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International Journal of Healthcare Management (20479700)17(1)pp. 63-75
Nowadays proper management of hospital resources has played an important role in reducing the economic pressure on hospitals. Unprecedented challenges have arisen for healthcare systems including further resource constraints due to the Covid-19 pandemic. So different countries faced the low capacity of hospitals' resources to admit and care for patients during the crisis. Medical staff, beds and personal protective equipment are among the scarce resources in this period. In this study, the patient flow in public hospitals has been modeled using System Dynamics (SD) simulation to manage the available capacity of intensive care units (ICU) and wards during the COVID-19 period. The model has been implemented in Vensim PLE and verified for public hospitals in Amol, Iran. The results have shown that hospitals face bed shortages in the period coinciding with the growing incidence of COVID-19, being forced to cancel or delay the admission of selected patients and nonemergency surgeries. Different scenarios based on possible strategies for managing hospital bed capacities have been also evaluated. The best strategy is the one in which the allocated bed capacities to COVID-19 patients are altered based on infection rates during different stages of the pandemic, resulting the shortage of beds is postponed the most. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Annals of Operations Research (02545330)328(1)pp. 727-754
This study considers a combination of strategic and tactical levels of operating room planning where two types of full-day and half-day blocks are considered. Constraints on available beds in hospital wards and different durations of stay for elective patients in the ward are the main assumptions of the problem. The aim is to minimize overtime and idleness of operating rooms, maximize surgeons' satisfaction and minimize the number of unscheduled surgeries in the master surgery schedule. We propose a mixed integer programming model as well as a novel heuristic algorithm by combining simulated annealing meta-heuristic and linear programming models. Real data from a teaching-educational hospital with 20 operating rooms and 47 surgeons’ groups as well as some random problem instances are used in the experiments. The results indicate high performance of the proposed heuristic algorithm in generating near-optimal Pareto solutions compared with the mathematical model and a local search algorithm from the literature. Sensitivity analysis is done on some parameters of the problem like overtime cost of operating rooms, maximum allowable overtime, available beds in the ward, and the number of attendance days preferred by each surgeon. The results from our case study show that 6% of operating room costs are related to the fact that some surgeons are unwilling to perform surgeries on some days of a week. Also, adding 20% to the capacity of ward beds results in 3% and 10% decrease in unscheduled surgeries and operating rooms idleness, respectively. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
International Journal of Industrial and Systems Engineering (17485037)43(4)pp. 435-463
In this research a master surgical scheduling problem in conditions of uncertainty of demand, duration of surgery and length of patients' stay is studied. First, an MIP model is developed in which the length of patients' stay is considered probabilistic. Then, allowing for uncertainty in demand, a robust model is presented. Finally, a simulation-optimisation approach is developed in which three parameters are considered as uncertain. In this approach, the Grey Wolf and genetic algorithms are designed as the optimisation, and the Mont Carlo simulation is used in the simulation module. The results show that the maximum gap in the comparison of the simulation-optimisation algorithms and the lower-bound solution of the mathematical models in small-scale problems is only 9.36% while the algorithms are much faster. In larger-scale problems, the average improvement percentage of the proposed approach with the Grey Wolf optimisation module as compared to the genetic algorithm module is 2.93%. Copyright © 2023 Inderscience Enterprises Ltd.
Lecture Notes on Data Engineering and Communications Technologies (23674512)181pp. 299-308
This study aims to identify the application of process mining techniques in health centres for the visualisation of healthcare activities. As a scoping review, this research was used and divided into three phases: literature collection, assessment, and selection. A literature search had done on Google Scholar, Web of Science, PubMed, Elsevier, and ProQuest, along with the impact of inclusion and exclusion criteria. Keywords have been addressed as follows: process mining, visualising, mapping, workflow mining, automated business process, discovery, process discovery, performance mining, healthcare, hospital, emergency department, emergency medical service, and apply. The findings showed that process mining can be used to analyse different activities in the field of healthcare, including workflow in healthcare, clinical and administrative processes, data analysis in information systems, events data in patients’ infectious, creation of dashboards, the discovery of unexpected, and hidden relationships. Finally, as the significance of this research, it has been argued that the use of process mining in healthcare allows health professionals to understand the actual implementation of processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
International Journal of Industrial Engineering : Theory Applications and Practice (1943670X)29(1)pp. 45-63
This paper investigates the integrated master surgical scheduling and case-mix planning problem with the objective of a weighted sum of minimizing the costs of overtime and idle time, increasing surgeons' preferences, and reducing uncovered demands. Considering the uncertainty in surgery demands, a robust optimization model is proposed. A two-stage method is designed for creating and updating this scheduling with respect to downstream resources. The first stage allocates the time blocks to each surgeon to determine the mix of surgery types assigned to each block. In the second stage, having the weekly waiting list of patients, the schedule is updated on the weekly horizon to cope with demand fluctuations and to maximize the use of operating rooms capacity. The proposed method is validated using actual data from a teaching hospital in Iran. The results of the proposed models significantly outperformed the actual plan of a hospital, which indicated the efficiency of the designed models. Comparing the deterministic and robust models shows that robust models lead to better results in over 70% of the test instances. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING
International Journal of Operational Research (17457653)43(4)pp. 479-497
In this article, the surgical case assignment problem (SCAP) with uncertain duration of surgeries is assessed. This problem is defined as assigning the subsets of patients on the waiting list to the time blocks of operating rooms (ORs) in a given planning horizon. To further increase the OR utility rate and service rate, a novel modified block scheduling strategy is proposed and modelled as a mixed integer programming model. Then a robust optimisation model is proposed to tackle uncertainties in surgery duration. A set of real-based instances from 'Al Zahra Hospital', a teaching hospital in Iran, is applied to verify the proposed deterministic model. The optimal solution is compared with the actual hospital plan indicating the efficiency of this proposed model in practice. A robust model is evaluated through a set of instances. Numerical results indicate that the average percent of overtime reduction is 51.95% by applying the robust model. Copyright © 2022 Inderscience Enterprises Ltd.
International Journal of Industrial Engineering : Theory Applications and Practice (1943670X)27(3)pp. 321-344
This research proposes a mathematical model in which the procurement and production lot-sizing are integrated with scheduling. In this model, the procurement lot-sizing comprises supplier selection and multiple transportation modes. This profit-maximizing model is developed with demand choice flexibility using two arrangements: with and without demand packages. The results represent that the objective function value of the model with demand packages is less than the model without packages while the computational time of the model with packages is greater than the other one. The impact of demand’s correlation on results is investigated through solving the problem with two correlation levels. Results show that the number of setups, total costs, and the number of incomplete packages are influenced by the demand correlation. Besides, the objective function value of the problem with positive demand correlation is higher. Results also certify the impact of discount schemes on productions, purchases, costs, and revenues. © INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING
Annals of Operations Research (02545330)292(1)pp. 191-214
Planning and scheduling of operating rooms (ORs) is important for hospitals to improve efficiency and achieve high quality of service. Due to significant uncertainty in surgery durations, scheduling of ORs can be very challenging. In this paper, surgical case scheduling problem with uncertain duration of surgeries in multi resource environment is investigated. We present a two-stage stochastic mixed-integer programming model, named SOS, with the objective of total ORs idle time and overtime. Also, in this paper a two-step approach is proposed for solving the model based on the L-shaped algorithm. Proposing the model in a multi resources environment with considering real-life limitations in academic hospitals and developing an approach for solving this stochastic model efficiently form the main contributions of this paper. The model is evaluated through several numerical experiments based on real data from Hasheminejad Kidney Center (HKC) in Iran. The solutions of SOS are compared with the deterministic solutions in several real instances. Numerical results show that SOS enjoys a better performance in 97% of the cases. Furthermore, the results of comparing with actual schedules applied in HKC reveal a notable reduction of OR idle time and over time which illustrate the efficiency of the proposed model in practice. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Applied Mathematical Modelling (0307904X)84pp. 1-18
This study addresses the multi-level lot-sizing and scheduling problem with complex setups and considers supplier selection with quantity discounts and multiple modes of transportation. The present research proposes a mixed-integer linear programming (MILP) model in which the purchase lot-sizing from multiple suppliers, production lot-sizing with multiple machines and scheduling of various products of different families are accomplished at the same time. However, these decisions are not integrated in traditional environments and are taken separately. In this study, two different types of lot-sizing models called aggregated and disaggregated are developed for the problem to evaluate and compare the computational efficiency of them under deterministic and stochastic demands and provide some managerial insights. To deal with the stochastic demands, Chance-Constrained Programming (CCP) approach is applied. Based on the results of this study, the average profit of the separated (purchase from production) lot-sizing model under demand choice flexibility and stochastic demand is 24% and 22% less than the integrated model, respectively. Moreover, the results also confirm the effect of discount structure on the amount of purchases, productions, revenues and costs. © 2020 Elsevier Inc.
Operations Research for Health Care (22116923)23
Operating Room (OR) Scheduling is one of the most critical problems at the operational level for hospital managers. A useful strategy for OR scheduling, especially in large hospitals is the block strategy. In this strategy, a specific time is blocked for each surgeon or surgical team. This strategy usually leads to unused operating rooms’ capacity. To overcome this problem, in this article, a novel modified block strategy is presented for the daily scheduling of elective patients. This study aims to find the optimal sequence and schedule of patients by minimizing the cost of overtime, makespan and completion time of surgeons’ operations by considering the resource constraints. Considering the limitations and real conditions of Al-Zahra Hospital, the largest educational hospital in Isfahan, Iran, is also an aspect of this study. The problem is modeled by mixed integer programming and Constraint Programming (CP). The performance of the models is verified by several random test instances. The results indicate that CP is more efficient than mathematical modeling in terms of the computational time for solving the considered problems, especially for large-size instances. The average percent of improvement in computational time is about 53% using the CP model. The proposed CP model is also used to solve real problem instances from Al-Zahra hospital. The results show that by using the CP model, the completion time of surgeons’ operations is shortened by 9% and ORs’ overtime and makespan objectives are reduced by 55% and 20% respectively. © 2019
European Journal of Industrial Engineering (17515254)7(1)pp. 100-118
In this paper, the dynamic single-machine scheduling problem with a sequence-dependent setup time and with minimising total weighted tardiness of jobs as the objective is investigated. Due to the dynamic nature of the problem, a function-based approach is developed that can capture dynamic characteristics associated with the environment. In order to find a function which maps the environment's states to an action at each decision point, a combination of simulated annealing and a multi-layer feed-forward neural network is employed in an algorithm named SANN. The efficiency of the proposed function-based approach is compared with the most commonly used dispatching rules and with an agent-based approach, which employs the Q-learning algorithm to develop a decision-making policy. Numerical results reveal that the proposed approach outperforms dispatching rules and the Q-learning algorithm. The mean value of the results is about 93% better than the mean of the best results obtained with dispatching rules. Copyright © 2013 Inderscience Enterprises Ltd.
Advances in Operations Research (16879155)2012
Operating room scheduling is an important operational problem in most hospitals. In this paper, a novel mixed integer programming (MIP) model is presented for minimizing Cmax and operating room idle times in hospitals. Using this model, we can determine the allocation of resources including operating rooms, surgeons, and assistant surgeons to surgeries, moreover the sequence of surgeries within operating rooms and the start time of them. The main features of the model will include the chronologic curriculum plan for training residents and the real-life constraints to be observed in teaching hospitals. The proposed model is evaluated against some real-life problems, by comparing the schedule obtained from the model and the one currently developed by the hospital staff. Numerical results indicate the efficiency of the proposed model compared to the real-life hospital scheduling, and the gap evaluations for the instances show that the results are generally satisfactory. Copyright © 2012 Somayeh Ghazalbash et al.
Computers and Operations Research (03050548)36(8)pp. 2450-2461
In this paper, steel-making continuous casting (SCC) scheduling problem (SCCSP) is investigated. This problem is a specific case of hybrid flow shop scheduling problem accompanied by technological constraints of steel-making. Since classic optimization methods fail to obtain an optimal solution for this problem over a suitable time, a novel iterative algorithm is developed. The proposed algorithm, named HANO, is based on a combination of ant colony optimization (ACO) and non-linear optimization methods. The solution construction in HANO is broken up into two phases. The first phase determines the discrete variables (corresponding to job-machine assignment and sequencing), while the second phase determines the continuous ones (corresponding to timing of the jobs on their assigned machines) through a non-linear optimization method. The efficiency of HANO is compared with a heuristic algorithm as a real case used at Mobarakeh Steel Company (MSC), the biggest steel factory in the Middle East. In addition, the proposed algorithm is compared with Genetic Algorithm, as a search method for both discrete and continuous variables, through solving several instances. Numerical results reveal the higher efficiency of the proposed approach compared with the heuristic one used at MSC. Furthermore, the efficiency of HANO is compared with GA to show that HANO enjoys a better performance in more than 95% of the cases while in the remaining 5%, its performance efficiency shows no difference. © 2008 Elsevier Ltd. All rights reserved.
Journal Of Intelligent Manufacturing (15728145)20(4)pp. 347-357
This paper presents a novel approach to the facility layout design problem based on multi-agent society where agents' interactions form the facility layout design. Each agent corresponds to a facility with inherent characteristics, emotions, and a certain amount of money, forming its utility function. An agent's money is adjusted during the learning period by a manager agent while each agent tries to tune the parameters of its utility function in such a way that its total layout cost can be minimized in competition with others. The agents' interactions are formed based on market mechanism. In each step, an unoccupied location is presented to all applicant agents, for which each agent proposes a price proportionate to its utility function. The agent proposing a higher price is selected as the winner and assigned to that location by an appropriate space-filling curve. The proposed method utilizes the fuzzy theory to establish each agent's utility function. In addition, it provides a simulation environment using an evolutionary algorithm to form different interactions among the agents and makes it possible for them to experience various strategies. The experimental results show that the proposed approach achieves a lower total layout cost compared with state of the art methods. © 2008 Springer Science+Business Media, LLC.
