An integrated MCDM approach to evaluate public transportation systems in Tehran

Public transportation is one of the most important systems in transportation, especially in big and crowded cities. As a result, evaluation of public transportation systems is a strategic decision-making problem for both private and public sections. In this paper, the problem of public transportation passengers in Tehran is addressed and their satisfaction levels are assessed by using passenger satisfaction survey. An integrated MCDM approach is proposed for evaluation of public transportation systems based on Delphi method, group analytic hierarchy process (GAHP) and preference ranking organization method for enrichment of evaluations (PROMETHEE). The proposed model provides more reliable and realistic results and introduces directions for future improvements of public transportation service quality. A sensitivity analysis is applied to investigate the influence of criteria weights on the decision making problem. As a conclusion, the most important public transportation systems in Tehran orderly are: metro, taxi, BRT, bus and van. Therefore, Tehran Municipality and policy makers should encourage and support the previously mentioned systems

Maintenance Strategy Choice Supported by the Failure Rate Function: Application in a Serial Manufacturing Line

The purpose of this article is to choose a maintenance procedure for the critical equipment of a forging production line with five machines. The research method is quantitative modelling and simulation. The main research technique includes retrieving time between failure and time to repair data and find the most likely distribution that has produced the data. The most likely failure rate function helps to define the maintenance strategy. The study includes two kinds of maintenance policies, reactive and anticipatory. Reactive policies include emergency and corrective procedures. Anticipatory policies include predictive and preventive ones combined with a total productive maintenance management approach. The most suitable combination for the first three machines is emergency and corrective choice. For the other machines, a combination of total productive maintenance and a predictive approach is optimal. The study encompasses the case of a serial production manufacturing line and maximum likelihood estimation. The failure rate function defines a combination of strategies for each machine. In addition, the study calculates the individual and systemic mean time to failure, mean time to repair, availability, and the most likely number of failures per production order, which follows a Poisson process. The main contribution of the article is a structured method to help define maintenance choices for critical equipment based on empirical data

A real-time data-driven framework for the identification of steady states of marine machinery

While maritime transportation is the primary means of long-haul transportation of goods to and from the EU, it continues to present a significant number of casualties and fatalities owing to damage to ship equipment; damage attributed to machinery failures during daily ship operations. Therefore, the implementation of state-of-the-art inspection and maintenance activities are of paramount importance to adequately ensure the proper functioning of systems. Accordingly, Internet of Ships paradigm has emerged to guarantee the interconnectivity of maritime objects. Such technology is still in its infancy, and thus several challenges need to be addressed. An example of which is data preparation, critical to ensure data quality while avoiding biased results in further analysis to enhance transportation operations. As part of developing a real-time intelligent system to assist with instant decision-making strategies that enhance ship and systems availability, operability, and profitability, a data-driven framework for the identification of steady states of marine machinery based on image generation and connected component analysis is proposed. The identification of such states is of preeminent importance, as non-operational states may adversely alter the results outlined. A case study of three diesel generators of a tanker ship is introduced to validate the developed framework. Results of this study demonstrated the outperformance of the proposed model in relation to the widely implemented clustering models k-means and GMMs with EM algorithm. As such, the proposed framework can adequately identify steady states appropriately to guarantee the detection of such states in real-time, whilst ensuring computational efficiency and model effectiveness

RAYLI ULAŞIM SİSTEMLERİNDE BAKIM YÖNTEMLERİNİN VERİMLİLİK AÇISINDAN ÖNCELİKLENDİRİLMESİ

Amaç: Yapılan çalışmada, raylı ulaşım sistemlerinde kullanılan bakım yöntemlerini verimlilik açısından değerlendirebilmek için literatürdeki çalışmalar, raylı sistemler verimlilik hesaplama kriterleri incelenerek çok kriterli karar verme metotları (ÇKKV) ile önceliklendirme yapılması amaçlanmıştır.Yöntem: Bakımın verimlilik üzerindeki etkisi belirlenmiş, uygulanan bakım metotları ve bakım metotlarının ulaşım verimliliğini etkilediği kriterler belirlenmiştir. Belirlenen kriterler ÇKKV metotları ile hesaplanmıştır.Bulgular: Çalışmamızda, bakımın arıza oluşumu ilişkisi nedeniyle verimliliği etkilediği ve verimliliği etkileyen 10 ayrı bakım kriteri olduğu, AHP uygulaması sonucunda, 10 kriter arasında %23,96 etki ağırlığı ile bakım maliyetinin en yüksek etkiye sahip olduğu tespit edilmiştir. PROMETHEE uygulamasında, tercih edilmesi gereken bakım yöntemleri olarak izleme yöntemi ile bakımın 0,8634 ortalama akım değeri ile ilk sıradaki yöntem olduğu, bu yöntemi aktif bakım (0,2437), periyodik bakım (0,1377), önleyici bakım (0,0685) ve fırsat bakım (0,0396) uygulamalarının takip ettiği tespit edilmiştir. Negatif yönlü ortalama akım değerine sahip operatör bakımı (-0,4353), yerinde bakım (-0,4549) ve düzeltici bakım (-0,4627) uygulamalarının tercih önceliği bulunmadığı tespit edilmiştir.Özgünlük: Çalışmada PROMETHEE uygulamasının, raylı ulaşım sistemlerinde kullanılan bakım yöntemlerinin verimliliği üzerinde kullanılmadığı görülmüştür. Çalışmanın raylı ulaşım sistemlerinde verimliliği etkileyen diğer başlıklar üzerinde uygulanabileceği ve literatüre katkı sağlayacağı düşünülmektedir

Evaluation Framework for Key Performance Indicators of Railway ITS

The aim of this study is to develop a framework for investigating a comprehensive set of Key Performance Indicators (KPIs) for the assessment of railway Intelligent Transportation Systems (ITS). The framework is established through four main steps: (1) development of a comprehensive set of KPIs for railway ITS; (2) validation of developed KPIs and collection of judgments from experts through a Delphi questionnaire; (3) evaluation of KPIs weights for assessing railway ITS with the Group Analytical Hierarchy Process (GAHP); and (4) presentation of a SWOT analysis for the developed KPIs by the authors. The results of the framework are presented as a set of 25 indicators for evaluation of railway ITS and their impacts. The framework could be helpful for selecting KPIs of ITS in another mode of transportation. Monitoring of the contributions of ITS towards sustainable railway can be achieved by a developed set of indicators which are classified in accordance with sustainable dimensions

Investigating ship system performance degradation and failure criticality using FMECA and artificial neural networks

The goal of all maintenance methods is to eliminate failures or reduce their occurrence. Ex-tended downtime on key ships systems such as power generation plants can lead to undesirable consequences beyond economic and operational losses, especially considering naval vessels. One solution to overcome this challenge is through a system-specific analysis that identifies the most critical component and possible causes of delays be it technical or logistics. In this regard, this paper presents a methodology using FMECA approach that adopts the risk priority number differently to identify Mission Critical Components. This was supported with ANN classification using unsupervised learning to identify patterns in the data that signifies the onset of performance degradation and potential failures onboard an OPV. The study has identified some critical components and failure patterns that contribute to extended downtime based on survey and machinery maintenance reports. Recommendations were provided on preventing/mitigating the failures and how to prioritize existing ship systems maintenance

FAILURE MODE AND EFFECTS ANALYSIS OF SHIP SYSTEMS USING AN INTEGRATED DEMPSTER SHAFER THEORY AND ELECTRE METHOD

Failure Mode and Effects Analysis (FMEA) is a risk analysis tool which is used to define, identify, and eliminate known and/or potential failures from a system. The task is generally performed by team of experts. Each of the team of experts can express diverse opinions in rating of failure modes of systems which may be in the form of precise data and imprecise distribution ratings. However the RPN of FMEA is incapable of using these various forms of information in the prioritisation of risk of failure modes. This is one of the main limitations of FMEA. Furthermore the technique is limited to the use of three decision criteria thereby excluding other important decision criteria such as production loss in prioritising risk. To address these problems a novel FMEA tool is proposed which combines Dempster Shafer Theory with the ELECTRE method to provide a more efficient failure mode prioritisation method. With this technique the Dempster Shafer Theory is used in aggregating different failure mode ratings from experts and the ELECTRE method is applied in the ranking of failure modes. The applicability of the proposed technique is demonstrated with a case study of a marine diesel engine. Results showed that the proposed method can be applied in addressing risk prioritisation problem more efficiently than the FMEA and its variant

Integration of Fuzzy PIPRECIA and Fuzzy MOORA Methods for Maintenance Strategy Selection

In today’s competitive environment, there is a pressure on companies for reducing costs and increasing the quality by providing on time delivery. Maintenance, plays an important role in reducing cost, improving quality, reducing failures, minimizing machine downtime, increasing productivity and as a result achieving objectives of company. The aim of this paper is to select best maintenance strategy for a manufacturing company by using an integrated fuzzy MCDM (Multi-Criteria Decision Making) approach. This approach is based on fuzzy PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) and fuzzy MOORA (Multi Objective and Optimization on the Basis of Ratio Analysis) methods. The selection of maintenance strategy is a multi-criteria decision making (MCDM) problem. As this problem includes uncertainties and difficulty in evaluating alternatives and criteria with definite expressions, fuzzy MCDM approach is proposed for selecting the best maintenance strategy. As a result of the application of the proposed integrated method in the manufacturing company, the ranking of the maintenance strategies was obtained, and predictive maintenance strategy was determined as the most appropriate maintenance strategy for the company

The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty