Data-Driven Robust Resource Allocation with Monotonic Cost Functions

2021 ◽  
Author(s):  
Ye Chen ◽  
Nikola Marković ◽  
Ilya O. Ryzhov ◽  
Paul Schonfeld
Keyword(s):  
Resource Allocation ◽  
Demand Uncertainty ◽  
Discrete Event ◽  
Data Driven ◽  
Cost Functions ◽  
Logistics Systems ◽  
Event Simulation ◽  
Uncertainty Model ◽  
Using Data ◽  
Robust Resource Allocation

Using Data to Allocate Resources Efficiently In city logistics systems, a fleet of vehicles is divided between service regions that function autonomously. Each region finds optimal routes for its own fleet and incurs costs accordingly. More vehicles lead to lower costs, but the trade-off is that fewer vehicles are left for other regions. Costs are difficult to quantify precisely because of demand uncertainty but can be estimated using data. The paper “Data-driven robust resource allocation with monotonic cost functions” by Chen, Marković, Ryzhov, and Schonfeld develops a principled risk-averse approach for two-stage resource allocation. The authors propose a new uncertainty model for decreasing cost functions and show how it can be leveraged to efficiently find resource allocations that demonstrably reduce the frequency of high-cost scenarios. This framework combines statistics and optimization in a novel way and is applicable to a general class of resource allocation problems, encompassing facility location, vehicle routing, and discrete-event simulation.

scholarly journals Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

2021 ◽  
Author(s):  
G.J. Melman ◽  
A.K. Parlikad ◽  
E.A.B. Cameron
Keyword(s):  
Resource Allocation ◽  
Discrete Event Simulation ◽  
Elective Surgery ◽  
Discrete Event ◽  
Specific Patient ◽  
Event Simulation ◽  
The Impact ◽  
Allocation Decisions ◽  
Allocation Strategies ◽  
Resource Allocation Decisions

AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

scholarly journals Simulation Project for Logistics of Brazilian Soybean Exportation

2020 ◽  
Vol 19 (4) ◽  
pp. 571-582
Author(s):  
H. S. Lopes ◽  
R. S. Lima ◽  
F. Leal
Keyword(s):  
Integrated Management ◽  
Discrete Event ◽  
International Market ◽  
Simulation Method ◽  
Strategic Analysis ◽  
Logistics System ◽  
Logistics Systems ◽  
Event Simulation ◽  
Global Logistics ◽  
Systems Simulation

Decision-making in complex logistics systems involves high risks and associated impacts. A way to forecast the impacts of these decisions is through the use of systems simulation projects, where the systematic impacts of the parameters can be visualized. This study presents a project based on Discrete-Event Simulation (DES) that analyses Brazilian soybean export logistics from producing regions to main international customers. The strategic analysis of a global logistics system using DES is a particularity of this study. At the conception stage, the conceptual modelling was made using IDEF-SIM (Integrated Definition Methods – Simulation) method, which allowed a better abstraction of reality and more accurate model implementation. The experimental analysis took place through the construction of 39 scenarios with specific characteristics that verified system behaviours through proposed changes. The analyses and decisions are based on costs. The simulations indicated the necessity for: a) an integrated management between the systems agents; b) the development of internal transportation infrastructure, especially railways and waterways, to increase competitiveness of Brazilian soybeans in the international market.

Real-time data-driven discrete-event simulation for garment production lines

2020 ◽  
pp. 1-12 ◽  
Author(s):  
Woo-Kyun Jung ◽  
Hyungjung Kim ◽  
Young-Chul Park ◽  
Jae-Won Lee ◽  
Eun Suk Suh
Keyword(s):  
Real Time ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Data Driven ◽  
Time Data ◽  
Production Lines ◽  
Event Simulation ◽  
Real Time Data

scholarly journals How an election can be safely planned and conducted during a pandemic: Decision support based on a discrete event model

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261016
Author(s):  
Nadine Weibrecht ◽  
Matthias Rößler ◽  
Martin Bicher ◽  
Štefan Emrich ◽  
Günther Zauner ◽  
...  
Keyword(s):  
Simulation Model ◽  
Social Life ◽  
Discrete Event ◽  
The Novel ◽  
Event Simulation ◽  
District Council ◽  
Event Model ◽  
Using Data ◽  
The One ◽  
Democratic Elections

In 2020, the ongoing COVID-19 pandemic caused major limitations for any aspect of social life and in specific for all events that require a gathering of people. While most events of this kind can be postponed or cancelled, democratic elections are key elements of any democratic regime and should be upheld if at all possible. Consequently, proper planning is required to establish the highest possible level of safety to both voters and scrutineers. In this paper, we present the novel and innovative way how the municipal council and district council elections in Vienna were planned and conducted using an discrete event simulation model. Key target of this process was to avoid queues in front of polling stations to reduce the risk of related infection clusters. In cooperation with a hygiene expert, we defined necessary precautions that should be met during the election in order to avoid the spread of COVID-19. In a next step, a simulation model was established and parametrized and validated using data from previous elections. Furthermore, the planned conditions were simulated to see whether excessive queues in front of any polling stations could form, as these could on the one hand act as an infection herd, and on the other hand, turn voters away. Our simulation identified some polling stations where long queues could emerge. However, splitting up these electoral branches resulted in a smooth election across all of Vienna. Looking back, the election did not lead to a significant increase of COVID-19 incidences. Therefore, it can be concluded that careful planning led to a safe election, despite the pandemic.

scholarly journals Simulation to Enable a Data-Driven Circular Economy

2019 ◽  
Vol 11 (12) ◽  
pp. 3379 ◽  
Author(s):  
Fiona Charnley ◽  
Divya Tiwari ◽  
Windo Hutabarat ◽  
Mariale Moreno ◽  
Okechukwu Okorie ◽  
...  
Keyword(s):  
Circular Economy ◽  
Industrial Revolution ◽  
Discrete Event ◽  
Life Extension ◽  
Shop Floor ◽  
Event Simulation ◽  
Reduced Costs ◽  
Using Data ◽  
The Internet Of Things ◽  
Time Of Intervention

This paper presents an investigation on how simulation informed by the latest advances in digital technologies such as the 4th Industrial Revolution (I4.0) and the Internet of Things (IoT) can provide digital intelligence to accelerate the implementation of more circular approaches in UK manufacturing. Through this research, a remanufacturing process was mapped and simulated using discrete event simulation (DES) to depict the decision-making process at the shop-floor level of a remanufacturing facility. To understand the challenge of using data in remanufacturing, a series of interviews were conducted finding that there was a significant variability in the condition of the returned product. To address this gap, the concept of certainty of product quality (CPQ) was developed and tested through a system dynamics (SD) and DES model to better understand the effects of CPQ on products awaiting remanufacture, including inspection, cleaning and disassembly times. The wider application of CPQ could be used to forecast remanufacturing and production processes, resulting in reduced costs by using an automatised process for inspection, thus allowing more detailed distinction between “go” or “no go” for remanufacture. Within the context of a circular economy, CPQ could be replicated to assess interventions in the product lifecycle, and therefore the identification of the optimal CE strategy and the time of intervention for the current life of a product—that is, when to upgrade, refurbish, remanufacture or recycle. The novelty of this research lies in investigating the application of simulation through the lens of a restorative circular economic model focusing on product life extension and its suitability at a particular point in a product’s life cycle.

Validation of the finite state machine model for network simulation and a proposed testbed

2017 ◽  
Vol 15 (1) ◽  
pp. 65-77
Author(s):  
Scott Brown ◽  
Harold Brown
Keyword(s):  
Finite State Machine ◽  
Discrete Event ◽  
State Machine ◽  
Parallel Discrete Event Simulation ◽  
Machine Model ◽  
Event Simulation ◽  
Finite State ◽  
The Creation ◽  
Parallel Discrete Event ◽  
Using Data

Under the Cyber Army Modeling and Simulation (CyAMS) program a new model has been created to efficiently model various cyber events. The finite state machine model allows for the modeling of applications by creating behaviors that map to properties of real world applications. The finite state machine model is originally implemented by utilizing the ns-3 parallel discrete event simulator and validated using data from an emulation testbed experiment featuring malware applications. Following the completion of the ns-3 validation work, the CyAMS behavioral simulator was developed to allow for larger scale networks to be modeled, while also allowing both the network and applications to be defined using a behavioral network. This allows for the creation of an accurate mapping between both the simulation and the emulation applications. Validation tests are then carried out to determine both the validity of the model and the potential scalability. Finally, a testbed is proposed that will combine behavioral simulation, traditional parallel discrete event simulation, and emulation. The creation of this end-to-end environment for cyber simulation will allow past, present, and potentially future cyber events to be modeled in order to help understand and potentially mitigate future malicious attacks.

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