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In this article, we study the application of NetworkX, a Python library for dealing with traffic networks, to the problem of signal optimization at a single intersection. We use the shortest-path algorithms such as Bellman-Ford (Dynamic Programming), A star (A*), and Dijkstra’s algorithm to compute an optimal solution to the problem. We consider both undersaturated and oversaturated traffic conditions. The results show that we find optimal results with short Central Processor Unit (CPU) time using all the applied algorithms, where Dijkstra’s algorithm slightly outperformed others. Moreover, we show that bee colony optimization can find the optimal solution for all tested problems with different degrees of computational complexity for less CPU time, which is a new contribution to knowledge in this field.

The problem of public transit network design belongs to the class of NP hard combinatorial optimization problem. Since optimal solution of this problem is difficult to discover, many heuristic algorithms have been proposed in the literature. By applying various heuristic algorithms to one problem instance, various solutions could be generated. We assume, in this paper, that decisions related to public transit network choice should be made in the presence of trade-offs between two or more conflicting objectives (usually, the passengers' and operator's interests are in conflict). In this paper, we propose the model for public transit network choice. The proposed model is based on the Data Envelopment Analysis (DEA). The proposed model is tested on the one of the best known benchmark examples for the transit network design problem.

This paper is an extensive survey of the Bee Colony Optimization (BCO) algorithm, proposed for the first time in 2001. BCO and its numerous variants belong to a class of nature-inspired meta-heuristic methods, based on the foraging habits of honeybees. Our main goal is to promote it among the wide operations research community. BCO is a simple, but efficient meta-heuristic technique that has been successfully applied to many optimization problems, mostly in transport, location and scheduling fields. Firstly, we shall give a brief overview of the other meta-heuristics inspired by bees’ foraging principles pointing out the differences between them. Then, we shall provide the detailed description of the BCO algorithm and its modifications, including the strategies for BCO parallelization, and giving the preliminary results regarding its convergence. The application survey is elaborated in Part II of our paper. [Projekat Ministarstva nauke Republike Srbije, br. OI174010, br. OI174033 i br. TR36002]

Bee Colony Optimization (BCO) is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications. [Projekat Ministarstva nauke Republike Srbije, br. OI174010, OI174033, TR36002]