New model of planning and scheduling for job-shop production system with energy consideration

• Autorzy: Hassani Zineb Ibn Majdoub , El Barkany Abdellah , El Abbassi Ikram , Jabri Abdelouahhab

Identyfikatory

DOI

10.24425/mper.2019.128247

• Języki publikacji: EN

Abstrakty

EN

This paper reports a new multi-item planning and scheduling problem in a job-shop production system with the consideration of energy consumption. A mixed integer linear programming is proposed to integrate planning and scheduling with the consideration of energy aspect. In this study a new operational constraint is considered in the tactical level because of the huge interest given to energy consumption and its strong link existing with production system. To evaluate the performance of this model, computational experiments are presented, and numerical results are given using the software CPLEX and then discussed.

Słowa kluczowe

EN

production; planning; scheduling; MILP; energy; resolution; CPLEX

• Wydawca: Production Engineering Committee of the Polish Academy of Sciences ; Polish Association for Production Management
• Czasopismo: Management and Production Engineering Review
• Rocznik: 2019
• Tom: Vol. 10, No. 1
• Strony: 89--97
• Opis fizyczny: Bibliogr. 64 poz., tab.

Twórcy

autor

Hassani Zineb Ibn Majdoub

• Mechanical Engineering Laboratory, Faculty of Science and Techniques, Sidi Mohammed Ben Abdellah University, B.P. 2202 – Route d’Imouzzer – FEZ, Morocco

autor

El Barkany Abdellah

• Mechanical Engineering Laboratory, Faculty of Science and Techniques, Sidi Mohammed Ben Abdellah University, Morocco

autor

El Abbassi Ikram

• ECAM-EPMI, France

autor

Jabri Abdelouahhab

• Mechanical Engineering Laboratory, Faculty of Science and Techniques, Sidi Mohammed Ben Abdellah University, Morocco

Bibliografia

• [1] Melo T.V., Moura A.M.A., Use of seawed flour in the animal feeding, Arch. Zoot., 58, 99–107, 2009.
• [2] Klibi, W., Martel, A., Modeling approach for the design of resilient supply networks under disruptions, International Journal of Production Economics, 135, 2, 882–898, 2012.
• [3] Eskandarpour M., Dejax P., Miemczyk J., Péton O., Sustainable supply chain network design: an optimization-oriented review, Omega, Elsevier, 54, 11–32, 2015.
• [4] Huang H., Guo X., Li D., Liu M., Wu J., Ren H., Identification of crucial yeast inhibitors in bioethanol and improvement of fermentation at high pH and high total solids, Bioresour Technol., 102, 16, 7486–93, 2011.
• [5] Sarkar S.K., Hazra S.K., Sen H.S., Karmakar P.G., Tripathi M.K., Sunnhemp in India, ICARCentral Research Institute for Jute and Allied Fibres (ICAR), Barrackpore, West Bengal, 2015.
• [6] Huizhi Yi., Bhaba R. Sarker, An operational policy for an integrated inventory system under consignment stock policy with controllable lead time and buyers’ space limitation, Computers & Operations Research, 40, 11, 2632–2645, 2013.
• [7] Huang L., Massa L., Karle J., The Kernel Energy Method: application to a tRNA, Proc. Natl. Acad. Sci. USA, 103, 5, 1233–7, 2006.
• [8] Hassani Z.I.M., El Barkany A., Jabri A., El Abbassi I., Darcherif A., New approach to integrate planning and scheduling of production system: heuristic resolution, International Journal of Engineering Research in Africa, 39, 156–169, 2018.
• [9] Hassani Z.I.M., El Barkany A., Jabri A., El Abbassi I., Darcherif A., Models for solving integrated planning and scheduling problem: computational comparison, International Journal of Engineering Research in Africa, 34, 161–170, 2018.
• [10] Weinert N., Chiotellis S., Seliger G., Methodology for planning and operating energy – efficient production systems, CIRP Annals – Manufacturing Technology, 60, 1, 41–44, 2011, doi: 10.1016/j.cirp.2011.03.015.
• [11] Soroush A., Kadivar M., Mohajerani A., Ganji A., Bazargan A., Manifesto for Iran’s green movement, New Perspectives Quarterly, 27, 2, 32–33, 2010.
• [12] Herrman C., Thiede S., Process chain simulation to foster energy efficiency in manufacturing, Proceedings in the 17th CIRP China, pp. 23–28, 2009.
• [13] Michalski A., Eugen D., Hauschild J-P., Lange O., Wieghaus A., Makarov A., Nagaraj N., Cox J., Mann M., Horning S., Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer, Molecular & Cellular Proteomics, 10, 9, M111.011015, 2011.
• [14] Liu Y., Dong H., Lohse N., Petrovic S., Gindy N., An investigation into minimising total energy consumption and total weighted tardiness in job shops, Journal of Cleaner Production, 65, 87–96, 2014.
• [15] Feng Li, Tie Liu, Hao Zhang, Rong Zeng Cao, Wei Ding, Fasano J.P., Distribution center location for green supply chain, Service Operations and Logistics, and Informatics, IEEE/SOLI 2008, IEEE International Conference, 2, 2951–2956, 2008.
• [16] Fan W., Xiaofan L., Ning S., A multi-objective optimization for green supply chain network design, Multiple Criteria Decision Making and Decision Support Systems, 51, 2, 262–269, 2011.
• [17] Pishvaie A., Jaberipur G., Jahanian A., Improved CMOS (4; 2) compressor designs for parallel multipliers, Computers & Electrical Engineering, 38, 6, 1703–1716, 2012.
• [18] Mallidis I., Dekker R., Vlachos D., The impact of greening on supply chain design and cost: a case for a developing region, Journal of Transport Geography, 22, 118–128, 2012.
• [19] Ramudhin A., Chaabane A., Paquet M., Carbon market sensitive sustainable supply chain network design, International Journal of Management Science and Engineering Management, 5, 1, 30–38, 2010.
• [20] Chaabane A., Ramudhin A., Paquet M., Design of sustainable supply chains under the emission trading scheme, International Journal of Production Economics (Advances in Optimization and Design of Supply Chains), 135, 1, 37–49, 2012.
• [21] Harris I., Christine L-M., Naim M-M., A hybrid multi-objective approach to capacitated facility location with flexible store allocation for green logistics modeling, Transportation Research Part E: Logistics and Transportation Review, 66, 1–22, 2014.
• [22] Huizhi Yi., Bhaba R. Sarker, An operational policy for an integrated inventory system under consignment stock policy with controllable lead time and buyers’ space limitation, Computers & Operations Research, 40, 11, 2632–2645, 2013.
• [23] Absi N., Dauzère-Pérès S., Kedad-Sidhoum S., Penz B., Rapine C., The single-item green lot-sizing problem with fixed carbon emissions, European Journal of Operational Research, 248, 3, 849–855, 2016.
• [24] Absi N., Dauzère-Pérès S., Kedad-Sidhoum S., Penz B., Rapine C., Lot sizing with carbon emission constraints, European Journal of Operational Research, 227, 1, 55–61, 2013.
• [25] Velázquez-Martínez J-C., Fransoo J-C,. Edgar EB., Jaime M-V., The impact of carbon footprinting aggregation on realizing emission reduction targets, Flexible Services and Manufacturing Journal, 26, 1– 2, 196–220, 2014.
• [26] Akbalik A., Rapine C., The single item uncapacitated lot-sizing problem with time-dependent batch sizes: NP-hard and polynomial cases, European Journal of Operational Research, 229, 2, 353–363, 2013.
• [27] Mathijn J. Retel Helmrich, Raf Jans, Wilco van den Heuvel, Albert P.M. Wagelmans, The economic lotsizing problem with an emission capacity constraint, European Journal of Operational Research, 241, 1, 50–62, 2015.
• [28] Benjaafar S., Yanzhi L., Daskin M., Carbon footprint and the management of supply chains: insights from simple models, Automation Science and Engineering, IEEE Transactions, 10, 1, 99–116, 2013.
• [29] Sazvar Z., Javad Mirzapour E-M., Baboli A., Akbari Jokar M.R., A bi-objective stochastic programming model for a centralized green supply chain with deteriorating products, International Journal of Production Economics, 150, 140–154, 2014.
• [30] Hua G., Cheng T.C.E., Shouyang W., Managing carbon footprints in inventory management, International Journal of Production Economics, 132, 2, 178–185, 2011.
• [31] Bouchery Y., Ghaffari A., Jemai Z., Dallery Y., Including sustainability criteria into inventory models, European Journal of Operational Research, 222, 2, 229–240, 2012.
• [32] Din¸cer Konur, Carbon constrained integrated inventory control and truckload transportation with heterogeneous freight trucks, International Journal of Production Economics, 153, 268–279, 2014.
• [33] Bozorgi A., Pazour J., Nazzal D., A new inventory model for cold items that considers costs and emissions, International Journal of Production Economics, 155, 114–125, 2014.
• [34] Goldman C., Reid M., Levy R., Silverstein A., Coordination of energy efficiency and demand response, Ernest Orlando Lawrence Berkeley National Laboratory, Environmental Energy Technologies Division, LBNL-3044E, January 2010.
• [35] Shrouf F., Ordieres-Mere J., Garcia-Sanchez A., Ortega-Mier M., Optimizing the production scheduling of a single machine to minimize total energy consumption costs, Journal of Cleaner Production, 67, 197–207, 2014.
• [36] Mir Saman P., Seyyed A-T., Razmi J., Credibility based fuzzy mathematical programming model for green logistics design under uncertainty, Computers & Industrial Engineering, 62, 2, 624–632, 2012.
• [37] Moon J-Y., Shin K., Park J., Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency, The International Journal of Advanced Manufacturing Technology, 68, 1–4, 523– 535, 2013.
• [38] Castro P., Harjunkoski I., Grossmann I., New continuous-time scheduling formulation for continuous plants under variable electricity cost, Industrial & Engineering Chemistry Research, 48, 14, 6701– 6714, 2009.
• [39] Artigues C., Lopez P., Haït A., The energy scheduling problem: Industrial case-study and constraint propagation techniques, International Journal of Production Economics, 143, 1, 13–23, 2013.
• [40] Fernandez M., Li L., Sun Z., Just-for-Peak buffer inventory for peak electricity demand reduction of manufacturing systems, International Journal of Production Economics, 146, 1, 178–184, 2013.
• [41] Wang Y., Li L., Time-of-use based electricity demand response for sustainable manufacturing systems, Energy, 63, 233–244, 2013.
• [42] Luo H., Du B., Huang Q.G., Chen H., Li X., Hybrid flow shop scheduling considering machine electricity consumption cost, International Journal of Production Economics, 146, 2, 423–439, 2013.
• [43] Castro P.M., Harjunkoski I., Grossmann I.E., Optimal scheduling of continuous plants with energy constraints, Computers & Chemical Engineering, 35, 2, 372–387, 2011.
• [44] Xing Y., Yusuf O., Zechun H., Yonghua S., A review on price-driven residential demand response, Renewable and Sustainable Energy Reviews, Elsevier, 96(C), 411–419, 2018.
• [45] Solding P., Petku D., Mardan N., Using simulation for more sustainable production systems – methodologies and case studies, International Journal of Sustainable Engineering, 2, 2, 111–122, 2009.
• [46] Brundage M-P., Qing C., Yang L., Guoxian X., Jorge A., Energy efficiency management of an integrated serial production line and HVAC system, Automation Science and Engineering, IEEE Transactions, 11, 3, 789–797, 2014.
• [47] Castro P., Sun L., Harjunkoski I., Resource – task network formulations for industrial demand side management of a steel plant, Industrial & Engineering Chemistry Research, 52, 36, 13046–13058, 2013.
• [48] Yu Zheng H., Wang L., Reduction of carbon emissions and project makespan by a Pareto-based estimation of distribution algorithm, International Journal of Production Economics, 164, 421–432, 2015.
• [49] Moon J.-Y., Park J., Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage, International Journal of Production Research, 52, 13, 3922–3939, 2014.
• [50] Babu C.A., Ashok S., Peak load management in electrolytic process industries, Power Systems, IEEE Transactions, 23, 2, 399–405, 2008.
• [51] Ashok S., Peak-load management in steel plants, Applied Energy, 83, 5, 413–424, 2006.
• [52] Bego A., Li L., Sun Z., Identification of reservation capacity in critical peak pricing electricity demand response program for sustainable manufacturing systems, International Journal of Energy Research, 38, 6, 728–736, 2014.
• [53] Bruzzone A., Anghinolfi D., Paolucci M., Tonelli F., Energy-aware scheduling for improving manufacturing process sustainability: a mathematical model for flexible flow shops, CIRP Annals – Manufacturing Technology, 61, 1, 459–462, 2012.
• [54] Yildirim M-B., Mouzon G., Single-machine sustainable production planning to minimize total energy consumption and total completion time using a multiple objective genetic algorithm, IEEE Transactions on Engineering Management, 59, 4, 585–597, 2012.
• [55] May G., Stahl B., Taisch M., Prabhu V., Multiobjective genetic algorithm for energy-efficient job shop scheduling, International Journal of Production Research, 53, 23, 7071–7089, 2015, doi: 10.1080/00207543.2015.1005248.
• [56] Spanos A.C., Ponis S.T., Tatsiopoulos I.P., Christou I.T., Rokou E., A new hybrid parallel genetic algorithm for the job-shop scheduling problem, International Transactions in Operational Research, 21, 3, 479–499, 2014.
• [57] Goncalves J.F., Resende M.G., An extended Akers graphical method with a biased random-key genetic algorithm for job-shop scheduling, International Transactions in Operational Research, 21, 2, 215– 246, 2014.
• [58] Zhu Z.C., Ng K.M., Ong H.L., A modified tabu search algorithm for cost-based job shop problem, Journal of the Operational Research Society, 61, 4, 611–619, 2010.
• [59] Zhang R., Wu C., A neighbourhood property for the job shop scheduling problem with application to hybrid particle swarm optimization, IMA Journal of Management Mathematics, 24, 1, 111–134, 2013.
• [60] Zobolas G.I., Tarantilis C.D., Ioannou G., A hybrid evolutionary algorithm for the job shop scheduling problem, Journal of the Operational Research Society, 60, 2, 221–235, 2009.
• [61] ChenGuang L., Jing Y., Jie L., WenJuan L., Evans S., Yong Y., Sustainable performance oriented operational decision making of single machine systems with deterministic product arrival time, Journal of Cleaner Production, 85, 318–330, 2014.
• [62] Kemmoé S., Lamy D., Tchernev N., Job-shop like manufacturing system with time dependent energy threshold and operations with peak consumption, Interventions for the Co-Creation of InterOrganizational Business Process Change, pp. 617– 624, 2015.
• [63] Lvjiang Y., Xinyu L., Liang G., Chao L., Zhao Z., Energy-efficient job shop scheduling problem with variable spindle speed using a novel multi-objective algorithm, Advances in Mechanical Engineering, 9, 4, 1–21, 2017.
• [64] Masmoudi O., Yalaoui A., Ouazene Y., Chehade H., Multi-item capacitated lot-sizing problem in a flow-shop system with energy consideration, IFACPapersOnLine, 49, 12, 301–306, 2016.