A binary particle swarm optimization algorithm for ship routing and scheduling of liquefied natural gas transportation

With the increasing global demands for energy, fuel supply management is a challenging task of today’s industries in order to decrease the cost of energy and diminish its adverse environmental impacts. To have a more environmentally friendly fuel supply network, Liquefied Natural Gas (LNG) is suggested as one of the best choices for manufacturers. As the consumption rate of LNG is increasing dramatically in the world, many companies try to carry this product all around the world by themselves or outsource it to third-party companies. However, the challenge is that the transportation of LNG requires specific vessels and there are many clauses in related LNG transportation contracts which may reduce the revenue of these companies, it seems essential to find the best option for them. The aim of this paper is to propose a meta-heuristic Binary Particle Swarm Optimization (BPSO) algorithm to come with an optimized solution for ship routing and scheduling of LNG transportation. The application demonstrates what sellers need to do to reduce their costs and increase their profits by considering or removing some obligations

A Credit Rating Model in a Fuzzy Inference System Environment

One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting

Evaluating the performance of Colombian banks by hybrid multicriteria decision making methods

The aim of the study in this paper is to show how the performance of banks can be evaluated by ranking them based on Balanced Scorecard (BSC) and Multicriteria Decision Making (MCDM) methods. Nowadays, assessing the performance of companies is a vital work for finding their weaknesses and strengths. The banking sector is an important area in the service sector. Many people want to know which bank performs best when entrusting their money to them. For assessing the performance of banks, BSC can be used. This method helps to translate strategic issues to meaningful insights for the respective financial institutions. After that, the banks will be ranked based on performance indicators by the Weighted Aggregated Sum Product Assessment (WASPAS) method. Because this method is based on a decision matrix, weights are required. To find such weights, the Step-wise Weight Assessment Ratio Analysis (SWARA) method is applied. The results show that the International Bank of Colombia has a much better performance than other Colombian banks. Besides, further insights regarding the evaluation process based on BSC, SWARA, and WASPAS are obtained

An integrated grey-based multi-criteria decision-making approach for supplier evaluation and selection in the oil and gas industry

Purpose The oil and gas industry is a crucial economic sector for both developed and developing economies. Delays in extraction and refining of these resources would adversely affect industrial players, including that of the host countries. Supplier selection is one of the most important decisions taken by managers of this industry that affect their supply chain operations. However, determining suitable suppliers to work with has become a phenomenon faced by these managers and their organizations. Furthermore, identifying relevant, critical and important criteria needed to guide these managers and their organizations for supplier selection decisions has become even more complicated due to various criteria that need to be taken into consideration. With limited works in the current literature of supplier selection in the oil and gas industry having major methodological drawbacks, the purpose of this paper is to develop an integrated approach for supplier selection in the oil and gas industry. Design/methodology/approach To address this problem, this paper proposes a new uncertain decision framework. A grey-Delphi approach is first applied to aid in the evaluation and refinement of these various available criteria to obtain the most important and relevant criteria for the oil and gas industry. The grey systems theoretic concept is adopted to address the subjectivity and uncertainty in human judgments. The grey-Shannon entropy approach is used to determine the criteria weights, and finally, the grey-EDAS (evaluation based on distance from average solution) method is utilized for determining the ranking of the suppliers. Findings To exemplify the applicability and robustness of the proposed approach, this study uses the oil and gas industry of Iran as a case in point. From the literature review, 21 criteria were established and using the grey-Delphi approach, 16 were finally considered. The four top-ranked criteria, using grey-Shannon entropy, include warranty level and experience time, relationship closeness, supplier’s technical level and risks which are considered as the most critical and influential criteria for supplier evaluation in the Iranian oil and gas industry. The ranking of the suppliers is obtained, and the best and worst suppliers are also identified. Sensitivity analysis indicates that the results using the proposed methodology are robust. Research limitations/implications The proposed approach would assist supply chain practicing managers, including purchasing managers, procurement managers and supply chain managers in the oil and gas and other industries, to effectively select suitable suppliers for cooperation. It can also be used for other multi-criteria decision-making (MCDM) applications. Future works on applying other MCDM methods and comparing them with the results of this study can be addressed. Finally, broader and more empirical works are required in the oil and gas industry. Originality/value This study is among the first few studies of supplier selection in the oil and gas industry from an emerging economy perspective and sets the stage for future research. The proposed integrated grey-based MCDM approach provides robust results in supplier evaluation and can be used for future domain applications

Integration of Balanced Scorecard and Fuzzy FMEA for Designing Road Map

Abstract: All organizations look forwards to evaluate themselves. There are a lot of methods for such an evaluation. One popular method is Balanced Scorecard which evaluates an organization from four perspectives. This method is shows in which of the four perspectives, an organization is weak or strong. However, this method has some weaknesses. One of them is that managers do not know which performance driver has a higher priority over others. Such negligence may lead to the loose of markets and customers. The aim of this paper is to find the problem and rank by the risk by fuzzy FMEA.Fuzzy FMEA has shown the risk score of each items by RPN.In this paper we have combined BSC and Fuzzy FMEA methods to introduce a new method for ranking performance drivers and to find out which of the performance drivers has higher priority based on risk factors occurrence (O), severity (S) and detection (D).Also we design improvement projects for high priority risks which are found by Fuzzy FMEA and then we allocate our limited resources to them. This method can help increase the customer satisfaction and design the best improvement projects to reach to our vision. This method performance in Sepehr safety glas

A Credit Rating Model in a Fuzzy Inference System Environment

One of the most important functions of an export credit agency (ECA) is to act as an intermediary between national governments and exporters. These organizations provide financing to reduce the political and commercial risks in international trade. The agents assess the buyers based on financial and non-financial indicators to determine whether it is advisable to grant them credit. Because many of these indicators are qualitative and inherently linguistically ambiguous, the agents must make decisions in uncertain environments. Therefore, to make the most accurate decision possible, they often utilize fuzzy inference systems. The purpose of this research was to design a credit rating model in an uncertain environment using the fuzzy inference system (FIS). In this research, we used suitable variables of agency ratings from previous studies and then screened them via the Delphi method. Finally, we created a credit rating model using these variables and FIS including related IF-THEN rules which can be applied in a practical setting

House of excellence: Better BSC practice through QFD Plus Hoshin Kanri

In this paper, we are to propose a new methodology for organization evaluation by balanced Scorecard, Quality Function Deployment and Hoshin Kanri.This method covers the weaknesses of previous methods and helps managers design a road map. We believe that by combining these methods managers can ensure that limited resources of the organization are allocated properly to get a proper vision. The weakness of the previous methods was that they are more qualitative and very hard to understand for the staff. To remedy this weakness, first of all we execute strategic planning. Secondly a BSC is used translate qualitative factors into quantities factors. Furthermore, we rank these performance drivers by QFD and find out how we can get indicators by Voice of customer, Voice of Staff and Voice of Engineering.Ultimatly we design improvement projects based on these voices and using Hoshin Kanri. The proposed methodology was implemented in Iran Porcelain factory (IRANA)

House of excellence: Better BSC practice through QFD Plus Hoshin Kanri

In this paper, we are to propose a new methodology for organization evaluation by balanced Scorecard, Quality Function Deployment and Hoshin Kanri.This method covers the weaknesses of previous methods and helps managers design a road map. We believe that by combining these methods managers can ensure that limited resources of the organization are allocated properly to get a proper vision. The weakness of the previous methods was that they are more qualitative and very hard to understand for the staff. To remedy this weakness, first of all we execute strategic planning. Secondly a BSC is used translate qualitative factors into quantities factors. Furthermore, we rank these performance drivers by QFD and find out how we can get indicators by Voice of customer, Voice of Staff and Voice of Engineering.Ultimatly we design improvement projects based on these voices and using Hoshin Kanri. The proposed methodology was implemented in Iran Porcelain factory (IRANA)