Emergency logistics refers to the logistics activities caused by emergencies

Emergency logistics refers to the logistics activities caused by emergencies

Authors Biography

Mohammad Heydari is an Iranian associate professor, scientist, and author. He was born on August 14, 1992, in Tehran, Iran. He published more than 13 books and over 88 scientific papers with famous authors and high-level research groups in his research fields; Currently, his papers are published and accepted by 37 different countries.

Mohammad Heydari is currently working at Business College, Southwest University, one of the country’s 100 key national universities. In August 2020, Dr. Heydari was accepted as the youngest associate professor and faculty member of Management Science and Engineering (MS&E) at Business College, Southwest University, Chongqing, China. At the same time, he was nominated for the “National Young Talent Program” title, one of the highest awards for foreigners working in academia.

In 2019, he received the Chinese Government Ministry Award Education Scholarship for outstanding research and academic activities at the national level. In 2017, Dr. Heydari received the (Nanjing Municipal Government) scholarship in (MS&E). Dr. Heydari earned his DSc., Ph.D. from the School of Economics and Management, Nanjing University of Science and Technology, China. Dr. Heydari’s research is in the areas of (1) Human Resources and Business Administration, Organizational Psychology and Organizational Behavior and Organizational Studies; (2) Applied Mathematics, Optimization Algorithm & Operation, Supply Chain Management, and Decision Analysis; (3) Entrepreneurship Management and Psychological Entrepreneurship Research.

Author Email: MohammadHeydari1992@yahoo.com

Author Facebook: https://www.facebook.com/M.Heydari1992

Author Twitter: https://twitter.com/Dr_MH1992

Author Instagram: https://www.instagram.com/dr.mohammadheydari1992/

Mahdiye Saeidi is an Iranian researcher and author. She was born on February 17, 1978, in Arak, Iran. She published one book, accomplished many scientific papers with Dr. Mohammad Heydari and worked in high-level research groups. Mahdiye Saeidi is currently working at Business Operation Dept. in National Iranian Tanker Company, one of the biggest shipping companies in the world, as a vessels’ demurrage and freight account, controller and vessels’ operator from March 2004. In 2017 she reached her Master’s degree in Information Technology from Payam Noor University of Tehran. The title of her Master’s thesis was assessing the effective factors in the acceptance of Internet of Things technology in smart buildings. She researches in (1) Information Technology (2) Human Resources and Business Administration.

Author Email: mahdisan57@yahoo.com

Yuanyi Wang is a bachelor degree student, born on September 12, 2001 in Zuoquan County, Jinzhong City, Shanxi Province, China. She is currently studying in business College of Southwest University, one of the country’s 100 key national universities. She studied two majors, the major was International Economics and trade, and the minor was Mathematics and applied Mathematics.

In 2021, she won the National Encouragement Scholarship. At the same time, she was awarded the prize “The outstanding Student of the 2020 Undergraduate Military Training of Southwest University.” and the title of “Merit Student” of Southwest University. In 2022, she was awarded the title of “Outstanding Communist Youth League Cadre” of Southwest University in 2021.

Yuanyi Wang has a strong research desire and hope to further her master’s and doctor’s degree. She intend to research Applied Mathematics, Optimization Algorithm & Operation, and Decision Analysis subjects such as “Linear and Non-linear Models,” “Optimization Models” and “Mathematical Models.” Yuanyi Wang hopes to make achievements in these fields and looks for new horizons within these fields.

Author Email: yuanyi_wang0912@163.com

Healthcare Emergency & Disaster Logistics Processes: Principles and Practice

Emergency logistics refers to the logistics activities triggered by emergencies, such as the emergency logistics demand generated by emergencies and the emergency logistics supply activities necessary to meet these logistics requirements, optimize time efficiency, and minimize disaster losses. Emergency logistics facility location and material distribution are critical components of emergency decision-making and linked to rescue operations. In 2004, researchers coined “emergency logistics” to describe the logistics activities necessary to respond to natural disasters, public health incidents, significant accidents, and other emergencies (OU, Z. et al., 2004). According to the Tomas and Fritz Institute’s 2004 definition, humanitarian logistics is the process of planning, executing, and controlling the efficient and effective flow1 and storage of relief supplies and associated information from the point of supply to the point of consumption. Preparation, planning, procurement, transportation, warehousing, trucking and tracking, and customs clearance are all included (Tomas. A, 2004).

1 How to claim efficiency and make the patient flow smooth? (1) switch the nurses’ scheduling to a sensitivity based-system (it rewards loyalty); (2) ed start supply chain management tracking software (for instant restocking of critical items); (3) no more double and triple shifts (personnel needs rest or people die); (4) d or d algorithm which means you have ten minutes per patient to diagnose or discharge (this keeps the patients moving through).

However, when natural disasters strike, they frequently cause damage to road infrastructure and impair the efficiency of emergency supplies distribution due to various obstacles. The loss caused by inefficient distribution accounts for approximately 15% to 20% of the total loss caused by the disaster (Kou G. et al., 2013). According to Fritz Research Institute, humanitarian logistics is a critical component of emergency rescue operations, accounting for between 80% and 90% of the total cost of the rescue (Taylor, D, Pettit, S, 2009). The transfer speed of rescue personnel will determine the success of the emergency rescue operation. As a result, it is critical to increasing emergency logistics’ efficiency and capability.

After natural disasters and social hazards such as mudslides, outbreaks, major traffic accidents and manufacturing accidents, or terrorist attacks, the distribution of disaster relief materials and personnel in disaster areas is studied, emergency logistics focuses on the distribution of materials in an emergency.

Emergency logistics is more expensive and takes longer than conventional commercial logistics activities, and the goal is more time-sensitive. As a result, it exhibits abrupt and unpredictable characteristics, stochastic demand, the urgency of time constraints, peak value, a weak economy, unconventionality, government intervention, and market participation. The uniqueness and continuity of various disaster relief stages should be considered when developing study-related problems and models.

Researchers believe that effectively ensuring emergency supply distribution is rational location-allocation and scientific planning of the Vehicle Routing Problem (VRP). Additionally, LAP and VRP are interdependent and interact with one another. As a result, it is necessary to design and optimize them holistically, that is, to investigate the Location-Routing Problem (LRP) in the emergency logistics system.

The LAP problem is primarily concerned with the DM determining the number and location of facilities within a given geographical area according to the geographic distribution of customers and goods. It can be roughly classified into the eight following categories:

1) Topological Features

  • Continues Site Section
  • Network Location
  • Discrete Location

2) Goal of Decision

  • Covering Location
  • Selection of Distribution Centre
  • Median Location

3) Facility Characteristics

  • Unlimited Capacity
  • Limited Capacity

4) Supply Chain Characteristics

  • Multi Cover
  • Single Cover

5) Model Parameters

  • Uncertainty
  • Certainly

6) Time Frame

  • Static Location
  • Dynamic Location

7) Demand Type

  • Multi-Source Service
  • Single-Source Service

8) Solution

  • Exact Solution
  • Non-Exact Solution

In this article, typical problems and solution models are selected for analysis:

  • Site Selection and Allocation

The traditional location problem is primarily concerned with coverage, center, and median issues. The coverage problem is classified into set coverage models, maximum coverage models, and maximum available models. These conventional models are based on the premise that solutions are predetermined. Compared to the conventional location problem, the emergency supply location problem is stochastic and uncertain. As a result, some people demonstrate probability and fuzzy theory in emergency rooms.

Additionally, an article on determining facilities with uncertain demand in terms of service quantity and distance. In the case of Los Angeles, we solve the maximum location coverage problem using a chance-constrained model (Murali, P. et al., 2012). VLSN2 search algorithms for capacity facility location problems with single source constraints have been proposed and demonstrated by several academics. Facility opening and customer allocation costs are reduced when the goal of uncapacitated facility location (UFL) is to identify a subset of available facility locations and allocate each client to open facilities. Researchers use (generalized) Benders cuts to replace many allocation variables with a few continuous variables that directly model the customer allocation cost (Fischetti M. et al., 2017).

2 Very Large Scale Neighborhood (VLSN)

  • Research on Dispatch and Allocation

Distribution and dispatch of emergency supplies manage emergency supplies following a natural disaster. Generally, it is necessary to transport various materials quickly from various locations to several emergency material distribution centers and disaster areas. Although material allocation occurs after material dispatching, scheduling and allocation frequently occur concurrently, and thus many papers will examine both.

When some papers examine the problem of locating emergency source reserve sites in cities and towns, they use the Hongshan District of Wuhan as a case study. Eight suitable emergency source reserve nodes are chosen in the study area based on the six necessary conditions for locating emergency source reserve sites under the influence of the determining rainstorm disaster, and Freud is then consulted. The German approach determines distribution paths that include reserve points. When reserve points vary, it determines the relationship between their positions, aiming for the shortest total path distance (WU, K. et al., 2017).

When studying emergency logistics’ location-allocation problem, some scholars considered the severe shortage of emergency materials and supplies in the immediate post-earthquake period. They constructed the Max and total satisfaction to meet the diverse emergency requirements for multi-agent participation following the earthquake and ensure the fair and efficient distribution of public and private logistics resources. A genetic algorithm solves a multi-period, multi-objective, multi-material dynamic location-allocation algorithm with a minimum cost and minimum personnel loss. The objective is to maximize commodity satisfaction at the upper level and time satisfaction at the lower level. Finally, the model and algorithm’s validity are demonstrated through an earthquake in Wenchuan (SONG Yinghua et al., 2018).

  • Review of VRP Researches

VRP studies that to meet the customer’s demand for goods, delivery time, vehicle capacity constraints, mileage constraints, and time constraints, a series of customer requirements points are used to shape appropriate vehicle routes to achieve the shortest mileage, the lowest cost, the fastest time, and the smallest fleet size possible. Optimize the target for increased utilization.

In terms of distribution routing optimization, it primarily analyzes the degree of road damage and congestion caused by constructing a road network. It investigates the emergency material scheduling problem under uncertain vehicle travel times.

The Vehicle Routing Problem (VRP) was proposed for the first time in 1959 to investigate the transportation of gasoline to various gas stations (Dantizig G, Ramser J., 1959). The optimal model for allocating and transporting sources to multiple disaster sites following an earthquake is studied to minimize deaths. According to some researchers, the distribution of rescue materials via multiple modes of transportation is a network flow issue, and multi-material classification and construction of a dynamic distribution algorithm with time constraints. This function aims to minimize emergency supplies that are insufficient to meet demand. Several researchers have examined the sub-path ejection chain algorithm in the Vehicle Routing Problem (VRP) context under capacity and path length constraints. A stochastic vehicle routing model is proposed to address the evacuation problems of flood victims.

  • Studies on LRP

Location-Routing Problems (LRP) scholars are primarily concerned with the theoretical model and algorithm design. The location of the distribution center and the distribution route chosen are interdependent and mutually reinforcing in the actual operation of an emergency logistics system. The distribution center’s location will affect the distribution center’s size. As a result, it is necessary to consider emergency logistics distribution in the overall post-disaster emergency logistics distribution system.

Currently, LRP interpretation models include the facility location decision model (Chen, G. et al., 2016), robust programming (WANG, J. et al., 2009), and two-stage random mixed integer programming (Rawls, C. G., & Turnquist, M. A., 2010), among others. Additionally, some researchers examined the LRP model with uncertain parameters. They incorporate randomness into the model, convert stochastic variable demand to deterministic demand, and develop an LRP algorithm with multiple supply points and multiple vehicle Location-Routing for cost optimization with a single objective (Chan, Y., & Baker, S. F., 2005). Numerous others investigated the integrated optimization model of multi-mode, multi-stage, multi-objective emergency material allocation and multi-mode transportation in fuzzy situations and achieved dynamic location of emergency facilities and optimal dispatch of emergency materials via multi-objective dynamic weighting (Song yinghua, Wang lifag, 2017).

As a result of the increasing number of disasters, demand for humanitarian relief operations has increased. Each year, approximately 500 disasters occur, killing approximately 75,000 people and affecting another 200 million (Van Wassenhove, 2006). According to the International Federation of Red Cross and Red Crescent Societies (2005), disasters increased to approximately 707 between 1999 and 2003, affecting approximately 213 million people annually. We all witnessed the Tsunami in 2004, one of the most tragic natural disasters in human history, which killed approximately 165,708 people, impacted over 530,000 people, and resulted in economic losses of approximately US$ 4,451,600,000. Unfortunately, as a result of environmental degradation, changing weather patterns, human occupation of hazardous locations, rapid urbanization, and the spread of HIV/AIDS in developing countries, the number of natural and human-caused disasters are expected to increase in the coming years. According to Whybark (2007), the forces of population growth, human encroachment into risky areas, and changing climate patterns all work against our ability to effectively mitigate the effects of various catastrophic events, despite advancements in relevant technologies. As a result, the need to continue improving disaster relief is critical.

Logistics/supply chain activities have been one of the most critical issues in Disaster Relief Operations (DROs). Logistics encompasses assessing demand, procuring goods, allocating resources, and receiving, sorting, storing, tracing, and tracking deliveries in the DRO context. Using commercial logistics as an analogy, we can divide logistical activities into inbound logistics, which involves acquiring supplies from multiple provisioners and delivering them to the distribution channel, and outbound logistics, which involves delivering supplies from the distribution center to the affected areas (Sheu, 2007a, b).

Unlike commercial logistics, where the objective is to provide profitable services to customers, supply chain/logistics activities in DRO are focused on matching the demand and supply of goods such as food, shelter, tents, and medicine to those in need. While cost-effectiveness is critical, minimizing delivery time and maximizing supply availability are two of the most critical objectives in disaster relief or emergency SCs. According to Sheu, emergency logistics can be defined as;

Plan, manage and control the efficient flow of relief, information and services from origin to destination during an emergency. (2007) (Sheu, 2007a)

According to Thomas and Kopczak (2005), the primary challenge for disaster relief operations (DROs) may not be a lack of supplies but rather a lack of timely and sufficient distribution of those supplies to those in need. In many cases, the bottleneck in distributing supplies is caused by damaged infrastructure and a lack of accurate information about the number of supplies required, particularly during the first few days following the disaster. An oversupply of non-essential goods may slow down logistical responses in other scenarios. Following the December 2004 Tsunami that devastated several Asian regions, for example, the overwhelmed goods that arrived at airports exceeded the capacity of aid agencies to sort, store, and deliver goods effectively.


Chan, Y., & Baker, S. F. (2005). The multiple depot, multiple traveling salesmen facility-location problem: Vehicle range, service frequency, and heuristic implementations. Mathematical and Computer Modelling41(8-9), 1035-1053.

Chen, G., Zhang, J., & Fu, J. (2016). A multi-objective location-allocation model for emergency logistics in an uncertain information environment. China Safety Science Journal26(12), 163-168.

Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management science6(1), 80-91.

Fischetti, M., Ljubić, I., & Sinnl, M. (2017). Redesigning Benders decomposition for large-scale facility location. Management Science63(7), 2146-2162.

Kou, G., Zhao, X., & Ergu, D. (2013). Recommender System Based “Scenario-Response” Types Post-Disaster Emergency Supplies Planning. In The 19th International Conference on Industrial Engineering and Engineering Management (pp. 1225-1233). Springer, Berlin, Heidelberg.

Murali, P., Ordóñez, F., & Dessouky, M. M. (2012). Facility location under demand uncertainty: Response to a large-scale bio-terror attack. Socio-Economic Planning Sciences46(1), 78-87.

OU, Z., WANG, H., & JIANG, D. (2004). i, LU Baoliang, GAN Wenxu, LIANG Jing (The Institute of Modem Logistics, Logistics Engineering University, Chongqing 400016, China); Emergency Logistics [J]. Journal of Chongqing University (Natural Science Edition)3.

Rawls, C. G., & Turnquist, M. A. (2010). Pre-positioning of emergency supplies for disaster response. Transportation research part B: Methodological44(4), 521-534.

Sheu, J.B. (2007a) ‘Challenges of emergency logistics management’, Transportation Research Part E, Vol. 43, No. 6, pp.655–659.

Sheu, J.B. (2007b) ‘An emergency logistics distribution approach for quick response to urgent relief demand in disasters’, Transportation Research Part E, Vol. 43, No. 6, pp.687–709.

SONG Yinghua, NING Jingjing, LYU Wei, DU Lijing. (2018). “Research on dynamic LAP model of emergency logistics public-private collaboration in aftermath of earthquake,” China Safety Science Journal, Vol.28, No.3, pp. 173-178.

Song yinghua, Wang lifag. (2017). “Location-multimodal transportation problem for relief distribution in emergency logistics under fuzzy conditions,” China Safety Science Journal, vol.27, no.7, pp.169-174.

Taylor, D., & Pettit, S. (2009). A consideration of the relevance of lean supply chain concepts for humanitarian aid provision. International Journal of Services Technology and Management12(4), 430-444.

Thomas, A. (2004). Leveraging private expertise for humanitarian supply chains. Forced Migration Review21, 64-65.

Thomas, A.S. and Kopczak, L. (2005) From Logistics to Supply Chain Management: The Path Forward in the Humanitarian Sector, Fritz Institute, http://www.fritzinstitute.org/

Van Wassenhove, L.N. (2006) ‘Humanitarian aid logistics: supply chain management in high gear’, Journal of the Operational Research Society, Vol. 57, No. 5, pp.475–489.

WANG, J., ZHANG, L., & HUANG, J. (2009). The robust emergency logistics system based on uncertain demand. Mathematics inPractice and theory39(20), 53-60.

Whybark, D.C. (2007) ‘Issues in managing disaster relief inventories’, International Journal of Production Economics, Vol. 108, Nos. 1–2, pp.228–235.

WU, K., SONG, Y., & LYU, W. (2017). Research on siting of urban emergency resources depots and layout optimization considering rainstorm disaster and distribution route. China Safety Science Journal27(9), 170-174.