Sergio Fichera

A new efficient encoding/decoding procedure for the design of a supply chain network with genetic algorithms

Supply chain network (SCN) design is a strategic issue which aims at selecting the best combination of a set of facilities to achieve an efficient and effective management of the supply chain. This paper presents an innovative encoding-decoding procedure embedded within a genetic algorithm (GA) to minimize the total logistic cost resulting from the transportation of goods and the location and opening of the facilities in a single product three-stage supply chain network. The new procedure allows a proper demand allocation procedure to be run which avoids the decoding of unfeasible distribution flows at the stage of the supply chain transporting products from plants to distribution centers. A numerical study on a benchmark of problems demonstrates the statistical outperformance of the proposed approach vs. others currently available in literature in terms of total supply chain logistic cost saving and reduction of the required computation burden to achieve an optimal design.

Shewhart and EWMA t control charts for short production runs

Short-run productions are common in manufacturing environments like job shops, which are characterized by a high degree of flexibility and production variety. Owing to the limited number of possible inspections during a short run, often the Phase I control chart cannot be performed and correct estimates for the population mean and standard deviation are not available. Thus, the hypothesis of known in-control population parameters cannot be assumed and the usual control chart statistics to monitor the sample mean are not applicable. t-charts have been recently proposed in the literature to protect against errors in population standard deviation estimation due to the limitation of available sampling measures. In this paper the t-charts are tested for implementation in short production runs to monitor the process mean and their statistical properties are evaluated. Statistical performance measures properly designed to test the chart sensitivity during short runs have been considered to compare the performance of Shewhart and EWMA t-charts. Two initial setup conditions for the short run fixing the population mean exactly equal to the process target or, alternatively, introducing an initial setup error influencing the statistic distribution have been modelled. The numerical study considers several out-of-control process operating conditions including one-step shifts for the population mean and/or standard deviation. The obtained results show that the t-charts can be successfully implemented to monitor a short run. Finally, an illustrative example is presented to show the use of the investigated t charts. Copyright

Multiobjective economic design of an X control chart

The prevention of defective products is a fundamental principle of total quality management and control charts are a powerful statistical tool to reach this objective, but they are expensive and may increase the cost of production. For this reason an appropriate design is necessary before the chart is used. In this paper a new approach, based on an evolutionary algorithm, to solve this problem is proposed. The design of the chart has been developed considering the optimisation of the cost of the chart and at the same time the statistical proprieties. The proposed multiobjective approach has been compared to some well-known heuristics; the obtained results show the effectiveness of the evolutionary algorithm.

An evolutionary approach to multi-objective scheduling of mixed model assembly lines

In this paper a multi-objective genetic algorithm for the scheduling of a mixed model assembly line is proposed, pursuing the line stop time minimisation together with the component usage smoothing. Specific features of the developed GA are step by step random selection of diversified crossover and mutation operators, population control for the substitution of duplicate chromosomes, and in-process updating of GA control parameters. Three different formulation of the fitness function were been tested with some distinct line configurations.

AN EVOLUTIONARY ALGORITHM FOR PURE FUZZY FLOWSHOP SCHEDULING PROBLEMS

The pure flowshop scheduling problem is here investigated from a perspective considering me uncertainty associated with the execution of shop floor activities. Being the flowshop problem is NP complete, a large number of heuristic algorithms have been proposed in literature to determine an optimal solution. Unfortunately, these algorithms usually assume a simplifying hypothesis: the problem data are assumed as deterministic, i.e. job processing times and the due dates are expressed through a unique value, which does not reflect the real process variability. For this reason, some authors have recently proposed the use of a fuzzy set theory to model the uncertainty in scheduling problems. In this paper, a proper genetic algorithm has been developed for solving the fuzzy flowshop scheduling problem. The optimisation involves two different objectives: the completion time minimisation and the due date fulfilment; both the single and multi-objective configurations have been considered. A new ranking criterion has been proposed and its performance has been tested through a set of test problems. A numerical analysis confirms the efficiency of the proposed optimisation procedure.

Human factor policy testing in the sequencing of manual mixed model assembly lines

In this paper the human resource management in manual mixed model assembly U-lines is considered. The objective is to minimise the total conveyor stoppage time to achieve the full efficiency of the line. A model, that includes effects of the human resource, was developed in order to evaluate human factor policies impact on the optimal solution of this line sequencing problem. Different human resource management policies are introduced to cope with the particular layout of the proposed line. Several examples have been proposed to investigate the effects of line dimensions on the proposed management policies. The examples have been solved through a genetic algorithm. The obtained results confirm the effectiveness of the proposed model on the performance optimisation of the line.

A variable sampling interval S2-EWMA control chart for monitoring the process variance

This paper proposes a Variable Sampling Interval version of the Fixed Sampling Interval S2-EWMA control chart developed by Castagliola (2004) and dedicated to the monitoring of the sample variance of a process. In this paper, we explain how the various parameters of this VSI S2-EWMA control chart can be computed and how the use of the VSI feature significantly improves the statistical efficiency of FSI S2-EWMA chart, thus representing an effective tool in the detection of process out-of-control conditions. An optimal design strategy based on the Average Time to Signal (ATS) is presented and a comparison with the FSI procedure is performed.

The economic performance of the Shewhart t chart

In the production of small batches of customized parts, high flexibility and frequent switching of production from one product variant to another could not allow for the implementation of a control chart to monitor the process. In fact, when a short-run production should be started, the distribution parameters of the quality characteristics to be monitored are frequentlytextitunknown and the production run is too short to get sufficient Phase I samples. To overcome this problem, the statistical properties of Shewhart t charts monitoring a short production run have been recently discussed in literature. In this paper, we investigate their economic performance: the SPC inspection cost optimization is constrained by the manufacturing and the inspection activities configuration. The decision variables of the problem include the chart design parameters and the size of batches of parts to be worked and released to the local inspection area. A numerical analysis aimed at evaluating the economic performance of the Shewhart t chart vs the Shewhart chart with known parameters has been performed. The expected economic loss associated with the implementation of the Shewhart t chart is acceptable with respect to the ‘ideal’ condition of the control chart with known parameters when the cost optimization is achieved without a statistical constraint limiting the number of expected false alarms. Finally, the effect of an erroneous initial set-up on the correctness of the inspection cost estimation has been investigated. Copyright

A new CUSUM‐S2 control chart for monitoring the process variance

Purpose – The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S2 control chart designed to monitor the sample variance of samples from a normally distributed population.Design/methodology/approach – The proposed chart monitors a statistic computed as a logarithmic transformation of the sample variance; the introduction of the sample variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.Findings – A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S2 chart has been assessed through a comparison with an EWMA‐S2 control chart proposed in the literature to monitor the process dispersion.Research limitations/implications – The paper only deals with uncorrelated normally distributed data.Practical implications – The obtained results show how the CUSUM‐S2 ch...

Linking Six Sigma to simulation: a new roadmap to improve the quality of patient care

PURPOSE 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. DESIGN/METHODOLOGY/APPROACH 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. FINDINGS 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. PRACTICAL IMPLICATIONS 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. ORIGINALITY/VALUE 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.