Emrah Bulut

Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: A process model for shipping asset management

The aim of this paper is to develop a generic version of the conventional fuzzy-analytic hierarchy process (FAHP) method and investigate the shipping asset management (SAM) problem in the dry bulk shipping market. The recent literature has various applications of the FAHP, but these studies lack consistency control, use identical decision support rather than weighted expert choices, and lack measurable criteria. The proposed model, generic fuzzy-AHP (here after GF-AHP), provides a standard control of consistency on the decision matrix for the expert group. GF-AHP also improves the capabilities of the FAHP by executing direct numerical inputs without expert consultation. In practical business, some of the criteria can be easily calculated and expert consultation is a redundant process. Therefore, GF-AHP presents how to transform such numerical inputs to a priority scale. Finally, expertise differences on the decision group are reflected in the GF-AHP process by an expert weighting algorithm.

A fuzzy extended DELPHI method for adjustment of statistical time series prediction: An empirical study on dry bulk freight market case

This paper investigates the forecasting accuracy of fuzzy extended group decisions in the adjustment of statistical benchmark results. DELPHI is a frequently used method for implementing accurate group consensus decisions. The concept of consensus is subject to expert characteristics and it is sometimes ensured by a facilitators judgment. Fuzzy set theory deals with uncertain environments and has been adapted for DELPHI, called fuzzy-DELPHI (FD). The present paper extends the recent literature via an implementation of FD for the adjustment of statistical predictions. We propose a fuzzy-DELPHI adjustment process for improvement of accuracy and introduced an empirical study to illustrate its performance in the validation of adjustments of statistical forecasts in the dry bulk shipping index.

Regime switching fuzzy AHP model for choice-varying priorities problem and expert consistency prioritization: A cubic fuzzy-priority matrix design

The aim of this paper is to develop a regime switching design of the fuzzy analytic hierarchy process (FAHP) and to improve its functionality under the choice-varying priority (CVP) problem. In the conventional AHP decision process, priority matrices are identical and their values are invariant for a specific objective. However, in many Multi-Criteria Decision Making (MCDM) problems, the relative importance of criteria may differ according to the choices. A regime switching process is proposed for improving the CVP problem. Under the fuzzy-AHP (FAHP) framework, choice-varying priorities are presented in a cubic matrix form. Another novel contribution is suggested in the prioritization of the level of expert consistency. During the decision-making practice, experts may have different attitudes and their individual matrix consistencies might be superior or inferior in their overall practices. Individual consistency is one of the objective indicators of the quality of judgment. An expert consistency prioritization approach is proposed to deal with the classification of response stability. For the financial risk assessment part of the study, the loss probability of the intended projects is calculated by the fuzzy Monte-Carlo simulation framework.

Modeling seasonality using the fuzzy integrated logical forecasting (FILF) approach

Fuzzy time series forecasting (FTSF) is a useful tool for forecasting without expert consultation as well as a user-friendly solution for non-expert forecasters. Before selecting the proper forecasting model, analysis of data series is a key step in the implementation of fuzzy time series forecasting. Seasonality is one of the change-making dimensions of data series that also include temperature, rainfall, freight rates and vessel traffic. The aim of this paper is to improve the fuzzy integrated logical forecasting (FILF) model for the seasonal time series by using the bivariate fuzzy time series approach. The proposed model is applied on the volume of vessel traffic on the Istanbul Strait in order to compare the accuracy of the proposed model with benchmark methods. In addition, the histogram damping partition (HDP) is used to define the initial length of intervals for the fuzzy C-means clustering method.

Bivariate Long Term Fuzzy Time Series Forecasting of Dry Cargo Freight Rates

Abstract This paper proposes a bivariate long term fuzzy inference system for time series forecasting task in the field of freight market. Fuzzy time series methods are applied by many scholars, it is broadly accepted pattern recognition, forecasting tool. Previous studies mainly establish algorithms for high frequency time series data such as daily, monthly intervals. The proposed model performs similar techniques for long term annual base data, also extends the conventional method with multi-variate heuristic algorithm. Empirical work is accomplished on shipping freight rate data, life expectancy is used as a leading indicator in the bivariate fuzzy time series model.

A non-linear clustering method for fuzzy time series

The clustering problem is an emergent issue in fuzzy time series.The existing clustering methods deal with the number of clusters or their size.Optimized cluster paradox refers to that trade-off between size and number.The histogram damping algorithm (HDP) is proposed to deal with this problem by estimating a proper cluster form with number and size characteristics.The proposed model is tested against the conventional methods and the case of random data is also presented. Results indicated superiority of the proposed method. The aim of this paper is to investigate the problem of finding the efficient number of clusters in fuzzy time series. The clustering process has been discussed in the existing literature, and a number of methods have been suggested. These methods have several drawbacks, especially the lack of cluster shape and quantity optimization. There are two critical dimensions in a fuzzy time series clustering: the selection of a proper interval for fuzzy clusters and the optimization of the membership degrees among the fuzzy cluster set. The existing methods for the interval selection assume that the intended data has a short-tailed distribution, and the cluster intervals are established in identical lengths (e.g. Song and Chissom, 1994; Chen, 1996; Yolcu et al., 2009). However, the time series data (particularly in economic research) is rarely short-tailed and mostly converges to long-tail distribution because of the boom-bust market behavior. This paper proposes a novel clustering method named histogram damping partition (HDP) to define sub-clusters on the standard deviation intervals and truncate the histogram of the data by a constraint based on the coefficient of variation. The HDP approach can be used for many different kinds of fuzzy time series models at the clustering stage.

Market entry, asset returns, and irrational exuberance: asset management anomalies in dry cargo shipping

The aim of this paper is to investigate shipping assets and market entry decisions from the point of business cycles in the dry cargo shipping. One of the critical problems of the shipping business is based on the market entry-exit decisions and the investment timing for asset allocation. There are many indicators which define the investment climate in the shipping business and the optimised market entry may extremely contribute the cumulative financial results of a shipping asset. A number of indicators are investigated under the business cycle perspective and the fluctuations of the return on equity (here after ROE) is figured out in the long-run framework. The fluctuations of asset prices and ROE indicate that the ship investor tends to place the investment at the time of peaks of asset prices (new building or second hand) which extremely causes the loss of ROE rates in long-run. The statistical significance is tested and the market entry decision is investigated according to the maximum ROE constraint.

Service quality assessment in liner shipping industry: an empirical study on Asian shipping case

The aim of this paper is to improve service quality of liner shipping companies and investigates the quality function deployment (QFD) in terms of quality assessment method. Liner shipping is particularly characterised by the container transportation and the wider organisation including other logistics activities. The major customers of the logistics services are the industrial clients. The customer satisfaction is a key managerial task since competitiveness is a growing issue in the liner shipping industry. The QFD is one of the unique procedures to expose the requirements of customer and transform them into managerial tasks by cross-correlation analysis between requirements and technical measures. The empirical study is performed to investigate service quality of Asian liner shipping industry by focusing on a group of anonymous leading liner shipping companies. One of the major finding is the customer of liner shipping service tends to purchase a complete transport service including other logistics services. The important technical measures are the implementation of ISO 9001 framework, cheaper service and exemption on terminal handling fee.

Multi-Attribute Decision Making for Crew Nationality Pattern Selection in the Shipping Business: An Empirical Study for Turkish Shipping Case

This paper investigates selection of crew nationality pattern strategy. Manning task is one of the most reported problems in shipping business. Increasing life quality of countries influences popularity of on board employments and its cost has crucial share in total terms. Although many shipping firms have preferred domestic crew, because of excess crew costs and some other reasons, multinational employment is prominent alternative. On the other hand, multi-national manning has its own specific difficulties such as common language on board. An analytic hierarchy process design is developed to answer this problem due to several factors. Alternatives are based on national, fully foreigners and a joint strategy for crew appointments. An empirical study is conducted for Turkish shipping case.

Multi-attribute analysis of ship investments under technical terms: a fuzzy extended TOPSIS approach

This paper investigates investment analysis issue on shipping business and handling of technical terms as well as financial circumstances. Ship investments are evaluated by several financial tools such as net present value, payback period etc. However, these financial methods have restrictions for analysis of technical factors such as speed of ship, under keel distance (for entrance to further ports without limitation) etc. A fuzzy-TOPSIS method is applied for hybrid investment analysis problem and results are reported.