Taxonomy of batch queueing models in manufacturing systems

Batching plays an important role in performance evaluation of manufacturing systems. Three types of batching are commonly seen: transfer batches, parallel batches and serial batches. To model the batching behavior correctly, a comprehensive classification of batching is proposed. Eight types of batching behavior are classified and corresponding queueing models are given. The newly proposed models are validated by simulation.     

Aggregation of Malmquist productivity indexes allowing for reallocation of resources

In this paper we consider aggregate Malmquist productivity index measures which allow inputs to be reallocated within the group (when in output orientation). This merges the single period aggregation results allowing input reallocation of Nesterenko and Zelenyuk (2007) with the aggregate Malmquist productivity index results of Zelenyuk (2006) to determine aggregate Malmquist productivity indexes that are justified by economic theory, consistent with previous aggregation results, and which maintain analogous decompositions to the original measures. Such measures are of direct relevance to firms or countries who have merged (making input reallocation possible), allowing them to measure potential productivity gains and how these have been realised (or not) over time.     

The dynamics of price, quality and productivity improvement decisions

Although quality has received significant attention during the last decades and its economic benefits are beyond any doubt, lots of questions have remained unanswered as to how much, when, and in what to invest to maintain sustainable competitive advantage. A model is introduced here to guide a firm in addressing these questions. The firm produces a single product and operates in a market where monopolistic competition is effective. Demand for the product in the industry depends on both price and performance quality. Increasing productivity knowledge decreases unit production cost, but demand for the company’s product decreases over time, as competitors will be able to offer products with similar performance. Productivity and quality knowledge can be developed through induced and autonomous learning in order to strengthen company position. The paper provides an optimal control formulation of the problem and develops necessary conditions for optimality and characterizes the dynamics of optimal price, quality and investment decisions.     

Parameter study of electrospinning of polyamide-6

Importance of different solution and process parameters were evaluated in multi-nozzle electrospinning of polyamide-6. We were interested in how those parameters affect firstly on fibre diameters and fibre diameter distribution and secondly on production rate and disturbances of the process. The trial series was planned using orthogonal experimental design. Altogether eight parameters having three or two levels each were chosen for this study. The chosen solution parameters were polymer grade, viscosity of solution, salt content and solvent grade and process parameters, voltage, distance, nozzle size and feeding pressure of solution. This study was performed using continuous electrospinning equipment consisting of multiple nozzles and horizontal electric field. Literature findings about the influences of certain variables are often contradictory. These contradictions and their possible causes were discussed.     

Quantifying temperature and flow rate effects on the performance of a fixed-bed chromatographic reactor

Chromatographic reactors are based on coupling chemical reactions with chromatographic separation in fixed-beds. Temperature and flow rate are important parameters for the performance of such reactors. Temperature affects mainly adsorption, chemical equilibria, mass transfer and reaction kinetics, whereas flow rate influences residence time and dispersion. In order to evaluate the mentioned effects, the hydrolysis reactions of methyl formate (MF) and methyl acetate (MA) were chosen as case studies. These reactions were performed experimentally in a lab-scale fixed-bed chromatographic reactor packed with a strong acidic ion exchange resin. The chosen reactions can be considered to represent a relative fast (MF) and a relative slow (MA) reaction. The processes which take place inside the reactor were described and simulated using an isothermal equilibrium dispersive model. The essential model parameters were determined experimentally at different temperatures and flow rates. The performance of the chromatographic reactor was evaluated at several discrete constant temperature levels by quantifying product purity, productivity and yield. The work provides insight regarding the influence of temperature and flow rate on values of the model parameters and the performance criteria.     

Multi-period models for analyzing the dynamics of process improvement activities

Managing knowledge based resource capabilities has become very important in recent years and during a finite horizon it seems to be reasonable to develop the capabilities intensively at the beginning as one can utilize those over a longer period of time. With the help of multi-period models we check the validity of this idea and characterize the dynamics of development activities. The paper identifies the factors that shape these dynamics and from the behavior of these factors we conclude when the dynamics can be increasing or decreasing. We point out that in stable environment there is tendency for decreasing dynamics but future expectations can significantly modify this outcome. Relationships between the successful or less successful implementation of a business strategy and the dynamics of improvement activities are highlighted as well. For specific model structures explicit solutions are derived.     

Conditions for positive definiteness of M-matrices

The present paper concentrates on conditions that are necessary and sufficient for M-matrices to be positive definite. The obtained results can be used in the analysis of productivity of the Leontief input-output model.     

Two-dimensional efficiency decomposition to measure the demand effect in productivity analysis

This paper proposes a two-dimensional efficiency decomposition (2DED) of profitability for a production system to account for the demand effect observed in productivity analysis. The first dimension identifies four components of efficiency: capacity design, demand generation, operations, and demand consumption, using Network Data Envelopment Analysis (Network DEA). The second dimension decomposes the efficiency measures and integrates them into a profitability efficiency framework. Thus, each component’s profitability change can be analyzed based on technical efficiency change, scale efficiency change and allocative efficiency change. An empirical study based on data from 2006 to 2008 for the US airline industry finds that the regress of productivity is mainly caused by a demand fluctuation in 2007-2008 rather than technical regression in production capabilities.     

Asymmetric information and macroeconomic dynamics

We show how macroeconomic dynamics can be derived from asymmetric information. As an illustration of the utility of this approach we derive the equilibrium density, non-equilibrium densities and the equation of motion for the response to a demand shock for productivity in a simple economy. Novel consequences of this approach include a natural incorporation of time dependence into macroeconomics and a common information-theoretic basis for economics and other fields seeking to link micro-dynamics and macro-observables.     

Impact of productivity on cross-training configurations and optimal staffing decisions in hospitals

Cross-training of nursing staff has been used in hospitals to reduce labor cost, provide scheduling flexibility, and meet patient demand effectively. However, cross-trained nurses may not be as productive as regular nurses in carrying out their tasks because of a new work environment and unfamiliar protocols in the new unit. This leads to the research question: What is the impact of productivity on optimal staffing decisions (both regular and cross-trained) in a two-unit and multi-unit system. We investigate the effect of mean demand, cross-training cost, contract nurse cost, and productivity, on a two-unit, full-flexibility configuration and a three-unit, partial flexibility and chaining (minimal complete chain) configurations under centralized and decentralized decision making. Under centralized decision making, the optimal staffing and cross-training levels are determined simultaneously, while under decentralized decision making, the optimal staffing levels are determined without any knowledge of future cross-training programs. We use two-stage stochastic programming to derive closed form equations and determine the optimal number of cross-trained nurses for two units facing stochastic demand following general, continuous distributions. We find that there exists a productivity level (threshold) beyond which the optimal number of cross-trained nurses declines, as fewer cross-trained nurses are sufficient to obtain the benefit of staffing flexibility. When we account for productivity variations, chaining configuration provides on average 1.20% cost savings over partial flexibility configuration, while centralized decision making averages 1.13% cost savings over decentralized decision making.