematical programming and heuristic methods. Lately, the use of genetic algorithms (GAs) to solve resource allocation problems like the network pavement maintenance problem has received increased attention from researchers. GAs have been demonstrated to be better than traditional techniques in terms of solution quality and diversity. However, the performance of the GAs is affected by the method used to handle the many constraints present in the formulation of such resource allocation methods. Penalty as well as generate and repair methods are the usual techniques used to handle constraints, but these have their drawbacks in terms of computational efficiency and tendency to get trapped in sub-optimal solution spaces. The paper proposes a third method that is computationally more efficient than the previous methods. The method is based on prioritised allocation of resources to maintenance activities and the maximum utilisation of resources. Constraints on maximum resource availability are no longer used passively to check on solution feasibility (as in the previous methods) but are used to help generate feasible solutions during the resource allocation phase of the algorithm itself. It is demonstrated that the GA with the prioritised resource allocation method (PRAM) outperforms the traditional GA with repair or penalty methods. PRAM was able to consistently outperform the other two GA based methods, both in terms of solution quality as well as computational time. It is concluded that PRAM can be used as the basis of more efficient resource allocation procedures in the area of pavement maintenance management.     

Fleet size determination for a truckload distribution center

This work presents an analysis of the fleet size determination problem for a truckload distribution center in which each customer requests multi‐truckload services. A transformation is made to define this problem as a cutting stock problem which can be resolved by finding the proper set of patterns beneficial for an NP‐hard (non‐deterministic polynomial‐time hard) problem. The genetic algorithm (GA) is combined with cutting stock techniques to solve the problem. Experiments are carried out testing two types of problems, with and without time‐window constraints. To verify the solution quality obtained with the proposed approach, the problem is also solved using only GA. It is shown that the proposed approach can significantly improve the GA solution by 14.68%. Copyright © 2012 John Wiley & Sons, Ltd.     

The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services

The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real‐time requests, interval between call‐in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling process.     

Optimal timetable development for community shuttle network with metro stations

This paper investigates an issue for optimizing synchronized timetable for community shuttles linked with metro service. Considering a passenger arrival distribution, the problem is formulated to optimize timetables for multiple community shuttle routes, with the objective of minimizing passenger’s schedule delay cost and transfer cost. Two constraints, i.e., vehicle capacity and fleet size, are modeled in this paper. The first constraint is treated as soft, and the latter one is handled by a proposed timetable generating method. Two algorithms are employed to solve the problem, i.e., a genetic algorithm (GA) and a Frank–Wolfe algorithm combined with a heuristic algorithm of shifting departure times (FW-SDT). FW-SDT is an algorithm specially designed for this problem. The simulated and real-life examples confirm the feasibility of the two algorithms, and demonstrate that FW-SDT outperforms GA in both accuracy and effectiveness.     

Application of genetic algorithm for scheduling and schedule coordination problems

The problems on scheduling and schedule co‐ordination usually have conflicting objectives related to user's cost and operator's cost. Users want to spend less time to wait, transfer and travel by public buses. Operators are interested in profit making by lesser vehicle operating cost and having a minimum number of buses. As far as level of service is concerned users are interested in lesser crowing while operators are concerned with maximizing profit and thus to have higher load factors. In schedule co‐ordination problems transfer time plays an important role. Users are interested in coordinating services with in acceptable waiting time whereas operators prefer to have lesser services and want to meet higher demands, which invariably increases waiting time. These problems have multiple conflicting objectives and constraints. It is difficult to determine optimum solution for such problems with the help of conventional approaches. It is found that Genetic Algorithm performs well for such multi objective problems.     

Disruption Management of an Inequality-Based Multi-Fleet Airline Schedule by a Multi-Objective Genetic Algorithm

Abstract

This paper presents a novel application of a Method of Inequality-based Multi-objective Genetic Algorithm (MMGA) to generate an efficient time-effective multi-fleet aircraft routing algorithm in response to the schedule disruption of short-haul flights. It attempts to optimize objective functions involving ground turn-around times, flight connections, flight swaps, total flight delay time and a 30-minute maximum delay time of original schedules. The MMGA approach, which combines a traditional Genetic Algorithm (GA) with a multi-objective optimization method, can address multiple objectives at the same time, then explore the optimal solution. The airline schedule disruption management problem is traditionally solved by Operations Research (OR) techniques that always require a precise mathematical model. However, airline operations involve too many factors that must be considered dynamically, making a precise mathematical model difficult to define. Experimental results based on a real airline flight schedule demonstrate that the proposed method, Multi-objective Optimization Airline Disruption Management by GA, can recover the perturbation efficiently within a very short time. Our results further demonstrate that the application can yield high quality solutions quickly and, consequently, has potential to be employed as a real-time decision support tool for practical complex airline operations.     

A quantum evolutionary algorithm for the second-best congestion pricing problem in urban traffic networks

This paper investigates the congestion pricing problem in urban traffic networks. A first-best strategy, a second-best strategy for toll leveling in closed cordons and a second-best strategy for determining both toll levels and toll points are considered. The problem is known to be a mixed integer programming model and formulated as a bi-level optimization problem, with an objective of maximizing the social welfare. A method is presented to solve the problem, based on a novel metaheuristic algorithm, namely quantum evolutionary algorithm (QEA). To verify the proposed method, the widely used genetic algorithm (GA) is also applied to solve the problem. The problem is solved for a medium-size urban traffic network and the results of the QEA are compared against the conventional GA. Computational results show that the QEA outperforms the GA in solution quality.     

Cranes scheduling in frame bridges based automated container terminals

This paper investigates crane scheduling problems for a new type of automated container terminal system, which is based on multi-storey frame bridges. For the new design concept, this paper studies how to schedule two types of cranes, i.e., quay cranes and bridge cranes that transfer containers between different storeys. The schedules of these two types of cranes impact the operation efficiency of the terminal system. Mathematical models are proposed for the two scheduling problems. Meta-heuristics are developed to solve them. Numerical experiments are conducted to validate the effectiveness of the proposed models and the efficiency of the proposed solution method.     

Multi-objective optimization of train routing problem combined with train scheduling on a high-speed railway network

Based on train scheduling, this paper puts forward a multi-objective optimization model for train routing on high-speed railway network, which can offer an important reference for train plan to provide a better service. The model does not only consider the average travel time of trains, but also take the energy consumption and the user satisfaction into account. Based on this model, an improved GA is designed to solve the train routing problem. The simulation results demonstrate that the accurate algorithm is suitable for a small-scale network, while the improved genetic algorithm based on train control (GATC) applies to a large-scale network. Finally, a sensitivity analysis of the parameters is performed to obtain the ideal parameters; a perturbation analysis shows that the proposed method can quickly handle the train disturbance.     

Metaheuristics for efficient aircraft scheduling and re-routing at busy terminal control areas

Intelligent decision support systems for the real-time management of landing and take-off operations can be very effective in helping air traffic controllers to limit airport congestion at busy terminal control areas. The key optimization problem to be solved regards the assignment of airport resources to take-off and landing aircraft and the aircraft sequencing on them. The problem can be formulated as a mixed integer linear program. However, since this problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY solver (a state-of-the-art optimization solver to deal with complex routing and scheduling problems) in order to improve the possibility of finding good quality solutions quickly. The proposed framework starts from a good initial solution for the aircraft scheduling problem with fixed routes (given the resources to be traversed by each aircraft), computed via a truncated branch-and-bound algorithm. A metaheuristic is then applied to improve the solution by re-routing some aircraft in the terminal control area. New metaheuristics, based on variable neighbourhood search, tabu search and hybrid schemes, are introduced. Computational experiments are performed on an Italian terminal control area under various types of disturbances, including multiple aircraft delays and a temporarily disrupted runway. The metaheuristics achieve solutions of remarkable quality, within a small computation time, compared with a commercial solver and with the previous versions of AGLIBRARY.     

Future electricity demand from battery‐powered vehicles

An important decision faced by airline schedulers is how to adapt the flight schedule and aircraft assignment to unforeseen perturbations in an established schedule. In the face of unforeseen aircraft delays, schedulers have to decide which flights to delay, and when delays become excessive, which to cancel. Current scheduling models deal with simple decision problems of delay or cancellation, but not with both simultaneously. But in practice the optimal decision may involve results from the integration of both flight cancellations and delays. In Part I of this paper, a quadratic programming model for the integration decision problem is given. The model can formulate the integration of flight cancellations and delays as well as some special cases, such as the ferrying of surplus aircraft and the possibility of swapping different types of aircraft. In this paper, based on the special structure of the model, an effective algorithm is presented, sufficient computational experiments are conducted and some results are reported. These show that we can expect to obtain a sufficiently good solution in terms of reasonable CPU time.     

Bus maintenance systems and maintenance scheduling: model formulations and solutions

This paper deals with the problem of scheduling bus maintenance activities. The scheduling of maintenance activities is an important component in bus transit operations planning process. The other components include network route design, setting timetables, scheduling vehicles, and assignment of drivers. This paper presents a mathematical programming approach to the problem. This approach takes as input a given daily operating schedule for all buses assigned to a depot along with available maintenance resources. It, then, attempts to design daily inspection and maintenance schedules for the buses that are due for inspection so as to minimize the interruptions in the daily bus operating schedule, and maximize the utilization of the maintenance facilities. Three integer programming formulations are presented and different properties of the problem are discussed. Several heuristic methods are presented and tested. Some of these procedures produce very close to optimal solutions very efficiently. In some cases, the computational times required to obtain these solutions are less than 1% of the computational time required for the conventional branch and bound algorithm. Several small examples are offered and the computational results of solving the problem for an actual, 181-bus transit property are reported.     

Hybrid model of taxonomy and genetic algorithms for finding shortest path in transportation systems

This is research is aimed at elaborating a new methodology of shortest path finding by utilizing the methods of taxonomy and genetic algorithms. Combination of the two is developed and called Genetic Taxonomy Evaluator (GTE) which is expected to be an alternative tool to solve shortest path finding problems within the transportation networks While keeping the properties of transportation networks Taxonomy Reconstructor (TR) transforms the network representation into taxonomic structure, which is hierarchically shaped, based on problem to be solved. In the process TR also creates classification of nodes in the network. This classification provides facilities to isolate the problem to the core, and the criteria that can be inserted in the Genetic Algorithm (GA). A package program for GTE is then developed in C-Language and performance of model is analyzed upon a medium scale of Sioux-Falls City Network. In conclusion, it is found that to achieve fairly quick convergence of GTE computation several optimal parameters of GA should be determined prior to searching for the shortest paths. And since GTE has only been applied to limited case, it is suggested that the findings could be a threshold for further researches.