International Journal of Industrial Engineering: Theory, Applications and Practice
Not a member yet
943 research outputs found
Sort by
A ROBUST CREDIBILITY-BASED FUZZY PROGRAMMING FOR PRODUCTION-ROUTING PROBLEMS WITH CELL FORMATION
No full textProduction-routing problem (PRP) integrates different decisions along the supply chain simultaneously. Studies on PRP only determine the amount and time of production in a linear and continuous production system. While for the supply chain with scattered and different customers, it is necessary to have a flexible production system such as a cellular manufacturing system (CMS). The proposed model incorporates the cell formation problem (CFP) into a PRP. This designing problem in CMS determines the allocation of machines and products to cells and reduces the cost of intercellular movements. We apply a Robust credibility-based fuzzy programming (RCFP) approach versus the possibilistic chance-constrained programming (PCCP) for demand uncertainty. We optimize the model by Teaching-Learning-based Optimization (TLBO) and simulated annealing (SA) algorithms. The convergence in results shows the effectiveness of both algorithms, especially the TLBO algorithm. Furthermore, RCFP leads to a more robust solution with the intelligent choice of the confidence level for satisfying chance constraints and less sensitivity to the changes in non-deterministic parameters
SERVICE QUALITY ANALYSIS AND IMPROVEMENT: DEVELOPMENT OFA SYSTEMATIC FRAMEWORK
No full textAs the service sector is rapidly growing, one of the challenges faced by the service industry is the lack of effe·ctive methodologies for quality analysis and improvement. In service industries, the service quality serves as both a customer retention tool and a business differentiator in local and global competition. This paper aims at developing a systematic framework for service quality analysis and improvement. The proposed framework advantageously integrates quality function deployment (QFD) and structural equation modeling (SEM). More specifically, the framework utilizes QFD to collect, organize, and analyze qualitative information. The results of QFD are used as the basis for developing a service quality improvement strategy. Then, SEM is used in building and analyzing quantitative models to devise a detailed strategy for the improvement. The proposed framework is demonstrated through a case study on the asymmetric digital subscriber lines (ADSL) service of a major telecommunication company in Asia. This framework can be utilized for an effective analysis and improvement ofservice quality not just in the telecommunication industry, but also in any service industry which collects customer satisfaction and service performance data as part of its daily operation
Simultaneous Optimization of Robust Design with Quantitative and Ordinal Data
Get PDFThe Taguchi method traditionally focused on one quality characteristic to optimize the control factor settings, yet most products have more than one quality characteristic. Several studies have presented approaches optimizing the multiple quantitative quality characteristics design. Due to the inherent nature of the quality characteristic or the convenience of the measurement technique and cost-effectiveness, the data observed in many experiments are ordinal data. Few published articles have focused primarily on optimizing the multiple quality characteristics involving quantitative and ordinal data. This paper presents a simple approach to optimizing this problem based on the quality loss function. A numerical example of the polysilicon deposition process for minimizing surface defects and achieving the target thickness in a very large-scale integrated circuit can demonstrate the proposed approach’s effectiveness
A Fuzzy Group Decision-Making Method in the Relationship Between Customer Requirements and Technical Measures of Quality Function Deployment
No full textQuality function deployment (QFD) was developed in Japan and used extensively by Toyota and its suppliers. The process results in a matrix, referred to as the “house of quality,” for a particular product that relates customer requirements to technical measures. Determining the relationship between customer requirements and technical measures is a typical group decision-making process in QFD by a cross-functional team. Generally, different and/or even subjective opinions are happened often in a group decision-making process due to the limitations of experience and vagueness. Obviously, the relationship between customer requirements and technical measures are determined by a group of people with imprecision and vagueness. Under such circumstances, a fuzzy group decision-making method can be applied in the relationship between customer requirements and technical measures of QFD to deal with a group decision-making process when the information is filled with imprecision and fuzziness. Moreover, an example is presented as well as the procedures to show how this fuzzy group decision-making method can be effectively used in QFD to make decisions with imprecision and vagueness
SAFETY EMERGENCY MANAGEMENT STRATEGY OF INDUSTRIAL BUILDING CONSTRUCTION PROJECTS: BASED ON ANALYSIS METHODS OF POLYMORPHIC FUZZY RELIABILITY
No full textStrengthening emergency management of construction safety accidents is an urgent problem. There are few researches on how to ensure emergency intervention to achieve low casualties and property losses after accidents. The paper presented a novel safety emergency management strategy of construction projects: based on analysis methods of polymorphic fuzzy reliability. In the research process, the emergency management and system reliability methods are integrated to build an accident tree analysis model for safety emergency management in construction projects, which is transformed into a polymorphic Bayesian network based on reliability. It makes up for the problem that it is difficult to describe the polymorphism of events and the indeterminacy of logic relations in fault tree analysis. The authors added the fuzzy set theory into the Bayesian network to determine the exact probability of different states of nodes, then the posterior probability and critical importance degree of each root node are calculated. Research result shows that integrating reliability theory with emergency management can improves the reliability of the entire system by analyzing the weak links of the Bayesian network system. The research can provide a reference for safety accident analysis in a multi-state complex system
APPLICATION OF LEAN-KAIZEN CONCEPT FOR IMPROVING QUALITY SYSTEM OF MANUFACTURING FIRMS
No full textThis research paper demonstrates a case study of improving the quality system for ten small and medium-size fastener manufacturing industries by applying the Lean-Kaizen using data envelopment analysis (DEA) technique. The data has been composed by paying personal visits to the selected industries over three months for primarily two inputs (manpower and quality cost) and one output (on-time delivery). The efficiency score of the selected industries has been calculated through the constant returns to scale (CRS), Charnes Cooper & Rhodes (CCR) model having the most efficient industry as a benchmark. The input minimization is considered an objective function to explore the possibilities of input reduction. The Lean-Kaizen concept was to identify the slackness/ gap areas within the inputs with the 5-why method for eliminating the waste and identifying the root cause of the gap areas and Kaizen events. After applying the Kaizen events, the DEA CCR model has been used to analyze the collected data with improvement in efficiency score as findings for the selected industries. Out of ten selected industries,100% efficiency in the quality system has been reported in two industries with 9% to 52% enhancement in others. The study will be helpful to the managers and academicians to overcome the different inefficiencies in the organizations
ONE-TIME ORDER INVENTORY MODEL FOR DETERIORATING AND SHORT MARKET LIFE ITEMS WITH TRAPEZOIDAL TYPE DEMAND RATE
Get PDFDetermining the end of the sales period for a one-time order inventory policy for technology products that see rapid innovation and improvement, such as smartphones, is a vital decision. While the market life cycle is short, with long lead times and expensive deliveries. Such situations can force the number of orders to be few or even only once. Products with the latest technology consist of many components that allow for deterioration from the start. This study discusses the effect of the market life cycle, as indicated by the trapezoidal demand rate, on deteriorating item inventory policies. This study will provide new insights into inventory policy. Mathematical models with a non-linear generalized reduced gradient approach can find the optimal end of the selling period and the order size to achieve maximum profit. A sensitivity analysis showed several findings that provide insight for management
PRICING AND INVESTMENT STRATEGY FOR DIGITAL TECHNOLOGY IN A SUPPLY CHAIN
Get PDFThis study addresses the problem of the pricing and investment strategy for smart technology under a supply chain with one manufacturer and one retailer. The models are constructed to investigate the strategic choices of supply chain members for investing in digital/smart technology under three scenarios: the M–system, wherein only the manufacturer fully pays for the investment cost; the S–system, wherein the manufacturer and retailer share the investment cost; and the R–system, wherein the retailer fully pays for the investment cost. We formulate analytical models to determine the optimal wholesale price, retail price and investment strategy in a Stackelberg game setting. Our findings show that the S–system is the most appropriate choice for both the manufacturer and retailer. We also suggest the appropriate investment sharing ratio to achieve Pareto improvement under such an arrangement
A HYBRID APPROACH BASED ON MACHINE LEARNING IN DETERMINING THE EFFECTIVENESS OF HYDROELECTRIC POWER PLANTS
No full textThis study has developed a machine learning-based framework to determine whether the planned hydroelectric power plants (HEPPs) are effective. First of all, the performance of HEPPs in Turkey was examined via DEA, and efficiency measurement was performed using the output-oriented BCC model. Then, classification models based on machine learning were established by using the obtained efficiency scores and input variables used in DEA. When identical comparisons were made using seven different classification models, REPTree was found to be the superior model. Finally, an interface based on the decision rules derived from RepTree was created to facilitate the use of the established model. With this interface, the HEPP's efficiency can be determined by the relevant inputs before a HEPP investment decision is made. Thanks to this reasonable and intelligent framework, strategic decision support is provided to decision-makers in the field of energy
A Predictive Algorithm for Estimating the Quality of Vehicle Engine Oil
Get PDFRecently, with emerging technologies, visibility of vehicle information over the whole lifecycle becomes possible. The visibility opens up new challenging issues for improving the efficiency of vehicle operations. One of the most challenging problems arising during the middle of life (MOL) of vehicles is the predictive maintenance on engine oil. For this, in this study, we focus on developing a predictive algorithm to estimate the quality of the engine oil of a vehicle by analyzing its degradation status with mission profile data. For this purpose, we specify the relations between indicators of engine mission profiles and oil quality indicators using principal component analysis and regression method. Then, we develop a heuristic algorithm for estimating the value of a quality indicator of engine oil based on them. To evaluate the proposed approach, we carry out a case study and computational experiments