Scheduling Tool Research Articles

Customized quality inspection cycles for achieving sustainable manufacturing in the era of Zero Defect Manufacturing

Manufacturing businesses aim to provide high-quality products at the lowest cost possible. Quality management is crucial for improving productivity, sustainability, competitiveness, and profitability. Quality assurance (QA) methods are used to produce high-quality parts with minimal performance loss. However, traditional QA methods may not meet the needs of contemporary manufacturing standards and market needs. To address this issue, Zero Defect Manufacturing (ZDM) is an approach that can provide greater industrial sustainability. ZDM requires 100% product inspection to ensure that the desired product quality is achieved. However, increasing quality inspection to 100% can be time, cost, or resource-consuming, significantly increasing production costs. The current research work introduces a framework that considers customized product inspection cycles for minimizing the product inspection time and at the same time assuring product quality for discrete manufacturing systems. The proposed framework identifies the parameters, factors and Industry 4.0 technologies that manufacturers need to pay attention at and use to implement the customized quality inspection cycles. The proposed methodology is tested in a real industrial use case coming from the semiconductor domain. A dynamic scheduling tool was utilized for the conduction of the simulations. The results of the simulations revealed the benefits of such a radical method and also the implementation challenges were identified and discussed.

A user-induced proactive-reactive scheduling tool for dynamic flow-shop environments

This paper proposes a flexible scheduling tool for dynamic flow-shop production systems that is based on a non-preemptive version of the overlapping load adjustment approach. The developed tool adopts a novel two-phase proactive-reactive dynamic scheduling mechanism. The first phase characterises a set of possible scheduling alternatives without actually building any detailed schedule. This phase follows the earliest loading approach via exploiting the jobs’ slack times across all machines. During the second phase, the dynamic build-up of schedules takes place in a progressive manner through a user-induced module that utilises graphical displays capturing in real-time the impact of unforeseen events on the ongoing schedule. Extensive experiments involving450 randomly generated test instances of three different sizes are carried out to assess the performance of the scheduling tool. The flexibility of the tool is assessed by the number of possible scheduling alternatives offered to the end-user. The results unveiled a high number of scheduling possibilities ranging from 155 schedules for the small size instances to 96,202 schedules for the large size instances. The relatively low number of tardy jobs (0.79 jobs on average) clearly exemplifies the robustness and quality of the scheduling alternatives despite the high number of potentially available schedules.

Adoption of Project Scheduling Tools in the Nigerian Construction Industry

Many studies have reported the benefits of using Project Management scheduling tools. Their use in building projects in Nigeria has however not been given adequate attention. This study identified the project management scheduling tools used in the building construction industry in Southwestern Nigeria, examined the motivations for their adoption and assessed the impact of the tools. Primary data were collected using three sets of questionnaire administered on 50 Project Managers, 50 project clients and 150 Project Workforce from 25 purposively selected firms from Lagos, Oyo and Osun States in Southwestern Nigeria. The study revealed that 68% of the firms performed their project management scheduling activities with dedicated software, 32% executed theirs manually while another 32% used both methods. The study further shows that the most commonly adopted scheduling tool was Microsoft Project (84%), followed by Primavera (10%) and Customized software (6%). The study also revealed that project scheduling activities were shortened by about 32 hrs using scheduling tools. In addition, enhancing work performance, meeting stakeholder’s expectations, and promoting interactiveness among others were found to have an impact on the motivation to adopt scheduling tools. The adoption of scheduling tools was found to have a positive impact on parameters such as meeting customer expectations, completing projects within budget and estimated time and finishing projects. The study recommended as management practice the adoption of scheduling tools in the construction industry in Southwestern Nigeria.

Information constraint and farmers’ willingness to pay for an irrigation scheduling tool

Sub-Saharan Africa (SSA) has witnessed increasing water scarcity and demand for additional water resources due to climate change. However, lack of information and access to irrigation scheduling decision support tools may limit smallholder farmers’ ability to manage irrigation water efficiently for sustainable crop production. To overcome the complex nature associated with irrigation scheduling, simple low-cost, and user-friendly soil water monitoring tools such as the Wetting Front Detector (WFD) have been developed but the commercial viability of these tools relies on effective demand for the services. This paper assesses how information on the Wetting Front Detector influences farmers’ willingness to pay (WTP). We find firstly that about 57% of the farmers have information about the WFD and 98% reported a WTP higher than zero. Secondly, farmers are highly price sensitive, but most are willing to pay a small amount for the WFD. Thirdly, irrigation information increases farmers’ WTP for WFD by US$6 after controlling for variations existing among irrigation communities. A heterogeneity analysis indicates that WTP is higher for women, youth, and high-income earners than men, adults, and low-income earners. The results imply that increasing initial awareness and providing price subsidies for the new irrigation scheduling tool will increase future adoption.

The Time-Varying Effect of Participatory Shift Scheduling on Working Hour Characteristics and Sickness Absence: Evidence from a Quasi-Experiment in Hospitals.

Participatory shift scheduling for irregular working hours can influence shift schedules and sickness absence. We investigated the effects of using participatory shift scheduling and shift schedule evaluation tools on working hour characteristics and sickness absence. We utilized a panel data for 2015-2019 with 16,557 hospital employees (6143 in the intervention and 10,345 in the control group). Difference-in-differences regression with ward-level clustered standard errors was used to estimate the average treatment effect on the treated coefficients relative to timing of the intervention with 95% confidence intervals (CI). Using participatory scheduling tool increased long working hours and weekend work and had delayed effects on the short (1-3 days) sickness absences. Increased effects were observed: 0.2 [95% CI 0.0-0.4] days for the second, and 0.8 [95% CI 0.5-1.0] for the third year after the onset of intervention. An average increase of 0.5 [95% CI 0.1-0.9] episodes on all sickness absence episodes was observed for the third year. Using the shift schedule evaluation tool with the participatory shift scheduling tool attenuated the adverse effects. To conclude, participatory shift scheduling increased some potentially harmful working hour characteristics but its effects on sickness absence were negligible, and further attenuated by using the shift schedule evaluation tool.

Developing an Automated Python Surface Energy Balance System (PySEBS) Software for Calculating Actual Evapotranspiration-Software Development and Application Case in Jilin Province, China

In this study, Python Surface Energy Balance System (PySEBS) software was developed in the Python 2.7 programming language for continuous calculation of actual evapotranspiration (ETa) at regional scales. The software is based on the Surface Energy Balance System (SEBS) model, which uses basic meteorological data, MODIS remote sensing data, and Digital Elevation Model (DEM) data as the original input data and finally outputs daily-scale ETa in the form of raster data with a spatial resolution of 1 km × 1 km. To verify the reliability of the PySEBS model, the ETa of spring maize during the growing season in Jilin Province, China, from 2001 to 2020 was calculated and analyzed in this study and compared with the results of similar studies by others. The findings showed that the PySEBS model has a reasonable accuracy in estimating ETa within ±15% and is a robust model that can achieve the continuous calculation of ETa at a regional scale. Therefore, PySEBS software is a useful tool for regional irrigation scheduling and water resources management.

A Novel Stochastic Mixed-Integer-Linear-Logical Programming Model for Optimal Coordination of Hybrid Storage Systems in Isolated Microgrids Considering Demand Response

Storage systems and demand-response programs will play a vital role in future energy systems. Batteries, hydrogen or pumped hydro storage systems can be combined to form hybrid storage facilities to not only manage the intermittent behavior of renewable sources, but also to store surplus renewable energy in a practice known as ‘green’ storage. On the other hand, demand-response programs are devoted to encouraging a more active participation of consumers by pursuing a more efficient operation of the system. In this context, proper scheduling tools able to coordinate different storage systems and demand-response programs are essential. This paper presents a stochastic mixed-integer-lineal-logical framework for optimal scheduling of isolated microgrids. In contrast to other works, the present model includes a logical-based formulation to explicitly coordinate batteries and pumped hydro storage units. A case study on a benchmark isolated microgrid serves to validate the developed optimization model and analyze the effect of applying demand-response premises in microgrid operation. The results demonstrate the usefulness of the developed method, and it is found that operation cost and fuel consumption can be reduced by ~38% and ~82% by applying demand-response initiatives.

A Mathematical Model for Multi-Period Surgical Scheduling with Capacity Constraint

This study proposes a mathematical model for multi-period surgical scheduling problem with capacity constraint over a time horizon. The goal is to schedule a list of patients who must undergo various kinds of operations by different eligible hospitals. In particular, each operation must be performed in a particular time period and different operations of one patient can be performed by different eligible hospitals. In addition, each hospital has limited surgery capacity for each time period. The problem is formulated with a multi-objective model using the weighted sum approach of two objectives: minimization of makes pan and minimization of total least preference assignment score. The experiment is executed using the simulated data according to the real treatments of cleft lip and palate patients. The results show that the model yield the correct assignment and operation sequence respected to all constraints. Thus, this proposed mathematical model can be further used as smart decision tool in surgical scheduling in hospital network.

Parallel Scheduling of Machines, Tool Transporter and Tools in a Multi Machine FMS with Alternative Routing Using Flower Pollination Algorithm

Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in the underutilization of capital-intensive machines and a high machine idle time. Tools are transferred by a tool transporter (TT) between machines. Therefore, efficient scheduling of jobs, TT, and the tools with alternate machines enables a manufacturing system to increase machines’ utilization and decrease their idle times. This paper addresses machines, TT, and tools concurrent scheduling with alternate machines and only one copy of every tool variety is made available where the tools are costly, in a multi-machine flexible manufacturing system, taking into account tool transfer times for makespan (MS) minimization. The tools are placed in a central tool magazine, which shares and serves them to many machines to cut down the price of duplicating the tools in each machine. The problem is to assign machines from alternate machines, tools, and corresponding TT trips, including the deadheading trip and loaded trip, to job operations for MS minimization. This paper uses a nonlinear mixed-integer programming framework to present the problem, and a flower pollination algorithm (FPA) is employed to solve it. The results show that FPA outperforms the Jaya algorithm, and the usage of alternate machines for the operations reduces the MS. Reduction in MS indicates an improvement in utilization of resources.

Modelling the root causes of fatigue and associated risk factors in the Brazilian regular aviation industry

This work evaluates the potential root causes of fatigue and its relationships with accident risks using a bio-mathematical model approach and a robust sample (N = 8476) of aircrew rosters from the Brazilian regular aviation, extracted from the Fadigômetro database. The fatigue outcomes derive from the software Sleep, Activity, Fatigue, and Task Effectiveness Fatigue Avoidance Scheduling Tool (SAFTE-FAST), which considers the homeostatic process, circadian rhythms and the sleep inertia. The analyses include data from January 2019 until March 2020 and show relevant group effects comparing early 2019 and 2020, with the latter presenting lower fatigue outcomes in most cases. The average minimum SAFTE-FAST effectiveness during critical phases of flight (departures and arrivals) decreases cubically with the number of shifts that elapse totally or partially between mid-night and 6 a.m. within a 30-day period (NNS). As a consequence, the relative fatigue risk increases by 23.3% (95% CI, 20.4–26.2%) when increasing NNS from 1 to 13. The average maximum equivalent wakefulness in critical phases also increases cubically with the number of night shifts and exceeds 24 h for rosters with NNS above 10. The average fatigue hazard area in critical phases of flight varies quadratically with the number of departures and arrivals within 2 and 6 a.m. (NWocl). These findings demonstrate that both NNS and NWocl represent potential root causes of fatigue and therefore should be considered key performance indicators and kept as low as reasonably practical when building aircrew rosters, in order to properly manage the fatigue risk. The effectiveness scores obtained at 30-minute time intervals allowed a model estimate for the relative fatigue risk as a function of the time of the day, whose averaged values show reasonable qualitative agreement with previous measurements of pilot errors in the cockpit. Moreover, the 2019 data revealed a risk exposure factor two times (14%) higher than the figures reported by de Mello et al. (2008) (7%), within 0h00 do 05h59. Tailored analyses of the SAFTE-FAST inputs for afternoon naps before night shifts, commuting from home to station and vice-versa, and bedtime before early-start shifts were carried out using the responses of a questionnaire. The average fatigue hazard area in critical phases of flight increases by 43 to 63% switching off the afternoon naps, 14 to 21% increasing the commuting from one to two hours and 35 to 54% switching off the advanced bedtime criterion, with significant group effects in all cases (p<0.001), evidencing the need of a better and more accurate understanding of these parameters when modelling fatigue risk factors. The non-linear relationships between SAFTE-FAST outcomes and key roster’s parameters, such as NNS and NWocl, provided a novel statistical approach for risk assessment and led to few safety recommendations to the aviation sector regarding regulatory reviews and fatigue risk management and mitigation policies.

Assessment of Adverse Health effect of Levonorgestrel-releasing Intrauterine System (Mirena) among Women Attending Private Clinic

Background Despite their known efficacy, recent studies have found that there is adverse health problem accompanied with Mirena as the following. Therefore, the current study aims to assess adverse health problems among levonorgestrel-releasing intrauterine system (Mirena) users. Design A descriptive design was utilized for the study. Setting This study was conducted at the gynecological clinic in Rofayda Obstetrics and Gynecology Private Hospital, at El Shikh Zaied, El Giza Sector, Egypt. Purposive sample of 50 women using levonorgestrel-releasing intrauterine (Mirena) system for at least 6 months. Two tools were used for the study (a) structured interviewing questionnaire schedule tool and (b) adverse health effect of Mirena assessment tool. Results The mean age among the study sample was 35.56±6.42 years. The common health problems among the study sample were physical problems 24% in the form of headache, 40% had weight gain, dermatological problem, including 34% acne, and 22% of the study sample had menstrual disturbance. Regarding psychological disturbance, 32% had depression and 42% had mood swings. Regarding gynecological problems, 22% had a vaginal infection as well as 8% had a decrease in libido. Conclusion Mirena is a promising and effective option for contraceptive and noncontraceptive methods. It is associated with health problems, as reported by Mirena users. Recommendations Organized educational guidelines facilities and methods about uses, complaints, and health problems toward Mirena such as lectures, handout, and videos should be developed and become accessible to all women in the outpatient clinic.

Can financial incentives and other nudges increase COVID-19 vaccinations among the vaccine hesitant? A randomized trial

Despite rapid initial uptake, COVID-19 vaccinations in the United States stalled within a few months of widespread rollout in 2021. In response, many state and local governments, employers and health systems used public health messaging, financial incentives and creative scheduling tools to increase vaccine uptake. Although these approaches drew on evidence from influenza and other vaccination efforts, they were largely untested in the context of SARS-CoV-2. In mid-2021, months after vaccines were widely available, we evaluated vaccination intentions and vaccine uptake using a randomized control trial. To do this, we recruited unvaccinated members of a Medicaid managed care plan in California (n = 2,701) and randomly assigned them to different public health messages, $10 or $50 financial incentives for vaccination, a simple vaccination appointment scheduler, or control. While messages increased vaccination intentions, none of the interventions increased vaccination rates. Estimates for financial incentives rule out even relatively small increases in vaccination rates. Small financial incentives and other behavioral nudges do not meaningfully increase COVID-19 vaccination rates amongst the vaccine hesitant.

Understanding GIS in visualization and evaluation of construction schedule

Economically successful available scheduling tools such as Primavera and Microsoft Project fail to provide information about the geographical aspects of a construction project. A methodology using geographical information systems (GIS) is developed to represent the geographical aspects of the construction progress graphically by accompanying and linking it with the construction schedule. The spatial or geographical aspects are illustrated by the 3D model developed in AutoCAD and the construction schedule is generated via MS Excel sheets and linking it to ArcVIEW. Spatial and scheduling information are linked together into the GIS environment (ArcGIS) With a GIS, we can identify and analyze the land features, climate, boundaries, population, resources, and many other things about places that interest you. In the past, you needed to use paper maps, globes, and a variety of hard-copy data sources to perform these analyses. The main purpose of the research is to discover a simple and perceptive way of assigning the essential project information from a constructional CAD model to scheduling software and streamlining sequencing and organizing project activities. In achieving this goal, one of the most exhausting and time-consuming steps in producing a construction project schedule will be removed. By the end of this project, we will be able to understand the GIS system, and how to visualize it while scheduling the project and its uses. Keywords- Geographical Information System, spatial information, scheduling, visualization

Objective assessment of fatigue among aviation personnel using a bio-mathematical model: An experimental study

BackgroundThere are many subjective and objective tools to detect, assess, and quantify fatigue. This study is a novice attempt to assess the occupational fatigue among the aviation personnel employing a computerized work-rest schedule tool integrated with actigraphy. MethodsThirty-eight aviation personnel were assessed for their sleep by using an actigraphy device. A work-rest scheduling software program called Fatigue Avoidance Scheduling Tool (FAST) was used to obtain fatigue parameters like Fatigue Risk Time (FRT), Fatigue Free Time (FFT), and Fatigue Free Occupational Time (FFOT). ResultsThe percentages of crew having a night sleep of the duration of more than 6 hours were 50% (Mon), 44.7% (Tue), 44.7% (Wed), and 47.3% (Thu) for weekdays and 65.8% (Fri), 57.9% (Sat), and 57.9% (Sun) for the weekend. There was a gradual increase in FRT, FFT, and FFOT from Day 1 to Day 5 of the week, and the differences were statistically significant. ConclusionIncrease in the FRT with a reciprocal drop of FFT and FFOT was observed with the progress of the week. Total Sleep Time (TST) of less than 8 hours could be the reason for a gradual increase in sleep debt, leading to fatigue depicted as increase in fatigue risk parameter FRT and gradual decrease in fatigue preventing parameters like FFT and FFOT. It was further confirmed by regression analysis in which TST was found to be a statistically significant predictor for all fatigue parameters. Regression equation for FFOT as 498.53 + (0.39 x TST) - (58.8 x Day of the week) can be used.

Hydro-economics tradeoff surfaces to guide unit commitment in production cost models

Production cost models simulate optimal operations that reliably balance load and generation at the power system scale. These models typically minimize total system production costs under several grid and generation constraints. In regions where hydropower is not a significant share of the overall generation portfolio, it is most often represented as a no-fuel-cost technology with monthly hydropower targets but no direct modeling of sub-monthly water operations. On the other hand, hydropower plant operators generally rely on hydro scheduling tools (also called “hydro schedulers”) that optimize day-ahead unit commitment to maximize revenues at the power plant scale while satisfying complex hydraulic and environmental operating criteria. Integrating hydro scheduler tools in production cost models creates a computational challenge as well as a data challenge. We propose a new method that relies on hydro-economic surfaces (or bivariate functions) derived from a hydro scheduler tool to guide power dispatch in production cost models and to address those challenges. Surfaces are constructed under many hydrologic conditions (around 100 runs per surface). These parameter surfaces inherently include all the detailed water considerations that are endogenously incorporated into the hydro-scheduling tool. For an integrated set of hydropower plants located in the Upper Colorado River Basin, we demonstrate that the use of parameter surfaces improves the accuracy of dispatch and revenue calculations in production cost models. The approach is expected to support the industry in more accurately communicating the potential needs from new hydropower operations during energy transitions and accordingly designing markets to incentivize those needed operations.

Optimization Helps Scheduling Nursing Staff at the Long-Term Care Homes of the City of Toronto

The City of Toronto Long-Term Care Homes and Services (LTCH&amp;S) division is one of the largest providers of long-term care in the Canadian province of Ontario, providing care to 2,640 residents at 10 homes across Toronto. Our collaboration with LTCH&amp;S was initiated to facilitate the increasingly challenging task of scheduling nursing staff and reduce the high absenteeism rate observed among the part-time nurses. We developed a spreadsheet-based scheduling tool to automate the generation of schedules and incorporate nurses’ preferences for different shifts into the schedules. At the core of the scheduling tool is a hierarchical optimization model that generates a feasible schedule with the highest total preference score while satisfying the maximum possible demand. Feasible schedules had to abide by a set of complex seniority requirements, which prioritized more-senior nurses when allocating the available shifts. Our scheduling tool was implemented in a 391-bed home in Toronto. The tool allowed nursing managers to generate feasible schedules within a fraction of an hour, in contrast to the status quo manual approach, which could take up to tens of hours. In addition, the schedules successfully accounted for preferences with, on average, above 94% of the allocated shifts ranked as most preferred. History: This paper was refereed.

Developing a Scheduling Tool for Machining Centers Using Real-time Model Checking

Developing a Scheduling Tool for Machining Centers Using Real-time Model Checking

The Multi-Period Surgical Scheduling with Capacity Constraint: A Mathematical Modelling Approach

This research proposes a mathematical model for multi-period surgical scheduling problem with capacity constraint over a particular time horizon. The goal is to schedule a list of patients who must undergo various kinds of operations by different eligible hospitals. In particular, each operation must be performed in a particular time period and different operations of one patient can be performed by different eligible hospitals. In addition, each hospital has limited surgery capacity for each time period. The problem is formulated with a multi-objective model using the weighted sum approach of two objectives: minimization of makespan and minimization of total least preference assignment score. The experiment is executed using the simulated data according to the real treatments of cleft lip and palate patients. The results show that the model yield the correct assignment and operation sequence respected to all constraints. Thus, this proposed mathematical model can be further used as smart decision tool in surgical scheduling in hospital network.

Simulation of Demand Responsive Transport using a dynamic scheduling tool with SUMO

Demand responsive transport (DRT) has been increasingly tested and applied in recent years as a new form of transportation that seeks to address mobility problems in cities and rural areas. The planning of DRT systems is a challenging task for transport planners since the performance of the service depends significantly on the demand, how the scheduling is made, and how the routes are computed. Transport simulations are a useful option to evaluate these systems. The paper presents a Python tool, which aims to simulate diverse DRT services using the software package for microscopic simulations Eclipse SUMO (Simulation of Urban MObility) as a framework. The fleet and requests of the DRT are handled dynamically by the scheduling module of the tool. This module is also responsible for calling a solver algorithm for the Dial-a-Ride-Problem (DARP), processing its results, and dispatching the DRT vehicles according to them. The tool also enables easier implementation of other methods to solve the DARP. To demonstrate the use of the tool, a DRT service operating in two central neighborhoods of the city of Brunswick (Germany) is presented. The tool is called drtOnline.py and is included in SUMO since version 1.9.0.

Emergency Project Management Practice in Ethiopia

Emergency Project Management is a kind of project management that often attracts the most attention and resources and its management needs application of knowledge, skills, tools, and techniques to project activities to meet the immediate project output and goal. Proper project management practice is critical for the success of Emergency Project. Thus, the main objective of this study was to assess the extent to which Project Management Knowledge area were applied and identify the challenges in Emergency Project management. The study used descriptive research design and data were obtained through questionnaire. The finding revealed Scoping &amp; Scheduling tools and techniques were not utilized properly. Moreover, Procurement management needs improvement. Some of the knowledge areas were managed informal &amp; traditionally. The study recommended that workable policy documents to lead the procurement and overall Emergency Project management are vital.