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The traditional practice for maintenance, quality control and production scheduling is to plan independently irrespective of an interrelationship exist between them. The purpose of this paper is to develop an approach for integrating maintenance, quality control and production scheduling. The objective is to investigate the benefits of the integrated effect in terms of the expected total cost of system operation of the three functions.

The proposed approach is based on the conditional reliability of the components. Cost model for integrating selective maintenance, quality control using sampling-based procedure and production scheduling is developed using the conditional reliability. An integrated approach is such that, first an optimal schedule for the batches to be processed is obtained independently while the maintenance and quality control decisions are optimized considering the optimal schedule on the machine. The expected total cost of conventional approach, i.e. “No integration” is calculated to compare the effectiveness of integrated approach.

The integrated approach have shown a higher cost saving as compared to the independent planning approach. The approach is practical to implement as the results are obtained in a reasonable computational time.

The approach presented in this paper is generic and can be applied at planned as well as unplanned opportunities. The proposed integrated approach is dynamic in nature, as during maintenance opportunities, it is possible to optimize the decision on maintenance, quality control and production scheduling considering the current age of components and production requirement.

The originality of the paper is in the approach for integration of the three elements of shop floor operations that are usually treated separately and rarely touched upon by researchers in the literature.

The purpose of this paper is to mine information on the construction process of previous projects to develop a construction plan that meets both quality requirements and schedule constraints.

This paper uses a failure mode and effect analysis to evaluate the construction quality of 311 apartments in Shanghai. The authors also evaluate construction-scheduling control using the earned value management technique and implement an artificial neural network to correlate the results. The authors then develop a quality risk and schedule correlation model based on Big Data. The model can predict the relationship between the planned schedule and the project quality risk using multiple variables such as the number of layers, the schedule performance index and budget costs.

The methodology offers an innovative approach for assessment on the relationship between quality risk and project schedule. The authors have also built a multiple regression analysis model for comparative purposes with the model. The results show that the proposed model can better describe the relationship. The model can provide a quantitative quality risk value that changes with the planned schedule, as well as help project managers to understand the relationship between quality risk and project scheduling more accurately.

The research approach only focuses on quality risk under the impact of scheduling. Future efforts might focus on developing a model that connects failure models with project schedules and costs in order to improve the effort of quality management.

The model based on Big Data in this paper is developed using real projects and reflects the relationship between project quality risk and scheduling in real environments. The created application provides support for project managers to develop and adjust quality plans and schedules, thereby reducing deviations in quality and scheduling objectives.

The authors make full use of historical project data from the perspective of both quality and schedule management, and provide a novel method to intelligently and objectively analyze the relationship between quality risk and scheduling.

The purpose of this paper is to propose an integrated earned value management (EVM) approach to control quality, cost, schedule and risk of projects.

This study represents a new EVM framework by considering a quality control index. Particularly, some control indices and cumulative buffers are defined by two proposed, methods, namely the linear- and Taguchi-based methods. These methods are implemented in three different projects in different industries.

According to the results, integration of the quality index creates a better control situation by providing more accurate information. Hence, project managers could comprehensively monitor the status of important factors to make more precise decisions while maintaining the simplicity of their analysis.

From the methodological and theoretical features, this paper offers new visions because, to the best of authors’ knowledge, no comparable study has been conducted before.

Health care is an example of an organization where the needs of potential clients are much greater than the capabilities of the service delivery system. The implementation of any medical procedure, as well as the provision of any service, just like the manufacturing of any product, can be decomposed into a series of tasks. The purpose of this paper is to propose a model for measuring the effectiveness of quality assurance tasks in health-care delivery processes.

The authors analyze a system of factors that affect the implementation of tasks in a process. In their considerations, they have focused on four areas of science that describe conditions that are related to the implementation of tasks: Scheduling as a methodology for allocating resources to perform tasks; Capacity planning as a methodology for assigning values to given resources expressed by the number of tasks that can be executed with the resources; Queueing theory, used as a methodology for describing phenomena in which not all planned tasks are performed within the prescribed specification limits; and Quality management, as a methodology to ensure appropriate conditions for completing tasks (CCTs), where CCT is a representation of parameters of casual relationship between variables.

The authors show that the effectiveness of executing any scheduled tasks in the process is determined by the difference between the capacity of resources allocated (at a given time interval) and the number of tasks planned to be carried out at that time. The CCT conditions determine the level of capacity of the fixed amount of resources. It is shown that their deviation from the reference CCT specification may cause the nominally correct amount of resources be either too small (causing queue formation and longer wait time in hospitals) or too large to contribute to the waste in the system by creating idle capacity.

The scope of application of the model is wide. It covers tasks performed with different degrees of uncertainties regarding the capacity of resources. It applies in all areas of health care where unlike manufacturing, the services delivered and the tasks performed in the health-care delivery system are seldom identical. Every patient is treated differently than the one waiting next in line. The workloads are pre-arranged in the order they are needed and completed in accordance with the FI-FO (first in-first out) principle. The model presented in this paper makes it possible to better understand the mechanism of effectiveness and efficiency improvement and the role of humans as a specific carrier of capacity.

As most of the health-care organizations are still stuck in the soft side of quality assurance, there has been little research conducted to test the applicability of well-known productions/operation management methodologies and theories benefitting health-care systems. The formulation of a reference point of CCT in this study is to serve as a stabilizing control point with the same connotation as that of a central reference line in the statistical process control chart. The correct capacity planning is needed to determine with a high degree of probability of success in implementation of all tasks to assure quality all the time.

The purpose of this paper is to identify control systems that give rise to better construction project performance; and develop and test project performance predictive models based on control systems adopted in the project.

Research design was questionnaire survey. Data were collected via Electronic mails. The sampling frame was Singapore-based construction firms.

In all, 16 control mechanisms are significantly correlated with project outcomes. The more important control mechanisms are: adequacy of project information to develop the project schedule; adequacy of float in the schedule; and quality of techniques used to support risk identification. Two relatively robust predictive models were constructed and validated to predict schedule and quality outcomes of construction projects. Schedule performance may be predicted by adequacy of float and stringency of criteria to select suppliers. Quality outcome is most significantly affected by competency of quality manager, rather than the hard systems adopted in the project.

The limitations include low response rate, and subjective nature of the five-point Likert scale used to rate project outcomes and extent to which control mechanisms were adopted in the project.

The implication of the findings is that merely having good project management practices and adequate resources are not sufficient to achieve good project outcomes. Instead, construction projects need to have control systems in place, as they play an important role in project outcomes.

The paper has shown empirically that control systems affect project outcomes. They are needed not just to control the project, but also help the project to achieve good outcomes. The research designed and tested two relatively robust models to predict schedule and quality outcomes of a project. These models may be used to make an initial assessment of the project's likely outcome, based on the control systems that contractors are going to adopt.

The purpose of this paper is to investigate the performance of public projects in Beijing, Hong Kong, Singapore and Sydney to uncover which areas project managers should focus on when managing public projects in different countries.

Based on the literature review, a structured questionnaire was designed to collect data of completed public projects. In total, 244 sets of data of completed public projects were collected.

Significant cost and schedule overruns are found in all four cities. Hong Kong’s public projects have the highest cost and schedule overruns. Singapore’s public projects have the lowest cost overrun and Beijing’s projects have the lowest schedule overrun. Public projects in all four cities recorded significantly good project quality.

The findings are not easily generalizable due to the relatively small sample size in Sydney, low response rate and data being collected from only four cities. The research implication is that the plethora of project management strategies does not seem effective in preventing cost and schedule overruns in public projects.

This study found that across the four cities, there are significant cost and schedule overruns. Projects in Hong Kong perform the worst in terms of cost and schedule, when compared to Beijing, Singapore and Sydney. The implication is that more attention should be paid to managing cost and schedule, especially in Hong Kong.

The originality is that the study discovered which areas project managers should focus on when managing public projects in different countries. In laissez-faire or free market economies, more attention should be paid to managing project cost and schedule. When a country has lower transparency index, more attention should be paid to controlling project quality. Project team members should focus on delivering public projects to the highest level of quality in developed countries.

The aim of this paper is to make an attempt to find good values of onsite–offshore team strength; number of hours of communication between business users and onsite team and between onsite and offshore team to reduce cost and improve schedule for re-engineering projects in global software development environment.

The system dynamics technique is used for simulation model construction and policy run experimentation. The experts from Indian software outsourcing industry were consulted for model construction, validation and analysis of policy run results in both co-located and distributed software development environment.

The study results show that there is a drop in the overall team productivity in outsourcing environment by considering the offshore options. But the project cost can be reduced by employing the offshore team for coding and testing work only with minimal training for imparting business knowledge. The research results show that there is a potential to save project cost by being flexible in project schedule.

The study found that there could be substantial cost saving for re-engineering projects with a loss of project schedule when an appropriate onsite–offshore combination is used. The quality and productivity drop, however, were rather small for such combinations. The cost savings are high when re-engineering work is sent to offshore location entirely after completion of requirement analysis work at onsite location and providing training to offshore team in business knowledge The research findings show that there is potential to make large cost savings by being flexible in project schedule for re-engineering projects.

The software project manager can use the model results to divide the software team between onsite and offshore location during various phases of software development in distributed environment.

The study is novel as there is little attempt at finding the team distribution between onsite and offshore location in global software development environment.

A fast production scheduling system, the very fast scheduler (VFS), has been developed by the authors. It creates a capacity constrained production schedule within one minute of elapsed time for problems of a size encountered in industry. The quality of the schedules is comparable with the best alternative heuristic scheduling techniques. The speed of the scheduler is such that it can be used on a real‐time basis to plan capacity, adjust priorities and other parameters and derive new schedules.

This study aims to seek to advance a system dynamics-discrete event hybrid simulation modelling concept useful for taking improvement decisions where one needs to consider the interactions between human factors and process flow elements in lean manufacturing systems.

A unique approach is taken to hybrid simulation modelling where the whole problem situation is first conceptualized using a causal loop diagram and stock and flow diagram, before transmitting to a hybrid simulation model. The concept is intended to simplify the simulation modelling process and make the concept pliable for use in various types of lean manufacturing problem situations.

The hybrid simulation modelling concept was applied to a lean manufacturing case where quality performance was sporadic mainly because of production pressures. The hybrid modelling concept revealed a solution that advanced full compliance with lean and one that required changes in job scheduling policies to promote both continuous improvement and throughput increases.

Because non-tangible aspects of lean were objectively assessed using the hybrid modelling concept, the study is an advancement towards establishing a credible link between human resource aspects of lean and the performance of an organization.

The applied hybrid model enabled managers in the plant navigate the trade-off decision they often face when choosing to advance production output ahead of continuous improvement practices.

System dynamics-discrete event hybrid simulation modelling is a rarity in lean manufacturing systems.

Basic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction.

Upon reviewing the topic of DS, a worldwide internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.

The need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use and software integration with contemporary project management practices.

Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.

Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.

The paper illustrates the importance of DS in construction, identifies the user needs and overviews the development, verification and validation of a model that supports the generation of high-quality schedules beneficial to large-scale projects.