scholarly journals SELECTED ASPECTS OF SIMULATION MODELLING OF INTERNAL TRANSPORT PROCESSES PERFORMED AT LOGISTICS FACILITIES

2014 ◽  
Vol 30 (2) ◽  
pp. 43-56 ◽  
Author(s):  
Marek Karkula
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Simulation Modelling ◽  
Transport Processes ◽  
Transport Costs ◽  
Event Simulation ◽  
Cross Docking ◽  
The Right ◽  
Internal Transport

The transport is an important part of logistic systems. Improper management of transport operations may contribute to the low level of the usage of vehicles and to high transport costs, as well as to the formation of unnecessary high inventory at each location of storage, as well as prolonged time of order realization and not full use of company capacity. It is therefore important the appropriate dimensioning, planning of the transport system and performed transport operations so as to allow the supply of certain goods at the right time and the amount to the appropriate points of the system. The article presents the methods of transport operations modelling, taking into account different criteria based on discrete event simulation. In the article the case study of modelling transport operations in the small cross-docking centre is also presented

scholarly journals The application of a hybrid simulation modelling framework as a decision-making tool for TPM improvement

2019 ◽  
Vol 25 (3) ◽  
pp. 476-498 ◽  
Author(s):  
Omogbai Oleghe ◽  
Konstantinos Salonitis
Keyword(s):  
Discrete Event Simulation ◽  
Hybrid Model ◽  
Discrete Event ◽  
Simulation Modelling ◽  
Hybrid Modelling ◽  
Content Type ◽  
Event Simulation ◽  
Modelling Framework ◽  
Field Notes

Purpose The purpose of this paper is to promote a system dynamics-discrete event simulation (SD-DES) hybrid modelling framework, one that is useful for investigating problems comprising multifaceted elements which interact and evolve over time, such as is found in TPM. Design/methodology/approach The hybrid modelling framework commences with system observation using field notes which culminate in model conceptualization to structure the problem. Thereafter, an SD-DEShybrid model is designed for the system, and simulated to proffer improvement programmes. The hybrid model emphasises the interactions between key constructs relating to the system, feedback structures and process flow concepts that are the hallmarks of many problems in production. The modelling framework is applied to the TPM operations of a bottling plant where sub-optimal TPM performance was affecting throughput performance. Findings Simulation results for the case study show that intangible human factors such as worker motivation do not significantly affect TPM performance. What is most critical is ensuring full compliance to routine and scheduled maintenance tasks and coordinating the latter to align with rate of machine defect creation. Research limitations/implications The framework was developed with completeness, generality and reuse in view. It remains to be applied to a wide variety of TPM and non-TPM-related problems. Practical implications The developed hybrid model is scalable and can fit into an existing discrete event simulation model of a production system. The case study findings indicate where TPM managers should focus their efforts. Originality/value The investigation of TPM using SD-DES hybrid modelling is a novelty.

scholarly journals Optimizing real-world factory flows using aggregated discrete event simulation modelling

2019 ◽  
Vol 32 (4) ◽  
pp. 888-912 ◽  
Author(s):  
Simon Lidberg ◽  
Tehseen Aslam ◽  
Leif Pehrsson ◽  
Amos H. C. Ng
Keyword(s):  
Discrete Event Simulation ◽  
Real World ◽  
Lead Time ◽  
Discrete Event ◽  
Simulation Modelling ◽  
Industrial Systems ◽  
Event Simulation ◽  
Batch Sizes ◽  
Industrial Problem

AbstractReacting quickly to changing market demands and new variants by improving and adapting industrial systems is an important business advantage. Changes to systems are costly; especially when those systems are already in place. Resources invested should be targeted so that the results of the improvements are maximized. One method allowing this is the combination of discrete event simulation, aggregated models, multi-objective optimization, and data-mining shown in this article. A real-world optimization case study of an industrial problem is conducted resulting in lowering the storage levels, reducing lead time, and lowering batch sizes, showing the potential of optimizing on the factory level. Furthermore, a base for decision-support is presented, generating clusters from the optimization results. These clusters are then used as targets for a decision tree algorithm, creating rules for reaching different solutions for a decision-maker to choose from. Thereby allowing decisions to be driven by data, and not by intuition.

Applying Discrete Event Simulation (DES) in Healthcare

2015 ◽  
pp. 390-410
Author(s):  
Stavros T. Ponis ◽  
Angelos Delis ◽  
Sotiris P. Gayialis ◽  
Panagiotis Kasimatis ◽  
Joseph Tan
Keyword(s):  
Discrete Event Simulation ◽  
Capacity Planning ◽  
Planning Process ◽  
Discrete Event ◽  
Healthcare Facilities ◽  
Performance Improvements ◽  
Event Simulation ◽  
Health Insurance Provider ◽  
And Performance

This paper highlights the opportunities and challenges of applying Discrete Event Simulation (DES) to support capacity planning of a network of outpatient facilities. Despite an abundance of studies using simulation techniques to examine the operation and performance of outpatient clinics, the problem of capacity allocation and planning of medical services within a network of outpatient healthcare facilities appears to be underexplored. Here, a case study of a health insurance provider that operates a network of six outpatient medical facilities in the US is used to illustrate and explore the synthesizing and adaptive, yet parsimonious nature of using DES methodology for network design and capacity planning. Results of this case study demonstrate that significant performance improvements for the network operator can be achieved with applying DES method to support the network facility capacity planning process.

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