Effect of Price Increases on the Internet Search Traffic of Turkey

As being the result a series of research/study, several prevention/action plans has been implemented to avoid/decrease the habits of cigarette and similar tobacco products through the World. Our country has been regarded as the first country implementing these international action plans, entirely. In this context, one of the most important arrangements has been decreasing the attractiveness of smoking by increasing the prices of them. The increase in the cigarette prices may end with attempts to quit smoking or to find an alternative/cheaper product. The general trend in this search behavior can be analyzed via the analysis of internet searches. In this study, by the use of the data provided by Google Trends, the qualification and magnitude of this search behavior after the price increase realized in 2015 and 2016 is analyzed. In the light of the statistical findings; it is understood that, price increases in our country generally ends with finding cheaper or substitute products instead of quitting the cigarettes

Dynamic Optimization in a Dynamic and Unpredictable World

Dynamism is known as an attempt to explain the phenomena of the universe against to some immediate change. All scientists dealing with the systems and phenomena of the universe have unsurprisingly faced with a variety of immediate change. Therefore, they are usually obligated to ignore more than one variable to keep change at a time. However, with the storm of technological change, it has been difficult to deal with the increasing dynamism and the uncertainty with the existing manner. Providentially, new computing and programming utilities like agent technology has enabled more realistic modeling. In this respect, a typical operations research problem including constraints, objective functions and variables could have been altered with the more realistic ones where constraints, objective functions and domains of variables can be a matter of any kind of change at any time. To cope with such dynamism, there have been several efforts to adapt some of the meta-heuristics to work in a harmony and in integrity without ignoring the objective(s) of modeling. This usually requires agent based approaches letting the elements (such as ants, bees or gens) to communicate and negotiate with each other in order to adapt themselves in parallel to the changes in domain of variables, constraints and objective functions. Although meta-heuristic approaches for the solution of dynamic optimization problems are relatively new in the literature, this paper intends to review and analyze existing studies that is available in the literature. Our focus will be specifically on agent based approaches which makes use of negotiation metaphor for problem solving. It is safe to say that this emerging branch of operations research will find numerous applications in solving engineering and technology management problems

Agent-Based Solution Approaches for Dynamic Traveling Salesman Problem: Resolving or Adapting Existing Solutions to New Conditions?

Dynamic Travelling Salesman Problem (DTSP) is a novel type of TSP where the number of cities in the problem domain changes unpredictably. The approaches to handling dynamism in those DTSPs, has been solving the problems as they were static and recreating the models after each change. In this respect, multi-agent based strategies along with intelligent approaches provide an opportunity to deal with those difficulties. The proposed approach in this paper is based on the modification of existing solutions according to changes in the city domain. Thereby problem is not resolved while local city agents deliver their novel bids (solution proposals) for these new conditions. Finally, general manager agent makes a decision about the new solution. This study presents two different agent-based solution strategies for providing promising solutions to DTSP. One of these strategies is based on the competition of city agents in a greedy way and thereby city agents just search for randomly selected alternatives which are feasible for the new conditions. The second strategy covers competition of city agents by the use of great deluge algorithm as the search mechanism. Finally, both of those proposed strategies are compared against the solutions of rein-vented models. Agent-based strategies start to produce better results as the problem size increases

A multi-agent based approach to modeling and solving dynamic generalized travelling salesman problem

This paper introduces four different types of Generalized Travelling Salesman Problem (GTSP) which are actually dynamic variants of the well-known logistics problems. For all of these defined types, new cities are added to/deleted from the city domain during the travelling of the salesman. This city addition and deletion during the solution phase of the problem, differentiates the proposed types from the classical GTSP. Since these variants of GTSP are relatively complicated compared to classical forms, an agent-based strategy is proposed in this paper to handle complexity and dynamism. In this respect, proposed agent-based strategy employs a general manager and numerous region agents to control and coordinate the dynamism in their regions and in the central level. Region agents create solutions just for their regions and thereby complexity of obtaining a central solution for each change containing is avoided. Findings of the proposed agent-based strategy confirm that adaptation ability of agent-based strategy against the dynamism is significantly better than classical central solution approach. In this respect, this paper is expected to be novel in two respects. First, those four types of GTSP defined in this paper, are different from the classical GTSP since they have dynamic city domain. Second, the proposed novel agent-based solution strategy is capable to create solutions in a timely manner

Flow time analyses of a simulated flexible job shop by considering jockeying

It has been essential to include flexibility in manufacturing policy making since variability in demand and products are considerably increasing. However, it is important to know and to monitor the proper level and type of flexibility that is required to obtain full benefits from it. This paper analyses the effects of flexibility on flow time performance of a simulated job shop. For that purpose, several scenarios are developed under four flexibility levels with two different machine selection rule and three types of dispatching rules. Furthermore, effect of jockeying as a queuing policy on the flow time performance is also investigated through simulation modeling. Results indicated that full flexibility is a preferable state for most of the cases. However, in some cases, chain configurations perform similar results since it combines the benefits of pooling and specialization. In addition, it is observed that a queue control mechanism like jockeying is an effective way to improve performance even though it may increase complexity of controlling policy

A classification scheme for agent based approaches to dynamic optimization

Several papers in the literature employ agent-based modeling approach for providing reasonable solutions to dynamic optimization problems (DOPs). However, these studies employ a variety of agent-based modeling approaches with different strategies and features for different DOPs. On the other hand, there is an absence in the literature of a formal representation of the existing agent-based solution strategies. This paper proposes a representation scheme indicating how the solution strategies with agent-based approach can be summarized in a concise manner. We present these in a tabular form called "Agent Based Dynamic Optimization Problem Solution Strategy" (ABDOPSS). ABDOPSS distinguishes different classes of agent based algorithms (via communication type, cooperation type, dynamism domain and etc.) by specifying the fundamental ingredients of each of these approaches with respect to problem domain (problems with dynamic objective functions, constraints and etc.). This paper also analyzes 18 generic studies in the literature employing agent-based modeling based on ABDOPSS

Enhancing technology clustering through heuristics by using patent counts

International Patent Classification (IPC) system is a hierarchical classification structure used essentially to classify and explore patents along with the technical fields which they are concerned with. Therefore, corresponding number of patents for a certain IPC, can serve as an indicator of technical developments in the relevant area. These numbers can also form a basis for investigating state of the art for a particular field of technology. This paper proposes an approach for clustering of patents for those of the technologies listed by IPC via the number of patent counts. A set of n real numbers indicating the patent counts for different technologies is partitioned into k clusters such that the sum of the squared deviations from the mean-value within each cluster is minimized. With this purpose in mind, two different heuristics have been considered for clustering since complete enumeration would take considerable solution time. The first heuristic is specifically proposed for this study and the second one is Great Deluge Algorithm (GDA) which has been extensively used for solving complicated problems. The proposed heuristics are coded in visual basic (VB) 6.0 and a user interface is developed for the program. The developed program attempts to find the appropriate k value in order to make the best possible clustering. As an application of the proposed clustering approach, patent data that is retrieved from web site of Turkish Patent Institute (TPI) has been used for clustering technologies. (C) 2011 Elsevier Ltd. All rights reserved