Okan Duru

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 integrated logical forecasting model for dry bulk shipping index forecasting: An improved fuzzy time series approach

This study develops an improved fuzzy time series method via adjustment of the latest value factor and previous error patterns. There are many fuzzy extended applications in the literature, and the fuzzy time series is one successful implementation of fuzzy logical modelling. Fuzzy time series have been studied for over a decade, and many researchers have proposed to remove some of the drawbacks of the initial fuzzy time series algorithm. In this paper, fuzzy integrated logical forecasting (FILF) and extended FILF (E-FILF) algorithms are suggested for short term forecasting purposes. Empirical studies are performed over the Baltic Dry Index (BDI), and indicate the superiority of the proposed approach compared to conventional benchmark methods.

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.

A multivariate model of fuzzy integrated logical forecasting method (M-FILF) and multiplicative time series clustering: A model of time-varying volatility for dry cargo freight market

The aim of this paper is to improve the fuzzy logical forecasting model (FILF) by utilizing multivariate inference and the partitioning problem for an exponentially distributed time series by using a multiplicative clustering approach. Fuzzy time series (FTS) is a growing study field in computer science and its superiority is indicated frequently. Since the conventional time series analysis requires various pre-conditions, the FTS framework is very useful and convenient for many problems in business practice. This paper particularly investigates pricing problems in the shipping business and price-volatility relationship is the theoretical point of the proposed approach. Both FTS and conventional time series results are comparatively presented in the final section and superiority of the proposed method is explicitly noted.

Long Term Freight Market Index and Inferences

Abstract This paper proposes to establish a long term shipping freight index for dry cargo transportation and investigates its particulars among the cyclic fluctuations. Many scholars investigated dry cargo shipping markets and some of them attempted to construct a composite index of freight rates. Although, several critiques are indicated about the method of composition, these indices depicted long term movements in general. This paper also presents models for long term freight rates and seaborne trade with the recent data. A novel contribution is derived from using life expectancy as a long-run leading indicator.

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.

Heuristic estimation of container stacking and reshuffling operations under the containership delay factor and mega-ship challenge

ABSTRACT This paper addresses a highly researched area, the reshuffling problem in ports, using a new paradigm-modified containership service order in light of credit risk assessment. Container stacking and reshuffling operations can cause ship delays and additional risk. In deep-sea terminals, outbound containers are tightly stacked according to the retrieval sequence. Due to lack of space, terminals stack containers in multiple tiers. This means any delay in the arrival of a ship can impose extra handlings and reshuffling of containers delaying future cargo handling. This paper addresses the reshuffling problem with a concept similar to the credit scoring and rating of creditworthiness used in the banking industry. By utilizing this comparison to the banking credit risk concept, a heuristic estimation model is proposed that illustrates the side effects of unscheduled modifications in containership service order. Further, the mega-ship trend amplifies the reshuffling debate. Probability of delay, reshuffles given delay, and call size at delay are introduced as the three-point risk metrics of the model. Numerical simulations illustrate the functionality to develop terminal stacking strategies as well as emphasize the mega-ship phenomenon and its side effects on terminals (i.e. yard operation deadlock).