Katarina S. Vukadinovic

A fuzzy approach to the vessel dispatching problem

The paper discusses the process of loading, transport and unloading of gravel by inland water transport. A model for the dispatchers decision support is developed. The decision-making process concerning the number of barges left at or taken from ports is modelled by fuzzy logic. By comparing the results obtained through the model with those resulting from the dispatchers decision, it has been found that the model is highly compatible with reality.

Locating inspection facilities in traffic networks: an artificial intelligence approach

Abstract In order for traffic authorities to attempt to prevent drink driving, check truck weight limits, driver hours and service regulations, hazardous leaks from trucks, and vehicle equipment safety, we need to find answers to the following questions: (a) What should be the total number of inspection stations in the traffic network? and (b) Where should these facilities be located? This paper develops a model to determine the locations of uncapacitated inspection stations in a traffic network. We analyze two different model formulations: a single-objective optimization problem and a multi-objective optimization problem. The problems are solved by the Bee Colony Optimization (BCO) method. The BCO algorithm belongs to the class of stochastic swarm optimization methods, inspired by the foraging habits of bees in the natural environment. The BCO algorithm is able to obtain the optimal value of objective functions in all test problems. The CPU times required to find the best solutions by the BCO are found to be acceptable.

A neural network approach to the vessel dispatching problem

The paper discusses the process of loading, transport and unloading of gravel by inland water transportation. At the loading port, the problem that needs to be solved is the assignment of load barges to pusher tugs for the planned period of one day. However, disturbances of planned schedules are very common. Whenever a disturbance in a daily schedule appears, the dispatcher urgently attempts to mitigate negative effects resulting from the disturbance. Real-time operations limit the amount of time that dispatchers in charge of traffic control have to make decisions and increase the level of stress associated with quick and adequate response. This paper aims to demonstrate the feasibility of a dispatch decision support system that could decrease the work load for the dispatcher and improve the quality of decisions. The proposed neural network with the ability to adapt or learn from examples of decisions can simulate the dispatchers decision process.

An application of neurofuzzy modeling: The vehicle assignment problem

When assigning vehicles to transportation requests, dispatchers usually have built-in fuzzy rules which they use to assign a given amount of freight to be sent to a given distance a given vehicle. Fuzzy systems equipped with learning capabilities can be trained to control complex processes like the dispatcher. They usually begin with a few very crude rules obtained from the dispatcher. Or they may work out the rules from the observed dispatchers behavior. In this paper, a neural network is used to refine and adapt the fuzzy system to achieve better performance. As a result of the study, on a real set of numerical data, it was shown that the proposed feedforward adaptive neural networks with supervised learning capabilities can be used to tune the initial fuzzy systems.

A NEURAL NETWORK APPROACH TO MITIGATION OF VEHICLE SCHEDULE DISTURBANCES.

In all transportation industries, when planned schedules are modified by delays, breakdowns or peaks of demand, ‘real‐time’ human decision making takes place. Dispatchers in charge of traffic control usually are allowed a very short time to make decisions that should mitigate the negative effects resulting from the disturbance. This paper aims to demonstrate the feasibility of a dispatch decision support system that could decrease the work load for the dispatcher and improve the quality of decisions. The proposed neural network, with the ability to adapt or learn from examples of decisions, can simulate the dispatchers decision process.

Data envelopment analysis of AGV fleet sizing at a port container terminal

A decision-making approach based on Data Envelopment Analysis (DEA) for determining the efficient container handling processes (considering the number of employed Automated Guided Vehicles (AGVs)) at a port container terminal (PCT) is presented in this paper. Containers are unloaded from the ship by quay cranes and transported to the storage area by AGVs. We defined performance measures of proposed container handling processes and analysed the effects when changing the number of AGVs. The values of performance measures were collected and/or calculated from simulation. Container handling process, with a fixed number of quay cranes, when a different number of AGVs is used to transport containers from berth to assigned locations within storage area, represents a decision-making unit (DMU). We applied the basic CCR (Charnes, Cooper and Rhodes) DEA model with two inputs: average ship operating delay costs and average operating costs of employed equipment at a PCT, and two outputs: average number of handled import containers per ship and weighted average utilisation rate of equipment at a PCT. DEA method proved to be useful when testing different DMUs and when determining efficient DMUs for planning purposes. This study shows that efficiency evaluation of AGV fleet sizing and operations is useful for planning purposes at PCTs.

Technical-technological characteristics of ports on Serbian inland waterways

This paper reviews the current state of ports on Serbian inland waterways with their main technical - technological characteristics.

Presentation of Fuzzy Models Developed in Transportation Applications

Vehicle routing problems appear in various transportation activities. Hundreds of papers have been published in world literature during the past three decades treating different aspects of the vehicle routing problem. Significant reviews were given by Larson and Odoni (1981), Bodin et al. (1983), Solomon and Desrosiers (1988), and Golden and Assad (1988).

A Fuzzy Mathematical Programming Approach to Transportation

In the past three decades, linear, nonlinear, dynamic, integer and multicriteria programming have been used very successfully to solve various traffic and transportation problems. Typical problems include assigning traffic in a network, distributing personnel to jobs, distributing transport facilities to carry out planned schedules, and planning fleet development and transportation-location tasks. Their solutions can be found using one of the mathematical programming methods.

Basic Definitions of the Fuzzy Sets Theory

In the classic theory of sets, very precise bounds separate the elements that belong to a certain set from the elements outside the set. In other words, it is quite easy to determine whether an element belongs to a set or not. For example, if we denote by A the set of signalized intersections in a city, we conclude that every intersection under observation belongs to set A if it has a signal. Element x’S membership in set A is described in the classic theory of sets by the membership function μA(x), as follows: