Pedro L. González-R

Token-based pull production control systems: an introductory overview

Since the advent of Kanban, pull systems have been widely used in practice and have been widely studied by researchers. During the last decades, several types of pull systems have emerged. Numerous articles have been published to introduce new paradigms or new principles for just-in-time systems as well as new approaches to evaluate them or to optimize their performance. An important feature of these systems, which is common to many other production control systems, is the use of tokens, which usually consist of cards that authorize certain production tasks to be performed. Tokens can be used in various manners to control production and can be combined with several other mechanisms with the objective of reducing the work in progress and the lead times, while meeting customers’ demand. This article proposes an introductory overview of existing research works in this area. In this respect, we suggest a classification of pull-inspired production control systems, which allows us to distinguish up to 18 different systems. For each type of system, we study its basic principles, the flow control strategy, and the parameters affecting its performance. This survey aims at facilitating the understanding of the different proposals made by researchers and highlighting their common points and differences.

A multi-objective comparison of dispatching rules in a drum-buffer-rope production control system

The advantages of the TOC (Theory of Constraints) philosophy have been extensively documented in the literature since its introduction during the 1980s. At the operational level, TOC is implemented by means of the well-known DBR (drum–buffer–rope) production control system. In a multiproduct manufacturing environment, the performance of DBR is greatly affected by the dispatching rules employed in front of the bottleneck station. Furthermore, it has been proved that no single dispatching rule (DR) performs globally better than any others. Therefore, for systems usually influenced by variability conditions, the selection of a robust DR could help practitioners to reach a good system performance. In this paper we propose a methodology to obtain a robust DR (by means of Taguchi signal-to-noise ratio) from a set of previously selected rules according to the performance measures of the system pursued by the practicing managers. We study the performance of different dispatching rules for several conflicting objectives (namely average tardiness, maximum tardiness, and WIP) from a robustness viewpoint and for a range of manufacturing scenarios in a shop floor formed by five stations in line and three different products. Different variability sources, such as processing times, breakdowns and set-ups, are discussed. The results obtained are of special interest for practitioners.

The pull evolution: from Kanban to customised token-based systems

This work formalises a new approach for material flow control, i.e. the so-called customised token-based systems (CTBS). This approach represents a natural evolution from classical pull systems, such as Kanban. The article first describes the flow of materials (orders or tasks) from a management point of view, and discusses exact and approximate approaches to tackle the problem. The origin of Customised Production Control systems is explained, as well as the main advantages: high degree of generalisation, enhancement of the space of solutions and adaptation to the manufacturing environment. Next, we propose a method based on cross-entropy for the optimisation of CTBS. The computational experience carried out in a number of scenarios shows the CTBS to be profitable. Furthermore, the resulting control mechanisms seem to be relatively easy to implement in real-manufacturing environments.

A response surface methodology for parameter setting in a dynamic Conwip production control system

Pull production control systems are usually implemented by means of Kanban cards. Kanban cards can be established either statically (card setting) or dynamically (card controlling). As card controlling seems to have a number of advantages over card setting, several card controlling mechanisms (CCM) have been proposed in the literature. The CCM automatically adjust the number of cards required depending on the status of the system and on the values of certain constants or parameters. Therefore, the performance of a CCM is greatly affected by the correct setting of its parameters. In this paper, we address the parameter setting in a specific CCM designed for Conwip systems. We focus on this mechanism as it has proved to outperform other CCM for a variety of scenarios. We suggest a methodology based on response surface methodology (RSM) statistical procedure for parameter setting. In order to test its suitability, we apply this methodology to a five-station line. The results show that the performance of the CCM for the predicted values of parameters is very close to the optimal solution, obtaining savings of 82% in the number of simulations, as compared to the results obtained by an exhaustive search of all possible parameter combinations.

Deformability properties of timolol-loaded transfersomes based on the extrusion mechanism. Statistical optimization of the process

Abstract The purpose of this work was to analyze the deformability properties of different timolol maleate (TM)-loaded transfersomes by extrusion. This was performed because elastic liposomes may contribute to the elevation of amount and rate of drug permeation through the corneal membrane. This paper describes the optimization of a transfersome formulation by use of Taguchi orthogonal experimental design and two different statistical analysis approaches were utilized. The amount of cholesterol (F1), the amount of edge-activator (F2), the distribution of the drug into the vesicle (F3), the addition of stearylamine (F4) and the type of edge-activator (F5) were selected as causal factors. The deformability index, the phosphorous recovery, the vesicle size, the polydispersity index, the zeta potential and percentage of drug entrapped were fixed as the dependent variables and these responses were evaluated for each formulation. Two different statistical analysis approaches were applied. The better statistical approach was determined by comparing their prediction errors, where regression analysis provided better optimized responses than marginal means. From the study, an optimized formulation of TM-loaded transfersomes was prepared and obtained for the proposed ophthalmic delivery for the treatment of open angle glaucoma. It was found that the lipid to surfactant ratio and type of surfactant are the main key factors for determining the flexibility of the bilayer of transfersomes. From in vitro permeation studies, we can conclude that TM-loaded transfersomes may enhance the corneal transmittance and improve the bioavailability of conventional TM delivery.

Robust optimization of alginate-Carbopol 940 bead formulations.

Formulation process is a very complex activity which sometimes implicates taking decisions about parameters or variables to obtain the best results in a high variability or uncertainty context. Therefore, robust optimization tools can be very useful for obtaining high quality formulations. This paper proposes the optimization of different responses through the robust Taguchi method. Each response was evaluated like a noise variable, allowing the application of Taguchi techniques to obtain a response under the point of view of the signal to noise ratio. A L 18 Taguchi orthogonal array design was employed to investigate the effect of eight independent variables involved in the formulation of alginate-Carbopol beads. Responses evaluated were related to drug release profile from beads (t 50% and AUC), swelling performance, encapsulation efficiency, shape and size parameters. Confirmation tests to verify the prediction model were carried out and the obtained results were very similar to those predicted in every profile. Results reveal that the robust optimization is a very useful approach that allows greater precision and accuracy to the desired value.

A Decision-Making Tool for a Regional Network of Clinical Laboratories

For healthcare systems that operate in large, geographically dispersed areas, the quality of the services provided requires the effective management of a complex transportation problem. We present a decision support system to help healthcare managers improve the delivery of biological samples collected from patients in hospitals and outpatient clinics to laboratories that perform tests on them. We develop an optimization model for supporting strategic decisions on the transport of samples and the assignment of work in a large healthcare network with geographically dispersed hospitals, clinics, and testing laboratories. We embed our model in a Web-based tool to provide planners with interactive functions, enabling them to explore solutions and interactively access data to facilitate the analysis of what-if scenarios. The tool proved invaluable in helping the Andalusian Healthcare System obtain significant improvements in efficiency, quality of service, and outsourcing costs.

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

Abstract This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10 000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.

A methodology for the design and operation of pull‐based supply chains

Purpose – The aim of this article is to propose a methodology for the design and operation of a just‐in‐time coordination mechanism to control the flow of materials in the supply chain.Design/methodology/approach – A literature review of the problem is presented and a new supply chain coordination mechanism is then proposed.Findings – The paper reveals that the one aspect not deeply studied yet in the supply chain management is their short‐term control. A simple kanban coordination mechanism demonstrates that is practical in the flow coordination of materials among the different nodes (companies) in a supply chain.Practical implications – This paper shows recent approaches in the supply chain context, useful to companies looking forward new opportunities in coordination systems for the supply chain.Originality/value – The paper begins with a review of previous research in the material flow control in the supply chain. The authors then propose a new mechanism and an optimization methodology to deal with th...

Improved virtual build to order

The evolution of the markets in recent years caused the evolution of production systems to adapt to a new kind of demand. This demand is characterized by requiring products more varied, available within a short time and at a reasonable price. We observed an evolution of productive systems from mass production to mass customization strategies. The adaptation of order fulfilment and production strategies to this new paradigm of mass customization offers new opportunities for companies improving their competitive advantages. This work is devoted to perform a simulation study of a new order fulfilment procedure (i-VBTO) to be used in mass customized environments. The results are promising, although additional studies are needed to bring this system into practice.