Abstract: Decision making on the true planning of the public transportation systems to serve potential users is a must for metropolitan areas. To take attraction of travelers to projected modes of transport, adequately fair overall travel times should be provided. In this fashion, other benefits such as lower traffic congestion, road safety and lower noise and atmospheric pollution may be earned. The congestion which comes with increasing demand of public transportation is becoming a part of our lives and making residents’ life difficult. Hence, regulations should be done to reduce this congestion. To provide a constructive and balanced regulation in public transportation systems, right stations should be located in right places. In this study, it is aimed to design and implement a Decision Support System (DSS) Application to determine the optimal bus stop places for public transport in Istanbul which is one of the biggest and oldest cities in the world. Required information is gathered from IETT (Istanbul Electricity, Tram and Tunnel) Enterprises which manages all public transportation services in Istanbul Metropolitan Area. By using the most real-like values, cost assignments are made. The cost is calculated with the help of equations produced by bi-level optimization model. For this study, 300 buses, 300 drivers, 10 lines and 110 stops are used. The user cost of each station and the operator cost taken place in lines are calculated. Some components like cost, security and noise pollution are considered as significant factors affecting the solution of set covering problem which is mentioned for identifying and locating the minimum number of possible bus stops. Preliminary research and model development for this study refers to previously published article of the corresponding author. Model results are represented with the intent of decision support to the specialists on locating stops effectively.
Abstract: In this paper, we present a binary cat swarm
optimization for solving the Set covering problem. The set covering
problem is a well-known NP-hard problem with many practical
applications, including those involving scheduling, production
planning and location problems. Binary cat swarm optimization
is a recent swarm metaheuristic technique based on the behavior
of discrete cats. Domestic cats show the ability to hunt and are
curious about moving objects. The cats have two modes of behavior:
seeking mode and tracing mode. We illustrate this approach with
65 instances of the problem from the OR-Library. Moreover, we
solve this problem with 40 new binarization techniques and we select
the technical with the best results obtained. Finally, we make a
comparison between results obtained in previous studies and the new
binarization technique, that is, with roulette wheel as transfer function
and V3 as discretization technique.
Abstract: A new Meta heuristic approach called "Randomized gravitational emulation search algorithm (RGES)" for solving large size set covering problems has been designed. This algorithm is found upon introducing randomization concept along with the two of the four primary parameters -velocity- and -gravity- in physics. A new heuristic operator is introduced in the domain of RGES to maintain feasibility specifically for the set covering problem to yield best solutions. The performance of this algorithm has been evaluated on a large set of benchmark problems from OR-library. Computational results showed that the randomized gravitational emulation search algorithm - based heuristic is capable of producing high quality solutions. The performance of this heuristic when compared with other existing heuristic algorithms is found to be excellent in terms of solution quality.
Abstract: Set covering problem is a classical problem in
computer science and complexity theory. It has many applications,
such as airline crew scheduling problem, facilities location problem,
vehicle routing, assignment problem, etc. In this paper, three
different techniques are applied to solve set covering problem.
Firstly, a mathematical model of set covering problem is introduced
and solved by using optimization solver, LINGO. Secondly, the
Genetic Algorithm Toolbox available in MATLAB is used to solve
set covering problem. And lastly, an ant colony optimization method
is programmed in MATLAB programming language. Results
obtained from these methods are presented in tables. In order to
assess the performance of the techniques used in this project, the
benchmark problems available in open literature are used.
Abstract: Finding the minimal logical functions has important applications in the design of logical circuits. This task is solved by many different methods but, frequently, they are not suitable for a computer implementation. We briefly summarise the well-known Quine-McCluskey method, which gives a unique procedure of computing and thus can be simply implemented, but, even for simple examples, does not guarantee an optimal solution. Since the Petrick extension of the Quine-McCluskey method does not give a generally usable method for finding an optimum for logical functions with a high number of values, we focus on interpretation of the result of the Quine-McCluskey method and show that it represents a set covering problem that, unfortunately, is an NP-hard combinatorial problem. Therefore it must be solved by heuristic or approximation methods. We propose an approach based on genetic algorithms and show suitable parameter settings.