Robust Smart Energy Efficient Production Planning for a general Job-Shop Manufacturing System under combined demand and supply uncertainty in the presence of grid-connected microgrid

Abstract This paper is the first to introduce the concept of Smart Energy-Efficient Production-Planning (SEEPP) for a general Job-Shop manufacturing system in the presence of Grid-connected Microgrid with wind power generation. To cope with the unpredictability of wind speed and the uncertainties of demands, a novel risk-based Robust Mixed Integer Linear Programming (RMILP) model is mathematically formulated. The last aim of the model is to minimize the total day-ahead cost of the system considering peak demand charge. The results show the capability of the proposed integrated framework to obtain a constructive trade-off between scenario-based cost deviations, and heat and power imbalances considering the attitude of Decision Makers (DMs) toward risk. The results further indicate that the proposed SEEPP concept is able to produce products with at least 1.95% less cost than conventional manufacturing systems, although it supports a much wider range of demands for products. The performance of the model is analyzed and evaluated in terms of accuracy, robustness, and computational efficiency.

[1]  M. Stonis,et al.  A Memetic Algorithm for an Energy-Costs-Aware Flexible Job-Shop Scheduling Problem , 2017 .

[2]  Melvyn Sim,et al.  The Price of Robustness , 2004, Oper. Res..

[3]  Prasenjit Basak,et al.  A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid , 2012 .

[4]  Han-Lin Li,et al.  A robust optimization model for stochastic logistic problems , 2000 .

[5]  Mengna Hong,et al.  Integrating process optimization with energy-efficiency scheduling to save energy for paper mills , 2018, Applied Energy.

[6]  Mohammad A. S. Masoum,et al.  A decision support algorithm for assessing the engagement of a demand response program in the industrial sector of the smart grid , 2018, Comput. Ind. Eng..

[7]  J. Belwin Edward,et al.  A Review on Impacts of Power Quality, Control and Optimization Strategies of Integration of Renewable Energy Based Microgrid Operation , 2018 .

[8]  S. M. Johnson,et al.  Optimal two- and three-stage production schedules with setup times included , 1954 .

[9]  Fysikopoulos Apostolos,et al.  Energy Efficiency of Manufacturing Processes: A Critical Review☆ , 2013 .

[10]  Ram M Shrestha,et al.  General equilibrium effects of a supply side GHG mitigation option under the Clean Development Mechanism. , 2006, Journal of environmental management.

[11]  Amir Parnianifard,et al.  An overview on robust design hybrid metamodeling: Advanced methodology in process optimization under uncertainty , 2018 .

[12]  Morgan Bazilian,et al.  Fossil fuel subsidy removal and inadequate public power supply: Implications for businesses , 2012 .

[13]  Jatinder N. D. Gupta,et al.  Flowshop scheduling research after five decades , 2006, Eur. J. Oper. Res..

[14]  Hêris Golpîra,et al.  A novel Multiple Attribute Decision Making approach based on interval data using U2P-Miner algorithm , 2018, Data Knowl. Eng..

[15]  Jing Li,et al.  Energy consumption model and energy efficiency of machine tools: a comprehensive literature review , 2016 .

[16]  Hao Zhang,et al.  Operational optimization of a grid-connected factory with onsite photovoltaic and battery storage systems , 2017 .

[17]  Mostafa Zandieh,et al.  Robust bi-level optimization for green opportunistic supply chain network design problem against uncertainty and environmental risk , 2017, Comput. Ind. Eng..

[18]  Peng Li,et al.  Robust Optimization for Household Load Scheduling with Uncertain Parameters , 2018 .

[19]  Mostafa Zandieh,et al.  A multi-objective multi-echelon green supply chain network design problem with risk-averse retailers in an uncertain environment , 2017 .

[20]  Robert J. Vanderbei,et al.  Robust Optimization of Large-Scale Systems , 1995, Oper. Res..

[21]  Banu Çalis,et al.  A research survey: review of AI solution strategies of job shop scheduling problem , 2013, Journal of Intelligent Manufacturing.

[22]  Paul Schönsleben,et al.  Integrating energy efficiency performance in production management – gap analysis between industrial needs and scientific literature , 2011 .

[23]  M. Hadi Amini,et al.  A Decentralized Trading Algorithm for an Electricity Market with Generation Uncertainty , 2017, ArXiv.

[24]  Toon De Pessemier,et al.  A power data driven energy-cost-aware production scheduling method for sustainable manufacturing at the unit process level , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[25]  Ling Wang,et al.  A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption Threshold , 2019, IEEE Transactions on Cybernetics.

[26]  Hao Zhang,et al.  Scheduling of a Single Flow Shop for Minimal Energy Cost Under Real-Time Electricity Pricing , 2017 .

[27]  Christian Breyer,et al.  Analysis of 100% renewable energy for Iran in 2030: integrating solar PV, wind energy and storage , 2017, International Journal of Environmental Science and Technology.

[28]  Roberto F. Coelho,et al.  Architecture, components and operation of an experimental hybrid ac/dc smart microgrid , 2017, 2017 IEEE 8th International Symposium on Power Electronics for Distributed Generation Systems (PEDG).

[29]  Wei Yang,et al.  Optimization of carbon emission considering production planning at enterprise level , 2017 .

[30]  Amandeep Kaur,et al.  A review on microgrid central controller , 2016 .

[31]  Shahin Rahimifard,et al.  Minimising Embodied Product Energy to support energy efficient manufacturing , 2010 .

[32]  Jinwoo Park,et al.  Optimization of production scheduling with time-dependent and machine-dependent electricity cost for industrial energy efficiency , 2013 .

[33]  Iraj Mahdavi,et al.  A robust periodic capacitated arc routing problem for urban waste collection considering drivers and crew's working time. , 2018, Waste management.

[34]  Shuva Paul,et al.  A review of smart technology (Smart Grid) and its features , 2014, 2014 1st International Conference on Non Conventional Energy (ICONCE 2014).

[35]  Hêris Golpîra,et al.  Supply Chain Network Design Optimization with Risk-Averse Retailer , 2017, Int. J. Inf. Syst. Supply Chain Manag..

[36]  Socorro Rangel,et al.  A survey of case studies in production scheduling: Analysis and perspectives , 2017, J. Comput. Sci..

[37]  Axel Tuma,et al.  Energy-efficient scheduling in manufacturing companies: A review and research framework , 2016, Eur. J. Oper. Res..

[38]  Juan C. Vasquez,et al.  Microgrid supervisory controllers and energy management systems: A literature review , 2016 .

[39]  Xu Rong,et al.  A review on distributed energy resources and MicroGrid , 2008 .

[40]  Aie,et al.  Tracking Industrial Energy Efficiency and CO2 Emissions , 2007 .

[41]  Rahmat-Allah Hooshmand,et al.  A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems , 2017 .

[42]  Patrik Thollander,et al.  Energy management in industry – a systematic review of previous findings and an integrative conceptual framework , 2016 .

[43]  Marco Taisch,et al.  Toward Energy Efficient Manufacturing: A Study on Practices and Viewpoint of the Industry , 2012, APMS.

[44]  Seyed Mojtaba Sajadi,et al.  Multi-objective optimization of stochastic failure-prone manufacturing system with consideration of energy consumption and job sequences , 2018, International Journal of Environmental Science and Technology.

[45]  Konstantin Biel,et al.  Systematic literature review of decision support models for energy-efficient production planning , 2016, Comput. Ind. Eng..

[46]  Salah Bahramara,et al.  Robust optimization of micro-grids operation problem in the presence of electric vehicles , 2018 .

[47]  Omar Estrada,et al.  Energy gap method (EGM) to increase energy efficiency in industrial processes: Successful cases in polymer processing , 2018 .

[48]  Mehmet Mutlu Yenisey,et al.  Scheduling Practice and Recent Developments in Flow Shop and Job Shop Scheduling , 2009 .

[49]  George Q. Huang,et al.  Hybrid flow shop scheduling considering machine electricity consumption cost , 2013 .

[50]  Mohammad Marufuzzaman,et al.  Developing a CCHP-microgrid operation decision model under uncertainty , 2018, Comput. Ind. Eng..

[51]  Xu Chen,et al.  Robust optimization for integrated scrap steel charge considering uncertain metal elements concentrations and production scheduling under time-of-use electricity tariff , 2018 .

[52]  Arkadi Nemirovski,et al.  Robust solutions of uncertain linear programs , 1999, Oper. Res. Lett..

[53]  Sanja Petrovic,et al.  A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance , 2016 .

[54]  K. Palanisamy,et al.  Optimization in microgrids with hybrid energy systems – A review , 2015 .

[55]  Yaochu Jin,et al.  Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns , 2019, IEEE Transactions on Cybernetics.

[56]  Dimitris Bertsimas,et al.  Constructing Uncertainty Sets for Robust Linear Optimization , 2009, Oper. Res..

[57]  Hadi Mokhtari,et al.  An energy-efficient multi-objective optimization for flexible job-shop scheduling problem , 2017, Comput. Chem. Eng..

[58]  John W. Sutherland,et al.  Flow shop scheduling with peak power consumption constraints , 2013, Ann. Oper. Res..

[59]  Lazaros G. Papageorgiou,et al.  Economic and environmental scheduling of smart homes with microgrid: DER operation and electrical tasks , 2016 .

[60]  Lazaros G. Papageorgiou,et al.  Efficient energy consumption and operation management in a smart building with microgrid , 2013 .

[61]  Ben Richard Hughes,et al.  A review of energy simulation tools for the manufacturing sector , 2018 .

[62]  Athula D. Rajapakse,et al.  Microgrids research: A review of experimental microgrids and test systems , 2011 .

[63]  Hicham Chehade,et al.  Solving a capacitated flow-shop problem with minimizing total energy costs , 2017 .

[64]  Marc Lambrecht,et al.  Production scheduling and sequencing for multi-stage production systems , 1979 .

[65]  Y. Parag,et al.  Microgrids: A review of technologies, key drivers, and outstanding issues , 2018, Renewable and Sustainable Energy Reviews.

[66]  Bernard Penz,et al.  Capacity acquisition for the single-item lot sizing problem under energy constraints , 2016, Omega.

[67]  Lin Li,et al.  Potential capability estimation for real time electricity demand response of sustainable manufacturing systems using Markov Decision Process , 2014 .

[68]  Melvyn Sim,et al.  Constructing Risk Measures from Uncertainty Sets , 2009, Oper. Res..

[69]  Hêriş Golpîra.  Robust bi-level optimization for an opportunistic supply chain network design problem in an uncertain and risky environment , 2017 .

[70]  Patrick Rivett,et al.  Principles of Operations Research , 1972 .

[71]  Christoph H. Glock,et al.  Integration of energy aspects into the economic lot scheduling problem , 2019, International Journal of Production Economics.

[72]  Amin Kargarian,et al.  Diagonal Quadratic Approximation for Decentralized Collaborative TSO+DSO Optimal Power Flow , 2019, IEEE Transactions on Smart Grid.

[73]  Feng Qiu,et al.  A Data-Driven Approach to Improve Wind Dispatchability , 2017, IEEE Transactions on Power Systems.

[74]  Zaijun Wu,et al.  Modeling, planning and optimal energy management of combined cooling, heating and power microgrid: A review , 2014 .

[75]  Davide Giglio,et al.  Integrated lot sizing and energy-efficient job shop scheduling problem in manufacturing/remanufacturing systems , 2017 .

[76]  Hamid Davoudpour,et al.  A robust optimization model for cellular manufacturing system into supply chain management , 2017 .

[77]  Ambarnath Banerji,et al.  Microgrid: A review , 2013, 2013 IEEE Global Humanitarian Technology Conference: South Asia Satellite (GHTC-SAS).

[78]  Gunther Reinhart,et al.  Concurrent scheduling of a job shop and microgrid to minimize energy costs under due date constraints , 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[79]  George Kesidis,et al.  Optimal Peak Shaving Using Batteries at Datacenters: Characterizing the Risks and Benefits , 2017, 2017 IEEE 25th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS).

[80]  André Bardow,et al.  Robust multi-objective optimization for sustainable design of distributed energy supply systems , 2017, Comput. Chem. Eng..

[81]  Dongkun Luo,et al.  Driving force of rising renewable energy in China: Environment, regulation and employment , 2017 .

[82]  Ashutosh Tiwari,et al.  Energy-efficient scheduling of flexible flow shop of composite recycling , 2018, The International Journal of Advanced Manufacturing Technology.

[83]  Christoph H. Glock,et al.  Flow shop scheduling with grid-integrated onsite wind power using stochastic MILP , 2018, Int. J. Prod. Res..

[84]  E. H. Bowman.  THE SCHEDULE-SEQUENCING PROBLEM* , 1959 .

[85]  Christoph H. Glock,et al.  Prerequisites of efficient decentralized waste heat recovery and energy storage in production planning , 2017 .