Search results
1 – 10 of over 14000Indraneel Das, Dilbagh Panchal and Mohit Tyagi
This paper aims to presents a novel integrated fuzzy decision support system for analyzing the issues related to failure of a milk process plant unit.
Abstract
Purpose
This paper aims to presents a novel integrated fuzzy decision support system for analyzing the issues related to failure of a milk process plant unit.
Design/methodology/approach
Process failure mode effect analysis (PFMEA) approach was implemented to list failure causes under each subsystem/component and fuzzy ratings for three risk criteria, i.e. probability of failure occurrence (O_f), severity (S) and non-detection (O_d) are collected against the listed failure causes through experts feedback. A new doubly technique for order of preference by similarity to ideal solution (DTOPSIS) approach was implemented within fuzzy PFMEA tool for ranking of listed failure causes. The proposed decision support system overcomes the restrictions of classical PFMEA and IF-THEN rule base PFMEA approaches in an effective way.
Findings
Failure causes such as electrical winding failure (RM4), high pressure in plate region (C1), communication problem in supervisory control and data acquisition control (MS3), insulation problem (ST2), lever breakage (B2), gasket problem (D3), formation of holes (PHE5), cavitations (FP7), deposition of milk particle inside the pipeline because of improper cleaning (MHP2) were acknowledged as the most critical one with the application of proposed decision support system.
Research limitations/implications
The analysis results are based on subjective judgments of the experts and therefore correctness of risk ranking results are totally dependent upon the quality of input data/information available from these experts. However, the analyst has taken proper care for considering the vagueness of the raw data by incorporating fuzzy set theory within the proposed decision support system.
Practical implications
The proposed fuzzy decision support system has been presented with its application on milk pasteurization plant of a milk process industry. The analysis based ranking results have been supplied to maintenance manager of the plant and a consent was shown by him with these results. Once the top management of the plant took decision for the implementation of these results, the detailed robustness of the proposed decision support system could be evaluated further.
Social implications
The analysis result would be highly useful for minimizing sudden breakdowns and operational cost of the plant which directly contributes to plant's profitability. With the decrease in the chances of sudden breakdowns there would be high safety for the people working on/off the plant's site. Further, with increase in availability of the considered plant the societal daily demand related to dairy products could be easily fulfilled at reasonable prices.
Originality/value
The performance and proficiency of the proposed decision support system has been evaluated by comparing the ranking results with classical TOPSIS and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) approaches based results.
Details
Keywords
Yuliana Kaneu Teniwut, Marimin Marimin and Nastiti Siswi Indrasti
The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP…
Abstract
Purpose
The purpose of this paper is to develop a spatial intelligent decision support system (SIDSS) for increasing productivity in the rubber agroindustry by green productivity (GP) approach. The SIDSS was used to measure the productivity of rubber plantation and rubber agroindustry by GP approach, and select the best strategies for increasing the productivity of rubber agroindustry.
Design/methodology/approach
This system was developed by combining spatial analysis, GP, and fuzzy analytic network process (ANP) with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry. Rubber plantation productivity measurement model was used to find the productivity level of rubber plantation with fuzzy logic, and also to provide information and decision alternatives to all stakeholders regarding spatial condition of rubber agroindustry, production process flow, and analysis of the seven green wastes at each production process flow using the geographic information system. GP measurement model was used to determine the productivity performance of the rubber agroindustry with the green productivity index (GPI). The best strategy for increasing the productivity was determined with fuzzy ANP.
Findings
Rubber plantation measurement model showed that the average of plantation productivity was 6.25 kg/ha/day. GP measurement model showed that the GPI value of ribbed smoked sheet (RSS) was 0.730, whereas of crumb rubber (CR) was 0.126. The best strategy for increasing the productivity of rubber agroindustry was raw material characteristics control. Based on the best strategy, the GPI value of RSS was 1.340, whereas of CR was 0.228.
Research limitations/implications
This decision support system is still limited as it is based on static data; it needs further development so that it can be more dynamically based on developments in the rubber agroindustry related levels of productivity and environmental impact. In addition, details regarding the decision to increase the productivity of the rubber section by benchmarking efforts should be studied further, both among plantation as well as among countries such as Thailand so that the productivity of rubber plantation and agroindustry can be integrated.
Practical implications
This research can help the planters to select superior clones for rubber trees, to improve the technique of tapping latex, and to use a better coagulant. The good quality and quantity of raw material is a key factor in increasing the productivity of rubber agroindustry; if the quality of latex is good then the resulting product will also have a good quality and production cost can be reduced. In addition, the application of GP through the calculation of GPI value using improvement scenarios can be used as a reference and comparison for evaluating the performance of rubber agroindustry to reduce the waste generated by the activities of rubber processing plant.
Social implications
Reduction of waste generated by production activities can improve the quality of life of the workforce and the environment. The calculation of GPI value can also be used as a basis to use raw materials, water, and electricity more efficiently.
Originality/value
This system was developed by combining spatial analysis, GP, and fuzzy ANP with the model-based management system, which is able to provide comprehensive and meaningful decision alternatives for the development of natural rubber agroindustry.
Details
Keywords
Ajith Abraham, Sonja Petrovic‐Lazarevic and Ken Coghill
This paper aims to propose a novel computational framework called EvoPOL (EVOlving POLicies) to support governmental policy analysis in restricting recruitment of smokers. EvoPOL…
Abstract
Purpose
This paper aims to propose a novel computational framework called EvoPOL (EVOlving POLicies) to support governmental policy analysis in restricting recruitment of smokers. EvoPOL is a fuzzy inference‐based decision support system that uses an evolutionary algorithm (EA) to optimize the if‐then rules and its parameters. The performance of the proposed method is compared with a fuzzy inference method adapted using neural network learning technique (neuro‐fuzzy).
Design/methodology/approach
EA is a population‐based adaptive method, which may be used to solve optimization problems, based on the genetic processes of biological organisms. The Takagi‐Sugeno fuzzy decision support system was developed based on three sub‐systems: fuzzification, fuzzy knowledge base (if‐then rules) and defuzzification. The fine‐tuning of the fuzzy rule base and membership function parameters is achieved by using an EA.
Findings
The proposed EvoPOL technique is simple and efficient when compared to the neuro‐fuzzy approach. However, EvoPOL attracts extra computational cost due to the population‐based hierarchical search process. When compared to neuro‐fuzzy model the error values on the test sets have improved considerably. Hence, when policy makers require more accuracy EvoPOL seems to be a good solution.
Originality/value
When policy makers require more accuracy EvoPOL seems to be a good solution. For complicated decision support systems involving more input variables, EvoPOL would be an excellent candidate for framing if‐then rules with precise decision scores that could help the government representatives as to what extent to concentrate on available social regulation measures in restricting the recruitment of smokers.
Details
Keywords
Hongmei Yang, Chimay J. Anumba, John M. Kamara and Patricia Carrillo
Describes a study that exploits the potential of fuzzy systems in construction through the development of a decision support system which is capable of handling fuzziness in the…
Abstract
Describes a study that exploits the potential of fuzzy systems in construction through the development of a decision support system which is capable of handling fuzziness in the collaborative decision‐making process. The proposed system is intended to provide an objective and rational framework within which collaborative decisions can be made by virtual construction project teams. Given the often linguistic nature of the weightings ascribed by individual disciplines to decision criteria, the proposed system will utilise fuzzy systems theory to rank criteria and recommend an optimal decision alternative. Presents an example to illustrate how the proposed tool works.
Details
Keywords
S.I. Lao, K.L. Choy, G.T.S. Ho, Y.C. Tsim and C.K.H. Lee
With the increasing concerns about food management, attention is placed on the monitoring of different potential risk factors for food handling. Therefore, the purpose of this…
Abstract
Purpose
With the increasing concerns about food management, attention is placed on the monitoring of different potential risk factors for food handling. Therefore, the purpose of this paper is to propose a system that helps facilitate and improve the quality of decision making, reduces the level of substandard goods, and facilitates data capturing and manipulation, to help a warehouses improve quality assurance in the inventory‐receiving process with the support of technology.
Design/methodology/approach
This system consists of three modules, which integrate the radio frequency identification (RFID) technology, case‐based reasoning (CBR), and fuzzy reasoning (FR) technique to help monitor food quality assurance activities. In the first module, the data collection module, raw warehouse and work station information are collected. In the second module, the data sorting module, the collected data are stored in a database. In this module, data are decoded, and the coding stored in the RFID tags are transformed into meaningful information. The last module is the decision‐making module, through which the operation guidelines and optimal storage conditions are determined.
Findings
To validate the feasibility of the proposed system, a case study was conducted in food manufacturing companies. A pilot run of the system revealed that the performance of the receiving operation assignment and food quality assurance activities improved significantly.
Originality/value
In summary, the major contribution of this paper is to develop an effective infrastructure for managing food‐receiving process and facilitating decision making in quality assurance. Integrating CBR and FR techniques to improve the quality of decision making on food inventories is an emerging idea. The system development roadmap demonstrates the way to future research opportunities for managing food inventories in the receiving operations and implementing artificial intelligent techniques in the logistics industry.
Details
Keywords
Banu Tuğba Turğut, Gamze Taş, Ahmet Herekoğlu, Hakan Tozan and Ozalp Vayvay
The purpose of this paper is to propose a disaster logistics center location selection decision support system, based on analytic hierarchy/fuzzy analytic hierarchy process…
Abstract
Purpose
The purpose of this paper is to propose a disaster logistics center location selection decision support system, based on analytic hierarchy/fuzzy analytic hierarchy process methods, which will serve to fulfill the needs of disaster victims and rescue teams after a possible earthquake, and to implement the proposed systems for Istanbul.
Design/methodology/approach
Determining the appropriate location from among the possibilities by taking many sophisticated and inter‐related processes and parameters into consideration under stringent constraints is one of the keystones of logistics. Disaster logistics center location selections are extremely complex and vital. In this paper, analytic hierarchy/fuzzy analytic hierarchy process methods are used to compose a decision support system for determining the location of disaster logistics centers. The criteria and the weighting for the criteria that are used are determined via a questionnaire technique applied to specialists working in the Istanbul Center of Disaster Coordination.
Findings
Results gathered from the implementation of the proposed models to the chosen case illustrate that systems successfully determine the location, and both models point out the same result with different weights.
Originality/value
The paper introduces two disaster logistics center location selection decision support systems (fuzzy and crisp) and presents an empirical case study of the proposed models for Istanbul. The proposed model and outcomes from the application may shed light on future work, especially in the field of disaster logistics management.
Details
Keywords
The purpose of this paper is to provide an integrated group decision support system (GDSS) that will select the appropriate human resource (HR) capabilities for a firm by using…
Abstract
Purpose
The purpose of this paper is to provide an integrated group decision support system (GDSS) that will select the appropriate human resource (HR) capabilities for a firm by using existing decision algorithms and information technology (IT) software systems.
Design/methodology/approach
The proposed GDSS is constructed by taking advantage of the characteristics of some existing analytical and mathematical methods, including electronic focus groups, value chain, HR scorecard, synergy analysis, gap analysis, analytic hierarchy process based on genetic algorithms (GA‐AHP), similarity measures, fuzzy set theory, and fuzzy mathematics programming. A case study is performed to test and evaluate the performance and usability of the GDSS and to identify whether or not it achieved its designed purpose.
Findings
The results show that the proposed GDSS can create a flexible and user‐friendly environment that aids managers and other relevant staff members in evaluating all relevant factors in selecting a firm's HR capabilities.
Practical implications
HR capabilities have a significant effect on business performance in the long term. However, not every firm can easily develop suitable HR capability strategies due to lacking of the adapted support tool. The proposed GDSS is proposed to provide a complete procedure to support managers using a strategy‐oriented perspective to decide the right HR capability to be developed. As the result of using the proposed GDSS, tasks are simplified and the time for HR capability analysis can be significantly reduced.
Originality/value
Few studies have discussed the application of IT to the selection of HR capabilities in facilitating managers in the strategic formulation process. This paper particularly focuses on the question of how firms can actually identify HR capabilities. Thus, the model‐developing nature‐oriented support system is provided for managers in solving such decision‐making problems.
Details
Keywords
Davide Aloini, Riccardo Dulmin, Valeria Mininno and Pierluigi Zerbino
This paper aims to model a decision support system (DSS) that could overcome the oversimplified, subjective, compensatory decision logic of extant purchasing portfolio models…
Abstract
Purpose
This paper aims to model a decision support system (DSS) that could overcome the oversimplified, subjective, compensatory decision logic of extant purchasing portfolio models (PPMs) by leveraging the firms’ procurement-related knowledge base.
Design/methodology/approach
The DSS was developed through a fuzzy-based approach, whose design and application were framed within a case study in a multinational company.
Findings
The application of the fuzzy-based DSS to a product class suggests investing in the relationship with two specific suppliers and to loosen the relationship with a third one.
Research limitations/implications
Exploiting the fuzzy set theory and fostering the elicitation of procurement-related knowledge from the decision-makers, the DSS effectively tackles the concerns about the existing PPMs by including strategic-oriented priorities and contextual constraints in the evaluation.
Practical implications
The recommendations in output from the DSS are feasible, more analytical and easy to interpret, enabling knowledge sharing, group decision processes and better decision-making.
Originality/value
To the best of the authors’ knowledge, this manuscript is the first attempt to effectively integrate traditional PPMs with contextual, strategy-related factors to refine the purchasing directions and make them objective.
Details
Keywords
Javier Puente, Raúl Pino, Paolo Priore and David de la Fuente
This study describes an alternative way of applying failure mode and effects analysis (FMEA) to a wide variety of problems. It presents a methodology based on a decision system…
Abstract
This study describes an alternative way of applying failure mode and effects analysis (FMEA) to a wide variety of problems. It presents a methodology based on a decision system supported by qualitative rules which provides a ranking of the risks of potential causes of production system failures. By providing an illustrative example, it highlights the advantages of this flexible system over the traditional FMEA model. Finally, a fuzzy decision model is proposed, which improves the initial decision system by introducing the element of uncertainty.
Details
Keywords
Asli Aksoy, Nursel Ozturk and Eric Sucky
Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors'…
Abstract
Purpose
Demand forecasting in the clothing industry is very complex due to the existence of a wide range of product references and the lack of historical sales data. To the authors' knowledge, there is an inadequate number of literature studies to forecast the demand with the adaptive network based fuzzy inference system for the clothing industry. The purpose of this paper is to construct a decision support system for demand forecasting in the clothing industry.
Design/methodology/approach
The adaptive‐network‐based fuzzy inference system (ANFIS) is used for forecasting demand in the clothing industry.
Findings
The results of the proposed study showed that an ANFIS‐based demand forecasting system can help clothing manufacturers to forecast demand more accurately, effectively and simply.
Originality/value
In this study, the demand is forecast in terms of clothing manufacturers by using ANFIS. ANFIS is a new technique for demand forecasting, it combines the learning capability of the neural networks and the generalization capability of the fuzzy logic. The input and output criteria are determined based on clothing manufacturers' requirements and via literature research, and the forecasting horizon is about one month. The study includes the real life application of the proposed system and the proposed system is tested by using real demand values for clothing manufacturers.
Details