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  • br Introduction br Optimization models have


    1. Introduction
    Optimization models have been used often to evaluate the efficiency of healthcare systems. For example, Van Houdenhoven et al. [1] were able to increase operation room (OR) utilization by applying mathematical algorithms. They also argued that mathe-matical algorithms were not enough; hence, they Ifenprodil hemitartrate needed to lower the organizational barriers that were limiting departments to schedule surgeries in certain rooms. Similarly, Ozkarahan [2] used goal programming to allocate surgeries to ORs. Denton el al. [3] combined mathematical optimization with simulation to minimize wait times, idle times and overtime in an OR.
    Most of the articles found in the literature are focused either in ORs or emergency rooms (ER). For example, Reuter-Oppermann el al. [4] summarized several logistical problems arising from emer-gency medical services. They established connections between de-mand, response time, and workload which are typically considered separately in the literature. Baesler and Sepulveda [5,6] are among the few articles not related to ORs or ERs. They presented a case study on combining simulation and multi-objective optimization heuristics to target four objectives at a Cancer Center such as minimizing patient’s waiting time, maximizing chair utilization, minimizing closing time, and maximizing nurses’ utilization. They were able to increase inpatient throughput by 30% with the same
    ∗ Corresponding author. E-mail address: [email protected] (D. Claudio).
    resources [5,6]. In fact, Swisher et al. [7] demonstrated that un-der certain conditions staffing reductions could be made without sacrificing patient throughput or increasing staff overtime. They experimented with several models with different patient mixes. They also showed that scheduling more of a certain type of patient (patients that require extensive physician interaction; longer ser-vice time) in the morning reduces employee overtime significantly.
    Harper and Gamlin [8] tested several different appointment schedules and showed how patient wait times can be signifi-cantly reduced through improved planning of the schedule and management engagement. Furthermore, Rohleder and Klassen [9] studied the use of rolling horizon appointment scheduling and considered two common management policies; Overload Rules (OLR) and Rule Delay (RD). The results showed that managers of appointment scheduling systems must carefully consider which measures are most important to them since the best choices of OLR and RD vary substantially by measure. Ahmadi-Javid et al. [10] concluded that in the last decade outpatient appointment systems have become more complex and more difficult to solve partic-ularly when integrating environmental factors in the model. Lin et al. [11] developed a model incorporating nurse fatigue as a way to building a nurse scheduling system. They used survey-based and circadian function-based fatigue models. The objective was to obtain a Pareto-optimal schedule where the nurse fatigue levels are reduced according to the nurse preference. In metaphase research we implemented different techniques to measure staff workload taking into consideration mental and physical workload and in-tegrated them into a mathematical model. We then took into
    consideration the balance of human resource workload as a main component of the mathematical model. Previous studies have con-sidered human resources as a number representing a fix quantity of available entities without considering their mental capabilities. Mental workload was included in our model to assure a balance in the capacity of the human resources without overloading them. This research advances the work done by Lin et al. [11] by mea-suring and incorporating mental workload of nurses. It then in-corporates this variable as a capacity constraint of a mathematical model to build an optimal schedule. Fields such as psychology and human factors have conceptualized and measured workload in the last decades. Workload is conceptualized as a dynamic balance between the challenge of the tasks and the individual’s responses to a task or activity [12]. This concept compiles elements such as task allocation, level of performance, task demand, mental and physical effort, and operator’s perception [13]. Another concept frequently used is mental workload as the capacity of individuals to process, analyze, and manage information.