# A Systematic Literature Review on Mathematical Models of Humanitarian Logistics

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## Abstract

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## 1. Introduction

## 2. Research Methodology

## 3. Research in Humanitarian Logistics

#### 3.1. Facility Location Problems

#### 3.1.1. Deterministic Models

#### Median Problem

Indexes and Set: | |

$I$ | Set of demand points indexed by $i\in I$; |

$J$ | Set of facilities indexed by $j\in J$. |

Input Parameters: | |

${d}_{ij}$ | The distance between each demand point $i$ and candidate facility $j$; |

${w}_{i}$ | The weight associated with each demand point $i$; |

$p$ | Maximum number of facilities to be located. |

Decision Variables: | |

${x}_{j}$ | 1 if a facility is located at candidate node $j$ and 0 otherwise; |

${y}_{ij}$ | 1 if demand point $i$ is assigned to the facility at candidate node $j$ and 0 otherwise. |

#### Covering Problem

- Set Covering Problem

Input Parameters: | |

${c}_{j}$ | Fixed cost of facility $j$; |

${S}_{i}$ | Maximum distance for a facility to service demand node $i$. |

Decision variables: | |

${x}_{j}$ | 1 if a facility is located at candidate node $j$ and 0 otherwise. |

- b.
- Maximal Covering Problem

Decision Variables: | |

${z}_{i}$ | 1 if demand node $i$ is covered by a facility within distance $S$, otherwise 0. Note that $S$ indicates the distance limit. |

#### P-Centre Problem

Decision Variables: | |

$D$ | The maximum distance between a selected location and a demand point. |

#### Other Models on FLPs

#### 3.1.2. Non-Deterministic Models

#### Stochastic Programming Approach

#### Robust Optimization and Other Non-Deterministic Approaches

#### 3.2. Relief Distribution

#### 3.2.1. Deterministic Models

#### 3.2.2. Non-Deterministic Models

#### The Stochastic Programming Model for Relief Distribution

#### Robust Optimization and Others

#### 3.3. Mass Evacuation

#### 3.3.1. Public and Private Transport Evacuation Model

#### 3.3.2. Urban Area Evacuation Model

## 4. Future Research Direction

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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https://www.elsevier.com/search-results?query=humanitarian%20logistics&labels=journals | |

Emerald Insight | https://www.emeraldinsight.com |

Authors | Objective Function | Constraints | Decisions | Stage of the Disaster | Solution Method | Problem Type |
---|---|---|---|---|---|---|

Dekel et al., (2005) | Minimize facilities for each area with a given distance and maximize the probability of using facilities | Identify the location of the facility for each area | Location identification | Recovery | Pick-the-farthest algorithm | Set covering model |

McCall (2006) | Minimize (victim nautical miles, shortage) | FC, BC | Location selection, unmet demand, | Preparation | GAMS/CPLEX | P-median problem |

Kongsomsaksakul et al., (2005) | Minimize total evacuation time and evacuee travel time | FC, LC, DC, TT, VC | Shelter location selection, route and destination selection, | Response | GA | Location-allocation model |

Jia et al., (2007) | Maximize the demand with sufficient quantity of facility and quality level | FC, FA, DC, FA | Facility location selection, number of serviced facility | Response | CPLEX | Maximal covering, p-median, p-center |

Balcik et al., (2008) | Maximize demand coverage by distribution centers | IL, FC, BC, DC | Number and location of the distribution center, amount of relief supplies | Preparation and response | GAMS/CPLEX | Maximal covering location model |

Rath et al., (2011) | Minimize (depot opening cost, transportation cost), maximize the covered demand | FC, VC, VTT, BC, DC | Depot identification, quantity of relief item, maximum operative budget, arc selection for vehicle | Response | AECA, The constraint pool heuristic, CPLEX | Set covering and vehicle routing model |

Lin et al., (2012) | Minimize the operational cost | VC, FC, IL, FA | Depot location selection, number of vehicles, demand point selection | Response | A two-phase heuristic approach is coded in C language and interfaced with ILOG CPLEX | Minimum facility location |

Abounacer et al., (2014) | Minimize the transportation duration, number of agents (first-aiders) and total uncovered demand | FC, VC, LC, WT | Location selection, amount of commodity to deliver | Response | Epsilon constraint method, Exact Pareto front, CPLEX | Minimum set covering, maximal covering |

Barzinpour et al., (2014) | Maximize the cumulative coverage of the population in pixels of the region, minimize the setup cost and transportation cost | MCC, CTC, FC, IL, DC | Location of shelter, allocation of people, amount of commodity to be transferred or stored | Preparation | LINGO | Maximal covering |

Hu et al., (2014) | Minimize (total cost of shelter, total evacuation distance) | FC, CC, ACS | Location selection, shortest distance, assignment of the community to shelter, construction cost | Preparation | Genetic algorithm | Set covering |

Ye et al., (2015) | Minimize the number of warehouses | NWSE, LD, DSOW | Warehouse location selection, selection of open warehouse for emergency operation | Preparation | VNS algorithm, CPLEX | p-center problem |

Khayal et al., (2015) | Minimize logistics cost and penalty cost | FC, SC, CF, DS, TT, FA | Location of demand and supply point, resource allocation and transfer, coverage, back ordered demand | Response | CPLEX | Dynamic facility location |

Xu et al., (2016) | Minimize the total distance, maximize the coverage of all shelters, maximize the shelter coverage for people | FC, DPC, SRS | Evacuation shelter site selection | Response | Lagrangian heuristic algorithm and GIS | p-median and set covering |

Chen et al., (2016) | Minimize the assignment cost of facilities | FC, DS, MAF | Temporary EMS location selection | Preparation | Reduced LR and greedy algorithm, K-medoids algorithm | Capacitated facility location |

Perez-Galarce et al., (2017) | Minimization of total traveled distance by the victim | CR, AM | Number of the victim, location of the refugee center | Preparation | CPLEX | Uncapacitated facility location model |

M. Akbari et al., (2017) | Minimize total cost before and after interdiction | FC, BC, CAF | Customer assignment, location of facility | Response | Tabu search, Rainfall optimization, Random greedy search | A tri-level facility location r-interdiction median model; |

Cotes and Cantillo et al., (2019) | Minimize the sum of private cost (transportation, inventory, fixed) and deprivation cost | ADC, FC, FLC, DT, TT | Amount of prepositioned product | Preparation | GAMS/CPLEX | capacitated facility location |

Das Rubel (2018) | Maximize the coverage | NW, CLW, TOC | Location selection of local warehouse (LW) and regional warehouse, coverage of LW | Response | Open source python package solver GLPK and PULP | Maximal covering problem |

Tabana et al., (2017) | Minimize the total cost of procurement and preparation, minimize the total relief operational cost, minimize the total operational relief time | FC, IL, DC, VC, BC, SP | Location selection, amount of unused product, shortage of product, inventory level | Preparation and response | NSGA-ΙΙ and RPBNSGA-ΙΙ | Facility location, vehicle routing, and inventory management |

Wapee Manopiniwes et al., (2020) | Minimize the amount of unsatisfied demand | SC, VC, DS, NV | Amount of vehicle, amount of supplies, location selection | Response | Gurobi optimizer | Location and routing |

Authors | Objective Function | First Stage Decisions | Second Stage Decisions | Uncertain Components | Stage of Disaster | Solution Approach/Technique | Model Type |
---|---|---|---|---|---|---|---|

Chang et al., (2007) | Minimize the expected shipping distance | Location of rescue storehouse | The number of resources to be stored | Demand | Preparation | LINGO | Two-stage stochastic programming |

G. Rawls et al., (2010) | Minimize the total expected cost | Location selection, amount of pre-positioned commodity | Distribution of available supplies | Demand and transportation network availability | Preparation | LLSM algorithm, CPLEX | Two-stage stochastic programming |

G. Rawls et al., (2012) | Minimize the expected cost | Stocking quantity, location selection | – | Demand | Preparation and response | CPLEX | Stochastic programming |

Murali et al., (2012) | Maximize the number of people taking medication | Facility location selection, supply to be assigned to the facility, allocated supplies to demand point | – | Demand | Response | Locate–allocate heuristic | Probabilistic model (CCM) |

Rennemo et al., (2014) | Maximize the utility (in terms of demand satisfaction and monetary budget) | Location selection, Number of vehicle type, Amount of commodity type | Level of the residual budget, amount of commodity type, number of vehicle type | Demand, the size of the vehicle fleet, available medical personnel and state of infrastructure | Response | Xpress-IVE | Three-stage stochastic programming |

Hong et al., (2015) | Minimize the cost of opening facilities and purchasing the relief supplies (1st stage) and expected total cost (2nd stage) | Size and location of the facility | Amount of commodity to be shipped, amount of shortage and surplus, the inventory level of relief supplies | Demand and transportation capacities | Preparation | Preprocessing algorithm, combinatorial patterns, MATLAB, AMPL, CPLEX | Two-stage stochastic programming |

Renkli et al., (2015) | Minimize the total weighted distance between affected areas and their assigned disaster response facilities | Location selection of warehouse, amount of relief item to be sent | Amount of relief item | Preparation | CPLEX | Probabilistic model (CCM) | |

Amiri et al., (2016) | Minimize the maximum amount of shortage, total travel time, pre- and post-disaster cost | Location of the facility, amount of commodity to transfer, amount of commodity to procure, inventory level, tour selection | – | Procurement cost, transportation cost, demand, amount of stocked commodity | Preparation and response | є-constraint method, GAMS/CPLEX | Stochastic programming |

An et al., (2015) | Minimize the total expected system cost | Location of facility, service allocation | – | Disaster location | Preparation | Lagrangian relaxation | Stochastic programming |

Golabi et al., (2017) | Minimize the aggregated travel time of both people and the UAVs | Location selection, the flight time, required numbers of reload | – | Demand, shortest path length | Preparation | GA, MA | Stochastic programming |

Moreno et al., (2018) | Minimize logistics cost and deprivation cost | Location, procured number of vehicles | Procured number of vehicle in 2nd stage, amount of commodity to ship, inventory of commodity, unmet demand | Demand, incoming supply, available routes | Response | CPLEX, FXO, TSH, and TSH+FXO | Two-stage Stochastic programming |

Kinay et al., (2018) | Maximize minimum weight of facilities | Location selection of facilities, allocation of demand points to the open facilities | – | Demand | Preparation | CPLEX | Max–min probabilistic model (CCM) |

Rezaei-Malek et al., (2016) | Minimize total cost, weighted response time | Warehouse location selection, amount of commodity to transfer, shortage of commodity, stock level | – | Disruption, demand, transportation time | Preparation and response | GAMS/CPLEX | Robust stochastic optimization |

Muggy et al., (2017) | Maximize the cumulative weighted demand | Location of facility | – | Supply, demand | Response | CPLEX | Robust stochastic optimization |

Ni et al., (2018) | Minimize 1st stage cost (facility cost and commodity holding cost) and 2nd stage cost (transportation cost, penalty cost) | Location of facility, pre-positioned inventory amount | – | Demand, proportion of usable inventories, road link capacity | Preparation and response | CPLEX | Min–max robust optimization |

Yahyaei et al., (2018) | Minimize total cost (transportation, facility opening cost) | Location selection of facility (UDC, SDC), amount of shipped relief item | – | Number of affected people | Preparation and response | GAMS/CPLEX | Robust optimization |

Oksuz et al., (2020) | Minimize the setup cost of TMC and expected transportation cost | Medical center location selection | Assignment of causalities, medical center assignment for a specific patient | Capacity of hospital, number of causalities, distance of road | Response | CPLEX | Two-stage stochastic programming |

Julia Monzon et al., (2020) | Minimize the expected unsatisfied demand | Selection of arc, decision of inventory | Flow of goods, supply quantity | Demand and state of transportation network | Preparation | GAMS/CPLEX | Two-stage stochastic programming |

S. Mohammadi et al., (2020) | Minimize the total logistics cost, minimize the total time of relief operation | Supplier selection, distribution center selection, dispatching of injured people | Demand, capacity of facility, time, cost | Response | GAMS | Robust optimization | |

Phillip R. Jenkins et al., (2020) | Maximize the demand coverage, minimize the maximum number of located facilities and reallocation | Location selection, reallocation, aeromedical helicopter deployment | Aeromedical helicopter | Response | CPLEX | Robust optimization |

Authors | Objective Function | Constraints | Decisions | Stage of Disaster | Solution Approach/Technique | Problem Type |
---|---|---|---|---|---|---|

Ozdamar et al., (2004) | Minimize the sum of unsatisfied demand | FBC (commodity and vehicle), VC | NVT, ACT, AUDN | Preparation | LRIA | Relief distribution and transportation |

Tzeng et al., (2007) | Minimize total cost, minimize travel time, maximize satisfaction | SP, SD | ACT, CLSTD | Response | LINGO | Relief distribution |

Yi et al., (2007) | Minimize the weighted sum of (unsatisfied demand and unserved wounded people) | FBC (wounded people), NV, VC, VL | ACT, NWP, AUDC, NUWP, NVT | Response | ACO algorithm, CPLEX | Multi-commodity network flow |

Balcik et al., (2008) | Minimize the sum of routing and penalty cost | DT, VC, FC, DF | ARS, DDS, DDR | Preparation and response | GAMS/CPLEX | Last-mile relief distribution |

Yan et al., (2008) | Minimize the cost in emergency repair network and the relief distribution network | FCC, FBC (commodity), WTA, AF | Repair team, arc selection | Mitigation and response | CPLEX, ACO | Relief distribution and scheduling of emergency roadway repair |

Campbell et al., (2008) | Minimize the maximum travel time and minimize the average arrival time | STE, VC, AT, VRD | Vehicle travel decision | Response | Insertion heuristics and improvement algorithm | Relief distribution and Vehicle routing |

Horner et al., (2010) | Minimize the cost of distributing relief goods | FA, FC, MND | Quantity of relief item, distribution center type selection, affected area selection for distribution center | Response | CPLEX | Relief distribution and transportation |

Vitriano et al., (2011) | Minimize (time, cost), maximize (equity, reliability) | FBC (vehicle), NV, STE, VC, BC | Quantity of relief item, quantity of a stored item, number of vehicles | Response | GAMS/CPLEX | Relief distribution |

Afsar et al., (2012) | Minimize the total amount of weighted unsatisfied demand | FC, VC, FBC (commodity and vehicle) | Location selection, number of the vehicle, amount of commodity | Response | CPLEX | Relief distribution, location, and routing |

Liberatore et al., (2014) | Maximize demand satisfaction | AT, DC, AF, MRP, AR | The flow of people passing arc, the flow of people at arc, arrival time | Response and recovery | GAMS/CPLEX | Relief distribution |

Sheu et al., (2014) | Minimize (travel distance, operational cost, psychological cost) | FBC (evacuee), EFC, VC, FC | Distribution center selection, quantity of relief resource to transfer, number of injured people | Response | LINGO | Relief distribution and network design |

Wang et al., (2014) | Maximization of the maximum vehicle route traveling time, minimization of relief distribution cost, maximization of the minimum route reliability | FA, FC, VC, VAD | Location selection, node selection, quantity of relief item, quantity of unsatisfied demand | Response | NSGA-ΙΙ and NSDE algorithm | Location and routing |

Pradhananga et al., (2016) | Minimize pre-disaster cost and expected post-disaster cost | FC, FBC (supply point), AQ | SPS, LSCP, TQP, TQPP, QTD, IN, SQ, AQDC | Preparation and response | CPLEX | Relief distribution and allocation |

Rivera-Royero et al., (2016) | Minimize the total remaining fraction of unsatisfied demand | BC, VC, DC, IL | Number of trips, number of pallets, inventory of pallets, remaining budget | Response | Run and fix multi-period heuristic, run and fix multi-period multi-stage heuristic, greedy algorithm, simulated annealing | Relief distribution |

Lu et al., (2016) | Minimize total relief distribution time | FC, FCC, VC | Amount of commodity flow | Response | C++ programming language, GUROBI 6.5 | Relief distribution |

Al Theeb et al., (2017) | Minimize the quantities of unsatisfied demand, unserved wounded, and non-transferred workers | VT, VC, FBC (vehicle), NW | Quantity of commodity, number of workers, number of evacuees | Response | CPLEX, four-phased heuristic | Relief distribution and vehicle routing |

Mollah et al., (2017) | Minimize total cost (transportation and penalty) | FC, ET, VC | Available shelter selection, number of trips | Response | CPLEX, genetic algorithm | Shelter allocation and relief distribution |

Rabta et al., (2018) | Minimize a cost function (which represents the total traveling distance, total traveling time or total traveling costs) | DC, EC, PC | Number of moves by drone, quantity of package to carry | Response | GAMS | Last-mile distribution, drone routing system |

Wang et al., (2018) | Minimize the total service completion time | DC, FC, FBC (arc), STE | Service starting time, quantity of relief item | Response | ABC algorithm, the Rh algorithm | Medical team assistance scheduling and relief distribution |

Authors | Objective Function | Uncertain Components | Stage of Disaster | Solution Technique/Approach | Model Type | Problem Type |
---|---|---|---|---|---|---|

Barbarosoglu et al., (2004) | Minimize the total transportation cost and recourse cost | Demand, supply, capacity of vehicle | Response | GAMS/OSL | Two-stage stochastic programming | Relief distribution and transportation |

Salmeron et al., (2010) | Minimize expected casualties, minimize expected unmet transfer population | Demand, number of relief worker, travel time | Preparedness | CPLEX | Two-stage stochastic programming | Asset prepositioning and relief operations |

Mete et al., (2010) | Minimize the total warehouse operating cost and total transportation time | Transportation time, demand | Preparation | CPLEX | Two-stage stochastic programming | Location-routing and relief distribution |

Doyen et al., (2012) | Minimize the total cost (transportation, facility establishment, inventory holding, the penalty for shortage) | Capacity, unit transportation cost, demand, transportation time | Preparedness and response | Lagrangean relaxation-based heuristics, CPLEX | Two-stage stochastic programming | Location and distribution (network design) |

Li et al., (2011) | Minimize total cost (fixed cost of operating shelters, inventory cost) and total transportation cost | Evacuees number, transportation cost, the operational cost of one evacuee | Preparedness and response | CPLEX | Two-stage stochastic programming | Location and distribution (network design) |

Noyan et al., (2015) | Maximize the expected total accessibility | Demand, transportation network | Response | Branch and cut algorithm | Two-stage stochastic programming | Last mile relief distribution model (network) |

Tofigi et al., (2016) | Minimize the total cost (warehouse and distribution center operating, inventory), distribution time, maximum weighted travel time | Supply, demand, road availability | Preparedness and response | DEA | Two-stage stochastic programming | Relief distribution (network) |

Ahmadi et al., (2015) | Minimize the total distribution time, penalty cost of unsatisfied demand and fixed cost of opening DC | Road destruction, location | Response | GAMS, Neighborhood search algorithm | Two-stage stochastic programming | Location-routing and last mile relief distribution |

Moreno et al., (2015) | Minimize the total expected cost (opening and operating relief center, vehicle assignment, transportation, inventory, unmet demand, demand satisfaction) | Demand, supply, inventory, road availability | Response | Relax-and-fix heuristics, | Stochastic programming | Location and transportation |

Fix-and-optimize heuristics | ||||||

Alem et al., (2016) | Minimize the cost of stock prepositioning, vehicle hiring, inventory, and unmet demand | Demand, supply, budget | Preparedness | Two-phase heuristic | Two-stage stochastic programming | Relief distribution (network) |

Zheng et al., (2013) | Minimize total time delay, total transportation cost, and total transportation risk | Quantity of good, cost, arrival time, travel time | Preparation | MOTS, MOGA | Fuzzy optimization | Transportation planning and relief distribution |

Najafi et al., (2013) | - (a)
- Minimize the total weighted unserved injured people
- (b)
- Minimize the total weighted unsatisfied demand
- (c)
- Minimize the total number of vehicles utilized in response
| Demand, number of injured people, supply of the commodity | Response | CPLEX | Robust optimization, stochastic model | Transportation and relief distribution |

Fereidumi et al., (2017) | Minimize the total cost | Demand, rescue operation time, transportation cost, operational cost | Preparedness and response | GAMS | Robust optimization | Distribution and evacuation |

Hagi et al., (2017) | - (a)
- Minimize the total cost (facility establishment, commodity procurement, transportation, shortage, inventory holding)
- (b)
- Maximize the satisfaction level by minimizing the maximum shortage
- (c)
- Minimize the cost of health center establishment, casualty transportation
- (d)
- Maximize the satisfaction level by minimizing the sum of maximum neglected casualties
| Demand, supply, and cost | Preparedness and response | MOGSA | Robust stochastic optimization | Location and distribution |

Vahdani et al., (2018) | - (a)
- Minimize the cost (facility establishment, storing goods in facilities)
- (b)
- Minimize the vehicle travel cost
- (c)
- Minimize the vehicle travel time
- (d)
- Maximize the route reliability
| Storage capacity | Response | NSGAII and MOPSO | Robust optimization | Location, routing, and distribution |

Yuchen Li et al., (2020) | Minimize the fixed cost of opened supply facilities, and the cost of prepositioned relief goods | Demand, transportation time | Preparation and response | CPLEX, MATLAB | Three stage stochastic programming | Distribution and location |

Peiman Ghasemi1 et al., (2020) | - (a)
- Minimize the untreated injured people
- (b)
- Minimize the shortage of commodities
| Demand | Preparation and response | NSGAII | Two-stage stochastic programming | Distribution and evacuation |

Authors | Objective Function | Uncertain Components | Decisions | Deterministic Model | Non-Deterministic Model | Solution Technique/Approach |
---|---|---|---|---|---|---|

Murray-Tuite et al., (2003) | Minimize the travel time and evacuee waiting time | _ | Link selection, meeting place selection of people | √ | Traffic simulation software | |

Goerigk et al., (2013) | Minimize the maximum travel distance | – | Traveling decision of bus decision, travel time | √ | Greedy algorithm | |

Goerigk et al., (2014) | Minimize the total evacuation time | Number of evacuees | √ | CPLEX | ||

Margulis et al., (2006) | Maximize the total evacuated number of people | – | Bus trip selection | √ | ||

Swamy et al., (2017) | Minimize the total distance between the pickup locations and shelters | – | Evacuee pickup point selection | √ | Python 2.7 for simulation code generation and optimization solver Gurobi 6.5 | |

Bish et al., (2011) | Minimize the evacuation time and total cost | – | Number of evacuees, bus trip selection | √ | Two heuristic algorithms | |

Ashish et al., (2014) | Minimize the total evacuation time | Number of transit-dependent evacuees | Trip number of bus, pick up the point of evacuees, allocation of bus | √ | GAMS/CPLEX | |

Song et al., (2009) | Minimize the total evacuation time | Number of evacuees | Shelter selection, vehicles’ travel | √ | Hybrid GA, artificial neural network, hill climbing heuristic algorithms | |

Liu et al., (2006) | Maximize the total number of vehicles entering all destinations, minimize the total trip time (including the waiting time of evacuees) | – | Number of vehicles | √ | LINGO 8.0 | |

Wang et al., (2016) | Minimize the total evacuation times | Link travel times and link capacities | √ | Relaxation-based heuristic, K-shortest path | ||

Kongsomsaksakul et al., (2005) | Minimize the total travel time for all evacuees to safe shelters | – | Safe shelter selection | √ | Genetic algorithm | |

Sayyady et al., (2010) | Minimize the total evacuation time and number of casualties | – | Flow of evacuees | √ | Traffic simulation package, CPLEX | |

Bretschneider et al., (2011) | Minimize the average evacuation time | – | Number of the vehicle, number of lanes | √ | CPLEX | |

Ye et al., (2012) | Maximizing the coverage population | – | Number of a single residential building for evacuation | √ | Arc GIS, shortest path algorithm | |

Goerigk et al., (2014) | Minimize the evacuation time and number of used shelters | – | Number of evacuees using cars and bus | √ | Genetic algorithm | |

Kimms et al., (2018) | Minimize the total exposed hazard, minimize the deviation of cell capacity utilization | – | Number of the vehicle for starting the evacuation, number of vehicles used between two cells | √ | Path generation algorithm | |

Li Wang (2020) | Minimize the evacuation time | Travel time and link capacity | Flow of people in a specific link | √ | Lagrangian relaxation-based algorithm |

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**MDPI and ACS Style**

Hezam, I.M.; Nayeem, M.k.; Lee, G.M.
A Systematic Literature Review on Mathematical Models of Humanitarian Logistics. *Symmetry* **2021**, *13*, 11.
https://doi.org/10.3390/sym13010011

**AMA Style**

Hezam IM, Nayeem Mk, Lee GM.
A Systematic Literature Review on Mathematical Models of Humanitarian Logistics. *Symmetry*. 2021; 13(1):11.
https://doi.org/10.3390/sym13010011

**Chicago/Turabian Style**

Hezam, Ibrahim M., Moddassir k. Nayeem, and Gyu M. Lee.
2021. "A Systematic Literature Review on Mathematical Models of Humanitarian Logistics" *Symmetry* 13, no. 1: 11.
https://doi.org/10.3390/sym13010011