Dušan Teodorović

Optimal dispatching strategy on an airline network after a schedule perturbation

Abstract It often happens that one or more aeroplanes from an airline fleet are taken out of operation for technical reasons and the airline has to operate on the existing network with a reduced number of planes. This paper presents the results of an effort to define a new ad hoc schedule for this situation, so that the total passenger delay on an airline network is minimized. A network is formed, in which nodes represent flights on a given airline network, and arcs are the total time losses on individual flights. The problem of determining a new routing and scheduling plan for the airline fleet is solved by branch and-bound methods. A numerical example illustrates the efficiency of the model.

Computing with Bees: Attacking Complex Transportation Engineering Problems

The Bee System (an artificial bee swarm) is introduced in this paper. The proposed approach is applied to the Traveling Salesman Problem. The obtained results are very promising. The potential applications of the developed Bee System in the field of transportation engineering are discussed. The Bee System represents the new, successful application of emergent techniques based on natural metaphors, such as simulated annealing, genetic algorithms, and neural networks, to the complex engineering and management problems.

FUZZY LOGIC SYSTEMS FOR TRANSPORTATION ENGINEERING: THE STATE OF THE ART

The paper presents a classification and analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes. Fuzzy logic is shown to be a very promising mathematical approach to modeling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation engineering problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. Possibilities are shown regarding the further application of fuzzy logic in this field.

Bee Colony Optimization: Principles and Applications

The bee colony optimization metaheuristic (BCO) is proposed in the paper. The BCO represents the new metaheuristic capable to solve difficult combinatorial optimization problems. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. In addition to proposing the BCO as a new metaheuristic, we also describe in the paper two BCO algorithms that we call the bee system (BS) and the fuzzy bee system (FBS). In the case of FBS the agents (artificial bees) use approximate reasoning and rules of fuzzy logic in their communication and acting. In this way, the FBS is capable to solve deterministic combinatorial problems, as well as combinatorial problems characterized by uncertainty. The proposed approach is illustrated by three case studies

Intelligent parking systems

Abstract The basic concepts of the parking reservation system and parking revenue management system are discussed in this paper. The proposed “intelligent” parking space inventory control system that is based on a combination of fuzzy logic and integer programming techniques makes “on line” decisions whether to accept or reject a new driver’s request for parking. In the first step of the proposed model, the best parking strategies are developed for many different patterns of vehicle arrivals. These parking strategies are developed using integer programming approach. In the second step, learning from the best strategies, specific rules are defined. The uniqueness of the proposed approach is that the rules are derived from the set of chosen examples assuming that the future traffic arrival patterns are known. The results were found to be close to the best solution assuming that the future arrival pattern is known.

MODEL FOR OPERATIONAL DAILY AIRLINE SCHEDULING

Perturbations in carrying out a schedule are occurrences which happen from time to time and there are numerous factors behind them. Cancelled or delayed flights can be caused by meteorological reasons, technical reasons, late or absent crew members, etc. One or more aircraft from an airline fleet might be taken out of operation due to technical reasons and the airline has to operate with a reduced number of planes. This paper presents the results of an effort to define a new daily airline schedule in this situation so that the total number of cancelled flights is minimized. Should there be several airline schedules with an equal total number of cancelled flights, the schedule with the minimum total passenger delay on flights to be performed is chosen. A heuristic algorithm is developed to solve this lexicographic optimization problem and is tested on numerical examples.

The fuzzy set theory approach to the vehicle routing problem when demand at nodes is uncertain

Abstract The problem of vehicle routing when demand at the nodes is uncertain is considered. The quantities to be picked up at the nodes are assumed to be only approximately known. A network with one depot from which vehicles depart and to which they return after completing their service is considered. The paper develops a model to design vehicle routing when demand at the nodes is uncertain. The model is based on the heuristic “sweeping” algorithm, the rules of fuzzy arithmetic and fuzzy logic.

Bee Colony Optimization (BCO)

Swarm Intelligence is the part of Artificial Intelligence based on study of actions of individuals in various decentralized systems. The Bee Colony Optimization (BCO) metaheuristic has been introduced fairly recently as a new direction in the field of Swarm Intelligence. Artificial bees represent agents, which collaboratively solve complex combinatorial optimization problem. The chapter presents a classification and analysis of the results achieved using Bee Colony Optimization (BCO) to model complex engineering and management processes. The primary goal of this chapter is to acquaint readers with the basic principles of Bee Colony Optimization, as well as to indicate potential BCO applications in engineering and management.

Transportation route choice model using fuzzy inference technique

Fuzzy set theory is applied to solve the problem of traffic assignment between two alternative routes on a highway network. The drivers perceived travel time on each route is treated as a fuzzy number, and his choice of route is based on an approximate reasoning model. The model consists of rules which indicate the degree of preference for each route given the approximate travel time of the two routes. Applying the model to each driver and then aggregating the individual preferences, a fuzzy network loading algorithm assigns traffic to each route.<<ETX>>

A simulated annealing technique approach to the vehicle routing problem in the case of stochastic demand

Research work dealing with the vehicle routing problem has not paid adequate attention to the cases where the demand for services at certain nodes is a random variable. This paper develops an approach to the vehicle routing problem for the case of stochastic demand. The approach is based on the simulated annealing technique. It is illustrated with a numerical example.