Overtime Costs Research Articles

Examination of Daily Task Constraint in Technician Routing and Scheduling Problem

This paper deals with a multi-term technician routing and programming problem with a different concept. The problem is that technicians with various skills form teams, and these teams perform the tasks that take in distinct locations. Technicians have daily duty capacities while performing tasks. The cases where overtime costs are allowed by combining different technician numbers are analyzed. Paper provides a single-purpose mixed integer programming method for modeling the problem because the model implemented aims to optimize the travel cost while simultaneously minimizing the amount of overwork. The main issue also specifies the division of technicians with different skills into teams, assignment of tasks with different skill requirements to teams, and routes for each team at the same time. The number of customers visited on a route within the daily customer limit. In addition, the model included exceeding the daily customer limit in the model and bound it to a constraint. The various combinations of the daily normal and over-work number of customers were evaluated and examined their effects on the objective function. Finally, the paper presents computational experiments and analyses to evaluate the efficiency of the proposed mathematical formulation and solution approach. The results show that daily task constraints are effective in employee selection and amount of use.

Sequencing and scheduling appointments on multiple servers with stochastic service durations and customer arrivals

In this paper, we consider the problem of sequencing and scheduling appointments on multiple servers with stochastic service durations and customer arrivals. The objective is to minimize the weighted sum of server staffing cost and total expected cost of customer waiting, server idleness and overtime. To solve the problem, we first formulate it as a two-stage integer program, where the second stage involves multiple stochastic linear programs. Based on this, we then derive a deterministic mixed-integer linear program for the problem via sample average approximation and further strengthen the formulation by exploiting problem properties. Due to the high complexity of the problem, we also propose an efficient integer L-shaped based heuristic, which is further enhanced by variable neighborhood descent. Our computational experiments show that the proposed integer L-shaped heuristic dominates the strengthened deterministic program and integer L-shaped method especially for large-scale problems, while the incorporation of variable neighborhood descent can significantly improve the performance of the heuristic. Our computational results also reveal that variations in service durations and fluctuations as well as customer unpunctual times have significant impacts on the system performance, while optimizing appointment sequencing decision can help reduce operational cost.

Multi-depot home health care routing and scheduling problem with multimodal transportation: Mathematical model and solution methods

Providing appropriate home health care is one of the increasing concerns in the health care organizations. Home Health Care provides various services for disabled or elderly individuals at their homes. Also, deal with the current critical situation of the coronavirus disease (COVID-19) due to the limited capacity of hospitals and the feeling of insecurity in crowded places, home health care is more recommended. This paper addresses a Home Health Care Routing and Scheduling Problem (HHCRSP) with two modes of transportations including public and private modes. Also, multi-depot version of the problem is studied to enhance the service delivery in scattered points. In this study, a mathematical model is presented based on a Mixed Integer Linear P‌rogramming (MILP) whose objective function is minimization of the sum of the travel distance and overtime costs. Furthermore, three meta-heuristic algorithms including Invasive Weed Optimization (IWO), Grasshopper Optimization Algorithm (GOA) and Simulated Annealing (SA) are presented for solving the large-sized problems. Since the performance of meta-heuristic algorithms depends on setting the parameters, Taguchi method is used to statistically set parameters of the developed algorithms. The computational results have shown that the proposed IWO has worked better than the other two proposed algorithms statistically.

Crashing Project Evaluation by Managing Allocation Resource and Cost using CPM-PERT and MS,Project in Nurul Hayat Mosque’s Renovation

The implementation of project management must be calculated based on time and cost estimation. One of the efficiency measures that can take is to accelerate. Time, cost, and quality are factors that must consider while making project acceleration. Project renovation in Nurul Hayat's room was chosen in this research because this project experienced delays in implementation. Delays occur due to a lack of available manpower. This delay will certainly have an impact on costs. The purpose of this research is to speed up time and get optimum costs while still paying attention to quality standards. By CPM-PERT approach, and the evaluation of project acceleration with Ms. Project, the results obtained are scheduling the initial completion of the mosque renovation which initially has an estimated 42 days with a normal total cost of 17,227,500, can be accelerated for 36 working days with additional processing costs of Rp. 70,000 and overtime costs of Rp. 1,635,000. The total cost is Rp 18,932,500
 Keyword: Crashing Project, CPM-PERT, Ms. Project

Optimal Fuzzy Scheduling and Sequencing of Work-Intensive Multiple Projects Under Normal and Unexpected Events

This research proposed optimization models for task scheduling and sequencing in work-intensive multiple projects under normal and unexpected events. The objectives of scheduling model were minimizing the total overtime/under-time costs and maximizing satisfaction values on tasks due dates and processing standard times. Further, the sequencing model aimed to minimize the sum of tasks' start times, maximize resource utilization, and maximize satisfaction on project completion times. Illustrations of the proposed scheduling and sequencing optimization models were provided where the results showed effective scheduling and sequencing of project tasks at minimal costs and achieved the desired satisfaction levels on tasks and projects and significantly enhanced resource efficiency at minimal overtime and under-time costs. Further, optimization models were modified to deal with unexpected events. In conclusion, the proposed models may support project managers in planning project tasks in a cost-effective manner under normal and unexpected events.

A heuristic algorithm for medical staff's scheduling problems with multiskills and vacation control.

The main issue related to the duty schedule is to allocate medical staff to each medical department by considering personnel skills and personal vocation preferences. However, how to effectively use staff's multiskill characteristics and how to execute vocation control have not been well investigated. This article aims to develop duty scheduling and vacation permission decisions to minimize the sum of customers' waiting costs, the overtime cost of medical staff, the cost of failing to meet medical staff' vacation requirements, and the cost of mutual support between departments. This study formulated the problem as a multiperiod mixed integer nonlinear programming model and developed a hybrid heuristic based on evolutionary mechanism of genetic algorithm and linear programming to efficiently solve the proposed model. Five types of problems were solved through Lingo optimization and the proposed approach. For small-scale problems, both methods can find the optimal solutions. For a slightly larger problem, the solutions found by the proposed approach are superior those of Lingo. This research discusses the complex decision-making problem of on-duty arrangement and vacation control of medical staff in a multidepartmental medical center. This research formulates the medical staff's scheduling and vacation control problems as constrained mixed integer quadratic programming problems. Computational results indicate that the proposed approach can efficiently produce compromise solutions that outperform the solutions of the Lingo optimization software.

An implementor-adversary approach for uncertain and time-correlated service times in the nurse-to-patient assignment problem

Parameter uncertainty is common to several optimization problems, especially in health care where patients’ conditions and service times are highly uncertain. Moreover, in Home Health Care services, where patients are treated over a long time horizon, their clinical condition may change and result in uncertain but also highly time-correlated service times. To include time-correlated uncertain parameters is a difficult task in robust optimization. It has been only marginally addressed, especially in the health care management literature. In this paper, we address the nurse-to-patient assignment problem in Home Health Care services under three types of continuity of care, focusing on uncertain and time-correlated service times. In the literature, this problem has been tackled with stochastic programming, robust optimization, and heuristics; however, service times at different time periods have been treated as independent so far. We propose a robust assignment model that accounts for the time-dependency of patients’ service times, with either constant or increasing overtime cost, and an approach based on the implementor-adversary framework to solve it. Service times are modeled as stochastic parameters and time-correlation is included by limiting the difference in the service time requested by the same patient in two consecutive time periods. The goals are to minimize the cost of staff overtime and the number of reassignments that impair patients’ continuity of care. Results from a relevant realistic case show that the approach does not always converge, but the robust solutions provided, under suitable parameters for the uncertainty description, always outperform the deterministic ones even without full convergence.

Home care vehicle routing problem with chargeable overtime and strict and soft preference matching

A new scheduling problem arising in the home care context is addressed, whose novelty with respect to the literature lies in the way overtime is paid. In this problem, some clients are willing to pay a higher fee to cover the additional overtime cost, if such overtime is incurred because a caregiver works extra time with the client to preserve continuity of care. These overtime hours charged to clients unburden the company, which no longer has to balance between cost and continuity of care in a traditional way. The problem is also studied in a context that includes preferences expressed by both clients and caregivers. Strict preferences must be respected with a high priority, while soft preferences increase the satisfaction and should be preferably respected. We formalize the problem as a Mixed Integer Linear Problem and also propose a cluster-based decomposition to solve real-life instances. The problem is inspired by the real case study of a provider operating in the USA. Numerical results validate the model and confirm the capability of the decomposition approach to deal with real-life instances.

Symmetric Algorithm for Detection of Coverage Hole in Wireless Sensor Network

For the problems of traditional overburden hole detection methods, such as overtime, low accuracy and high running cost, etc. A multi factor reliable coverage hole detection method based on LEACH algorithm is proposed. Based on the analysis of the QoS architecture of wireless sensor networks and the research of representative network layer symmetry protocols, a symmetric network model is constructed according to the selection of gateway performance parameters, gateway selection criterion function and symmetry criterion, and a symmetric criterion design and optimization strategy is given. It uses this strategy to improve LEACH algorithm in three aspects. Two reliability optimization factors are added to the similarity calculation of coverage hole detection, which are node intrusion optimization factor and multipath transmission optimization factor respectively. Multipath transmission optimization factor is used in data transmission. In hostile environments, in order to avoid a large number of malicious nodes interfering with real data, the data containing noise should be filtered and processed. Then, the similarity is calculated by multi-path transmission optimization factor, and the coverage holes of wireless sensor networks are detected by weighted average. The experimental results show that the proposed method takes less time to detect the covering holes, which has higher detection accuracy and lower operation cost.

Cloud accounting information systems: Threats and advantages

When the Corona-virus pandemic hit the world, executing and performing the jobs of the remote world became an imperative, this pandemic became as motivator to create this study which aimed to reveal the advantages and threats that encounter the implementing of Cloud Accounting Information Systems CAIS from the view point of external auditors. For that purpose, a questionnaire was developed and distributed then from 198 valid questionnaires which were analyzed by a T- test. It has been found that the main advantages are a reduction of labor and overtime costs because of the ability to access the system from anywhere, while penetration, interruption, and confidentiality are the main threats, also it was found that there is a strong relationship between implementing cloud accounting information systems (CAIS) and the limiting manipulation of financial information. The study therefore recommended upgrading the security procedures of cloud accounting systems and holding courses for auditors to enhance their abilities in auditing CAIS.

A Novel and Efficient Exact Technique for Integrated Staffing, Assignment, Routing, and Scheduling of Home Care Services Under Uncertainty

We model and solve an integrated multi-period staffing, assignment, routing, and scheduling for home care services under uncertainty. The goal is to construct a weekly schedule that adheres to related operational considerations and determines optimal staffing of caregivers by minimizing caregivers’ fixed- and overtime costs. For tractability, we incorporate a priori generated visit patterns—an existing practical approach that deals effectively with hard assignment decisions in. First, we propose a novel mixed-integer program (MIP) for the nominal problem. We then incorporate uncertainty in service and travel times and develop a robust counterpart by hybridizing interval and polyhedral uncertainty sets. Second, we show that there is a special mathematical structure within the model that allows us to develop a novel logic-based Benders branching-decomposition algorithm that systematically delays the resolution of difficult routing/ scheduling problems and efficiently solves both the nominal and robust MIP models. Using a dataset from the literature, we show that CPLEX can solve our nominal and robust models with an average optimality gaps of 44.56% and 45.53%, respectively. Using the same dataset, we demonstrate that our new exact technique can solve our nominal and robust mixed-integer models to an average optimality gap of 2.8% and 4.5%, respectively. Third, we provide practical insights into (i) the price of robustness and (ii) the impacts of nurse flexibility and overtime. The average total cost does not increase beyond 12.7% than the nominal solution and the cost-savings of nurse flexibility is approximately five times higher than that of overtime.

MULTI-START LOCAL SEARCH FOR ONLINE SCHEDULING IN PARALLEL OPERATING THEATRE

The present work is considered online operating theatre scheduling for regular patients and also it is involving emergency patients on a day. The emergency patients urge to schedule the earliest as their level of priority is higher than the regular patients and it is the importance of online scheduling problem. The local search (LS) method was tested for scheduling the parallel operating theatre with an objective to minimize the delay and overtime cost of operating theatre. A modification of the local search algorithm was performed to improve the solutions and it is called multi-start local search (MSLS). This research found that, MSLS method performed better than LS method since it has reduced the total cost of the operating theatre significantly.

A Matheuristic Approach for the Home Care Scheduling Problem With Chargeable Overtime and Preference Matching

Home care (HC) services represent an effective solution to face the health issues related to population aging. However, several scheduling problems arise in HC, and the providers must make several scheduling and routing decisions, e.g., the assignment of caregivers to clients, in order to balance operating costs and client satisfaction. Starting from the analysis of a real HC provider operating in New York City, NY, USA, this article addresses a scheduling problem with chargeable overtime and preference matching and formulates it as an integer programming model. The objective is to minimize a cost function that includes traveling costs, the overtime cost paid by the provider, the preference mismatch, and a penalty related to the continuity of care violation. To solve this problem, we design a matheuristic algorithm that integrates a specific variable neighborhood search with a set covering model. The results demonstrate the applicability and efficiency of our approach to solving real-size instances. Sensitivity analyses are also performed to discuss practical insights. Note to Practitioners-This article provides a decision support tool to HC managers, which appropriately assigns caregivers to clients and makes routing decisions over a long horizon. Chargeable overtimes and preference matching enclosed in this tool are rarely considered in the literature, despite matching is relevant in HC caregiver-to-client assignments and chargeable overtime has potential in tailoring the service level based on the specific client. We formulate the scheduling problem as a mathematical model. Then, we propose a matheuristic algorithm to efficiently solve the problem in real-size instances. The results show the applicability and efficiency of our method. Thus, HC managers can exploit it to efficiently make assignment and routing decisions and to analyze the impact on other operating costs when adjusting any of them.

Integrasi forecasting pada rantai pasok manufaktur komponen otomotif Jepang di Indonesia dengan penerapan metode classic dan regresi

The need of spark plugs as a replacement component has a potential demand, especially for a Company from Japan which establish in the last 40 year ago as manufacture of spark plugs in Indonesia. Sales forecast of spark plugs that develop by Company for aftermarket class has big error. It makes a question ‘how to improve the accuracy of forecasting spark plugs in the aftermarket class to reduce losses in the supply chain process’, such as loss due to inventory, production and transportation. In this study, it was found that using ARIMA and MLR methods could increase level of accuracy than current forecasting method by Company. It was found that the increment in forecasting accuracy by using ARIMA and MLR was able to reduce operational costs up to 25.05% per year in overtime costs and 40.21% per year in finished goods inventory costs. In addition, reducing the cost of shipping materials by 24.90% per year, and reducing inventory costs on suppliers by 25.74% per year.

Operating Room Staffing and Scheduling

Problem definition: We consider two problems faced by an operating room (OR) manager: (1) how many baseline (core) staff to hire for OR suites, and (2) how to schedule surgery requests that arrive one by one. The OR manager has access to historical case count and case length data, and needs to balance the fixed cost of baseline staff and variable cost of overtime, while satisfying surgeons’ preferences. Academic/practical relevance: ORs are costly to operate and generate about 70% of hospitals’ revenues from surgical operations and subsequent hospitalizations. Because hospitals are increasingly under pressure to reduce costs, it is important to make staffing and scheduling decisions in an optimal manner. Also, hospitals need to leverage data when developing algorithmic solutions, and model tradeoffs between staffing costs and surgeons’ preferences. We present a methodology for doing so, and test it on real data from a hospital. Methodology: We propose a new criterion called the robust competitive ratio for designing online algorithms. Using this criterion and a robust optimization approach to model the uncertainty in case mix and case lengths, we develop tractable optimization formulations to solve the staffing and scheduling problems. Results: For the staffing problem, we show that algorithms belonging to the class of interval classification algorithms achieve the best robust competitive ratio, and develop a tractable approach for calculating the optimal parameters of our proposed algorithm. For the scheduling phase, which occurs one or two days before each surgery day, we demonstrate how a robust optimization framework may be used to find implementable schedules while taking into account surgeons’ preferences such as back-to-back and same-OR scheduling of cases. We also perform numerical experiments with real and synthetic data, which show that our approach can significantly reduce total staffing cost. Managerial implications: We present algorithms that are easy to implement and tractable. These algorithms also allow the OR manager to specify the size of the uncertainty set and to control overtime costs while meeting surgeons’ preferences.

Adaptive Capacity Planning for Ambulatory Surgery Centers

Problem Definition: Problem Definition: We develop a framework to plan capacity for ambulatory surgery centers (ASCs) typically consisting of three stages. The problem is to determine the capacity in each stage that efficiently covers possible daily patient demand and to coordinate the three stages of patient visits. The objective is to minimize the total cost incurred in satisfying the daily patient demand, where the total cost is defined as the sum of overtime cost and amortized construction cost for the three stages. Academic/Practical Relevance: Timely capacity adjustment is important for ASC practitioners, but related research is limited. We explicitly consider three sequential stages that are typical in ASCs: the pre-operative at holding room (HR), intra-operative at operating room (OR), and post-operative at post-anesthesia care unit (PACU). Such ASCs can be modeled as a hybrid flow shop (HFS) in the scheduling literature. The interdependence of activities and uncertainties in patient-mix as well as their durations pose a significant challenge to manage the capacity of each activity and achieving a smooth patient flow. Methodology: In contrast to the traditional top-down approach to capacity planning, our approach contributes by proposing a bottom-up strategy based on optimization methods combined with analytics that are informed by operational-level archival patient data. Specifically, we use analytics tools to classify patients into groups, which reduces the complexity of the capacity planning problem and improves the model's practicality. Later, we develop several mathematical formulations and heuristics based on scheduling theory to derive the most cost-efficient capacity solution. Results: Given the trade-off between overtime cost and amortized capacity construction cost, this study relaxes the fixed capacity assumption of traditional HFS problems. Because of the computational complexity of the exact HFS model, we develop a heuristic that is straightforward and easy to implement to find cost-efficient capacities for the three stages. Our computational study examines how uncertain business parameters, e.g., patient-mix, service durations, overnight-stay probabilities, affect the capacity planning decision. Managerial implications: This study highlights the benefit of considering multiple stages together in capacity planning, rather than focusing solely on the operating rooms exclusively. We expect our approach to guide the more than 5,000 ASCs in the U.S., performing 23 million surgeries annually, to make appropriate investments that will improve ASC operations via capacity adjustment and patient scheduling.

Coordinated appointment scheduling with multiple providers and patient-and-physician matching cost in specialty care

To achieve effective care, it is critical to match patients with capable physicians in specialty care. Motivated by the rising popularity of patient-and-physician matching applications in specialty care, this study optimizes the matching and appointment scheduling problems simultaneously in a stochastic environment, in which a decision-maker determines the patient-and-physician pair assignment and the starting times of services. We develop a stochastic optimization model to minimize the matching and operational costs (i.e., patients’ waiting time costs, service providers’ idle time and overtime costs). This paper is the first study that incorporates matching and appointment scheduling problems together. The benefits of combining these two problems are enormous. The experimental results show that the operational costs gap is as large as 51% between the ill-matched and the well-matched patient-and-physician scenarios. We first reformulate this problem as a two-stage optimization problem. With the analysis for the optimal solution of the second stage problem, a Benders decomposition algorithm is developed. To improve the efficiency of the proposed algorithm, we also prove a low bound of our problem and use it to construct a set of feasibility cuts. Then, we extend our method to incorporate no-shows. Our algorithm can solve problems efficiently, and it can obtain optimal solutions for medium-size problems within 2 or 3 min. In contrast, traditional optimal methods require nearly 2 h. For large-size problems, our algorithm can obtain optimal solutions within 5 or 6 min, whereas traditional optimal methods cannot generate a result within 5 h. Finally, numerical experiments are conducted to evaluate the performance of our proposed algorithm and to investigate the variation of the optimal solutions in different scenarios. To provide quality care as well as minimize the total cost of appointment scheduling in specialty care, we suggest that physicians should develop or train their specialties based on the local patients’ disease pattern. We also disclose that the no-show has less influence on the service system when the weight of the matching cost is substantial.

The Positive Impact of Establishing an Internal Transfer Center

Health systems are facing greater pressures than ever before, with factors ranging from mergers and acquisitions and changing reimbursement models to capacity challenges and staffing shortages. A transfer center, where with 1 simple call from a referring physician, a decision to accept a critically ill patient can be made, placing them at the most appropriate level of care, the first time. Baptist Health in Jacksonville, Florida, went from an outsourced to an in-house center in 3 months—in the first 28 days alone, patient transfers increased from 900 to 1,500, growing to an average of 1,583 transfers per month.

Appointment Scheduling for a Health Care Facility with Series Patients

This study focuses on determining the appointment scheduling for healthcare facilities with series patients. “Series” patients are patients who are scheduled for a series of appointments instead of a single appointment. Examples of healthcare services with series patients include radiotherapy/chemotherapy for cancer, physical therapy, kidney dialysis, diabetes treatment, etc. The aim of this study is to design appointment scheduling policies taking into account revenues per service per patient, costs of staffing, overtime, overbooking and delay. The appointment scheduling problem is formulated using an MDP model. However, due to the huge state space, computing the optimal policy is impractical. Hence, we propose the Index Policy (IP) based on a one‐step policy improvement algorithm applied to the MDP model. We study a further simplification obtained by approximating the distribution of the number of patient visits by a Geometric distribution. A key analytical contribution is to prove the MDP to be a uni‐chain, which implies that there exists an optimal policy that maximizes the long‐run average profit. We also find that the IP provides a significant improvement over the other policies. Especially with the Geometric approximation, the IP requires minimal effort in implementing, and works almost as well. To test the effectiveness of our proposed IP in a real‐world setting, we use the data from a local physical therapy center to compare its performance with two other commonly used policies, namely, the Next Available Day Policy and the Shortest Queue Policy. We recommend the IP with Geometric approximation for series patients’ scheduling, which is computationally efficient and can significantly increases profits by incorporating the series feature of the patients’ appointments. Finally, we provide analysis that incorporates several practical considerations such as accounting for patient heterogeneity in number of visits and inter‐visit times, the option to reject new patients when the system is at full capacity, and incorporating patients with known number of visits at the time of the scheduling decision.

A Spatial Analytics Framework to Investigate Electric Power-Failure Events and Their Causes

The U.S. electric-power infrastructure urgently needs renovation. Recent major power outages in California, New York, Texas, and Florida have highlighted U.S. electric-power unreliability. The media have discussed the U.S. aging power infrastructure and the Public Utilities Commission has demanded a comprehensive review of the causes of recent power outages. This paper explores geographic information systems (GIS) and a spatially enhanced predictive power-outage model to address: How may spatial analytics enhance our understanding of power outages? To answer this research question, we developed a spatial analysis framework that utilities can use to investigate power-failure events and their causes. Analysis revealed areas of statistically significant power outages due to multiple causes. This study’s GIS model can help to advance smart-grid reliability by, for example, elucidating power-failure root causes, defining a data-responsive blackout solution, or implementing a continuous monitoring and management solution. We unveil a novel use of spatial analytics to enhance power-outage understanding. Future work may involve connecting to virtually any type of streaming-data feed and transforming GIS applications into frontline decision applications, showing power-outage incidents as they occur. GIS can be a major resource for electronic-inspection systems to lower the duration of customer outages, improve crew response time, as well as reduce labor and overtime costs.