Order picking is a labor-intensive operation and contributes significantly to overall operational costs in a warehouse. This research develops a simulation model to evaluate the performance of order picking with varying numbers of autonomous mobile robots (AMRs) and pickers, considering both traditional and cross-aisled warehouse layouts.
In this paper, researchers introduce two case studies that show how simulation and optimization help in overcoming many of the challenges associated with data science techniques. The methodology is easily extended to a wide range of industries and use cases and enables an organization to improve its decision-making and generate business value.
Simulation is one of the most used approaches to analyze semiconductor manufacturing systems. However, most simulation models are designed for single-use applications and study a limited set of problems that are not reusable afterwards. This paper proposes a generic, data-driven simulation model to evaluate and analyze a wide range of problems arising in modern semiconductor manufacturing systems.
Based on an authentic model of system dynamics, the authors propose a new approach to simulation modeling of the dissemination of electric vehicles in a given region. The proposed system dynamics model made it possible to identify measures required to stimulate EV demand in a region with a low level of electric transport development.
Simulation models can be used to project the long-term outcomes associated with implementing a public health intervention or program at the population level. The main goal of simulation modeling in this paper was to estimate the long-term colorectal cancer outcomes associated with a first screening through the Colorectal Cancer Control Program among low-income and underserved patients in the U.S.
This paper introduces a simulation study of interventions to ensure a stable supply of a generic medicine in Norway. A hybrid simulation modeling framework is proposed to evaluate the effect of alternative supply chain shortage interventions in response to various disruptions to support national decision making with respect to preparedness planning and emergency response.
Urban logistics is becoming increasingly important due to the global rise of e-commerce with home deliveries of small but frequent orders from consumers. The introduction of self-collection delivery systems is an innovation for last mile delivery operations in urban areas and brings new benefits. This paper introduces the application of hybrid modeling approach for the self-collection delivery costs optimization and estimation of future demand based on several socio-economic parameters.
Material requirements planning (MRP) is one of the main production planning approaches implemented in enterprise resource planning systems, and one that is broadly applied in practice. In this paper, a multi-stage and multi-item production system is simulated by considering random customers’ demands and other sources of uncertainty.
In this paper, researchers used a discrete event simulation to evaluate the performance of outlier correction methods and extended the forecast generation process to increase forecast accuracy.
Physical distancing is recommended as the most efficient strategy for defending individuals from long-term global pandemics. It lowers the risk of community spreading, especially for indoor spaces. This paper aims to model physical-distancing behaviors in an educational facility, using agent-based simulation, and evaluate the impact of measures (e.g., controlling classroom capacity, breaktime scheduling) on physical distance violation risks.