A hybrid simulation modeling framework for regional food hubs

References

• Ahumada, O., & Villalobos, J. R. (2009). Application of planning models in the agri-food supply chain: A review. European Journal of Operational Research, 196(1), 1–20.
   Web of Science ®Google Scholar
• Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In R. Conte, R. Hegselmann, & P. Terna (Eds.), Simulating social phenomena (pp. 21–40). Berlin: Springer.
   Google Scholar
• Barham, J., Tropp, D., Enterline, K., Farbman, J., Fisk, J., & Kiraly, S. (2012). Regional food hub resource guide. Washington, DC: U.S. Dept. of Agriculture-Agriculture Marketing Service. doi:10.9752/MS046.04-2012
   Google Scholar
• Barton, P., Bryan, S., & Robinson, S. (2004). Modelling in the economic evaluation of health care: selecting the appropriate approach. Journal of Health Service Research Policy, 9(2), 110–118.
   PubMedGoogle Scholar
• Behdani, B. (2012). Evaluation of paradigms for modeling supply chains as complex socio-technical systems. In Proceedings of the 2012 Winter Simulation Conference (pp. 1–15). IEEE.
   Google Scholar
• Bergh, J. V., Beliën, J., Bruecker, P. D., & Demeulemeester, E. (2013). Personnel scheduling: A literature review. European Journal of Operational Research, 226(3), 367–385.
   Web of Science ®Google Scholar
• Borshchev, A., & Filippov, A. (2004). From system dynamics and discrete event to practical agent based modeling: Reasons, techniques, tools. In Proceedings of the 22nd International Conerence of the System Dynamics Society (p. 22).
   Google Scholar
• Brailsford, S. C., Viana, J., Rossiter, S., Channon, A., & Lotery, A. J. (2013). Hybrid simulation for health and social care: The way forward, or more trouble than it’s worth? In Proceedings of the 2013 Winter Simulation Conference (pp. 258–269). IEEE.
   Google Scholar
• Chan, W. K., Son, Y. J., & Macal, C. M. (2010). Agent-based simulation tutorial-simulation of emergent behavior and differences between agent-based simulation and discrete-event simulation. In Proceedings of the 2010 Winter Simulation Conerence (pp. 135–150). IEEE.
   Google Scholar
• Dyer, J. S. (2005). MAUT—multiattribute utility theory. In S. Greco (Ed.), Multiple criteria decision analysis: state of the art surveys (pp. 265–292). New York, NY: Springer.
   Google Scholar
• Fischer, M., Hamm, M., Pirog, R., Fisk, J., Farbman, J., & Kiraly, S. (2013). Findings of the 2013 national food hub survey. Michigan: Michigan State University-Center for Regional Food Systems & The Wallace Center at Winrock International.
   Google Scholar
• Fisk, J., & Matteson, G. (2013). Counting values—Food hub financial benchmarking study. National Good Food Network.
   Google Scholar
• Giunipero, L. C. (1990). Motivating and monitoring JIT supplier performance. Journal of Purchasing & Materials Management, 26(3), 19–24.
   Google Scholar
• Gopakumar, B., Sundaram, S., Wang, S., Koli, S., & Srihari, K. (2008). A simulation based approach for dock allocation in a food distribution center. In Proceedings of the 40th Conference on Winter Simulation (pp. 2750–2755). IEEE.
   Google Scholar
• Gore, R., & Reynolds, P., Jr. (2010). INSIGHT: Understanding unexpected behaviours. Journal of Simulation, 4(3), 170–180.
   Web of Science ®Google Scholar
• Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760–2768.
   Web of Science ®Google Scholar
• Gu, J., Goetschalckx, M., & McGinnis, L. F. (2007). Research on warehouse operation: A comprehensive review. European Journal of Operational Research, 177(1), 1–21.
   Web of Science ®Google Scholar
• Heath, S. K., Brailsford, S. C., Buss, A., & Macal, C. M. (2011). Cross-paradigm simulation modeling: Challenges and successes. In Simulation. Proceedings of the 2011 Winter Simulation Conference (pp. 2783–2797). IEEE.
   Google Scholar
• Interview. (2015). Manager, food hub in Central Iowa ( A. Mittal, Interviewer). Iowa, United States.
   Google Scholar
• Janssen, M. A., & Ostrom, E. (2006). Empirically based, agent-based models. Ecology and Society, 11(2), 37.
   Web of Science ®Google Scholar
• Krause, D. R., Handfield, R. B., & Scannell, T. V. (1998). An empirical investigation of supplier development: Reactive and strategic processes. Journal of Operations Management, 17(1), 39–58.
   Google Scholar
• Law, A. M., & Kelton, W. D. (1991). Simulation modeling and analysis (Vol. 2). New York, NY: McGraw-Hill.
   Google Scholar
• Low, S. A., Adalja, A., Beaulieu, E., Key, N., Martinez, S., Melton, A., & Jablonski, B. B. (2015). Trends in US local and regional food systems. U.S. Dept. of Agriculture—Economic Research Service.
   Google Scholar
• Majid, M. A., Aickelin, U., & Siebers, P. O. (2009). Comparing simulation output accuracy of discrete event and agent based models: A quantitative approach. In Proceedings of the 2009 Summer Computer Simulation Conference (pp. 177–184). Society for Modeling & Simulation International.
   Google Scholar
• Mittal, A. (2016). Hybrid simulation modeling for regional food systems (MS Thesis). Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA.
   Google Scholar
• Mittal, A., & Krejci, C. C. (2015). A hybrid simulation model of inbound logistics operations in regional food supply systems. In Proceedings of the 2015 Winter Simulation Conerence (pp. 1549–1560). IEEE.
   Google Scholar
• Modi, S. B., & Mabert, V. A. (2007). Supplier development: Improving supplier performance through knowledge transfer. Journal of Operations Management, 25(1), 42–64.
   Web of Science ®Google Scholar
• Mustafee, N., Sahnoun, M. H., Smart, A., Godsiff, P., Baudry, D., & Louis, A. (2015). Investigating execution strategies for hybrid models developed using multiple M&S methodologies. In Proceedings of the 48th Annual Simulation Symposium (pp. 78–85). Society for Computer Simulation International.
   Google Scholar
• Owen, C. (2013). Selection of simulation tools for improving supply chain performance (Doctoral dissertation). Aston University.
   Google Scholar
• Pinchot, A. (2014). The economics of local food systems: A literature review of the production, distribution, and consumption of local food. University of Minnesota Extension—Center for Community Vitality.
   Google Scholar
• Pugh, G. A. (2006). Agent-based simulation of discrete-event systems. In Proceedings of the 2006 Illinois-Indiana and North Central Joint Section Conferences.
   Google Scholar
• Punj, G., & Stewart, D. W. (1983). Cluster analysis in marketing research: Review and suggestions for application. Journal of Marketing Research, 20(2), 134–148.
   Web of Science ®Google Scholar
• Rogoff, J. (2014). Improving systems of distribution and logistics for regional food hubs. MIT, Department of Urban Studies and Planning.
   Google Scholar
• Sargent, R. G. (2005). Verification and validation of simulation models. In Proceedings of 2005 Winter Simulation Conerence (pp. 30–143). IEEE.
   Google Scholar
• Schreinemachers, P., Berger, T., Sirijinda, A., & Praneetvatakul, S. (2009). The diffusion of greenhouse agriculture in northern Thailand: Combining econometrics and agent-based modeling. Canadian Journal of Agricultural EconomicslRevue Canadienne d’agroeconomie, 57(4), 513–536.
   Web of Science ®Google Scholar
• Sopha, B. M., Klöckner, C. A., & Hertwich, E. G. (2013). Adoption and diffusion of heating systems in Norway: Coupling agent-based modeling with empirical research. Environmental Innovation and Societal Transitions, 8, 42–61.
   Google Scholar
• Soysal, M., Bloemhof-Ruwaard, J. M., Meuwissen, M. P., & van der Vorst, J. G. (2012). A review on quantitative models for sustainable food logistics management. International Journal on Food System Dynamics, 3(2), 136–155.
   Google Scholar
• Stroink, M. L. (2013). Complexity and food hubs: Five case studies from Northern Ontario. Local Environment, 18(5), 620–635.
   Web of Science ®Google Scholar
• Vanderburgh-Wertz, D., & Moraghan, M. R. (2014). Food hub business assessement toolkit. Wholesome Wave.
   Google Scholar
• Vespignani, A. (2009). Predicting the behavior of techno-social systems. Science, 325(5939), 425–428.
   PubMed Web of Science ®Google Scholar
• Viana, J., Rossiter, S., Channon, A. A., Brailsford, S. C., & Lotery, A. (2012). A multi-paradigm, whole system view of health and social care for age-related macular degeneration. In Proceedings of the 2012 Winter Simulation Conerence (p. 95). IEEE.
   Google Scholar
• Zulkepli, J., Eldabi, T., & Mustafee, N. (2012). Hybrid simulation for modelling large systems: An example of integrated care model. In Proceedings of the 2012 Winter Simulation Conerence (pp. 1–12). IEEE.
   Google Scholar