A Fuzzy Based Approach for New Product Concept Evaluation and Selection

Product developers make many decisions during the early stages of product development which have a profound impact on the final cost of the product. These decisions include selecting a product concept that best meet customer needs.  Product concept selection involves using the collective knowledge of many experts who possess different backgrounds and expertise in various fields to evaluate a set of product concepts developed to meet certain customer needs. This paper proposes a concept evaluation and selection methodology capable of capturing the fuzziness and vagueness impeded in concept evaluation. The proposed methodology integrates the Weighted Concept Selection Matrix with the Analytical Hierarchal Process (AHP) under a Fuzzy environment. The developed methodology has the capability of capturing the fuzziness and vagueness in the concept evaluators’ ratings. The methodology consists of eight steps that begins with retrieving the product concepts, developing the evaluation criteria and selecting the evaluators, and ends up by choosing the best concept. The criteria are prioritized and assigned fuzzy weights according to their importance with respect to the nature of the product and based on the capabilities of the manufacturing company.  Furthermore, the evaluators are prioritized and assigned fuzzy weights with respect to the criteria based on their different backgrounds. These weights are aggregated with the concepts’ fuzzy rating done by the evaluators in order to compute a final score for each concept. The usage of the methodology is verified and tested by using an illustrative example. Keywords: Product Design, Fuzzy systems, Multi-criteria Decision Making, Analytical Hierarchal Proces

Improved Genetic and Simulating Annealing Algorithms to Solve the Traveling Salesman Problem Using Constraint Programming

The Traveling Salesman Problem (TSP) is an integer programming problem that falls into the category of NP-Hard problems. As the problem become larger, there is no guarantee that optimal tours will be found within reasonable computation time. Heuristics techniques, like genetic algorithm and simulating annealing, can solve TSP instances with different levels of accuracy. Choosing which algorithm to use in order to get a best solution is still considered as a hard choice. This paper suggests domain reduction as a tool to be combined with any meta-heuristic so that the obtained results will be almost the same. The hybrid approach of combining domain reduction with any meta-heuristic encountered the challenge of choosing an algorithm that matches the TSP instance in order to get the best results

Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks

Wireless Sensor Networks (WSNs) can be used in many real applications (environmental monitoring, habitat monitoring, health, etc.). The energy consumption of each sensor should be as lower as possible, and methods for grouping nodes can improve the network performance. In this work, we show how organizing sensors in cooperative groups can reduce the global energy consumption of the WSN. We will also show that a cooperative group-based network reduces the number of the messages transmitted inside the WSNs, which implieasa reduction of energy consumed by the whole network, and, consequently, an increase of the network lifetime. The simulations will show how the number of groups improves the network performance. © 2011 Springer Science+Business Media, LLC.García Pineda, M.; Sendra Compte, S.; Lloret, J.; Canovas Solbes, A. (2013). Saving Energy and Improving Communications using Cooperative Group-based Wireless Sensor Networks. Telecommunication Systems. 52(4):2489-2502. doi:10.1007/s11235-011-9568-3S24892502524Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Journal of Computer Networks, 38(4), 393–422.Garcia, M., Bri, D., Sendra, S., & Lloret, J. (2010). Practical deployments of wireless sensor networks: a survey. Journal on Advances in Networks and Services, 3(1&2), 1–16.Lloret, J., Garcia, M., Bri, D., & Sendra, S. (2009). A wireless sensor network deployment for rural and forest fire detection and verification. Sensors, 9(11), 8722–8747.Mainwaring, A., Polastre, J., Szewczyk, R., & Culler, D. (2002). Wireless sensor networks for habitat monitoring. In ACM workshop on sensor networks and applications (WSNA’02), Atlanta, GA, USA, September.Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2010, in press). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, pp. 1–9. doi: 10.1049/iet-com.2010.0654 .Sinha, A., & Chandrakasan, A. (2001). Dynamic power management in wireless sensor networks. IEEE Design & Test of Computers, 18(2), 62–74.Garcia, M., Coll, H., Bri, D., & Lloret, J. (2008). Using MANET protocols in wireless sensor and actor networks. In The second international conference on sensor technologies and applications (SENSORCOMM 2008), Cap Esterel, Costa Azul, France, 25–31 August.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In Wireless and mobile networking: Vol. 284 (Chap. 13, pp. 161–172). Berlin, Heidelberg, Boston: Springer.Lloret, J., García, M., & Tomás, J. (2008). Improving mobile and ad-hoc networks performance using group-based topologies. In Wireless sensor and actor networks 2008 (WSAN 2008), Ottawa, Canada, 14–15 July. Berlin, Heidelberg, New York: Springer.Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improving networks using group-based topologies. Journal of Computer Communications, 31(14), 3438–3450.Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: a group-based protocol for large wireless ad hoc and sensor networks. Journal of Computer Science and Technology, 23(3), 461–480.Lloret, J., García, M., Boronat, F., & Tomás, J. (2008). MANET protocols performance in group-based networks. In 10th IFIP international conference on mobile and wireless communications networks (MWCN 2008), Toulouse, France, 30 September–2 October.Garcia, M., Sendra, S., Lloret, J., & Lacuesta, R. (2010). Saving energy with cooperative group-based wireless sensor networks. In LNCS: Vol. 6240. Cooperative design, visualization, and engineering: CDVE 2010 (pp. 231–238), September. Berlin: Springer.Lloret, J., Sendra, S., Coll, H., & García, M. (2010). Saving energy in wireless local area sensor networks. Computer Journal, 53(10), 1658–1673.Meiyappan, S. S., Frederiks, G., & Hahn, S. (2006). Dynamic power save techniques for next generation WLAN systems. In Proceedings of the 38th southeastern symposium on system theory (SSST), Cookeville, Tennessee, USA, 5–7 March.Raghunathan, V., Schurgers, C., Park, S., & Srivastava, M. (2002). Energy aware wireless microsensor networks. IEEE Signal Processing Magazine, 19(2), 40–50.Min, R., Bhardwaj, M., Cho, S.-H., Shih, E., Sinha, A., Wang, A., & Chandrakasan, A. (2001). Low power wireless sensor networks. In Proceedings of international conference on VLSI design, India, Bangalore, 3–7 January.Salhieh, A., Weinmann, J., Kochha, M., & Schwiebert, L. (2001). Power efficient topologies for wireless sensor networks. In Proceedings of the IEEE international conference on parallel processing (pp. 156–163), 3–7 September.Jayashree, S., Manoj, B. S., & Murthy, C. S. R. (2004). A battery aware medium access control (BAMAC) protocol for Ad-hoc wireless network. In Proceedings of the 15th IEEE international symposium on personal, indoor and mobile radio communications (PIMRC 2004), Barcelona, Spain, 5–8 September (Vol. 2, pp. 995–999).Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. In Proceedings IEEE INFOCOM 2002, the 21st annual joint conference of the IEEE computer and communications societies, New York, USA, 23–27 June.Ching, C., & Schindelhauer, C. (2010). Utilizing detours for energy conservation in mobile wireless networks. Journal of Telecommunication Systems. doi: 10.1007/s11235-009-9188-3 .Gao, Q., Blow, K., Holding, D., Marshall, I., & Peng, X. (2004). Radio range adjustment for energy efficient wireless sensor networks. Journal of Ad Hoc Networks, 4(1), 75–82.Li, D., Jia, X., & Liu, H. (2004). Energy efficient broadcast routing in static ad hoc wireless networks. IEEE Transactions on Mobile Computing, 3(1), 1–8.Camilo, T., Carreto, C., Silva, J., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks. In Lecture notes in computer science: Vol. 4150. Ant colony optimization and swarm intelligence (pp. 49–59). Berlin: Springer.Younis, M., Youssef, M., & Arisha, K. (2002). Energy-aware routing in cluster-based sensor networks. In Proceedings of the 10th IEEE international symposium on modeling, analysis, and simulation of computer and telecommunications systems (MASCOTS ’02) (pp. 129–136). Washington: IEEE Computer Society.Cheng, Z., Perillo, M., & Heinzelman, W. B. (2008). General network lifetime and cost models for evaluating sensor network deployment strategies. IEEE Transactions on Mobile Computing, 7(4), 484–497.Heo, N., & Varshney, P. K. (2005). Energy-efficient deployment of intelligent mobile sensor networks. IEEE Transactions on Systems, Man and Cybernetics Part A Systems and Humans, 35(1), 78–92.Vlajic, N., & Xia, D. (2006). Wireless sensor networks: to cluster or not to cluster? In International symposium on a world of wireless, mobile and multimedia networks, WoWMoM 2006.Garcia, M., & Lloret, J. (2009). A cooperative group-based sensor network for environmental monitoring. In LNCS: Vol. 5738. Cooperative design, visualization, and engineering: CDVE 2009. (pp. 276–279). Berlin: Springer.Garcia, M., Bri, D., Boronat, F., & Lloret, J. (2008). A new neighbour selection strategy for group-based wireless sensor networks. In 4th int. conf. on networking and services, ICNS 2008. 16–21 March (pp. 109–114).Kaplan, E. D. (1996). Understanding GPS: principles and applications. Boston: Artech House.Stojmenovic, I. (2002). Position based routing in ad hoc networks. IEEE Communications Magazine, 40(7), 128–134.Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660–670.Bhardwaj, M., Garnett, T., & Chandrakasan, A. P. (2001). Upper bounds on the lifetime of sensor networks. In: International conference on communications (ICC’01). June 2001 (pp. 785–790).Gibbons, A. (1985). Algorithmic graph theory. Cambridge: Cambridge University Press.Fraigniaud, P., Pelc, A., Peleg, D., & Perennes, S. (2000). Assigning labels in unknown anonymous networks. In Proceedings of the 19th annual ACM SIGACT-SIGOPS symposium on principles of distributed computing, Portland, OR, USA (Vol. 1, pp. 101–111).OPNET Modeler® Wireless Suite network simulator (2011). Available at http://www.opnet.com/solutions/network_rd/modeler_wireless.html