Discrete-Event Control and Predictive Optimization of Fuel Tankers and Pumps Allocation

Adrian  Ramanda; Nur Faizatus Sa'idah; Rully  Tri Cahyono

doi:10.61221/jriem.v1i1.8

Authors

Adrian Ramanda Fakultas Teknologi Industri, Program Studi Teknik Industri, ITB
Nur Faizatus Sa'idah Fakultas Teknologi Industri, Program Studi Teknik Industri, ITB
Rully Tri Cahyono Fakultas Teknologi Industri, Program Studi Teknik Industri, ITB

DOI:

https://doi.org/10.61221/jriem.v1i1.8

Keywords:

berth allocation, discrete-event systems, fuel terminals, model predictive control

Abstract

This paper deals with a dynamic scheduling model of a general fuel tankers and pumps operations. The models are dynamics because the ships’ arrival time is allowed to vary. The goal of the study is to determine the optimal berth allocation/scheduling and the policy recommendations, which minimize ships’ total waiting time, berth occupancy ratio, and ships’ charter cost. Discrete-event Systems (DES) modeling is chosen due to aperiodicity in ships’ arrival time and asynchrony operations’ time among different berthing positions. Two DES models are developed, i.e.: (1) multiple cost allocation problem (MBAP) for the supply jetty and (2) simple berth allocation problem (SBAP) for the consignment jetty. Furthermore, we use model predictive control (MPC) to optimize the DES model, and we also provide mathematical analysis of the proposed algorithm. Numerical examples examining two cases (tidal and non-tidal) in each of the two models are presented to illustrate the optimal solution. The problem encountered is that current berth allocation is not working efficiently, as indicated by the average waiting time for ships at the supply jetties (jetties 1 and 2) which is above the standard (14 hours), while the consignment jetty (jetty 3) is well below standard.

References

Ministry of Transportation of the Republic of Indonesia, 2018.

Cohen, G., Dubois, D., Quadrat, J., and Viot, M., A linear-system-theoretic view of discrete-event processes and its use for performance evaluation in manufacturing, IEEE Transactions on Automatic Control. 30(3) (1985) 210-220.

Ramadge, P.J.G., and Wonham, W.M., The control of discrete event systems, Proceedings of the IEEE. l(77) (1989) 81-98.

Cahyono, R.T., Discrete-Event Control and Optimization of Container Terminal Operations, Ph.D. dissertation, The Engineering and Technology Institute (ENTEG), Faculty of Science and Engineering, University of Groningen, The Netherlands. (2021).

Cassandras, C.G., and Lafortune, S., Introduction to Discrete Event Systems: Second Edition. New York: Springer Science+Business Media, LLC. (2008).

Hermansson, A.W., Syafiiey, S., and Mohd Noorz, S.B., Multiple model predictive control of nonlinear pH neutralization system, 2010 IEEE International Conference on Industrial Engineering and Engineering Management, Macao, China. (2010) 301-304.

Cahyono, R.T., and Jayawardhana, B., On the optimal input allocation of discrete-event systems with dynamic input sequence, 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France. (2019) 1062-1067.

Forbes, M.G., Patwardhan, R.S., Hamadah, H., and Gopaluni, R.B., Model Predictive Control in Industry: Challenges and Opportunities, IFAC-PapersOnLine. 48(8) (2015) 531.

Xi, Y.G., Li, D.W., and Lin, S., Model Predictive Control — Status and Challenges, Acta Automatica Sinica. 39(3) (2013) 222-236.

Oyelere, S.S., The Application of Model Predictive Control MPC to Fast Systems such as Autonomous Ground Vehicles (AGV), IOSR Journal of Computer Engineering. 16(3) (2014) 22-37.

Hai, D., Hao, Z., and Ping, L.Y., Model Predictive Control for inventory Management in Supply Chain Planning, Procedia Engineering. 15 (2011) 1154-1159.

Ehlinger, V.M., and Mesbah, A., Model Predictive Control of Chemical Processes: A Tutorial, Coulson and Richardson's Chemical Engineering, Berkeley: Elsevier Ltd. (2017) 367-402.

Wang, W., Rivera, D.E., Kempf, K.G., and Smith, K., A Model Predictive Control Strategy for Supply Chain Management in Semiconductor Manufacturing under Uncertainty, Proceedings of the American Control Conference. (2004) 1-7.

Flonk, E.J., Berth and Quay Crane Allocation at the Seaport of Jakarta, Thesis, Industrial Engineering and Management Program, University of Groningen, The Netherlands. (2014).

Golias, M., Saharidis, G., and Boile, M., The berth allocation problem: Optimizing vessel arrival time, Marit Econ Logist. 11 (2009) 358–377.

Imai, A., Nishimura, E., and Papadimitriou, S., The dynamic berth allocation problem for a container port, Transportation Research Part B: Methodological. 35(4) (2001) 401-417.