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Perishables

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Energy-Related Material Flow Simulation in Production and Logistics

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Abstract

Perishable goods such as fruits and vegetables require timely and accurate handling routines to ensure a high degree of product quality across all stages of the supply chain. Consequently, they constitute a fundamental business factor for organizations that needs to be managed in a delicate and prudent fashion. The perishability of products characterizes a challenging environment that requires dynamic planning and evaluation approaches to avoid or countervail the negative energetic impacts of inefficient operations. By providing a sophisticated conceptualization of the given system and its dynamic evolution over time, computer simulation serves as viable tool for analyzing and optimizing energy-related aspects of production and logistics systems for perishable items. This chapter reviews the current state of research for simulating energy-related aspects of perishable products and highlights common energy performance indicators such as food waste, emissions, and temperature. To outline contextual interdependencies and provide practical insights into the use of simulation to assess energy aspects of perishables, three use cases are presented. These cases elaborate on the energetic implication of a juice production plant in Sweden, the estimation of food quality losses in regional strawberry supply chains in Austria, and the energy and media consumption of a beverage bottling plant in Germany.

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References

  • Aiello G, La Scalia G, Micale R (2012) Simulation analysis of cold chain performance based on time–temperature data. Prod Plann Control 23(6):468–476

    Article  Google Scholar 

  • Akkerman R, Van Donk DP, Gaalman G (2007) Influence of capacity- and time-constrained intermediate storage in two-stage food production systems. Int J Prod Res 45(13):2955–2973

    Article  Google Scholar 

  • Alexander B, Barton G, Petrie J, Romagnoli J (2000) Process synthesis and optimization tools for environmental design: methodology and structure. Comput Chem Eng 24(2–7):1195–1200

    Article  Google Scholar 

  • Andersson J, Skoogh A, Johansson B (2012) Evaluation of methods used for life-cycle assessments in discrete event simulation. In: Laroque C, Himmelspach J, Pasupathy R, Rose O, Uhrmacher AM (eds) Proceedings of the 2012 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1–12

    Google Scholar 

  • Auf der Landwehr M, Trott M, von Viebahn C (2021) Environmental sustainability as food for thought! Simulation-based assessment of fulfillment strategies in the e-grocery sector. In: Feng B, Pedrielli G, Peng Y, Shashaani S, Song E, Corlu CG, Lee LH, Lendermann P (eds) Proceedings of the 2021 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1–12

    Google Scholar 

  • Azapagic A, Clift R (1999) Life cycle assessment as a tool for improving process performance: a case study on boron products. Int J Life Cycle Assess 4(3):133–142

    Article  Google Scholar 

  • Bajželj B, Richards KS, Allwood JM, Smith P, Dennis JS, Curmi E, Gilligan CA (2014) Importance of food-demand management for climate mitigation. Nat Clim Chang 4(10):924–929

    Article  Google Scholar 

  • Banks J, Carson JS, Nelson BL (1996) Discrete-event system simulation, 2nd edn. Prentice Hall, Upper Saddle River, New Jersey

    Google Scholar 

  • Bär RM, Voigt T (2021) A metamodeling approach for the simulation of energy and media demand for the brewing industry. J Adv Manuf Process 3(2):1–20

    Article  Google Scholar 

  • Behdani B, Adhitya A, Lukszo Z, Srinivasan R (2012) Mitigating supply disruption for a global chemical supply chain—application of agent-based modeling. Comput Aided Chem Eng 31:1070–1074

    Article  Google Scholar 

  • Bottani E, Ferretti G, Montanari R, Rinaldi M (2014) Analysis and optimisation of inventory management policies for perishable food products: a simulation study. Int J Simul Process Model 9(1–2):16–32

    Article  Google Scholar 

  • Brailsford SC, Eldabi T, Kunc M, Mustafee N, Osorio AF (2019) Hybrid simulation modelling in operational research: a state-of-the-art review. Eur J Oper Res 278(3):721–737

    Article  MathSciNet  Google Scholar 

  • Burek J, Nutter DW (2020) Environmental implications of perishables storage and retailing. Renew Sustain Energy Rev 133:1–20

    Article  Google Scholar 

  • Calvet L, de la Torre R, Goyal A, Marmol M, Juan AA (2020) Modern optimization and simulation methods in managerial and business economics: a review. Adm Sci 10(3):1–23

    Article  Google Scholar 

  • Chatzidakis SK, Athienitis A, Chatzidakis KS (2004) Computational energy analysis of an innovative isothermal chamber for testing of the special equipment used in the transport of perishable products. Int J Energy Res 28(10):899–916

    Article  Google Scholar 

  • Crippa M, Solazzo E, Guizzardi D, Monforti-Ferrario F, Tubiello FN, Leip AJNF (2021) Food systems are responsible for a third of global anthropogenic GHG emissions. Nat Food 2(3):198–209

    Article  Google Scholar 

  • Cuéllar AD, Webber ME (2010) Wasted food, wasted energy: the embedded energy in food waste in the United States. Environ Sci Technol 44(16):6464–6469

    Article  Google Scholar 

  • Czerniak LL, Daskin MS, Lavieri MS, Sweet BV, Erley J, Tupps MA (2021) Improving simulation optimization run time when solving for periodic review inventory policies in a pharmacy. In: Feng B, Pedrielli G, Peng Y, Shashaani S, Song E, Corlu CG, Lee LH, Lendermann P (eds) Proceedings of the 2021 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1–12

    Google Scholar 

  • De Keizer M, Haijema R, Bloemhof JM, Van Der Vorst JG (2015) Hybrid optimization and simulation to design a logistics network for distributing perishable products. Comput Ind Eng 88:26–38

    Article  Google Scholar 

  • Ding H, Benyoucef L, Xie X (2009) Stochastic multi-objective production-distribution network design using simulation-based optimization. Int J Prod Res 47(2):479–505

    Article  Google Scholar 

  • Dong Y, Miller SA (2021) Assessing the lifecycle greenhouse gas (GHG) emissions of perishable food products delivered by the cold chain in China. J Clean Prod 303:1–13

    Article  Google Scholar 

  • Eriksson A (2014) A discrete-event simulation approach to improve efficiency in stump fuel supply chains. Licensiate thesis, Swedish University of Agricultural Sciences, Department of Energy and Technology, Uppsala

    Google Scholar 

  • Fan Y, de Kleuver C, de Leeuw S, Behdani B (2021) Trading off cost, emission, and quality in cold chain design: a simulation approach. Comput Indus Eng 158:107442. https://doi.org/10.1016/j.cie.2021.107442

  • FAO (2019). The state of food and agriculture 2019. Moving forward on food loss and waste reduction. https://www.fao.org/3/ca6030en/ca6030en.pdf. Accessed 10 Mar 2022

  • Figueira G, Almada-Lobo B (2014) Hybrid simulation–optimization methods: a taxonomy and discussion. Simul Model Pract Theory 46(11):118–134

    Article  Google Scholar 

  • Fikar C (2018) A decision support system to investigate food losses in e-grocery deliveries. Comput Ind Eng 117:282–290

    Article  Google Scholar 

  • Fikar C (2020) Model-driven decision support to facilitate efficient fresh food deliveries. Die Bodenkultur: J Land Manage Food Environ 71(1):1–9

    Article  Google Scholar 

  • Forster T (2013) Modellierung und Simulation von Getränkeabfüll- und Verpackungsanlagen unter Berücksichtigung von Energie- und Medienverbräuchen. In: Dangelmaier W, Laroque C, Klaas A (eds) Simulation in Produktion und Logistik 2013. HNI Verlagsschriftenreihe, Paderborn, Germany, pp 197–206

    Google Scholar 

  • Fredriksson A, Liljestrand K (2015) Capturing food logistics: a literature review and research agenda. Int J Log Res Appl 18(1):16–34

    Article  Google Scholar 

  • Gäbel K, Tillman A-M (2005) Simulating operational alternatives for future cement production. J Clean Prod 13(13–14):1246–1257

    Article  Google Scholar 

  • Gharehyakheh A, Krejci CC, Cantu J, Rogers KJ (2020) A multi-objective model for sustainable perishable food distribution considering the impact of temperature on vehicle emissions and product shelf life. Sustainability 12(16):1–21

    Article  Google Scholar 

  • Goldratt E (1990) Theory of constraints. North River Press, Great Barrington

    Google Scholar 

  • Gruler A, Fikar C, Juan AA, Hirsch P, Contreras-Bolton C (2017) Supporting multi-depot and stochastic waste collection management in clustered urban areas via simulation–optimization. J Simul 11(1):11–19

    Article  Google Scholar 

  • Haass R, Dittmer P, Veigt M, Lütjen M (2015) Reducing food losses and carbon emission by using autonomous control—a simulation study of the intelligent container. Int J Prod Econ 164:400–408

    Article  Google Scholar 

  • Haijema R, van Dijk N, van der Wal J, Sibinga CS (2009) Blood platelet production with breaks: optimization by SDP and simulation. Int J Prod Econ 121(2):464–473

    Article  Google Scholar 

  • Hatami-Marbini A, Sajadi SM, Malekpour H (2020) Optimal control and simulation for production planning of network failure-prone manufacturing systems with perishable goods. Comput Indus Eng 146:106614. https://doi.org/10.1016/j.cie.2020.106614

  • Holmgren J, Davidsson P, Persson JA, Ramstedt L (2012) TAPAS: a multi-agent-based model for simulation of transport chains. Simul Model Pract Theory 23:1–18

    Article  Google Scholar 

  • Hu G, Mu X, Xu M, Miller SA (2019) Potentials of GHG emission reductions from cold chain systems: case studies of China and the United States. J Clean Prod 239:1–11

    Article  Google Scholar 

  • ISO (2006a) ISO 14040:2006 environmental management—life cycle assessment—principles and framework. International Organization for Standardization, Geneva Switzerland

    Google Scholar 

  • ISO (2006b) ISO 14044:2006 environmental management—life cycle assessment—requirements and guidelines. International Organization for Standardization. Geneva Switzerland

    Google Scholar 

  • Ivanov D, Rozhkov M (2020) Coordination of production and ordering policies under capacity disruption and product write-off risk: an analytical study with real-data based simulations of a fast moving consumer goods company. Ann Oper Res 291(1):387–407

    Article  MathSciNet  Google Scholar 

  • Jbira A, Jaoua A, Bouchery Y, Jemai Z (2018) Simulation based optimisation model for a joint inventory pricing problem for perishables. In Boukachour J, Abdelkader S, Alaoui AEH, Benadada Y (eds) Proceedings of the 4th international conference on logistics operations management. IEEE, Piscataway, NJ, pp 1–9

    Google Scholar 

  • Jödicke G, Zenklusen O, Weidenhaupt A, Hungerbühler K (1999) Developing environmentally sound processes in the chemical industry: a case study on pharmaceutical intermediates. J Clean Prod 7:159–166

    Article  Google Scholar 

  • Johansson B, Johnsson J, Kinnander A (2003) Information structure to support discrete event simulation in manufacturing systems. In: Chick S, Sanchez PJ, Ferrin D, Morrice DJ (eds) Proceedings of the 2003 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1290–1295

    Google Scholar 

  • Johansson B, Stahre J, Berlin J, Östergren K, Sundström B, Tillman AM (2008) Discrete event simulation with lifecycle assessment data at a juice manufacturing system. In: Proceedings of FOODSIM 2008 conference, Dublin, 26th–28th June, pp 165–169

    Google Scholar 

  • Kather A, Voigt T (2005) Weihenstephan standards for the production data acquisition in bottling plants—Part 3: data evaluation and reporting. TU München, LVT, Freising

    Google Scholar 

  • Kelton WD, Sadowski RP (2009) Simulation with ARENA, 5th edn. McGraw-Hill, New York

    Google Scholar 

  • Ketzenberg M, Bloemhof J, Gaukler G (2015) Managing perishables with time and temperature history. Prod Oper Manag 24(1):54–70

    Article  Google Scholar 

  • Ketzenberg M, Gaukler G, Salin V (2018) Expiration dates and order quantities for perishables. Eur J Oper Res 266(2):569–584

    Article  MathSciNet  Google Scholar 

  • Kheawhom S, Hirao M (2004) Decision support tools for environmentally benign process design under uncertainty. Comput Chem Eng 28:1715–1723

    Article  Google Scholar 

  • Kogler C, Rauch P (2019) A discrete-event simulation model to test multimodal strategies for a greener and more resilient wood supply. Can J for Res 49(10):1298–1310

    Article  Google Scholar 

  • Kohl J, Spreng S, Franke J (2014) Discrete event simulation of individual energy consumption for product-varieties. Procedia CIRP 17:517–522

    Article  Google Scholar 

  • Kouki C, Sahin E, Jemaï Z, Dallery Y (2013) Assessing the impact of perishability and the use of time temperature technologies on inventory management. Int J Prod Econ 143(1):72–85

    Article  Google Scholar 

  • La Scalia G, Micale R, Miglietta PP, Toma P (2019) Reducing waste and ecological impacts through a sustainable and efficient management of perishable food based on the Monte Carlo simulation. Ecol Ind 97:363–371

    Article  Google Scholar 

  • Law AM, McComas MG (1999) Simulation of manufacturing systems. In: Farrington PA, Nembhard HB, Sturrock DT, Evans GW (eds) Proceedings of the 1999 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 56–59

    Google Scholar 

  • Law AM, Kelton WD (1999) Simulation modeling and analysis, 3rd edn. McGraw-Hill, New York

    Google Scholar 

  • Leithner M, Fikar C (2019) A simulation model to investigate impacts of facilitating quality data within organic fresh food supply chains. Ann Oper Res 314:529–550

    Article  Google Scholar 

  • Lin JT, Yeh KC, Sheu LC (1996) A context-based object-oriented application framework for discrete event simulation. Comput Ind Eng 30(4):579–597

    Article  Google Scholar 

  • Lin X, Negenborn RR, Duinkerken MB, Lodewijks G (2017) Quality-aware modeling and optimal scheduling for perishable good distribution networks: the case of banana logistics. In: Bektaş T, Coniglio S, Martinez-Sykora A, Voß S (eds) International conference on computational logistics. Springer, Cham, pp 483–497

    Chapter  Google Scholar 

  • Lind S, Krassi B, Johansson B, Viitaniemi J, Heilala J, Stahre J, Vatanen S, Fasth Å (2008) SIMTER: a production simulation tool for joint assessment of ergonomics, level of automation and environmental impacts. In de Vin LJ, Ng AHC, Thorvald P, Sullivan W G, Ahmad M (eds) Proceedings of the 18th international conference on flexible automation and intelligent manufacturing. University of Skövde, Skövde, Sweden, pp 1025–1032

    Google Scholar 

  • Lütjen M, Dittmer P, Veigt M (2013) Quality driven distribution of intelligent containers in cold chain logistics networks. Prod Eng Res Devel 7(2):291–297

    Article  Google Scholar 

  • Ma AJ, Zhao HZ, Ren FZ (2010) Study on food life cycle carbon emissions assessment. Procedia Environ Sci 2:1983–1987

    Article  Google Scholar 

  • Mani M, Johansson B, Lyons KW, Sriram RD, Ameta G (2013) Simulation and analysis for sustainable product development. Int J Life Cycle Assess 18(5):1129–1136

    Article  Google Scholar 

  • McHaney R (1991) Computer simulation: a practical perspective. Academic press, San Diego

    Google Scholar 

  • Melouk SH, Freeman NK, Miller D, Dunning M (2013) Simulation optimization-based decision support tool for steel manufacturing. Int J Prod Econ 141(1):269–276

    Article  Google Scholar 

  • Mustafee N, Powell J, Brailsford SC, Diallo S, Padilla J, Tolk A (2015) Hybrid simulation studies and hybrid simulation systems: Definitions, challenges, and benefits. In: Yilmaz L, Chan WKV, Moon I, Roeder TMK, Macal C, Rossetti MD (eds) Proceedings of the 2003 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1678–1692

    Google Scholar 

  • Noordhoek M, Dullaert W, Lai DS, de Leeuw S (2018) A simulation–optimization approach for a service-constrained multi-echelon distribution network. Transp Res Part E: Logistics Transp Rev 114:292–311

    Article  Google Scholar 

  • Nunes MC, Nicometo M, Emond JP, Melis RB, Uysal I (2014) Improvement in fresh fruit and vegetable logistics quality: berry logistics field studies. Philos Trans Royal Soc A 372:20130307. https://doi.org/10.1098/rsta.2013.0307

  • Osterroth I, Klein S, Nophut C, Voigt T (2017) Operational state related modelling and simulation of the electrical power demand of beverage bottling plants. J Clean Prod 162:587–600

    Article  Google Scholar 

  • Persson D, Karlsson J (2007) Flow simulation of food industry production. Master thesis, Chalmers University of Technology, Department of Product and Production Development, Gothenburg

    Google Scholar 

  • Pirard F, Iassinovski S, Riane F (2011) A simulation based approach for supply network control. Int J Prod Res 49(24):7205–7226

    Article  Google Scholar 

  • Polotski V, Gharbi A, Kenne JP (2021) Production control of unreliable manufacturing systems with perishable inventory. Int J Adv Manuf Technol 116(7):2473–2496

    Article  Google Scholar 

  • Psomas AN, Nychas GJ, Haroutounian SA, Skandamis PN (2011) Development and validation of a tertiary simulation model for predicting the growth of the food microorganisms under dynamic and static temperature conditions. Comput Electron Agric 76(1):119–129

    Article  Google Scholar 

  • Rijpkema WA, Rossi R, van der Vorst JGAJ (2014) Effective sourcing strategies for perishable product supply chains. Int J Phys Distrib Logist Manag 44(6):494–510

    Article  Google Scholar 

  • Ritchie H, Roser M (2020) Environmental impacts of food production. Our world in data. https://ourworldindata.org/environmental-impacts-of-food. Accessed 14 April 2022

  • Rohrer MW (2003) Maximizing simulation ROI with Auto-Mod. In: Chick S, Sanchez PJ, Ferrin D, Morrice DJ (eds) Proceedings of the 2003 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 201–209

    Google Scholar 

  • Safaei AS, Moattar Husseini SM, Z-Farahani R, Jolai F, Ghodsypour SH (2010) Integrated multi-site production-distribution planning in supply chain by hybrid modelling. Int J Prod Res 48(14):4043–4069

    Google Scholar 

  • Santos R, Nunes J, Silva PDD, Gaspar PD, Andrade LP (2013) Computational tool for the analysis and simulation of cold room performance in perishable products industry. In: Proceedings of the 2nd IIR international conference on sustainability and the cold chain. Curran Associates, Red Hook, NY, pp 1–8

    Google Scholar 

  • Sargent RG (2000) Verification, validation, and accreditation of simulation models. In: Joines JA, Barton RR, Kang K, Fishwick PA (eds) Proceedings of the 2000 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 50–59

    Google Scholar 

  • Scott EP, Heldman DR (1990) Simulation of temperature dependent quality deterioration in frozen foods. J Food Eng 11(1):43–65

    Article  Google Scholar 

  • Sel Ç, Bilgen B (2014) Hybrid simulation and MIP based heuristic algorithm for the production and distribution planning in the soft drink industry. J Manuf Syst 33(3):385–399

    Article  Google Scholar 

  • Shamsi AA, Raisi AA, Aftab M (2014) Pollution-inventory routing problem with perishable goods. In: Golinska P (ed) Logistics operations, supply chain management and sustainability. Springer, Cham, pp 585–596

    Chapter  Google Scholar 

  • Solding P, Thollander P (2006) Increased energy efficiency in a Swedish iron foundry through use of discrete event simulation. In: Perrone LF, Wieland FP, Liu J, Lawson BG, Nicol DM, Fujimoto RM (eds) Proceedings of the 2006 Winter Simulation Conference. IEEE, Piscataway, NJ, pp 1971–1976

    Google Scholar 

  • Soto-Silva WE, Nadal-Roig E, González-Araya MC, Pla-Aragones LM (2016) Operational research models applied to the fresh fruit supply chain. Eur J Oper Res 251(2):345–355

    Article  MathSciNet  Google Scholar 

  • Teller C, Holweg C, Reiner G, Kotzab H (2018) Retail store operations and food waste. J Clean Prod 185:981–997

    Article  Google Scholar 

  • United Nations Environment Programme (2021) Food waste index report 2021. https://wedocs.unep.org/bitstream/handle/20.500.11822/35280/FoodWaste.pdf. Accessed 1 March 2022

  • Utomo DS, Onggo BS, Eldridge S (2018) Applications of agent-based modelling and simulation in the agri-food supply chains. Eur J Oper Res 269(3):794–805

    Article  MathSciNet  Google Scholar 

  • Van Der Vorst JG, Tromp SO, Zee DJVD (2009) Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics. Int J Prod Res 47(23):6611–6631

    Article  Google Scholar 

  • Vicino DA (2015) Improved time representation in discrete-event simulation. Ph.D. thesis, Université Nice Sophia Antipolis, Informatique, Sophia Antipolis

    Google Scholar 

  • Voigt T (2004) Neue Methoden für den Einsatz der Informationstechnologie bei Getränkeabfüllanlagen. Ph.D.thesis, TU München, Wissenschaftszentrum Weihenstephan, Weihenstephan

    Google Scholar 

  • Wohlgemuth V, Page B, Mäusbacher M, Staudt-Fischbach P (2004) Component-based integration of discrete event simulation and material flow analysis for industrial environmental protection: a case study in wafer production. EnviroInfo 1:303–312

    Google Scholar 

  • Wu W, Beretta C, Cronje P, Hellweg S, Defraeye T (2019) Environmental trade-offs in fresh-fruit cold chains by combining virtual cold chains with life cycle assessment. Appl Energy 254:113586. https://doi.org/10.1016/j.apenergy.2019.113586

  • Zhang Y, Lu H, Zhou Z, Yang Z, Xu S (2021) Analysis and optimisation of perishable inventory with stocks-sensitive stochastic demand and two-stage pricing: a discrete-event simulation study. J Simul 15(4):326–337

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty VII for Life Sciences: Food, Nutrition and Health, Chair of Food Supply Chain Management, University of Bayreuth, Fritz-Hornschuch-Strasse 13, 95326, Kulmbach, Germany

    Christian Fikar

  2. Department of Industrial and Materials Science, Chalmers University of Technology, Hörsalsvägen 7A, 412 96, Gothenburg, Sweden

    Björn Johansson

  3. SimPlan AG—Dresden Branch, Gostritzer Straße 63, 01217, Dresden, Germany

    Karsten Beyer

  4. Fakultät Wirtschaft und Informatik, Institute for Applied Data Science Hanover, Hochschule Hannover, Ricklinger Stadtweg 120, 30459, Hannover, Germany

    Marvin Auf der Landwehr

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  1. Christian Fikar
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  2. Björn Johansson
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  3. Karsten Beyer
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  4. Marvin Auf der Landwehr
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Correspondence to Christian Fikar .

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Editors and Affiliations

  1. Fachbereich Maschinenbau Fachgebiet Produktionsorganisation und Fabrikplanung, Universität Kassel, Kassel, Germany

    Sigrid Wenzel

  2. IT in Produktion und Logistik, TU Dortmund, Maschinenbau, Dortmund, Germany

    Markus Rabe

  3. Fakultät für Maschinenbau, Technische Universität Ilmenau, Ilmenau, Germany

    Steffen Strassburger

  4. Fakultät IV - WI, Hochschule Hannover, Hannover, Niedersachsen, Germany

    Christoph von Viebahn

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Fikar, C., Johansson, B., Beyer, K., Auf der Landwehr, M. (2024). Perishables. In: Wenzel, S., Rabe, M., Strassburger, S., von Viebahn, C. (eds) Energy-Related Material Flow Simulation in Production and Logistics. Springer, Cham. https://doi.org/10.1007/978-3-031-34218-9_6

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