Agnihothri, S. R., Mishra, A. K., and Simmons, D. E. 2003. Workforce cross-training decisions in field service systems with two job types. Journal of the Operational Research Society 54(4):410–418.Google Scholar

Ahn, H.-S., Righter, R., and Shanthikumar, J. G. 2005. Staffing decisions for heterogeneous workers with turnover. Mathematical Methods of Operations Research 62(3):499–514.CrossRefGoogle Scholar

Allport, A., Styles, E. A., and Hsieh, S. 1994. Shifting intentional set: Exploring the dynamic control of tasks. In Umilta, C. and Moscovitch, M., eds., Attention and Performance XV: Conscious and Unconscious Information Processing, 421–452. Cambridge, MA: MIT Press.Google Scholar

Anzanello, M. J., and Fogliatto, F. S. 2011. Learning curve models and applications: Literature review and research directions. International Journal of Industrial Ergonomics 41(5): 573–583.Google Scholar

Aral, S., Brynjolfsson, E., and Van Alstyne, M. 2012. Information, technology, and information worker productivity. Information Systems Research 23(3-part-2): 849–867.Google Scholar

Arlotto, A., Chick, S. E., and Gans, N. 2014. Optimal hiring and retention policies for heterogenous workers who learn. Management Science 60(1): 110–129.CrossRefGoogle Scholar

Armbruster, D., Gel, E. S., and Murakami, J. 2007. Bucket brigades with worker learning. European Journal of Operational Research 176(1):264–274.Google Scholar

Attia, E.-A., Duquenne, P., and Le-Lann, J.-M. 2014. Considering skills evolutions in multi-skilled workforce allocation with flexible working hours. International Journal of Production Research 52(15): 4548–4573.CrossRefGoogle Scholar

Badiru, A. B. 1995. Multivariate analysis of the effect of learning and forgetting on product quality. The International Journal of Production Research 33(3):777–794.Google Scholar

Bailey, C. D. 1989. Forgetting and the learning curve: A laboratory study. Management Science 35(3):340–352.Google Scholar

Batt, R. J., and Terwiesch, C. 2012. Doctors under load: An empirical study of service time as a function of census. https://opimweb.wharton.upenn.edu/files/?whdmsaction=public:main.file\&fileID=5133.Google Scholar

Bentefouet, F., and Nembhard, D. A. 2013. Optimal flow-line conditions with worker variability. International Journal of Production Economics 141(2):675–684.Google Scholar

Bersin, J. 2013. The datafication of human resources. Forbes.com, July 19.Google Scholar

Bevis, F., Finniear, C., and Towill, D. 1970. Prediction of operator performance during learning of repetitive tasks. International Journal of Production Research 8(4):293–305.Google Scholar

Bianco, F. J., Cronin, A. M., Klein, E. A., Pontes, E. E., Scardino, P. T., and Vickers, A. J. 2010. Fellowship training as a modifier of the surgical learning curve. Academic Medicine: Journal of the Association of American Medical Colleges 85(5):863–868.CrossRefGoogle ScholarPubMed

Block, L. 2015. Work Rules! New York: Twelve.Google Scholar

Blume, B. D., Ford, J. K., Baldwin, T. T., and Huang, J. L. 2010. Transfer of training: A meta-analytic review. Journal of Management 36(4):1065–1105.CrossRefGoogle Scholar

Bokhorst, J., and Gaalman, G. 2009. Cross-training workers in dual resource constrained systems with heterogeneous processing times. International Journal of Production Research 47(22): 6333–6356.Google Scholar

Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., and Zhao, S. 2005. Statistical analysis of a telephone call center. Journal of the American Statistical Association 100(469): 36–50.Google Scholar

Bukchin, J., Luquer, R., and Shtub, A. 2002. Learning in tele-operations. IIE Transactions 34(3):245–252.Google Scholar

Buzacott, J. 2002. The impact of worker differences on production system output. International Journal of Production Economics 78(1):37–44.Google Scholar

Cachon, G., and Terweisch, C. 2012. Matching Supply with Demand, 3rd ed. New York: McGraw-Hill Education.Google Scholar

Campbell, G. M. 2010. A two-stage stochastic program for scheduling and allocating cross-trained workers. Journal of the Operational Research Society 62(6): 1038–1047.Google Scholar

Campbell, G. M., and Diaby, M. 2002. Development and evaluation of an assignment heuristic for allocating cross-trained workers. European Journal of Operational Research 138(1): 9–20.Google Scholar

Chen, X., Thomas, B. W., and Hewitt, M. 2015. The technician routing problem with experience-based service times. http://myweb.uiowa.edu/bthoa/iowa/Research.html (accessed August 17, 2017).Google Scholar

Cordeau, J.-F., Laporte, G., Pasin, F., and Ropke, S. 2010. Scheduling technicians and tasks in a telecommunications company. Journal of Scheduling 13(4): 393–409.CrossRefGoogle Scholar

Corominas, A., Olivella, J., and Pastor, R. 2010. A model for the assignment of a set of tasks when work performance depends on experience of all tasks involved. International Journal of Production Economics 126(2): 335–340.CrossRefGoogle Scholar

Dar-El, E. M. 2000. Human Learning: From Learning Curves to Learning Organizations. Boston, MA: Kluwer Academic Publishers.Google Scholar

Deming, W. E. 1982. Quality, Productivity, and Competitive Position. Boston, MA: Massachusetts Institute of Technology, Center for Advanced Engineering Study.Google Scholar

Detert, J. R., Treviño, L. K., Burris, E. R., and Andiappan, M. 2007. Managerial modes of influence and counterproductivity in organizations: A longitudinal business-unit-level investigation. Journal of Applied Psychology 92(4): 993–1005.Google Scholar

Donin, O., Feigin, P. D., Mandelbaum, A., Zeltyn, S., Trofimov, V., Ishay, E., Khudiakov, P., and Nadjharov, E. 2006. DataMOCCA: DATA model for call center analysis. Technical report 4.1., Tel Aviv, Israel: Industrial Engineering and Management, Technion University.Google Scholar

Eitzen, G., Panton, D., and Mills, G. 2004. Multi-skilled workforce optimisation. Annals of Operations Research 127(1–4): 359–372.Google Scholar

Fine, C. H. 1986. Quality improvement and learning in production systems. Management Science 10: 1301–1315.Google Scholar

Firat, M., and Hurkens, C. A. J. 2011. An improved MIP-based approach for a multi-skill workforce scheduling problem. Journal of Scheduling 15: 363–380.Google Scholar

Fowler, J., Wirojanagud, P., and Gel, E. 2008. Heuristics for workforce planning with worker differences. European Journal of Operational Research 190(3): 724–740.CrossRefGoogle Scholar

Gans, N., and Zhou, Y.-P. 2002. Managing learning and turnover in employee staffing. Operations Research 50(6): 991–1006.Google Scholar

Gans, N., Koole, G., and Mandelbaum, A. 2003. Commissioned paper: Telephone call centers: Tutorial, review, and research prospects. Manufacturing and Service Operations Management 5(2): 79–141.Google Scholar

Gans, N., Liu, N., Mandelbaum, A., Shen, H., and Han, Ye. 2010. Service times in call centers: Agent heterogeneity and learning with some. In Berger, J. O., Cai, T. T., and Johnstone, I. M., eds., Borrowing Strength: Theory Powering Applications – A Festschrift for Lawrence D. Brown, 6: 99–123. Beachwood, OH: Institute of Mathematical Statistics.Google Scholar

Greeno, J. G., Smith, D. R., and Sternberg, R. J. 1993. Transfer of situated transfer of situated learning. In Detterman, D. K. and Sternberg, R. J., eds., Transfer on Trial: Intelligence, Cognition, and Instruction, 99–167. New York: Ablex.Google Scholar

Gutjahr, W. J., Katzensteiner, S., Reiter, P., Stummer, C., and Denk, M. 2008. Competence-driven project portfolio selection, scheduling and staff assignment. Central European Journal of Operations Research 16(3): 281–306.Google Scholar

Heimerl, C., and Kolisch, R. 2009. Scheduling and staffing multiple projects with a multi-skilled workforce. OR Spectrum 32(2): 343–368.Google Scholar

Heimerl, C., and Kolisch, R. 2010. Work assignment to and qualification of multi-skilled human resources under knowledge depreciation and company skill level targets. International Journal of Production Research 48(13): 3759–3781.Google Scholar

Hendrickson, G., and Schroeder, W. B. 1941. Transfer of training in learning to hit a submerged target. Journal of Educational Psychology 32(3): 205–213.Google Scholar

Hewitt, M., Chacosky, A., Grasman, S., and Thomas, B. W. 2015. Integer programming techniques for solving non-linear workforce planning models with learning. European Journal of Operational Research 242(3): 942–950.Google Scholar

Hopp, W., and Van Oyen, M P. 2004. Agile workforce evaluation: A framework for cross- training and coordination. IIE Transactions 36(10): 919–940.Google Scholar

Hopp, W. J., Tekin, E., and Van Oyen, M. P. 2004. Benefits of skill chaining in serial production lines with cross-trained workers. Management Science 50(1): 83–98.Google Scholar

Huang, H.-C., Lee, L.-H., Song, H., and Thomas Eck, B. 2009. SimMan – A simulation model for workforce capacity planning. Computers and Operations Research 36(8): 2490–2497.Google Scholar

Hunter, J. E., Schmidt, F. L., and Judiesch, M. K. 1990. Individual differences in output variability as a function of job complexity. Journal of Applied Psychology 75(1): 28–42.Google Scholar

Jaber, M. Y. 2006. Lot sizing for an imperfect production process with quality corrective interruptions and improvements, and reduction in setups. Computers and Industrial Engineering 51(4): 781–790.CrossRefGoogle Scholar

Jaber, M. Y., and Bonney, M. 2003. Lot sizing with learning and forgetting in set-ups and in product quality. International Journal of Production Economics 83: 95–111.Google Scholar

Jaber, M. Y., and Givi, Z. S. 2014. Imperfect production process with learning and forgetting effects. Computational Management Science 12(1): 129–152.Google Scholar

Jaber, M. Y., and Guiffrida, A. L. 2004. Learning curves for processes generating defects requiring reworks. European Journal of Operational Research 159(3): 663–672.Google Scholar

Jaber, M. Y., and Sikström, S. 2004. A numerical comparison of three potential learning and forgetting models. International Journal of Production Economics 92(3): 281–294.Google Scholar

Jordan, W. C., and Graves, S. C. 1995. Principles on the benefits of manufacturing process flexibility. Management Science 41(February): 577–594.Google Scholar

Judd, C. H. 1908. The relation of special training to general intelligence. Educational Review 36: 28–42.Google Scholar

Kaminsky, J. A., and Sloutsky, V. M. 2011. Representation and transfer of abstract mathematical concepts. In Reyna, V. F., ed., The Adolescent Brain: Learning, Reasoning, and Decision Making, 67–93. Washington, DC: American Psychological Association.Google Scholar

KC, D. S. 2014. Does multitasking improve performance? Evidence from the emergency department. Manufacturing and Service Operations Management 16(2): 168–183.Google Scholar

Kim, S., and Nembhard, D. A. 2010. Cross-trained staffing levels with heterogeneous learning/ forgetting. IEEE Transactions on Engineering Management 57(4): 560–574.Google Scholar

Kovacs, A. A., Parragh, S. N., Doerner, K. F., and Hartl, R. F. 2011. Adaptive large neighborhood search for service technician routing and scheduling problems. Journal of Scheduling 15 (5): 579–600.Google Scholar

Kuntz, L., Mennicken, R., and Scholtes, S. 2014. Stress on the ward: Evidence of safety tipping points in hospitals. Management Science 61(4): 754–771.Google Scholar

Lapre, A. S., Mukherjee, , and Van Wassenhove, L. N. 2000. Behind the learning curve: Linking learning activities to waste reduction. Management Science 46(5): 597–611.Google Scholar

Levy, F. K. 1965. Adaptation in the production process. Management Science 11(6): B136–B154.Google Scholar

Lim, D. H., and Johnson, S. D. 2002. Trainee perceptions of factors that influence learning transfer. International Journal of Training and Development 6(1): 36–48.Google Scholar

Lohr, S. 2013. Big data, trying to build better workers. The New York Times, BU4.Google Scholar

Marlin Steel. 2015. How cross-training drives employee engagement in manufacturing jobs. https://www.marlinwire.com/blog/how-cross-training-drives-employee-engagement-in-manufacturing-jobs (accessed September 30, 2015).Google Scholar

Mazur, J. E., and Hastie, R. 1978. Learning as accumulation: A reexamination of the learning curve. Psychological Bulletin 85(6): 1256–1274.Google Scholar

McDonald, T., Ellis, K. P., Van Aken, E. M., and Koelling, C. P. 2009. Development and application of a worker assignment model to evaluate a lean manufacturing cell. International Journal of Production Research 47(9): 2427–2447.Google Scholar

Nembhard, D., and Uzumeri, M. 2000a. An individual-based description of learning within an organization. IEEE Transactions on Engineering Management 47(3): 370–378.Google Scholar

Nembhard, D., and Uzumeri, M. V. 2000b. Experiential learning and forgetting for manual and cognitive tasks. International Journal of Industrial Ergonomics 25(4): 315–326.Google Scholar

Nembhard, D. A. 2001. Heuristic approach for assigning workers to tasks based on individual learning rates. International Journal of Production Research 39(9): 1955–1968.Google Scholar

Nembhard, D. A., and Bentefouet, F. 2012. Parallel system scheduling with general worker learning and forgetting. International Journal of Production Economics 139(2): 533–542.Google Scholar

Nembhard, D. A., and Bentefouet, F. 2014. Selection policies for a multifunctional workforce. International Journal of Production Research 52(16): 4785–4802.Google Scholar

Nembhard, D. A., and Bentefouet, F. 2015. Selection, grouping, and assignment policies with learning-by-doing and knowledge transfer. Computers and Industrial Engineering 79: 175–187.Google Scholar

Nembhard, D. A., and Norman, B. A. 2007. Cross training in production systems with human learning and forgetting. In Nembhard, D. A., ed., Workforce Cross Training, 111–129. Boca Raton, FL: CRC Press.Google Scholar

Nembhard, D. A., and Shafer, S. M. 2008. The effects of workforce heterogeneity on productivity in an experiential learning environment. International Journal of Production Research 46(14): 3909–3929.Google Scholar

Olivella, J., Corominas, A., and Pastor, R. 2013. Task assignment considering cross-training goals and due dates. International Journal of Production Research 51(3): 37–41.Google Scholar

Peck, D. 2013. They’re Watching You at Work. The Atlantic (December): 72–82, 84 .Google Scholar

Pierce, L., Snow, D., and Mcafee, A. 2015. Cleaning house: The impact of information technology monitoring on employee theft and productivity. Management Science 61(10): 2299–2319.Google Scholar

Pisano, G. P., Bohmer, R. M., and Edmondson, A. C. 2001. Organizational differences in rates of learning: Evidence from the adoption of minimally invasive cardiac surgery. Management Science 47(6): 752–768.Google Scholar

Powell, A., Savin, S., and Savva, N. 2012. Physician workload and hospital reimbursement: Overworked physicians generate less revenue per patient. Manufacturing and Service Operations Management 14(4): 512–528.CrossRefGoogle Scholar

Qin, R., Nembhard, D. A., and BarnesII, W. L. 2015. Workforce flexibility in operations management. Surveys in Operations Research and Management Science 20(1): 19–33.Google Scholar

Sabar, M., Montreuil, B., and Frayret, J. M. 2012. An agent-based algorithm for personnel shift-scheduling and rescheduling in flexible assembly lines. Journal of Intelligent Manufacturing 23(6): 2623–2634.Google Scholar

Salomon, G., and Globerson, T. 1987. Skill may not be enough: The role of mindfulness in learning and transfer. International Journal of Educational Research 11(6): 623–637.Google Scholar

Sayin, S., and Karabati, S. 2007. Assigning cross-trained workers to departments: A two-stage optimization model to maximize utility and skill improvement. European Journal of Operational Research 176(3): 1643–1658.Google Scholar

Shafer, S. M., Nembhard, D. A., and Uzumeri, M. V. 2001. The effects worker learning, forgetting, and heterogeneity on assembly line productivity. Management Science 47(12): 1639–1653.Google Scholar

Son, J. Y., and Goldstone, R. L., , J. Y. 2009. Contextualization in perspective. Cognition and Instruction 27: 51–89.Google Scholar

Speier, C., Valacich, J. S., and Vessey, I. 1999. The influence of task interruption onindividual decision making: An information overload perspective. Decision Sciences 30(2): 337–360.Google Scholar

Stevenson, W. J. 2015. Operations Management, 12th ed. New York: McGraw-Hill.Google Scholar

Sze, D. Y. 1984. OR practice – A queueing model for telephone operator staffing. Operations Research 32(2): 229–249.Google Scholar

Tan, T. F., and Netessine, S. 2014. When does the devil make work? An empirical study of the impact of workload on worker productivity. Management Science 60(6): 1574–1593.Google Scholar

U.S. Bureau of Labor Statistics. 2016. Labor force statistics from the current population survey. http://www.bls.gov/cps/cpsaat47.htm (accessed September 30, 2016).Google Scholar

Uzumeri, M., and Nembhard, D. A. 1998. A population of learners: A new way to measure organizational learning. Journal of Operations Management 16(5): 515–528.Google Scholar

Winch, J. K., Cai, X., and Vairaktarakis, G. L. 2007. Cyclic job scheduling in paced assembly lines with cross-trained workers. International Journal of Production Research 45(4): 803–828.Google Scholar

Wirojanagud, P., Gel, E. S., Fowler, J. W., and Cardy, R. 2007. Modelling inherent worker differences for workforce planning. International Journal of Production Research 45 (3): 525–553.Google Scholar

Womack, J. P., and Jones, D. T. 2003. Lean Thinking. New York: Free Press.Google Scholar

Wright, T. P. 1936. Factors affecting the cost of airplanes. Journal of Aeronautical Sciences 3(4): 122–128.Google Scholar

Yan, J.-h., and Wang, Z.-m. 2011. GA based algorithm for staff scheduling considering learning-forgetting effect. 2011 IEEE 18th International Conference on Industrial Engineering and Engineering Management: 122–126.Google Scholar

Zhu, G., and Sherali, H. 2009. Two-stage workforce planning under demand fluctuations and uncertainty. Journal of the Operational Research Society 60(1): 94–103.Google Scholar

Zumbrun, J. 2015. Behind lingering job listings. The Wall Street Journal, June 19 A3.Google Scholar