Nebojsa J. Bojovic

A general system theory approach to rail freight car fleet sizing

Abstract The transportation of goods from shippers to consignees is a railroads major activity. Rail freight cars are enormously expensive and a rail vehicle fleet represents one of the largest capital resources of most railroads. Resource allocation to rail freight cars is an extraordinary complex managerial problem. This paper describes the determination of an optimal number of rail freight cars so as to satisfy the demand, on one hand, and minimize the total cost, on the other. A new mathematical model relying on optimal control theory is developed. The problem is formulated as the problem of finding an optimal regulator for a linear system, excited by Gaussian white noise, a quadratic performance index, and random initial conditions. The model has been tested on numerical examples.

Optimal allocation of buffer times to increase train schedule robustness

Reliability and punctuality of railway traffic are among the key performance indicators, which have a significant impact on user satisfaction. A way to improve the reliability and on-time performance in the timetable design stage is by improving the timetable robustness. In order to increase the robustness, most railway companies in Europe insert a fixed amount of buffer time between possibly conflicting events in order to reduce or prevent delay propagation if the first event occurs with a delay. However, this often causes an increase of capacity consumption which is a problem for heavily utilised lines. A sufficient amount of buffer time can therefore not be added between every two conflicting events. Thus, buffer times need to be allocated carefully to protect events with the highest priority. In this paper we consider the problem of increasing the robustness of a timetable by finding an optimal allocation of buffer times on a railway corridor. We model this resource allocation problem as a knapsack problem, where each candidate buffer time is treated as an object with the value (priority for buffer time assignment) determined according to the commercial and operational criteria, and size equal to its time duration. The validity of the presented approach is demonstrated on a case study from a busy mixed-traffic line in Sweden.

Benchmarking distribution centres using Principal Component Analysis and Data Envelopment Analysis: A case study of Serbia

The efficiency of distribution systems is largely affected by the performances of distribution centres. The main objective of this paper is to develop and propose a DEA model for distribution centres efficiency measuring that can help managers in decision making and improving the efficiency. Due to numerous indicators that describe DCs operating, the main problem is indicators selection. In order to improve discriminatory power of classical DEA models PCA-DEA approach is used. This paper analysis the efficiency of distribution centres of one trading company in Serbia. Proposed models integrate operational, quality, energy, utilisation and equipment warehouse and transport indicators. Several hypotheses are tested in this paper. The results showed that small distribution centres are more efficient than large.

A Comparative Assessment of Transport-Sustainability in Central and Eastern European Countries with a Brief Reference to the Republic of Serbia

ABSTRACT This report develops a framework for a cross-country comparative assessment of transport sustainability in central and eastern European countries. The objective was to assess where national transportation systems stand relative to each other according to sustainability criteria. The core of the method is the evaluation of the current status and trends in transport performance using an Analytic Hierarchy Process approach. As a result, countries have been positioned in a static-dynamic space related to sustainability sub-themes. The significance of the approach is in its ability to monitor and analyze transport system functionality regarding sustainability concerns, thus raising awareness of these problems among policy makers.

Fuzzy modeling approach to the rail freight car inventory problem

Techniques to improve freight car fleet use are of considerable interest to the railroad industry. In this paper, we present a fuzzy inventory control approach applied to the sizing of empty cars on a rail network. We address the problem of deciding the optimal inventory level and the optimal ordering quantity for a rail freight car fleet system in which demand and travel time are uncertain variables represented as triangular fuzzy numbers. Based on the fuzzy economic order quantity (EOQ) formula, a modified fuzzy EOQ model is set up and the optimal policy is developed using the signed distance method to defuzzify the fuzzy total cost. Computational results made for the Serbian rail network case verify the proposed model as well as the efficiency of the approach.

The best rail fleet mix problem

Every rail operator wishes to minimise the size of rail freight car fleet in order to reduce costs. The best rail fleet mix problem for railway is to determine an optimal rail freight car fleet in order to respond to the actual transport demand. A rail company must have capabilities to respond to the actual transport demand if it wants to reach a raised level of competitiveness on the transport market. In other words, it is necessary that the structure of freight rolling stock correspond to the structure of the transport demand. Various types of railcars are used for carrying goods by rail, caused by different performances of goods, for example, by manifestation form, aggregate condition and resistance to atmosphere influences. This paper treats a problem of determining an optimal rail fleet mix. A successful application of a multicriteria decision method, analytic hierarchical process (AHP) for solving of this problem is proposed. According to the selected number and types of criteria, as well as given level of importance, optimal type of rail freight car for all homogenous types of goods is derived. Obtained result shows a potential of practical application of AHP method for the problem of determining the best rail fleet mix.

A Fuzzy Simulated Annealing approach for project time-cost tradeoff

Managers often face difficulties when making project decisions that involve large number of interrelated activities --the planning and scheduling of which is project management. Most problems often arise in areas such as product development, production planning and control and setting up the production facilities. One important aspect of project management is activity crashing, i.e. reducing the activity time by adding more resources such as workers, overtime and so on. It is important to decide the optimal crash plan to complete the project within the desired time period. A fuzzy simulated annealing crashing method is introduced in this paper to evaluate project networks and determine the optimum crashing configuration that minimizes the average project cost, due to lateness penalties and crashing costs in the presence of vagueness and uncertainty. Through a real project example the proposed approach is shown effective and efficient in conducting time-cost tradeoff analysis.

A hybrid model for forecasting the volume of passenger flows on Serbian railways

The accuracy of predicting the volume of railway passenger flows is very significant because of the vital role in the basic functions of transportation resources management. Although dealing with this problem is very often based on the use of the neural networks, the uncertainty which dominates in the functioning of transportation systems is of great significance. The neural networks have been used for the time-series prediction with good results. This research compared two methods the parametric and the non-parametric approach. This study aims at presenting a hybrid model based on the integration of the genetic algorithm (GA) and the artificial neural networks (ANN) for forecasting the monthly volume of passengers on the Serbian railways. This innovative hybrid demonstrates how the genetic algorithms can be used to optimize the network architecture. By applying the idea of genetic algorithms in the neural networks, the integration is used so that on the basis of the input data, the selected population represents the number of neurons in the middle. In order to assess performances, the developed approach is compared to the traditional SARIMA model and the proposed method GAANN is better.

Train driver rostering optimization

The rail crew scheduling problem is based on assigning train drivers to scheduled trains over a time horizon at minimal cost while honoring operational and contractual requirements. Currently within the most railways, decisions related to crew are made manually. In this paper, we made an introduction to rail crew scheduling problem and propose a new mathematical model for optimizing a train driver rostering problem. Computational experiments are performed for a set of real case instances.

Fuzzy reasoning approach for a postal company restructuring project

Modern organizations and companies exist in time of progressiveness in almost all segments of life and work influenced by large, rapid and dynamic changes. In such conditions, business world is enforced to adapt to these changes. Character of resulting changes is such that leads to question the existing social theories, especially in the macroeconomic field. Project management is a new and specialized management discipline. The main goal of project management is an efficient project realization within the planned time and planned costs in the conditions of significant external and internal changes. In this paper we developed a fuzzy reasoning approach for a Postal company restructuring project management. We combined Fuzzy PERT method with Fuzzy Delphi for activity time forecasting and finding the critical path. This approach has been applied on a project of Serbian postal company restructuring and obtained results appeared as very consistent.