Surrogate Estimators for Collaborative Decision

Fatiha Bendali, Alejandro Olivas Gonzales, Alain Quilliot, Hélène Toussaint

Abstract


We deal here with job scheduling under the constraint of encapsulated renewable and non-renewable resources. For the sake of understanding, we rely here on a case study related to energy production by a photovoltaic platform. This context means synchronizing production and consumption in order to both minimize production cost and achieve the jobs according to specific purposes. Because of both the complexity of resulting bi-level model and the fact that synchronization most often involves distinct players with their own agenda and non shared information, we shortcut the production level with the help of surrogate estimators. Those estimators involve flexible pricing mechanisms and machine learning devices. According to this, we first perform a structural analysis of our model, before designing and testing several algorithms that implement this surrogate based approach.

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References


Adulyasak Y., Cordeau J. F., Jans R.: The production

routing problem: A review of formulations and solutions.

Computers and Operations Research, 55, p 141-

, (2015). https://doi.org/10.1016/j.cor.2014.01.011.

Ahuja R.K., Magnanti T.L., Orlin J.B., Reddy M.R.:

Applications of network optimization. Handbook of

Operation Research and Management Sci. 7, p 1-83,

Albrecht A., Pudney P.: Pickup and delivery

with a solar-recharged vehicle. Ph.D.

thesis Australian Society for O.R (2013).

www.asor.org.au/conferences/asor2013.

Almuhtady A., Lee S., Romeijn E., Wynblatt M.,

Ni J.: A degradation informed battery swapping policy

for fleets of electric or hybrid electric vehicles.

Transportation Science 48, 4, p 609-618 (2014).

https://doi.org/10.1287/trsc.2013.0494.

Balbiyad S.: Collective self consumption: computing

the optimal energy distribution coefficients considering

local energy management. ENSTA/EDF OSIRIS,

(2019).

Bendali F., Mole Kamga E., Mailfert J., Quilliot A.,

Toussaint H.: Synchronizing Energy Production and

Vehicle Routing. RAIRO-O.R, 55 (4),pp. 2141-2163.

(2021). https://doi.org/10.1051/ro/2021093.

Berge C. : Théorie des Jeux à n personnes . Gauthier-

Villars, Paris, Memorial Sciences Maths 138, (1957).

http://www.numdam.org/item/?id=MSM_1957_138_1_0.

Bondareva O. N. : Some applications of linear programming

methods to the theory of cooperative games.

Problemy Kibernetica 10, p 119-139, (1963).

Bsaybes S., Quilliot A., Wagler A.: Vehicle fleet management

using flows in time-expanded networks. TOP,

p 1-24, (2019). DOI: 10.1007/s11750-019-00506-4

Caprara A., Carvalho M., Lodi A., Woeinger G. J.: A

study on the complexity of the bilevel knapsack problem.

SIAM Journal on Optimization 24 (2), p 823-838,

(2014).

Chen L., Zhang G.: Approximation algorithms

for a bi-level Knapsack problem. Theoretical

Computer Sciences 497, p 1-12, (2013).

https://doi.org/10.1016/j.tcs.2012.08.008

Chen L., Englund C.: Cooperative intersection management:

a survey. IEEE Transactions on Intelligent

Transportation Systems, 17-2, p 570-586, (2016).

https://doi.org/10.1109/TITS.2015.2471812

Chen Z. L.: Integrated production and distribution

scheduling: Review and extensions.

Operations Research 58, p 130-148, (2010).

https://doi.org/10.1287/opre.1080.0688.

Chen Z. L., Vairaktarakis: Integrated production and

distribution operations. Management Science 51, p

-628, (2005). DOI: 10.1287/mnsc.1040.0325

Chretienne P., Hazir O., Khadad-Sidhoum S.: Integrated

batch sizing and scheduling on a single machine.

Journal of Scheduling 14-6, p 541-55, (2011). DOI :

1007/s10951-011-0229-x

Colson B., Marcotte P., Savard G.: Bi-level programming:

A survey. 4OR Vol 3 (2), p 87-107, (2005).

DOI:10.1007/s10288-005-0071-0.

Deb S., Tammi K., Kalita K., Mahanta P.: Impact

of electric vehicle charging station load on distribution

network; Energies 11 (1), p 178-185, (2018).

https://doi.org/10.3390/en11010178

Dempe S., Kalashnikov V., Perez-Valdez G.,

Kalashikova N.: Bi-level Programming Problems

Theory, Springer (2015). ISBN 3662458276,

Erdelic T., Caric T.: A survey on the electric vehicle

routing problem. Journal of Advanced Transportation,

(2019). https://doi.org/10.1155/2019/5075671.

Garey M. R., Johnson D. S.: Computers and

Intractability: A Guide to the Theory of NPCompleteness.

Freeman and Co. Ed. (1979). ISBN

-7167-1044-7

Granot D., Granot F.: On some network flow

games. Maths O.R 17, p 792-841, (1992).

https://doi.org/10.1287/moor.17.4.792

Grimes C., Varghese O., Ranjan S.: Light, water,

hydrogen: The solar generation of hydrogen by water

photoelectrolysis. Springer-Verlag US, (2008). •

DOI:10.5860/choice.45-6194

Hall N. G., Potts C. N.: The coordination of

scheduling and batch deliveries. Annals of Operations

Research, 135, p 41-64, (2005). DOI

https://doi.org/10.1007/s10479-005-6234-8.

Hartman S., Briskorn D. : An updated survey of variants

and extensions of the resource-constrained project

scheduling problem. EJOR 297, 1, p 1-14, (2022).

https://doi.org/10.1016/j.ejor.2021.05.004.

Irani S., Pruhs K.: Algorithmic problems in power

management. SIGACT News, 36, 2, p 63-76, (2003).

DOI:10.1145/1067309.1067324.

Kleinert T., Labbé M., Ljubic I., Schmidt M.:

A survey on mixed integer programming techniques

in bilevel optimization. EURO Journal

on Computational Optimization 9, 21 p, (2021).

https://doi.org/10.1016/j.ejco.2021.100007.

Koc C., Jabali O., Mendoza J., Laporte G.: The

electric vehicle routing problem with shared charging

stations. ITOR, 26 , p 1211-1243, (2019).

doi:https://doi.org/10.1111/itor.12620.

M.Krzyszton M.: Adapative supervison: method of

reinforcement learning fault elimination by application

of supervised learning. Proceedings of the 2018

FEDCSIS AI Conference, p 139-149, (2018). DOI:

http://dx.doi.org/10.15439/978-83-949419-5-6.

J. Kumar J., Ranga V.: Multi-robot coordination

analysis, taxonomy and future scope. Journal of Intelligent

and Robotic Systems, 102:10, (2021). DOI:

1007/s10846-021-01378-2

Luthander R., Widen J., Nilsson D., Palm J.:

Photovoltaic self-consumption in buildings: A review.

Applied Energy 142, p 80-94, (2015). DOI:

1016/j.apenergy.2014.12.028

Macrina G., Pugliese L.D, Guerriero F.: The greenvehicle

routing problem: A survey. In Modeling and

Optimization in Green Logistics, Cham: Springer

International Publishing. p. 1-26, (2020). DOI:

https://doi.org/10.1007/978-3-030-45308-4_1.

Orji.M.J, Wei.S. Project Scheduling Under Resource

Constraints: A Recent Survey. Inter. Journal of Engineering

Research and Technology (IJERT) Vol. 2 Issue

, (2013). DOI : 10.17577/IJERTV2IS2508.

Owen G. : Game Theory.Academic Press, (1982).

ISBN 0125311508, 9780125311502.

Rizk Y., Awad M., Tunstel E.: Cooperative

heterogenous mutlti-robot systems: a survey.

ACM Computing Surveys 29, (2019). DOI :

https://doi.org/10.1145/3303848.

Sarmiento A.M., Nagi R.: A review of integrated

production-distribution systems. IEE

Transactions 31, 1061-1074, (1999). DOI :

https://doi.org/10.1023/A:1007623508610.

Smarter Together: Reports on collective

self-consumption of photo-voltaic. Technical

Report Smarter Together, (2016). https:

//ec.europa.eu/research/participants/

documents/downloadPublic?documentIds=

e5adb475db&appId=PPGMS

Souffran G., Liegeville L., Guerin P.: Simulation of

real world vehicle missions using a stochastic Markov chain model for optimal power train sizing. IEE Transactions

on Vehicular Technology 61, 8, p 3454-3465,

(2012). https://doi.org/10.1109/TVT.2012.2206618

Tamiselvi S., Gunasundari S., Karuppiah N., Razak

A., Madhusudan S., Nagarajan V., Sathish T., Shamim

M., Saleel A., Afzal A.: A review of battery modelling

techniques. Sustainability 13, p 2-26, (2021).

https://doi.org/10.3390/su131810042

Tremblay O., Dessaint L.: Experimental validation

of a battery dynamic model for E.V applications.

World Electric Vehicle Journal 3, (2009).

https://doi.org/10.3390/wevj3020289

Trotta M., Archetti C., Feillet D., Quilliot A.: A

pickup and delivery problem with electric vehicles and

local energy production. Proc. Triennial Symposium on

Transportation Analysis (TRISTAN), 6 pages, (2022).

https://hal.science/hal-03723282v1

Verma A.: Electric vehicle routing problem with time

windows, recharging stations and battery swapping stations.

Euro Journal of Transportation Logistics 7, p

-451, (2018). https://doi.org/10.1007/s13676-018-

-9.

Wegener I.: Complexity Theory. Springer (2005). Wegener

I.: Complexity Theory. Springer (2005).

Wojtuziak J., Warden T., Herzog O.: Machine learning

in agent based stochastic simulation: Evaluation

in transportation logistics. Computer and Mathematics

with Applications 64, p 3658-3665, (2012).

https://doi.org/10.1016/j.camwa.2012.01.079.

Zsiboracs H., Baranyai H., Vincze A., Haber I., Pinter

G.: Economic and technical aspects of flexible storage

photovoltaics systems in Europe. Energies 11, p 1445-

, (2018). https://doi.org/10.3390/en11061445




DOI: https://doi.org/10.31449/inf.v48i4.4768

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