Modelling and analysis of dynamic capacity complexity in multi-stage production

The uncertainty associated with managing dynamic capacity problem is the main source of its complexity. This article presents a system dynamics approach to model and analyse operational complexity of dynamic capacity in multi-stage production. The unique feature of this approach is that it captures the stochastic nature of three main sources of complexity associated with dynamic capacity. These are the demand, internal manufacturing delay and capacity scalability delay. The developed model was demonstrated by an industrial case study of multi-stage printed circuit board assembly line. The analysis of simulation experiments showed that ignoring complexity sources can lead to wrong decisions concerning both scaling levels and backlog management decisions. In addition, a general trade-off between the controllability and complexity of the dynamic capacity was illustrated. Finally, comparative analysis of the effect of each of these sources on the complexity level revealed that internal delay has the highest impact on dynamic capacity efficiency. Guidelines and recommendations for better capacity management and reduction of its complexity are presented

A multiple performance analysis of market-capacity integration policies

A model that uses simulation augmented with Design of Experiments (DOE) is presented to analyse the performance of a Make-to-Order (MTO) reconfigurable manufacturing system with scalable capacity. Unlike the classical capacity scaling policies, the proposed hybrid capacity scaling policy is determined using multiple performance measures that reflect cost, internal stability and responsiveness. The impact of both tactical capacity and marketing policies and their interaction on the overall performance was analysed using DOE techniques and real case data. In addition to the different insights about the trade-offs involved in capacity planning decisions, the presented results challenged the conventional capacity planning wisdoms in MTO about the negative role of the capacity scalability delay time. Finally the analysis demonstrated the importance of inter-functional integration between capacity and marketing policies

Variety and volume dynamic management for value creation in changeable manufacturing systems

In today’s uncertain market and continuously evolving technology, managing manufacturing systems are more complex than ever. This paper studies the dynamics of managing variety and volume to enhance value creation in manufacturers implementing system-level advanced and automated manufacturing technology (AAMT). The demand is composed of heterogeneous customers who make purchasing decisions depending on the variety levels and lead times of the firm’s product offerings. The cost structure adopted calculates profit as the difference between customer value creation rate (VCR) and costs associated with the process of creating this value. Reported results contribute to the variety and volume management literature by offering analytical clarity of factors affecting product platforms and capacity scalability management for systems with AAMT. In addition, insightful answers to the trade-offs between profit maximising market coverage and investments, smoothing demand policies and system stability for this type of environment are presented. Furthermore, the value of market information in deciding the industrial technology investment and also the impact of product life cycle on the same investment is captured

Assessing capacity scalability policies in RMS using system dynamics

This paper presents a model for assessing different capacity scalability policies in Reconfigurable Manufacturing System (RMS) for different changing demand scenarios. The novelty of this approach is two fold: (1) it is the first attempt to explore different capacity scalability policies in RMS based on multiple performance measures, mainly scaling rate, Work In Process level, inventory level and backlog level; and (2) the dynamic scalability process in RMS is modeled for the first time using System Dynamics. Different policies for capacity scalability for various demand scenarios were assessed. Numerical simulation results obtained using the developed capacity scalability model showed that the best capacity scalability policy to be adopted for RMS is dependent on the anticipated demand pattern as well as the various manufacturing objectives. The presented assessment results will help the capacity scalability planners better decide the different tradeoffs between the competing strategic and operational objectives of the manufacturing enterprise, before setting the suitable capacity scalability plan parameters

Effect of Time-Based Parameters on the Agility of a Dynamic MPC System

This paper presents a dynamic manufacturing planning and control (MPC) system that can maintain agility through the ability to dynamically switch between different policies due to varying market strategies. The dynamic behavior of the developed system is investigated by studying the effect of the time based parameters on responsiveness and cost effectiveness of the system reflected in the natural frequency and the damping of its different configurations. Results showed that the agility requirements are directly affected by the time based parameters of the MPC system: production lead time, capacity scalability delay, and shipment time. This resulted in a better understanding of the requirements for a well designed agile MPC system

Seismic hazard studies in Egypt

Abstract The study of earthquake activity and seismic hazard assessment of Egypt is very important due to the great and rapid spreading of large investments in national projects, especially the nuclear power plant that will be held in the northern part of Egypt. Although Egypt is characterized by low seismicity, it has experienced occurring of damaging earthquake effect through its history. The seismotectonic sitting of Egypt suggests that large earthquakes are possible particularly along the Gulf of Aqaba–Dead Sea transform, the Subduction zone along the Hellenic and Cyprean Arcs, and the Northern Red Sea triple junction point. In addition some inland significant sources at Aswan, Dahshour, and Cairo-Suez District should be considered. The seismic hazard for Egypt is calculated utilizing a probabilistic approach (for a grid of 0.5° × 0.5°) within a logic-tree framework. Alternative seismogenic models and ground motion scaling relationships are selected to account for the epistemic uncertainty. Seismic hazard values on rock were calculated to create contour maps for four ground motion spectral periods and for different return periods. In addition, the uniform hazard spectra for rock sites for different 25 periods, and the probabilistic hazard curves for Cairo, and Alexandria cities are graphed. The peak ground acceleration (PGA) values were found close to the Gulf of Aqaba and it was about 220 gal for 475 year return period. While the lowest (PGA) values were detected in the western part of the western desert and it is less than 25 gal

The spectrum of the random environment and localization of noise

We consider random walk on a mildly random environment on finite transitive d- regular graphs of increasing girth. After scaling and centering, the analytic spectrum of the transition matrix converges in distribution to a Gaussian noise. An interesting phenomenon occurs at d = 2: as the limit graph changes from a regular tree to the integers, the noise becomes localized.Comment: 18 pages, 1 figur

Prediction of wear rates of UHMWPE bearing in hip joint prosthesis with support vector model and grey wolf optimization

One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness (Ra) values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments

Deterministic seismic hazard assessment for Sultanate of Oman

The Sultanate of Oman forms the southeastern part of the Arabian plate, which is surrounded by relatively high active tectonic zones. Studies of seismic risk assessment in Oman have been an important on-going socioeconomic concern. Using the results of the seismic hazard assessment to improve building design and construction is an effective way to reduce the seismic risk. In the current study, seismic hazard assessment for the Sultanate of Oman is performed through the deterministic approach with particular attention on the uncertainty analysis applying a recently developed method. The input data set contains a defined seismotectonic model consisting of 26 seismic zones, maximum magnitudes, and 6 alternative ground motion prediction equations that were used in four different tectonic environments: obduction zone earthquake (Zagros fold thrust belt), subduction zone earthquakes (Makran subduction zones), normal and strike-slip transform earthquakes (Owen and Gulf of Aden zones), and stable craton seismicity (Arabian stable craton). This input data set yielded a total of 76 scenarios at each point of interest. A 10 % probability that any of the 76 scenarios may exceed the largest median ground acceleration is selected. The deterministic seismic hazards in terms of PGA, 5 % damped spectral acceleration at 0.1, 0.2, 1.0 and 2.0 s are performed at 254 selected points. The ground motion was calculated at the 50th and 84th percentile levels for selected probability of exceeding the median value. The largest ground motion in the Sultanate of Oman is observed in the northeastern part of the country.Oman Ministerial Cabinet (Project 22409017