Multimodal processes optimization subject to fuzzy operation time constraints: declarative modeling approach<FootNote> A preliminary version was presented at the 12th International Conference on Distributed Computing and Artificial Intelligence, June 3–5, 2015, Spain </FootNote>

Abstract

We present an extension of the resource-constrained multi-product scheduling problem for an automated guided vehicle (AGV) served flow shop, where multiple material handling transport modes provide movement of work pieces between machining centers in the multimodal transportation network (MTN). The multimodal processes behind the multi-product production flow executed in an MTN can be seen as processes realized by using various local periodically functioning processes. The considered network of repetitively acting local transportation modes encompassing MTN’s structure provides a framework for multimodal processes scheduling treated in terms of optimization of the AGVs fleet scheduling problem subject to fuzzy operation time constraints. In the considered case, both production takt and operation execution time are described by imprecise data. The aim of the paper is to present a constraint propagation (CP) driven approach to multi-robot task allocation providing a prompt service to a set of routine queries stated in both direct and reverse way. Illustrative examples taking into account an uncertain specification of robots and workers operation time are provided.

Keywords

Automated guided vehicles (AGVs) / Scheduling / Multimodal process / Fuzzy constraints / Optimization

References

[1] Abara, J., 1989. Applying integer linear programming to the fleet assignment problem. Interfaces, 19(4):20–28.http://www.jstor.org/stable/25061245

[2] Bocewicz, G., Nielsen, I., Banaszak, Z., 2014. Automated guided vehicles fleet match-up scheduling with production flow constraints. Eng. Appl. Artif. Intell., 30:49–62. http://dx.doi.org/10.1016/j.engappai.2014.02.003

[3] El Moudani, W., Mora-Camino, F., 2000. A dynamic approach for aircraft assignment and maintenance scheduling by airlines. J. Air Transp. Manag., 6(4):233–237. http://dx.doi.org/10.1016/S0969-6997(00)00011-9

[4] Hall, N.G., Sriskandarajah, C., Ganesharajah, T., 2001. Operational decisions in AGV-served flowshop loops: fleet sizing and decomposition. Ann. Oper. Res., 107:189–209. http://dx.doi.org/10.1023/A:1014955216633

[5] Lu, S.P., Kong, X.T.R., Luo, H., , 2013. Dynamic scheduling of AGVS for tobacco automatic warehouse: a case study. Proc. Int. Conf. on Computers and Industrial Engineering, p.725–733.

[6] Polak, M., Majdzik, P., Banaszak, Z., , 2004. The performance evaluation tool for automated prototyping of concurrent cyclic processes. Fundam. Inform., 60(1-4): 269–289.http://dl.acm.org/citation.cfm?id=2370556.2370576

[7] Relich, M., Jakabova, M., 2013. A decision support tool for project portfolio management with imprecise data. Proc. 10th Int. Conf. on Strategic Management and Its Support by Information Systems, p.164–172.

[8] Sitek, P., Wikarek, J., 2015. A hybrid framework for the modelling and optimisation of decision problems in sustainable supply chain management. Int. J. Prod. Res., 53(21):6611–6628. http://dx.doi.org/10.1080/00207543.2015.1005762

[9] von Kampmeyer, T., 2006. Cyclic Scheduling Problems. PhD Dissertation, Mathematik/Informatik,Universität Osnabrück, Gernamy.

[10] Wójcik, R., Bzdyra, K., Crisostomo, M.M., , 2005. Constraint programming approach to design of deadlock-free schedules in concurrent production systems. Proc. 10th IEEE Conf. on Emerging Technologies and Factory Automation, p.135–142. http://dx.doi.org/10.1109/ETFA.2005.1612512

[11] Wójcik, R., Nielsen, I., Bocewicz, G., , 2015. Multimodal processes optimization subject to fuzzy operation time constraints. Proc. 12th Int. Conf. on Distributed Computing and Artificial Intelligence, p.313–322. http://dx.doi.org/10.1007/978-3-319-19638-1_36

[12] Zaremba, M.B., Jedrzejek, K.J., Banaszak, Z.A., 1998. Design of steady-state behaviour of concurrent repetitive processes: an algebraic approach. IEEE Trans. Syst. Man Cybern. A, 28(2):199–212. http://dx.doi.org/10.1109/3468.661147