Creating resident shift schedules under multiple objectives by generating and evaluating the Pareto frontier

Hong, Young-Chae; Cohn, Amy; Epelman, Marina A.; Alpert, Aviva

doi:10.1016/j.orhc.2018.08.001

Cited by 5 publications

(2 citation statements)

References 36 publications

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“…In any organization, employees face the prospect of being assigned tasks they do not prefer. Encouraging the equal distribution of non-preferred tasks is a common conceptualization of fair outcomes in personnel decisions (e.g., Carter and Lapierre 2001, Cohn et al 2009, Elomri et al 2015, Erhard et al 2018, F ügener et al 2015, Gross et al 2019, Hong et al 2019, Proano and Agarwal 2018, Stolletz and Brunner 2012, Tan and Staats 2020. Equality of outcomes is often encouraged by incorporating soft constraints into multi-objective optimizations (Ernst et al 2004a, 2004b, Van den Bergh et al 2013.…”

Section: Fairness In Personnel Managementmentioning

confidence: 99%

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Unequal but Fair: Incorporating Distributive Justice in Operational Allocation Models

Rea

Froehle

Masterson

et al. 2021

Production and Operations Management

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Fairness is a natural concern when model‐based decisions affect human beings. In personnel‐management decisions, employees’ perceptions of fairness depend both upon the decision processes as well as the resulting outcomes. Acting as a key part of decision processes, operational models often emphasize equality, or sameness, of outcomes among individuals. Fair outcomes, as a manifestation of distributive justice, involve balancing two competing aspects: equality and equity. Equity, in opposition to equality, is concerned with an individual’s outcomes being commensurate with their inputs. While both aspects of distributive justice can be an expectation of members of an organization, they present an inherent trade‐off; more of one requires less of the other. In order to balance the trade‐off between equity and equality, we propose a bi‐objective, non‐linear optimization model, which is then extended to a mixed‐integer formulation in a service‐oriented case study. Specifically, the case study model allocates physicians’ contracted clinical time across multiple emergency department locations. Deviations from physicians’ equity‐weighted preferences for where they work are minimized and a Pareto frontier of objectively fair solutions is derived. As a result, the time physicians were allocated to locations they did not prefer was substantially reduced. In addition, pre‐ and post‐implementation surveys revealed statistically significant improvements in employee reported perceptions of fairness, transparency, and overall satisfaction with the work‐time‐allocation process. The evidence supports the conclusion that decision models designed to result in unequal outcomes can still be perceived as fair by the employees they affect.

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Section: Fairness In Personnel Managementmentioning

confidence: 99%

“…2019, Hong et al. 2019, Proano and Agarwal 2018, Stolletz and Brunner 2012, Tan and Staats 2020). Equality of outcomes is often encouraged by incorporating soft constraints into multi‐objective optimizations (Ernst et al.…”

Section: Introductionmentioning

confidence: 99%