Saturday, May 20, 2023

Simulation & Optimization Techniques for the Mitigation of Disruptions to Supply Chains

Our last paper at the Annual Modeling and Simulation Conference (ANNSIM) is entitled "Simulation and Optimization Techniques for the Mitigation of Disruptions to Supply Chains" where we (Raj Patel, Abhisekh Rana, Sean Luke, Carlotta Domeniconi, Hamdi Kavak, Jim Jones and myself) build upon our previous work which explored how the actions of criminal networks and agents might impact supply chains. 
 
This paper extends this research to incorporate both disruption and mitigation modeling into the same simulation.  By using evolutionary computation optimization techniques (e.g., Covariance Matrix Adaptation Evolution Strategy) we demonstrate how we can optimize both the disruption and mitigation scenarios in a pharmaceutical supply chain (which we call PharmaSIM). Our results demonstrate how  evolutionary computation techniques could be used to not only identify worst-case disruption scenarios but to also optimize the allocation of the mitigations to counter their effects.  If this sounds of interest, below we provide the abstract to the paper, some of the figures we use to support our discussion and results. While at the bottom of the post we provide the full reference to the paper along with a link to a preprint of it. 

Abstract
The COVID-19 pandemic has clearly highlighted the importance of supply chains to the function of the world economy. Moreover, the global nature of most modern supply chains along with their complexity has left them vulnerable to a wide-ranging set of disruptive scenarios. This increase in complexity has also led to a corresponding increase in disruptions to supply chains from criminal networks. In this paper, we demonstrate how a generic pharmaceutical supply chain network can be successfully modeled using discrete event simulation. We outline how disruptions by criminal networks and mitigation strategies to counter them can be effectively incorporated into the same model. Finally, we show how optimization techniques, such as evolutionary computation, can be used to not only identify worst-case disruptions and find mitigations for them, but also be used to identify mitigation strategies that are effective against a diverse set of damaging disruption scenarios.

Keywords: Simulation, Optimization, Supply Chains, Disruptions, Mitigation. 


Topology of the generic pharmaceutical supply chain (PharmaSIM) model.

Fitness after evolutionary optimization of attack configurations and corresponding safety stock allocation for different budgets.

Fitness by generation for the coevolution of attack vectors and mitigation configurations.

Full Reference:

Rana, R., Patel, R., Luke, S., Domeniconi, C., Kavak, H., Jones, J. and Crooks, A.T.  (2023), Simulation And Optimization Techniques for the Mitigation of Disruptions to Supply Chains, The Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON. (pdf)

Spiral Software Development Process for ABM

Readers of this blog might gather that we are constantly developing agent-based models to study and better understand a wide range of problems but unlike in say the software industry, agent-based model development is rather ad hoc in terms of a standardized software development process. To this end  Maxim Malikov, Fahad  Aloraini, Hamdi Kavak and William Kennedy from George Mason University and myself  have a paper entitled "Developing a Large-Scale Agent-Based Model Using the Spiral Software Development Process" which we will be presenting at the upcoming Annual Modeling and Simulation Conference (ANNSIM).

In the paper, we review the unique aspects of agent-based models and discuss the challenges faced in the development of our own large-scale agent-based model, which simulates the impact of a disaster on the infrastructure and the population of a city. This project combines the expertise of teams with multiple disciplines, and therefore must be able to adjust to novel input from these teams over the life of the project.  Furthermore, we describe our solution to these challenges in the form of a variation of the Spiral model of software development and the ways this approach helped us address the exploratory nature of agent-based modeling.  

If this sounds of interest, below we provide the abstract to the paper, some of the figures we use to support our discussion. At the bottom of the post we provide the full reference to the paper along with a link to a preprint of it. 

Abstract:

As the level of complexity of agent-based models grows, so does the complexity of their development. At the time of writing, the discipline of agent-based modeling does not have an established standard for the software development process to support this increasing complexity. We hope to address this need by introducing our variation of the Spiral model of software development and demonstrating an application of this process through a simple use case. We argue that the Spiral model of software development is a flexible approach that can be tailored to fit the needs of almost any project type. Further, our agent-based modeling variation of the Spiral model is an effective approach that is capable of guiding and supporting large interdisciplinary teams participating in a project, while providing sufficient flexibility to account for the uncertainty in the requirements that may arise during the development period.

Keywords: Software development, Agent-based Modeling, Spiral Development, Disaster.

 

Spiral model with adjustments made to account for the specifics of complex agent-based models. Adopted from Boehm (2000).


Prototype 1 of the city infrastructure simulation. This graphical user interface shows agent and infrastructure changes after a disaster.

Full reference:

Malikov, M., Aloraini, F., Crooks, A.T., Kavak, H. and Kennedy, W.G. (2023), Developing a Large-Scale Agent-Based Model Using the Spiral Software Development Process, The Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON. (pdf)

Tuesday, May 16, 2023

Modeling Forced Migration

At the upcoming Annual Modeling and Simulation Conference (ANNSIM) we have several papers being presented. One of which is with Troy Curry and Arie Croitoru entitled "Modeling Forced Migration: A System Dynamic Approach." In this paper we study how forced migration can be modeled through a systems dynamics perspective. 
 
To some extent this  paper builds upon our previous work on refugees especially making use new open data sources that allow us to study forced migration. Using ideas from  systems thinking which incorporates notions  non-linearity, interconnectedness, relationships, causality and feedbacks we build a systems dynamics model of the Syrian refugee crisis from January 2012 until December 2018. The model itself  explores refugee-producing variables that have been linked as determinants of forced migration including human rights violations, political violence, generalized violence, and civil war. We use these refugee-producing variables  to simulate the flow of refugees from Syria to Greece, Turkey, Lebanon and Jordan. 
 
If this sounds of interest, below you can read the abstract of the model, see a high-level causal loop diagram for our forced migration model along with our validation attempts  such as comparing predicted system dynamics model refugee counts vs. reference United Nations High Commissioner for Refugees (UNHCR) refugee counts. We also have included a movie of one such model scenario however,  readers can also run the model here. Finally at the bottom of the page you can find the full reference to paper along with a link to a pre-print.

Abstract: 

Forced migration of populations is a topic of increasingly national and international importance due to security, international relations, and humanitarian considerations. Despite its importance, there has been a dearth of quantitative research to support modeling and simulation of this topic, thus hindering our ability to better understand this phenomenon. Motivated by this gap, this research leverages the recent availability of diverse set of data related to forced migration, including regime legitimacy, violence, human rights violations, conflict, socio-political mobilization, intervening opportunities, and social media. The purpose of this article is to explore the applicability and utility of open-source data in a system dynamics model to forecast population displacement, and to illustrate the benefits of using a system dynamics approach to modeling displaced population on a national and international scale. Our results suggest that this proposed approach can be used to understand such migration processes and simulate possible scenarios.

Keywords: forced migration, refugee, system dynamics, prediction model, Middle East.


High-level causal loop diagram for forced migration.

Migration routes in simulation (i.e., Greece, Turkey, Lebanon, Jordan).

Simulation refugee counts for paths to different countries (i.e., Greece, Turkey, Lebanon, Jordan).

Model validation - comparing predicted system dynamics model refugee counts vs. reference UNHCR refugee counts.



Full reference:

Curry, T., Croitoru, A. and Crooks, A.T. (2023), Modeling Forced Migration: A System Dynamic Approach, The Annual Modeling and Simulation Conference (ANNSIM), Hamilton, ON. (pdf)