Least cost scheduling for repetitive projects

1993 ◽  
Vol 20 (5) ◽  
pp. 834-843 ◽  
Author(s):  
Osama Moselhi ◽  
Khaled El-Rayes
Keyword(s):  
Dynamic Programming ◽  
Learning Curve ◽  
Cost Optimization ◽  
Indirect Costs ◽  
Optimization Process ◽  
Planning And Scheduling ◽  
Weather Impact ◽  
Project Duration ◽  
Curve Effect ◽  
The Impact

Dynamic programming has been utilized to optimize scheduling of repetitive projects. The optimization criterion of existing techniques, however, is limited to minimizing the project duration and does not account for the impact of such optimization on the project cost. While the minimization of the project duration may reduce the project indirect costs, it does not guarantee a minimum total cost for the project. In practice, minimizing the overall cost of a project is frequently regarded to be more important than minimizing its duration. The objective of this paper is to present a flexible model that incorporates cost in the optimization process. In addition, the model is capable of considering the weather impact and the learning curve effect in the optimization process, simulating two important factors affecting productivity on this class of projects. The model utilizes dynamic programming and performs the solution in two stages: first, a forward path to identify local minimum conditions; and then a backward path to ensure a global minimum state. A numerical example from the literature is analyzed in order to demonstrate the use of the model, test its validity, and illustrate the significance of incorporating cost, weather impact, and the learning curve effect in the optimization process. Key words: planning and scheduling, repetitive projects, linear scheduling, cost optimization, dynamic programming, learning curve effect, weather impact.

Multi-objective optimization for repetitive scheduling under uncertainty

2019 ◽  
Vol 26 (7) ◽  
pp. 1294-1320 ◽  
Author(s):  
Tarek Salama ◽  
Osama Moselhi
Keyword(s):  
Cost Optimization ◽  
Indirect Costs ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Software Module ◽  
Content Type ◽  
Project Duration ◽  
Multi Objective ◽  
Work Interruptions ◽  
Cost Calculations

Purpose The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters. Design/methodology/approach The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module. Findings For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules. Originality/value Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.

Evaluation of Surgical Learning Curve Effect on Obstructive Sleep Apnea Outcomes in Upper Airway Stimulation

2020 ◽  
pp. 000348942095873
Author(s):  
Christopher Larsen ◽  
Christopher Boyd ◽  
Mark Villwock ◽  
Armin Steffen ◽  
Clemens Heiser ◽  
...  
Keyword(s):  
Learning Curve ◽  
Operative Time ◽  
Upper Airway ◽  
Patient Specific ◽  
Complication Rates ◽  
Upper Airway Stimulation ◽  
Curve Effect ◽  
The Impact ◽  
Surgical Learning

Objective: An increasing number of facilities offer Upper Airway Stimulation (UAS) with varying levels of experience. The goal was to quantify whether a surgical learning curve exists in operative or sleep outcomes in UAS. Methods: International multi-center retrospective review of the ADHERE registry, a prospective international multi-center study collecting UAS outcomes. ADHERE registry centers with at least 20 implants and outcomes data through at least 6-month follow-up were reviewed. Cases were divided into two groups based on implant order (the first 10 or second 10 consecutive implants at a given site). Group differences were assessed using Mann-Whitney U-tests, Chi-squared tests, or Fisher’s Exact tests, as appropriate. A Mann-Kendall trend test was used to detect if there was a monotonic trend in operative time. Sleep outcome equivalence between experience groups was assessed using the two one-sided tests approach. Results: Thirteen facilities met inclusion criteria, contributing 260 patients. Complication rates did not significantly differ between groups ( P = .808). Operative time exhibited a significant downward trend ( P < .001), with the median operative time dropping from 150 minutes for the first 10 implants to 134 minutes for the subsequent 10 implants. The decrease in AHI from baseline to 12-month follow-up was equivalent between the first and second ten (22.8 vs 21.2 events/hour, respectively, P < .001). Similarly, the first and second ten groups had equivalent ESS decreases at 6 months (2.0 vs 2.0, respectively, P < .001). ESS outcomes remained equivalent for those with data through 12-months. Conclusions: Across the centers’ first 20 implants, an approximately 11% reduction operative time was identified, however, no learning curve effect was seen for 6-month or 12-month AHI or ESS over the first twenty implants. Ongoing monitoring through the ADHERE registry will help measure the impact of evolving provider and patient specific characteristics as the number of implant centers increases.

scholarly journals An Estimation of the Learning Curve Effect on Project Duration with Monte Carlo Simulation

2018 ◽  
Vol 49 (1) ◽  
pp. 66-71 ◽  
Author(s):  
Levente Mályusz ◽  
Anita Varga
Keyword(s):  
Learning Curve ◽  
Learning Effect ◽  
Test Problems ◽  
Activity Time ◽  
Project Duration ◽  
Curve Effect ◽  
Normal Duration ◽  
Minimum Estimate ◽  
Repetitive Activities ◽  
Total Project

The aim of this paper is to estimate learning curve effect on project duration with the mean of project scheduling techniques. To measure this effect only one assumption is taken: the activity time individuals / groups take to perform an activity decreases at a given rate as experience is gained with the activity. Unfortunately this effect directly is not taken into account by project management software. In some software after scheduling, supervisor manually can switch on the "as soon as possible" or "as late as possible" buttons on an activity.Monte Carlo simulation was used to model the risks in the total project durations. It is assumed that the (normal) durations of the activities can vary according to the beta distribution. The minimum estimate is 95 % of the original (normal) duration, and the maximum estimate is 140 % of the original (normal) duration. We assumed that most likely value is the (normal) duration of each activity. The learning effect on project duration with the help of test problems and real problems was investigated. In test problems learning effect can occur between two consecutive activities. These pairs are chosen randomly. After calculating project duration, these pairs are allocated to be closer to each other using the predecessor's total float time. It is assumed that the duration of impending repetitive activities is shorter due to the learning curve effect if the gap between consecutive activities is small enough. This iteration is carried out until it is not possible to shorten the successor's activity time in a pair. It is shown that this effect brings a 2-3 % shorter project duration meanwhile variance is also left in a 1-2 % range. Numerical tests were implemented by XPRESS-Mosel Optimization Software.

A time – cost trade-off algorithm for nonserial linear projects

1996 ◽  
Vol 23 (1) ◽  
pp. 134-149 ◽  
Author(s):  
Ahmed B. Senouci ◽  
Neil N. Eldin
Keyword(s):  
Dynamic Programming ◽  
Time Cost ◽  
Calculation Algorithm ◽  
Planning And Scheduling ◽  
Trade Off ◽  
Project Duration ◽  
Total Cost ◽  
Current Formulation ◽  
Discrete Functions ◽  
Linear Projects

In the last decade, a number of methods based on the theories of linear and dynamic programming were developed for the scheduling of linear projects. However, these methods are only capable of handling sequential or serial activities. In this paper, a dynamic programming formulation for the scheduling of nonsequential or nonserial activities is presented. The objective of the current formulation is to determine the project time–cost profile, which determines possible project durations and their minimum project total costs. The formulation considers the effects of crew formations, interruptions, and lags for production activities in determining the minimum project total cost, minimum project direct cost, and minimum project duration. In addition, the approach presented is capable of handling activity durations and interruptions described by continuous and discrete functions. The formulation is also capable of handling both serial and nonserial linear projects. An example project is provided to illustrate the computational steps, validate the calculation algorithm, and show the capabilities of the proposed method. Key words: time-cost trade-off, linear projects, planning and scheduling, project control, project management, nonserial linear projects.

The Learning Curve in Arthroscopic ACL Reconstruction. The impact on the Surgical Time and Postoperative Clinical Results

2018 ◽  
Vol 69 (10) ◽  
pp. 2874-2876
Author(s):  
Teodor Negru ◽  
Stefan Mogos ◽  
Ioan Cristian Stoica
Keyword(s):  
Anterior Cruciate Ligament ◽  
Learning Curve ◽  
Clinical Outcomes ◽  
Positive Impact ◽  
Cruciate Ligament ◽  
Clinical Results ◽  
Single Bundle ◽  
Surgical Time ◽  
Anterior Cruciate ◽  
The Impact

Rupture of the anterior cruciate ligament (ACL) is a common injury. The objective of the current study was to evaluate if the learning curve has an impact on surgical time and postoperative clinical outcomes after anatomic single-bundle anterior cruciate ligament reconstruction (ACLR) using an outside-in tunnel drilling hamstrings technique. The learning curve has a positive impact on surgical time but has no influence on postoperative clinical outcomes at short time follow-up.

scholarly journals The impact of end-userś participation (cancer patients) during the optimization process

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S Houwaart
Keyword(s):  
End Users ◽  
Public Health Care ◽  
Optimization Process ◽  
Patient Organization ◽  
End User ◽  
The Public ◽  
Patient Organizations ◽  
Crucial Information ◽  
The Individual ◽  
The Impact

Abstract End-user (e.g. patients or the public) testing of information material is becoming more common in the German public health care system. However, including the end-user (in this case patients) in an optimisation process and thus enabling a close collaboration while developing PIMs is still rare. This is surprising, given the fact that patients provide the exact perspective one is trying to address. Within the isPO project, a patient organization is included as a legal project partner to act as the patient representative and provide the patient's perspective. As such, the patient organization was included in the PHR approach as part of the PIM-optimisation team. During the optimisation process, the patients gave practical insights into the procedures of diagnosing and treating different types of cancer as well as into the patient's changing priorities and challenges at different time points. This was crucial information for the envisioned application of the individual PIMs and their hierarchical overview. Moreover, the developed PIM-checklist enabled the patients to give detailed feedback to the PIMs. With their experience of being in the exact situation in which the PIMs will be applied, their recommendations, especially on the wording and layout of the materials, have been a valuable contribution to the PIM optimisation process. In this part of the seminar, we will take a closer look at the following skill building aspects: What is gained from including patients as end-users in the development and optimization of PIM?How can we reach patients to contribute to a PIM optimization process? Which requirements and prerequisites do patients have to provide to successfully work on an optimisation team?How to compromise and weigh opinions when different ideas occur? Altogether, this part will construct a structured path of productive patient involvement and help to overcome uncertainties regarding a collaboration with patient organizations.

scholarly journals The Value of Vaccination in Older Adults: The Why

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 806-807
Author(s):  
Philip Buck
Keyword(s):  
Older Adults ◽  
Time Horizon ◽  
Pneumococcal Disease ◽  
Population Aging ◽  
Indirect Costs ◽  
Health Behavior Change ◽  
Vaccine Preventable Diseases ◽  
Time Period ◽  
The Us ◽  
The Impact

Abstract The incidence of vaccine-preventable diseases remains high among older adults in the US, despite longstanding immunization recommendations, and is projected to increase as the population ages. The impact of US population aging on the burden of four vaccine-preventable diseases (influenza, pneumococcal disease, shingles, and pertussis) was modeled over a 30-year time horizon, with cumulative direct and indirect costs increasing from $378 billion over 10 years to $1.28 trillion over 30 years. Compared to current levels of vaccination coverage, increasing coverage was predicted to avert over 33 million cases of disease and greater than $96 billion in disease-associated costs, with a corresponding increase in vaccination costs of approximately $83 billion over the entire 30-year time period. Specific examples of cost-effectiveness analyses that assess the epidemiologic and economic impact of vaccination against shingles and pertussis in older adults will be discussed. Part of a symposium sponsored by the Health Behavior Change Interest Group.

scholarly journals It’s the Weather: Quantifying the Impact of Weather on Retail Sales

2021 ◽  
Author(s):  
Natalie Rose ◽  
Les Dolega
Keyword(s):  
Weather Condition ◽  
Weather Conditions ◽  
Influential Factor ◽  
Retail Sector ◽  
Retail Sales ◽  
Weather Impact ◽  
High Street ◽  
Sector Decision ◽  
The Impact ◽  
The Relationship

AbstractThe weather is considered as an influential factor on consumer purchasing behaviours and plays a significant role in many aspects of retail sector decision making. As a result, better understanding of the magnitude and nature of the influence of variable UK weather conditions can be beneficial to many retailers and other stakeholders. This study addresses the dearth of research in this area by quantifying the relationship between different weather conditions and trading outcomes. By employing comprehensive daily sales data for a major high street retailer with over 2000 stores across England and adopting a random forest methodology, the study quantifies the influence of various weather conditions on daily retail sales. Results indicate that weather impact is greatest in the summer and spring months and that wind is consistently found to be the most influential weather condition. The top five most weather-dependent categories cover a range of different product types, with health foods emerging as the most susceptible to the weather. Also, sales from out-of-town stores show a far more complex relationship with the weather than those from traditional high street stores with the regions London and the South East experiencing the greatest levels of influence. Various implications of these findings for retail stakeholders are discussed and the scope for further research outlined.

scholarly journals POS0530 HOW DOES MULTIMORBIDITY IMPACT ON THE DIRECT AND INDIRECT COSTS IN PATIENTS WITH RHEUMATOID ARTHRITIS?

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 498.3-499
Author(s):  
P. H. Hsieh ◽  
C. Geue ◽  
O. Wu ◽  
E. McIntosh
Keyword(s):  
Rheumatoid Arthritis ◽  
Early Rheumatoid Arthritis ◽  
Employment Status ◽  
Functional Disability ◽  
Hospital Admissions ◽  
Information Service ◽  
Indirect Costs ◽  
Direct Costs ◽  
Direct And Indirect Costs ◽  
The Impact

Background:Comorbidities are prevalent in patients with rheumatoid arthritis (RA) and associated with worse outcomes as well as higher economic burden. Little is known about the impact of multimorbidity on the direct and indirect costs of RA. Evidence of the incremental scale of these multimorbidity costs will usefully inform RA interventions and policies.Objectives:The aim of this study was to describe how multimorbidity impacts on the cost-of-illness, including direct and indirect costs, in patients with RA.Methods:The Scottish Early Rheumatoid Arthritis (SERA) is a registry of patients newly presenting with RA since 2011. It contains data on patient characteristics, clinical outcomes, health-related quality of life, and employment status data. These data were linked to routinely recorded hospital admissions and primary care prescribing data. Direct costs were estimated by applying relevant unit costs to healthcare resource use quantities. Indirect cost estimates were obtained from information on employment status and hospital admissions, valued by age and sex specific wages. Two-part models (probit followed by generalized linear model) were used to estimate direct and indirect costs, adjusting for age, gender, and functional disability. The Charlson Comorbidity Index (CCI) score was calculated using patient ICD-10 diagnoses from hospital records. The number of comorbidities was categorized into “RA alone”, “single comorbidity” and “multimorbidity (>1 comorbidity)”.Results:Data were available for 1,150 patients, 65.7% were female and a mean age of 57.5±14 years. The majority of patients only had RA (54.1%), followed by a single comorbidity (23.4%) and multimorbidity (22.5%). Annual total costs were significantly higher for patients with multimorbidity (£6,669 95% CI £4,871-£8,466; OR 11.3 95% CI 8.14-15.87) and for patients with a single comorbidity (£2,075 95% CI £1,559-£2,591; OR 3.52 95% CI 2.61-4.79), when compared with RA alone (£590). The excess costs were mainly driven by direct costs (£6,281 versus £1,875 versus £556). Although the difference in indirect costs between patients with multimorbidity and a single comorbidity were not statistically significant (£1,218 versus £914, p=0.11), patients with multimorbidity were associated with significantly higher costs than those with RA only (£594, p<0.01).Conclusion:The presence of comorbidity contributes significant excess to both direct and indirect costs among RA patients. In particular, patients with multimorbidity incurred substantially higher direct costs than those with a single comorbidity or RA only.Acknowledgements:The study analysed the data from the Scottish Early Rheumatoid Arthritis (SERA) study with a linkage to routinely recorded health data from Information Service Division, National Service Scotland. We would like to thank all the patients, clinical and nursing colleagues who have contributed their time and support to the study, the SERA steering committee for the approval, and Allen Tervit from the Robertson Centre for Biostatistics, University of Glasgow for the timely technical supports.Disclosure of Interests:Ping-Hsuan Hsieh: None declared, Claudia Geue: None declared, Olivia Wu Consultant of: OW has received consultancy fees from Bayer, Lupin and Takeda outside the submitted work., Emma McIntosh: None declared

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