Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 97 Designing Efficient Material Handling Systems Via Automated Guided Vehicles (AGVs) Carlos Llopis-Albert1, Francisco Rubio1, Francisco Valero1 1Centro de Investigación en Ingeniería Mecánica (CIIM). Universitat Politècnica de València – Camino de Vera s/n, 46022 – Valencia, Spain Corresponding autors: Carlos Llopis-Albert: cllopisa@upvnet.upv.es Francisco Rubio: frubio@mcm.upv.es Francisco Valero: fvalero@mcm.upv.es Received: 2018-04-08; Accepted: 2018-08-15 Abstract The designing of an efficient warehouse management system is a key factor to improve productivity and reduce costs. The use of Automated Guided Vehicles (AVGs) in Material Handling Systems (MHS) and Flexible Manufacturing Systems (FMS) can help to that purpose. This paper is intended to provide insight regarding the technical and financial suitability of the implementation of a fleet of AGVs. This is carried out by means of a fuzzy set/qualitative comparative analysis (fsQCA) by measuring the level of satisfaction of managerial decision makers. Keywords Fuzzy sets, qualitative comparative analysis, autonomous guided vehicles, conflict resolution, decision-making, material handling systems, flexible manufacturing systems. Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 98 1. Introduction Logistic processes are a key factor in the industrial performance (Sarker and Gurav, 2005), which can be improved with the use of Automated Guided Vehicles (AGVs). They are programmable and driverless vehicles used in industrial applications by means of a communication and navigation system to carry materials around a manufacturing facility (Pillac et al., 2013). The benefits of its use span from more efficient warehousing systems and inventory control, lower labor costs, increased safety and production, to more Flexible Manufacturing Systems (FMS). The latter is because they can easily deal with changes in both products demand and labor force (Fazlollahtabar et al., 2015). The design of an efficient AGV network system should also take into account the dimensions of the aisles, stacking areas, workstations, and fixed structures on the facility layout. There are several ways of navigation and guidance technologies of AGVs, which range from physical guide-paths using laser, magnetic tape, optical sensors, wire, to gyroscope based inertial guidance and wireless. The latter provides the advantage that can be easily modified. In addition, the AGVs can connect different points of the facility using unidirectional, bidirectional, multi-lane and mixed systems. There are many traffic control algorithms to tackle the routing and scheduling problems, which cover static and dynamic approaches (Biçer and Seifert, 2017). In static approaches the route is known in advance, thus hampering the adaptation to changes in both traffic conditions and logistic system. In dynamic approaches the route uses real-time information (for instance, with regard traffic conditions and obstacles) to calculate the route, thus providing calable MHS and FMS (Gourgand et al., 1995). Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 99 2. Methodology This paper designs an efficient AGV fleet using the fuzzy set/qualitative comparative analysis (fsQCA) (Ragin, 2008). The methodology considers many factors or conditions, covering management, financial and technical ones. The factors or antecedent conditions used in the fsQCA are presented in Table 1. The aim is to determine which configuration of conditions leads to the actors’ satisfaction, which cover shareholders, managers, labor unions and workers. The fuzzy set/qualitative comparative analysis, uses Boolean algebra and fuzzy logic to develop principles of comparison in the qualitative studies of social and economic phenomena (Ragin, 2008; Berbegal-Mirabent and Llopis-Albert, 2015; Llopis-Albert and Palacios-Marqués 2016; Llopis-Albert et al., 2017). An exhausted explanation of the methodology can be found in Mendel and Korjani 2012, 2013. The methodology is intended to find which combination of conditions (configuration) are minimally necessary and/or sufficient for obtaining a certain outcome (Meyer et al., 1993). The methodology is applied to small and medium-sized enterprises (SMEs) of several industrial sectors in Spain based on a wide range of information such as reports, surveys and expert judgment. Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 100 Table 1. Factors or antecedent conditions used in the fsQCA. Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 101 The actors’ preference degree regarding the conditions is determined using a continuous fuzzy set, ranging from 0 (for low degree of acceptance) to 1 (for high degree of acceptance). These values are obtained based on the available information and after a calibration process. This allows to define the truth table, which is subsequently analyzed by means of the fsQCA software (Ragin, 2008). Because there are 13 factors, so that the matrix dimensions are (213) rows and 13 columns, which lead to 8192 possible configurations. Table 2 shows the necessary conditions for the actors’ satisfaction and also for the negation of those factors, which is marked with the tilde (~) sign. Necessary conditions have a consistency score that exceeds the threshold of 0.9 (Schneider et al., 2010). Only 2 (C2 and C4) out of 13 conditions show a consistency above the threshold. However, there are other existing conditions in most of the configurations (e.g., annual savings). This shows that the outcome (stakeholders’ satisfaction) depends on a several conditions because of the high heterogeneity of stakeholders with conflicting interests. Another step is to reduce the number of rows in the matrix, which is performed using the Quine–McCluskey algorithm (Quine, 1952). In this way the matrix is minimized by means of Boolean algebra to obtain a set of combinations of causal conditions, where each of them is minimally sufficient to lead to the outcome. The minimization step is performed through the coverage and consistency values. Table 3 depicts the 13 solutions that are found by the algorithm, in which black circles () indicate the presence of a condition, white circles (�) denote its absence, and blank cells represent ambiguous factors (Ragin and Fiss, 2008). The variety of these configurations suggests that they are sufficient but not necessary. Hence, any configuration explains by itself the actors’ satisfaction. Additionally, all configurations present acceptable consistency values (<0.80) and raw coverage values. Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 102 Table 2. Analysis of necessary conditions and consistency and coverage values for all conditions Table 3. Sufficient configurations of factors for stakeholders’ satisfaction Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 103 4. Conclusions Nowadays, the use AGVs are becomingly more common in the manufacturing industries for transport and warehousing purposes. This paper presents a powerful technique, based on the fsQCA, to design efficient warehouse management system and improve productivity and reduce costs. The method has been successfully applied to a real case study of small and medium-sized enterprises (SMEs) of several industrial sectors in Spain. It can help the decision-making process when designing and implementing a network flow of AGVs systems. Results show that actors are more interested in management and financial conditions than in technical ones. Nevertheless, shareholders and managers pay more attention to the achievement of profits, while labor unions and workers are more prone to keep their jobs and concerned about the technical factors that can hinder the MHS and in safety issues. The unique necessary conditions are the improvement of productivity and performance and the attainment of FMS, since they presumably favor all stakeholders. The methodology increases the business performance and leads to flexible manufacturing systems that can quickly reconfigurable due to production changes. Moreover, it provides more agreement among stakeholders, thus reducing possible delays in the implementation of the AGVs system because of work union strikes. References Berbegal-Mirabent, J., Llopis-Albert, C. (2015). Applications of Fuzzy Logic for Determining the Driving Forces in Collaborative Research Contracts. J. Bus. Res., 69 (4), 1446–1451. DOI: 10.1016/j.jbusres.2015.10.123. Biçer, I., Seifert, R.W., (2017). Optimal Dynamic Order Scheduling under Capacity Constraints Given Demand-Forecast Evolution. Production and Operations Management 26(12), 2266–2286. DOI: 10.1111/poms.12759. Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. Sci. Vol. 5 Nº 2 (2018): 97-105 | 104 Fazlollahtabar, H., Saidi-Mehrabad, M. (2015). Autonomous Guided Vehicles: Methods and Models for Optimal Path Planning. Springer. DOI: 10.1007/978-3-319-14747-5. ISBN 978-3-319-14747-5. Gourgand, M., Sun, X.C., Tchernev, N., 1995. Choice of the guide path layout for an AGV based material handling system. Emerging Technologies and Factory Automation, 1995. ETFA '95, Proceedings. 1995 INRIA/IEEE Symposium on. DOI: 10.1109/ETFA.1995.496688. Llopis-Albert, C., Rubio, F., Valero, F. (2015). Improving productivity using a multi- objective optimization of robotic trajectory planning. Journal of Business Research 68, 1429–1431. DOI: 10.1016/j.jbusres.2015.01.027. Llopis-Albert, C., Palacios-Marqués, D. (2016). Applied Mathematical Problems in Engineering. Multidisciplinary Journal for Education 3(2), 1-14. DOI: 10.4995/muse.2016.6679. Llopis-Albert, C., Merigó, J.M., Xu, Y., Liao, H. (2017). Application of Fuzzy Set/Qualitative Comparative Analysis to Public Participation Projects in Support of the EU Water Framework Directive. Water Environment Research, 89. DOI: 10.2175/WERD1600372. Mendel, J. M.; Korjani, M. M. (2012). Charles Ragin’s Fuzzy Set Qualitative Comparative Analysis (fsQCA) Used for Linguistic Summarizations. Inf. Sci., 202, 1–23. Mendel, J. M., Korjani, M. M. (2013). Theoretical Aspects of Fuzzy Set Qualitative Comparative Analysis (fsQCA). Inf. Sci., 237, 137–161. Meyer, A. D., Tsui, A.S. and Hinings, C.R. (1993). Configurational approaches to organizational analysis. Academy of Management Journal, 36(6), 1175–1195. Quine, W. V. (1952). The problem of simplifying truth functions. The American Mathematical Monthly, 59(8), 521–531. Multidisciplinary Journal for Education, http://dx.doi.org/10.4995/muse.2018.10722 Social and Technological Sciences ISSN: 2341-2593 Llopis-Albert et al. (2018) http://polipapers.upv.es/index.php/MUSE/ Mult. J. Edu. Soc & Tec. 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