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The search of the optimal economic design of the Bayesian adaptive control charts for finite production runs can be a long and tedious procedure due to the intrinsic structure of the optimization problem, which requires a dynamic programming approach to select the best decision at each sampling epoch during the production horizon of the process. This paper aims to propose a new efficient procedure implementing a genetic algorithm neighbourhood search scheme embedded within the dynamic programming procedure with the aim of reducing the computational burden and achieving significant cost savings in the chart implementation.

The efficiency of the developed procedure has been verified through a comparison with another existing exhaustive approach working exclusively on one‐sided X¯ Bayesian control charts; then, it has been extended to the design of two‐sided Bayesian control charts.

The proposed procedure implementing the genetic algorithm neighbourhood search is very fast and efficient in detecting optimal solutions: it allows significant quality control cost savings to be achieved during the Bayesian charts implementation thanks to the possibility of investigating larger spaces of decisions than the existing optimization procedures.

With reference to discrete part manufacturing, where the assumption of finite production runs is often realistic, the design and implementation of adaptive Bayesian control charts by means of the proposed procedure allows significant cost savings to be achieved with respect to the fixed parameters Shewhart charts.

The exhaustive optimization procedure cannot be executed in a reasonable computational time when the space of decisions to select Bayesian chart design parameters significantly enlarges, which is the case of two‐sided control charts. The paper documents the proposed procedure which overcomes this problem and allows the two‐sided Bayesian chart to be designed and proposed as an efficient means to monitor short production runs.

The use of control charts to monitor a product quality characteristic requires the selection of their design parameters. To select feasible design parameters, the constraints related to the inspection resources available at the workstation, its configuration and the process operating parameters should be taken into account. The aim of this research is to discuss the design of Shewhart and EWMA control charts monitoring dispersion in the presence of these constraints and for processes characterized by a random shift size.

In this paper the design of the investigated control charts has been intended in a broader sense as the design of a local inspection procedure constrained by a set of resource/environmental process factors characterizing the workstation configuration. An economic objective pursuing the minimization of the total inspection cost has been considered.

Constraining the design of a control chart is an essential issue to find chart design parameters that can be effectively implemented by quality practitioners. The influence of the process‐operating parameters has been modelled and some guidelines have been suggested through the use of contour plots.

The economic design of control charts has been scarcely implemented by quality practitioners due to the difficulty of adapting them to the multifaceted complexity and constraints present within manufacturing environments. This paper tries to contribute to the existing literature by improving the available mathematical models through modelling the actual workstation configuration and resource allocation; furthermore, for random shift processes it investigates the performance of the Shewhart and EWMA control charts monitoring process dispersion.

Improving the quality of patient care is a challenge that calls for a multidisciplinary approach, embedding a broad spectrum of knowledge and involving healthcare professionals from diverse backgrounds. The purpose of this paper is to present an innovative approach that implements discrete‐event simulation (DES) as a decision‐supporting tool in the management of Six Sigma quality improvement projects.

A roadmap is designed to assist quality practitioners and health care professionals in the design and successful implementation of simulation models within the define‐measure‐analyse‐design‐verify (DMADV) or define‐measure‐analyse‐improve‐control (DMAIC) Six Sigma procedures.

A case regarding the reorganisation of the flow of emergency patients affected by vertigo symptoms was developed in a large town hospital as a preliminary test of the roadmap. The positive feedback from professionals carrying out the project looks promising and encourages further roadmap testing in other clinical settings.

The roadmap is a structured procedure that people involved in quality improvement can implement to manage projects based on the analysis and comparison of alternative scenarios.

The role of Six Sigma philosophy in improvement of the quality of healthcare services is recognised both by researchers and by quality practitioners; discrete‐event simulation models are commonly used to improve the key performance measures of patient care delivery. The two approaches are seldom referenced and implemented together; however, they could be successfully integrated to carry out quality improvement programs. This paper proposes an innovative approach to bridge the gap and enrich the Six Sigma toolbox of quality improvement procedures with DES.

The healthcare industry is facing several challenges such as the reduction of costs and quality improvement of the provided services. Engineering studies could be very useful in supporting organizational and management processes. Healthcare service efficiency depends on a strong collaboration between clinical and engineering experts, especially when it comes to analyzing the system and its constraints in detail and subsequently, when it comes to deciding on the reengineering of some key activities. The purpose of this paper is to propose a case study showing how a mix of representation tools allow a Manager of a Radiology Department to solve some human and technological resource re‐organizational issues, which have to be faced due to the introduction of a new technology and a new portfolio of services.

In order to simulate the activities within the radiology department and examine the relationship between human and technological resources, different visual diagrammatic language (VDL) techniques have been implemented to get knowledge about the heterogeneous factors related to the healthcare service delivery. In particular, flow charts, IDEF0 diagrams and Petri nets have been integrated each other with success as a modelisation tools.

The simulation study performed through the application of the aforementioned VDL techniques suggests the opportunity of re‐organizing the nurse activities within the radiology department.

The re‐organization of a healthcare service and in particular of a radiology department by means of joint flow charts, IDEF0 diagrams and Petri nets is a poorly investigated topic in literature. This paper demonstrates how flow charts and IDEF0 can help people working within the department to understand the weak points of their organization and constitute an efficient base of knowledge for the implementation of a Petri net aimed at improving the departmental performance.