Employment Of Temporary Workers And Use Of Overtime To Achieve Volume Flexibility Using Master Production Scheduling: Monetary And Social Implications

Flexibility and in particular volume flexibility is an important topic for industrial manufacturing companies. In this context, the harmonization of the available and required capacity is a central task, especially with increasing fluctuations in customer demand. In classical approaches, this is considered only by the use of additional capacities and there are only a few approaches that combine aspects of personnel planning with production planning. Therefore, this article presents a linear optimization model for master production scheduling that includes aspects of personnel requirements planning. It is used to investigate different strategies for the use of overtime and temporary workers in order to achieve different levels of volume flexibility. With regard to the monetary and social impacts, the results indicate that overtime has a stronger influence to achieve volume flexibility than the use of temporary workers. However, both are affected by substantial deficits in human working conditions. But the results also imply a promising potential for improving the social aspects without a significant increase in costs.

Capacity Loss Estimation For Li-Ion Batteries Based On A Semi-Empirical Model

Understanding battery capacity degradation is instrumental for designing modern electric vehicles. In this paper, a Semi-Empirical Model for predicting the Capacity Loss of Lithium-ion batteries during Cycling and Calendar Aging is developed. In order to redict the Capacity Loss with a high accuracy, battery operation data from different test conditions and different Lithium-ion batteries chemistries were obtained from literature for parameter optimization (fitting). The obtained models were then compared to experimental data for validation. Our results show that the average error between the estimated Capacity Loss and measured Capacity Loss is less than 1.5% during Cycling Aging, and less than 2% during Calendar Aging. An electric mining dumper, with simulated duty cycle data, is considered as an application example.

Modeling And Analyzing Cloud Auto-Scaling Mechanism Using Stochastic Well-Formed Coloured Nets

Auto-scaling is one of the most important features in Cloud computing. This feature promises cloud computing customers the ability to best adapt the capacity of their systems to the load they are facing while maintaining the Quality of Service (QoS). This adaptation will be done automatically by increasing or decreasing the amount of resources being leveraged against the workload’s resource demands. There are two types and several techniques of auto-scaling proposed in the literature. However, regardless the type or technique of auto-scaling used, over-provisioning or under-provisioning problem is often observed. In this paper, we model the auto-scaling mechanism with the Stochastic Well-formed coloured Nets (SWN). The simulation of the SWN model allows us to find the state of the system (the number of requests to be dispatched, the idle times of the started resources) from which the auto-scaling mechanism must be operated in order to minimize the amount of used resources without violating the service-level agreements (SLA).

Discrete Event Simulation Of The COVID-19 Sample Collection Point Operation

The year 2020 was very challenging for everyone due to the COVID-19 pandemic. Many people turn their lives upside down from day to day. Politicians had to impose completely unprecedented measures, and doctors immediately had to adapt to the huge influx of patients and the massive demand for testing. Of course, not all processes could be planned completely efficiently, given that the situation literally changes from minute to minute, but sometimes better planning could improve the real processes. This contribution deals with the application of simulation software SIMUL8 to the analysis of the COVID-19 sample collection process in a drive-in point in a hospital. The main aim is to create a model based on the real data and then to find out the suitable number of other staff (medics) helping a doctor during the process to decrease the number of unattended patients and their waiting times.

Using Semantic Technology To Model Persona For Adaptable Agents

In state of the art research a growing interest in the application of agent models for the simulation of road traffic can be observed. Software agents are particularly suitable for the representation of travellers and their goal-oriented behaviour. Although numerous applications based on these types of models are already available, the options for modelling and calibration of the agents as goal-oriented individuals are either simplified to aggregated parameters or associated with overly complex and opaque implementation details. This makes it difficult to reuse available simulation models. In this paper, we demonstrate how the combination of persona models together with semantic methods can be applied to achieve a well-structured agent model that allows for improved reusability.

Investigating The Load-Bearing Capacity Of Additively Manufactured Lattice Structures

Additive manufacturing provides unprecedented design freedom from the product’s external appearance to the internal structure. Additively manufactured parts, objects can be designed with cellular lattice structures as infills. The application of lattice structures can reduce the required amount of material and desired properties can be assigned to certain objects. There are several different lattice structures each with its own unique, exclusive property or properties. In this study a wide spectrum of so called ‘auxetic’ and standard lattice structures will be compared using finite element method and compression laboratory tests. The considered auxetic and non-auxetic cellular structures are based on the result of other researches. Along with the aforementioned existing lattices several new structures were proposed. Nine distinct additively manufactured specimens were compared.

Make-To-Order Production Planning With Seasonal Supply In Canned Pineapple Industry

This research studies a make-to-order production planning in a canned pineapple industry. Pineapple is a seasonal perishable fruit. Thus, the cost of fresh pineapple which is the main raw material in canned pineapple is inexpensive during its season. The color of the pineapple also determines the price of the canned pineapple. However, the availability of different colors (called “choice” and “standard”) is dependent. Specifically, if the ratio of the choice color is more, the ratio of the other color is less. There are several costs involve such as fresh pineapple cost, can cost, sugar cost, water cost, labor cost, energy cost, and inventory cost. The problem is formulated as a mathematical model to maximize the total profit over four-months planning horizon. Two supply uncertainty cases are tested which are low and high ratios of the choice color. The results show that the profit depends on available color ratios of the pineapple. The production planning is best if it matches with the availability of the color ratios. In certain months, some fresh pineapple purchased exceed the need of the production because of the dependency of the two colors. The inventory holding cost also influences the production decision—whether to produce the canned pineapple in earlier months or it is better to produce only the canned pineapple when it is needed to serve the customer orders.

Robust Simulation Of Imaging Mass Spectrometry Data

Mass spectrometry imaging (MSI) with high resolution in mass and space is an analytical method that produces distributions of ions on a sample surface. The algorithms for preprocessing and analysis of the raw data acquired from a mass spectrometer should be evaluated. To do that, the ion composition at every point of the sample should be known. This is possible via the employment of a simulated MSI dataset. In this work, we suggest a pipeline for a robust simulation of MSI datasets that resemble real data with an option to simulate the spectra acquired from any mass spectrometry instrument through the use of the experimental MSI datasets to extract simulation parameters.

Change Detection For Area Surveillance Using A Moving Camera

This paper tackles change detection for area surveillance with a moving device. None of the existing datasets for change detection meets a surveillance scenario where a camera is mounted on a moving platform and pointed in the direction of moving. Thus, this paper creates a new dataset including several challenging points. For this dataset, this paper employs a composable method and proposes some components. To evaluate the proposed components, some corresponding classic methods were also tested on the dataset. As a result, the proposals outperformed them. Moreover, this paper investigated the relationship between the parameters of the components and their performance.

Pedestrian Simulation In SUMO Through Externally Modelled Agents

Pedestrian simulation is often forgotten or implemented poorly in most high profile traffic simulators. This is the case of SUMO, where the pedestrian models are very simple and not based in real human behaviour, making it impossible to study pedestrian safety with it. With this in mind, the ability to externally control pedestrians in SUMO was explored. Using Unity3D to create an external three dimensional representation of a running SUMO simulation, we were able to create and control pedestrians through the TraCI API. This also opened the possibility to use virtual reality immersed subjects to participate in the simulation, opening the door to study real pedestrian behaviour to create more elaborate models. It also allowed us to completely offload the pedestrian simulation from SUMO to Unity3D, which was tested with the external implementation of the social forces model, without losing SUMO's interactions between pedestrians and motorized vehicles.