Interdependent Network Design Problem (INDP)

Interdependent Network Design Problem (INDP)#

Description

This analysis takes a decentralized approach to solve the Interdependent Network Design Problem (INDP), a family of centralized Mixed-Integer Programming (MIP) models, which find the optimal restoration strategy of disrupted networked systems subject to budget and operational constraints.

Contributors

  • Implementation: Hesam Talebiyan, Chen Wang, and NCSA IN-CORE Dev Team

Related publications

  • Talebiyan, Hesam. “Interdependent Restoration of Infrastructure Networks with Humans in the Loop: decentralized and strategic decision processes.” (2021) Diss., Rice University. https://hdl.handle.net/1911/111232

Input parameters

key name

type

name

description

network_type *

str

Network Type

Type of the network, which is set to from_csv for Seaside networks. e.g. from_csv, incore.

MAGS *

list

MAGS

The earthquake return period.

sample_range *

range

Sample Range

The range of sample scenarios to be analyzed.

dislocation_data_type

str

Dislocation Data Type

Dislocation Data Type.

return_model

str

Return Model

Type of the model for the return of the dislocated population. Options: step_function and linear.

testbed_name

str

Testbed Name

Sets the name of the testbed in analysis.

extra_commodity *

dict

Extra Commodity

Multi-commodity parameters dictionary.

RC *

list

Resource Caps

List of resource caps or the number of available resources in each step of the analysis. Each item of the list is a dictionary whose items show the type of resource and the available number of that type of resource. For example: * If network_type=from_csv, you have two options:* if, for example, R_c= [{“budget”: 3}, {“budget”: 6}], then the analysis is done for the cases when there are 3 and 6 resources available of type “budget” (total resource assignment).* if, for example, R_c= [{“budget”: {1:1, 2:1}}, {“budget”: {1:1, 2:2}}, {“budget”: {1:3, 2:3}}] and given there are 2 layers, then the analysis is done for the case where each layer gets 1 resource of type “budget”, AND the case where layer 1 gets 1 and layer 2 gets 2 resources of type “budget”, AND the case where each layer gets 3 resources of type “budget” (Prescribed resource for each layer).

layers *

list

Layers

List of layers in the analysis.

method *

str

Method

There are two choices of method: 1. INDP: runs Interdependent Network Restoration Problem (INDP). 2. TDINDP: runs time-dependent INDP (td-INDP). In both cases, if “time_resource” is True, then the repair time for each element is considered in devising the restoration plans.

t_steps

int

Time steps

Number of time steps of the analysis.

time_resource

bool

Time Resource

If time resource is True, then the repair time for each element is considered in devising the restoration plans.

save_model

bool

Save Model

If the optimization model should be saved to file. The default is False.

solver_engine

str

Solver Engine

Solver to use for optimization model. Default to use SCIP solver.

solver_path

str

Solver Engine Path

Executable Path to the Solver to use for optimization model. Default to SCIP solver.

solver_time_limit

int

Solve Time Limit

Solving time limit in seconds.

Input datasets

key name

type

name

description

wf_repair_cost *

incore:repairCost

Water Facility Repair Cost

Repair cost for each water facility.

wf_restoration_time *

incore:waterFacilityRepairTime

Water Facility Repair Time

Repair time at certain functionality recovery for each class and limit state.

epf_repair_cost *

incore:repairCost

Electric Power Facility Repair Cost

Repair cost for each electric power facility.

epf_restoration_time *

incore:epfRepairTime

Electric Power Facility Repair Time

Repair time at certain functionality recovery for each class and limit state.

pipeline_repair_cost *

incore:pipelineRepairCost

Water Pipeline Repair Cost

Repair cost for each water pipeline.

pipeline_restoration_time *

incore:pipelineRestorationVer1

Water Pipeline Resotarting Time

Pipeline restoration times.

power_network *

incore:epnNetwork

Electric Power Network Dataset

Electric power network dataset.

water_network *

incore:waterNetwork

Water Network Dataset

Water network dataset.

powerline_supply_demand_info *

incore:powerLineSupplyDemandInfo

Powerline Supply Demand Info

Supply and demand information for powerlines.

epf_supply_demand_info *

incore:epfSupplyDemandInfo

Electric Power Facility Supply Demand Info

Supply and demand information for electric power facilities.

wf_supply_demand_info *

incore:waterFacilitySupplyDemandInfo

Water Facility Supply Demand Info

Supply and demand information for water facilities.

pipeline_supply_demand_info *

incore:pipelineSupplyDemandInfo

Water Pipeline Supply Demand Info

Supply and demand information for water pipelines.

interdep *

incore:interdep

Interdependency

Interdepenency between water and electric power facilities.

wf_failure_state *

incore:sampleFailureState

Water Facility Failure State

MCS failure state of water facilities.

wf_damage_state *

incore:sampleDamageState

Water Facility Damage State

MCS damage state of water facilities.

pipeline_failure_state *

incore:sampleFailureState

Water Pipeline Failure State

Failure state of pipeline from pipeline functionality analysis.

epf_failure_state *

incore:sampleFailureState

Electric Power Facility Failure State

MCS failure state of electric power facilities.

epf_damage_state *

incore:sampleDamageState

Electric Power Facility Damage State

MCS damage state of electric power facilities

pop_dislocation *

incore:popDislocation

Population Dislocation

Population dislocation.

dt_params

incore:dTParams

Dislocation time parameters

Parameters for population dislocation time.

bldgs2elec

incore:bldgs2elec

Building To Electric Power Facility

Relation between building and electric power facility.

bldgs2wter

incore:bldgs2wter

Building To Water Facility

Relation between building and water facility.

Output datasets

key name

type

parent key

name

description

action *

incore:indpAction

Action

Restoration action plans.

cost *

incore:indpCost

Cost

Restoration cost plans

runtime *

incore:indpRuntime

Run Time

Run time duration (in second) to execute computations for each time step

(* required)

Execution

code snippet:

    indp_analysis = INDP(client)
    indp_analysis.set_parameter("network_type", "from_csv")
    indp_analysis.set_parameter("MAGS", [1000])
    indp_analysis.set_parameter("sample_range", sample_range)
    indp_analysis.set_parameter("dislocation_data_type", "incore")
    indp_analysis.set_parameter("return_model", "step_function")
    indp_analysis.set_parameter("testbed_name", "seaside")
    indp_analysis.set_parameter("extra_commodity", {1: ["PW"], 3: []})
    indp_analysis.set_parameter("RC", [{"budget": 240000, "time": 700}, {"budget": 300000, "time": 600}])
    indp_analysis.set_parameter("layers", [1, 3])
    
    indp_analysis.set_parameter("method", "INDP")
    # indp_analysis.set_parameter("method", "TDINDP")
    
    indp_analysis.set_parameter("t_steps", 10)
    indp_analysis.set_parameter("time_resource", True)
    
    indp_analysis.set_parameter("save_model", False)
    # indp_analysis.set_parameter("save_model", True)
    
     # scip
    indp_analysis.set_parameter("solver_engine", "scip")
    indp_analysis.set_parameter("solver_path", "/usr/local/bin/scip")

    # glpk
    # indp_analysis.set_parameter("solver_engine", "glpk")
    # indp_analysis.set_parameter("solver_path", "/usr/local/bin/glpsol")

    # cbc
    # indp_analysis.set_parameter("solver_engine", "cbc")
    # indp_analysis.set_parameter("solver_path", "/usr/local/bin/cbc")

    # gurobi
    # indp_analysis.set_parameter("solver_engine", "gurobi")

    indp_analysis.set_parameter("solver_time_limit", 3600)  # if not set default to never timeout

    indp_analysis.set_input_dataset("wf_restoration_time", wf_restoration_time)
    indp_analysis.set_input_dataset("wf_repair_cost", wf_repair_cost_result)
    indp_analysis.set_input_dataset("epf_restoration_time", epf_restoration_time)
    indp_analysis.set_input_dataset("epf_repair_cost", epf_repair_cost_result)
    indp_analysis.set_input_dataset("pipeline_restoration_time", pipeline_restoration_time)
    indp_analysis.set_input_dataset("pipeline_repair_cost", pipeline_repair_cost_result)
    indp_analysis.set_input_dataset("power_network", power_network_dataset)
    indp_analysis.set_input_dataset("water_network", water_network_dataset)  # with distribution noes
    indp_analysis.load_remote_input_dataset("powerline_supply_demand_info", powerline_supply_demand_info_id)
    indp_analysis.load_remote_input_dataset("epf_supply_demand_info", epf_supply_demand_info_id)
    indp_analysis.load_remote_input_dataset("wf_supply_demand_info", wf_supply_demand_info_id)
    indp_analysis.load_remote_input_dataset("pipeline_supply_demand_info", pipeline_supply_demand_info_id)
    indp_analysis.load_remote_input_dataset("interdep", interdep_id)
    indp_analysis.set_input_dataset("wf_failure_state", wterfclty_sample_failure_state)
    indp_analysis.set_input_dataset("wf_damage_state", wterfclty_sample_damage_states)
    indp_analysis.set_input_dataset("pipeline_failure_state", pipeline_sample_failure_state)
    indp_analysis.set_input_dataset("epf_failure_state", epf_sample_failure_state)
    indp_analysis.set_input_dataset("epf_damage_state", epf_sample_damage_states)
    indp_analysis.set_input_dataset("pop_dislocation", pop_dislocation_result)

    # # optional inputs
    # indp_analysis.load_remote_input_dataset("bldgs2elec", bldgs2elec_id)
    # indp_analysis.load_remote_input_dataset("bldgs2wter", bldgs2wter_id)

    # Run Analysis
    indp_analysis.run_analysis()

full analysis: indp.ipynb