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Optimization of Utility Companies’ Mutual Assistance Using Agent-Based Modeling


When people are impacted by a natural or man-made disaster, utility companies look for ways to provide resources as soon as possible, and reduce outage time. To assist and better coordinate with each other, Canadian companies from closely located territories created alignments. So, if a disaster happens, and a local utility company does not have enough resources, another company from a closely located region can come to assist. However, taking expenses and distances to cover into account, the responding company might question the assistance possibility, whereas the requesting company needs to find assistance shortly.

Engineers from York University applied simulation modeling to provide the utility companies with a better decision making tool for managing the process of mutual assistance.


To identify the criteria that contribute to decision making, experienced people were interviewed. Overall, 13 criteria were chosen and then grouped into 3 categories:

  • Mutual aid responding criteria - including emergency conditions, availability of resources, etc.
  • Mutual aid requesting criteria - including distance to responding company, extent of damage, etc.
  • Disaster criteria - including size of disaster, disaster type, etc.

Criteria were assigned with numerical values and weights, showing the importance of a parameter in a particular situation.

Operational Planning Simulation

An agent-based simulation model was designed to test various mutual aid scenarios. The model’s interface allowed users to choose agents, acting as requesting and responding companies, which would then be marked in GIS space. It was also possible to set weight and value for each criterion.

Emergency Situation Simulation Modeling

Natural Disaster Simulation Model

This state chart of a utility company shows the decision making process when a responding company gets a call from a requesting company. To decide whether the help could be provided, the model's algorithm calculates the score, based on preset values and weights of criteria.

Decision Making Simulation Model

When the decision is made, the crews of the responding companies start moving to the place of emergency. While in route, the crews may be distributed among several places of emergency. At the same time, it is possible to see the following outputs:

  • Number of utility companies available, as well as ones that agreed to assist
  • Number of crews deployed and arrived
  • Crews’ distances between departure and arrival points
  • Time it will take for each utility crew to cover the distance

Decision Making Simulation Tool

Emergency Responce Simulation


AnyLogic simulation modeling helped develop a tool for better planning, according to which mutual assistance might be recommended. Simulation modeling made it possible to optimize the mutual assistance process by running various experiments and avoiding mistakes in the decision making process. GIS capabilities allowed users to visualize the routing of utility companies’ crews and redirect them if needed.


More Case Studies

  • Optimizing Energy Systems with AnyLogic Simulation Modeling
    European Institute for Energy Research (EIFER) is an organization that deals with the decentralization of energy systems on various territories, and promotes renewable energy sources. Unlike classical energy systems, localized ones are less hierarchical, thus providing the prospects of power storage, renewable energy inclusions, and demand forecasting. EIFER engineers chose AnyLogic simulation modeling to find out how the localized systems should be planned and operated.