Humanitarian Logistics

In Chapter xv, Humanitarian Logistics Planning in Disaster Relief Operations, classifications of different types of disasters and their effects on human lives are given.

From: Logistics Operations and Management , 2011

Humanitarian Logistics Planning in Disaster Relief Operations

Ehsan Nikbakhsh , Reza Zanjirani Farahani , in Logistics Operations and Direction, 2011

15.4 Humanitarian Logistics

Humanitarian logistics is a branch of logistics dealing with the preparedness and response phases of a disaster management system. Humanitarian logistics can be divers as:

… the procedure of planning, implementing and controlling the efficient, cost-effective flow and storage of goods and materials, also as related information, from the point of origin to the point of consumption for the purpose of alleviating the suffering of vulnerable people. The function encompasses a range of activities, including preparedness, planning, procurement, transport, warehousing, tracking and tracing, and customs clearance [4].

Humanitarian logistics is crucial to the effectiveness and speed of relief operations and programs [24]. These logistics systems are usually required to procure, store, and transport food, water, medicine, and other supplies as well as human resources, necessary machinery and equipments, and the injured during the pre- and postdisaster periods.

The variety of logistical operations in disaster relief are and so extensive that they make humanitarian logistics the most expensive part of disaster relief operations, bookkeeping for well-nigh lxxx% of them [25]. Besides, relief operations require deploying a huge number of logistical vehicles, equipments, and personnel. For instance, in the Wenchuan earthquake on May 12, 2008, in China [26], 6 cargo-ship planes and nineteen helicopters were sent to the region in 24 hours. Virtually 5800 military medical and rescue personnel and 150 tons of supplies were conveyed to the affected surface area. The effective and efficient implementation of such a huge operation, considering the chaotic nature of the situation (e.1000., public panic and the devastation of transportation and communication infrastructures), is actually a complex and difficult one.

In the remaining parts of this section, we start encounter cursory comparison of humanitarian logistics systems and commercial supply chains in Section 15.4.1. So the principal stages of a generic humanitarian logistics system and required items and equipments in relief operations are discussed in Sections xv.4.ii and fifteen.4.three, respectively. For more information on concepts, methods, and models in the fields of humanitarian logistics and disaster operations direction, run across [3,21,24,25, 27,28].

15.4.1 Humanitarian Logistics Systems Versus Commercial Supply Chains

In practise, managing humanitarian logistics systems can be considered to exist very different from managing their commercial counterparts. This is mainly because of different inherent characteristics of need in each system. In commercial supply chains, the demand for the production is usually either estimated using proper forecasting techniques (i.due east., push production system) or initiated by the customer (i.e., pull production arrangement). Therefore, commercial supply chain managers try their all-time to eliminate the elements of doubt equally much every bit possible. However, the nature of demand in humanitarian logistics is very uncertain considering disaster time, location, and intensity—and hence exact relief requirements—are not known until after a disaster occurs. Based on the above explanations, the specific attributes of humanitarian logistics systems are as follows [24,25,27–32]:

1.

The missions of not-for-profit organizations are unlike from profit-making entities (i.due east., ensuring speedy and lifesaving responses instead of maximizing profits and reducing costs).

2.

In that location are more complicated merchandise-offs of objectives considering of different types of stakeholders, including governments, relief organizations, donors, and people affected by the disaster.

3.

Circuitous characteristics of demand include:

a.

incertitude of demand in features such as location, fourth dimension, type, and quantity;

b.

suddenly occurring demand and therefore urgently shorter pb times;

c.

high stakes associated with adequate and timely delivery.

4.

Complex operational weather exist because of:

a.

the chaotic nature of events during the postdisaster menstruation;

b.

a lack of resources (e.g., vehicles, equipments, food and h2o supplies, and medical supplies);

c.

a lack of proper access to vital infrastructures (e.one thousand., transportation and communication);

d.

a lack of experienced and professional man resources;

e.

a lack of security in the regions affected past the disaster.

v.

Coordination betwixt organizations participating in relief operations is often lacking.

6.

Relief organizations must act in accordance with humanity, neutrality, and principles of impartiality.

7.

At that place is often a politicized environment in which it is hard to maintain a humanitarian perspective to operations.

eight.

In that location is no way to punish ineffective organizations because of absence of the humanitarian logistics system terminal beneficiaries' vocalization in the performance appraisement and evaluation process. Since the afflicted people are non directly involved in this process, provided they are not expressionless, they usually cannot claim for more than than their damages, which is usually paid past insurances and governments, whereas in a commercial supply chain, an ineffective member has to pay for its own inefficiencies.

Information technology is worth noting that nearly all of the items listed are serious challenges to the operation of any supply chain organisation, not only humanitarian logistics systems. For example, a lack of proper transportation infrastructures forces humanitarian relief teams to use various modes of transportation ranging from avant-garde modes (and usually more expensive) such as helicopters and cargo planes to more primitive modes such as animals (due east.g., elephants and donkeys).

Although, until almost 10 years ago, logistics was considered to be not necessary by many humanitarian organizations [24,25,27–32], but today many of them are trying to implement many of the concepts and practices used by commercial supply bondage as advocated by researchers [24,xxx]. As well as mentioned in [24,25,29], commercial supply chains can learn a lot from humanitarian logistics systems, peculiarly well-nigh important topics such as supply chain risk and disruption management. Therefore, mapping the awarding of best practices of each side to the other is of import ongoing research in the field of supply chain management.

15.iv.2 Humanitarian Logistics Chain Structure

The humanitarian logistics concatenation construction consists of three main stages ( Figure 15.4): supply acquisition and procurement, pre-positioning and warehousing, and transportation [25]. The start stage in whatever humanitarian logistics chain is acquisition and procurement of necessary items and equipments. Any relief system is required to obtain its necessary items and equipments from local or global suppliers using various procurement techniques such equally direct purchasing and tenders. The main challenges in this stage are reducing the purchasing costs (considering the possible inflation of prices in local markets after disasters), ensuring the availability of supplies during the necessary times, reducing lead times, and coordinating in-kind donations with respect to other acquired items [29].

Figure 15.4. Humanitarian logistics chain structure [29].

After acquiring necessary items and equipments for the predisaster and postdisaster periods, the responsible relief organizations are obliged to pre-position and store their items and equipments in suitable locations considering the location of disaster-decumbent areas. Challenges of this phase include the high costs of opening and operating permanent warehouses [29], inventory holding costs, and possible deterioration of items. Also, there is a high gamble that warehouses will be destroyed during disasters, then those used for humanitarian logistics should have college resistance against disasters and be located wisely.

Finally, transportation is the last important phase of whatever humanitarian logistics chain in which human personnel, equipment, and necessary items are sent to predefined key distribution centers (CDCs), distribution intermediary points, local distribution centers, and finally regions affected past the disaster. Transportation during the postdisaster period is somehow the most difficult stage of humanitarian logistics even if dissimilar kinds of preventive measures and plans have been taken into business relationship [27]. This is mainly because transportation infrastructures and equipment are usually damaged and in poor condition after a disaster. Also, the geographical, weather conditions, and insecurities of the affected regions might restrict the types of send vehicles and their usage methods.

15.4.3 Required Items and Equipments in Humanitarian Logistics

Usually after a disaster in a region, there is a high demand for various items and equipments for facilitating the relief operations. Based on data from the Pan American Wellness Organization and the World Health System [33], an extended list of required items and equipment includes but is not express to the post-obit:

ane.

Food

2.

Water and germ-free items

3.

Environmental wellness equipments and items (e.g., water-handling equipment and items)

4.

Medicine (including both general pharmaceutical products and specific pharmaceutical products in possible cases of epidemics)

5.

Wellness kits and supplies for supporting health-care processes

6.

Field hospitals

7.

Vesture and blankets

eight.

Items associated with infants and children (e.g., instant milk, diapers, formula, and toys)

9.

Shelters and temporary housing facilities (e.thou., tents)

x.

Electrical power generating equipment

11.

Fuel (e.g., coal, gas, or oil)

12.

Field kitchen equipment and utensils

13.

Cleaning supplies

14.

Agricultural commodities and livestock

xv.

Specialized equipment for treatment hazardous materials

16.

Communication equipment

17.

Firefighting equipment

eighteen.

Debris-removal equipment and vehicles

19.

Construction equipment and vehicles

Proceed in heed that the list is general, so different items and various amounts of them would be required based on the characteristics of each disaster and its specific state of affairs. The urgency level of each item differs from others and is based on each specific situation; some of them are top priority and should be delivered during the early stages of postdisaster menstruation. For example, in case of an convulsion during cold winter, the timely commitment of enough clothing, blankets, and fuel is disquisitional. Also, in some countries specific items are required co-ordinate to their cultural rules. For case, delivering plenty chadors afterwards a disaster is an important feature of disaster relief operations in Muslim countries. Finally, another of import aspect of required items is the perishability of some items such every bit food and medicine. This characteristic calls for specifically designed ordering and inventory systems for such items because they cannot be feasibly stocked for longer terms.

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The Business and Government Interface

Darren J. Prokop , in Global Supply Chain Security and Management, 2017

Some Examples

The Federal Emergency Management Agency's (FEMA'south) choice of vendors to partner with represents a PPP in order to facilitate humanitarian logistics. The goal is to restore police force and order, as well as safety and security. The PPP relationship among the partners is not 1 of regulation or policing. In this way, information technology is very similar to a standard business organisation-to-business (B2B) relationship when market forces are driving information technology. Even so, as a authorities entity, FEMA may not experience the pressure that private sector firms practise in having to cull appropriate partners (based on, say, cost or quality) and setting upwardly strong contractual relationships. Indeed, the transaction price approach is noted for the view that contracts tin never be written to cover all possible contingencies. This means there is always room for opportunism (or working for cocky-interested private goals). In the public setting transaction costs may be college than otherwise. 5

Vendor partnership with FEMA is voluntary and the vendors await to be paid (unless they are willing to donate their services). To amend guess PPP structure, consider the seven "all-time practices" proposed past the National Council for Public-Private Partnerships half-dozen :

ane.

Public sector champion

2.

Statutory environment

3.

Public sector's organized structure

four.

Detailed contract (business plan)

v.

Clearly defined revenue stream

6.

Stakeholder support

vii.

Selection your partner advisedly (non necessarily the lowest bid)

In terms of these seven points the ones which are the about challenging for FEMA are (3), (4), and (7) because they go to the heart of whether or not the contract/plan is based on a proactive, reactive, or hybrid arroyo (every bit discussed in Chapter 4). However, in one case this is decided upon everything else becomes more workable.

C-TPAT, as discussed in Chapter iv, is also a PPP; but it is not market place-based. Regulations and policing mix in with CBP'due south interest in the partnership. As a voluntary program, C-TPAT relies on security exchanges betwixt the individual and public sectors in order to be successful. 7 Ex ante costs may exist reduced for authorities and business members since the security plans are collaborative. The negotiating and contracting process may exist more cordial. Ex mail service costs are probable to be reduced since authorities tin inspect facilities across US ports of entry and devote more attention to shippers and carriers which are non C-TPAT members. This gives the regime a meliorate feel for how secure the supply chain is. Also, monitoring, at least for C-TPAT members, would be more random than obligatory. This is because C-TPAT is designed to give the shippers and carriers faster clearance, which, for their part, lower their own ex post costs.

Customs brokers, which tin be hired to act as intermediaries between importers and CBP, have their own PPP with CBP. This is intended to smooth the process of community clearance. Similar to the voluntary nature of C-TPAT the National Customs Brokers and Forwarders Association of America (NCBFAA) partnered with CBP to course the Broker-Known Importer Program (BKIP). Customs brokers who are members of BKIP share trade intelligence with CBP. More importantly, for new importers or those changing their merchandise patterns, banker members may vouch for an importer before the item(s) reaches the port of entry. The agreement is that the broker has had an in-depth conversation with the importer-client about all applicable trade regulations. ACE is designed to receive "known importer" transmissions from broker members and, every bit such, may prevent whatsoever red flags from being raised during the import screening process. It is upwards to the banker to decide which clients, if any, to represent via BKIP. As well, it is the NCBFAA which provides the guidance for what to consider as opposed to CBP. Suggested characteristics of the client include 8 :

▪

Closeness and longevity of the relationship

▪

Volume and value of items handled

As such, this PPP is less restrictive than is C-TPAT. In fact, while CBP handles the periodic reviews and inspections of shippers and carriers, information technology is the BKIP member broker which takes on the role of reviewing the relationship with the importer. CBP appears willing to let the brokers do the vetting since they are considered a trusted source. Recall that brokers are licensed by the federal regime and well versed in trade compliance. CBP, however, does asking that details relating to the conversations between brokers and known importers be documented and made available on request.

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Experiences

Chen Reis , Tania Bernath , in Condign an International Humanitarian Aid Worker, 2017

6.6 Resources

Educational Resource—Chief's degrees

Association of Professional Schools in International Affairs: http://www.apsia.org/

Network of Humanitarian Activeness, International Clan of Universities: http://world wide web.nohanet.org

Educational Resources—Certificates

Child Protection: http://cpwg.net/what-we-do/capacity-edifice/cpie-diploma/

Humanitarian Logistics: https://world wide web.humanitarianlogistics.org

Humanitarian online resources: http://www.anarchapistemology.net/archives/1213

ELHRA: http://www.elrha.org/professional-development/

Online Courses and Training

DisasterReady: https://www.disasterready.org/courses

Red R UK: http://www.redr.org.united kingdom

Volunteer Opportunities

Peace Corps: https://www.peacecorps.gov/volunteer/volunteer-openings/

VSO: http://www.vsointernational.org

UNV: http://www.unv.org

Earth Volunteers Spider web: http://www.worldvolunteerweb.org

Funding for Internships

http://www.diversityabroad.com/guides/funding-study-internships-abroad/guide-to-funding-written report-internships-away

Crowd Funding Sites

https://www.gofundme.com

https://www.indiegogo.com/

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Humanitarian Drones: A Review and Research Calendar

Abderahman Rejeb , ... Horst Treiblmaier , in Internet of Things, 2021

4.2.3 Sustainability (T6)

In the context of HL and supply chains, sustainability is associated with economic development, rehabilitation, climatic change prevention, and ecology sustainability [46]. The utilize of drones in HL can especially foster the latter. For instance, Jeong et al. [53] contend that the technology has been unremarkably employed to evaluate extreme ecology events similar volcanic eruptions or the monitoring of air pollution. Gärtner et al. [39] posit that lightweight UAVs minimize energy consumption while increasing the functioning time for transportation to a disaster location. Similarly, Guettier and Lucas [44] indicate that UAVs with large wingspans enable medium-altitude flights and reduce energy consumption. As a event, the benefits of drones go across their humanitarian and life-saving value, fortifying the ecological and environmental dimensions of HL. Moreover, drones improve situational awareness, prophylactic, and working conditions of rescue teams because they can be leveraged when it is not applied, possible, or rubber to apply first responders [37]. Because the deployment of drones is not limited by having admission to operation transportation networks, it offers the possibility of helping endangered people, thus providing equity and inclusivity for people otherwise less likely to receive emergency aid. Thus, the application of drones in the field of HL and public safety services is expected to amplify the protective and preventive abilities of rescue and safe service providers, contributing to a more livable and secure society [27].

As summarized in Table 4, the bear upon of humanitarian drones on HL operation is axiomatic in terms of increased HL flexibility and responsiveness, cost reduction, and sustainability. Drones support responsiveness and toll-efficiency, which are viewed as the most disquisitional factors in humanitarian relief bondage [2,vi,31,53,113]. The transition of humanitarian organizations toward more sustainable HL can be significantly accelerated by the integration of drones as they further the cause of environmental and social sustainability.

Table 4. HL performance outcomes of humanitarian drones

HL performance outcomes Main findings References
Flexibility and responsiveness
•

Offer operational flexibility and effectiveness for disaster operations

•

Reach places which rescue teams can hardly approach

•

Provide quick first-hand information and assistance

•

Offer increased mobility and reliability of wireless networks in disaster regions

•

Ensure swift decision making and quick reactions to disasters

 [48,lx,66,80]
Price reduction
•

Increase cost-effectiveness of disaster operations

•

Reduce the toll of providing medical facilities

•

Provide a cheap and fast mode to monitor and survey disaster areas

•

Reduce the cost of spatial data collection

•

Provide more affordable designs and logistical options than manned aircraft

 [l,89,119,121]
Sustainability
•

Foster environmental sustainability

•

Enable equity and inclusivity

•

Minimize energy consumption

•

Reduce CO2 emissions

•

Increment the safety of disaster responders

•

Ameliorate the ecology and social performance of HL

 [26,37,39,43,53]

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OR models with stochastic components in disaster operations management: A literature survey

Maria Camila Hoyos , ... Raha Akhavan-Tabatabaei , in Computers & Industrial Engineering, 2015

4 Stochastic components

In their review on Incertitude in Humanitarian Logistics for Disaster Management, Liberatore et al. (2013) established five major parameters considering doubtfulness in humanitarian logistics: one. Need, that can comprehend the number of affected population and/or the quantity of required relief appurtenances, 2. Demand location and iii. Affected areas, parameters that are straight related to the demography of the location and the disaster's impact, iv. Supply, that considers the products' quality and availability in a post-disaster scenario, and finally, 5. Transportation network, where all possible amercement to the infrastructure or congestions are included. Based on this nomenclature but dividing the "afflicted areas" parameter into two different factors: one considering disaster hazard and occurrence dubiousness and the other 1 to consider the site or infrastructure damage, and including a new parameter "Human beliefs", we have conducted an assay on stochastic components for all of the 101 papers reviewed in this article, in which the stochastic component is shown forth with the technique used to model the doubt and the disaster phase when found. The summary of this analysis is presented in Table 1.

Table 1. Stochastic components.

Stochastic parameter Stochastic component Papers addressing the component Stage of disaster Approach to model stochasticity
Disaster hazard and occurrence uncertainty Earthquake and other disasters take a chance, exposure and vulnerability Bayraktarli et al. (2005), Yi and Özdamar (2007), El-Anwar et al. (2009), Guikema (2009), Kailiponi (2010), Legg et al. (2010), Barker and Haimes (2009), Hashemi and Alesheikh (2011), Lagaros and Karlaftis (2011), Qi and Altinakar (2011), Rottkemper et al. (2011), Verma and Gaukler (2011), Wang, Yang et al. (2012), Wang, Lin et al. (2012), Guanquan and Jinhui (2012), Maqsood and Huang (2013), Song et al. (2012), Van Steenbergen et al. (2012), Wang, Lin et al. (2012) Mitigation and Response Utilise of probability functions and hazard maps from major international building codes and studies, Monte Carlo simulation and statistical learning to establish run a risk and vulnerability parameters and distributions. Some also use Bayesian probability networks to study the interdependence between the different parameters
Ocurrence and severity of droughts, floods, storms, snowfall or other disasters Lee and Evangelista (2006), Mishra and Desai (2006), Brito and de Almeida (2009), Paulo and Pereira (2007), Li et al. (2007), Lee (2008), Wu et al. (2008), Nefeslioglu et al. (2008), Chauhan et al. (2010), Han et al. (2010), Pradhan and Lee (2010), Dong et al. (2011), Wang, Yang et al. (2012), Akgun (2012), Danso Amoako (2012), Jacobson et al. (2012), Kung et al. (2012), Liang et al. (2012), Liao et al. (2012), Xu et al. (2012) Mitigation Apply of ARIMA, Logistic regression, recursive multi-step artificial neural networks and Markov chains methods to model the behavior of the phenomenon, forecasting the disaster impacts or establishing accident scenario probabilities
Probability of a disaster occurrence Zhai et al. (2007), Verma and Gaukler (2011), Döyen et al. (2012), Wang, Yang et al. (2012), Wang, Lin et al. (2012) Mitigation and Response Consider multiple scenarios, each with a given probability, to cope with the uncertainty of disaster occurrence
Occurrence and severity of droughts Moreira et al. (2008) Mitigation Use of a loglinear model to develop brusk term prediction of drought severity classes. They too evaluated confidence intervals in order to understand the drought evolution and to estimate drought course transition probabilities

Demand doubtfulness Forecasting demand of commodities afterwards a disaster Xu et al. (2010) Preparedness and Response Utilise of EMD-ARIMA method to forecast the need of certain bolt subsequently a disaster impacts a certain region
Need doubtfulness Lee et al. (2009), Yao et al. (2009), Liu et al. (2012), Xu et al. (2012) Preparedness and Response Use of Genetic algorithms or example-base reasoning to establish demand in dissimilar points of the network or robust optimization with constraints regarding the uncertainty in demand
Ben-Tal et al. (2011) Preparedness and Response Apply of AARC methodology to deal with the uncertainty in demand, where each demand belongs to a box uncertainty set and use joint constraints to create an upper bound for possible values
Blecken et al. (2010), Li and Li (2012), Rottkemper et al. (2012), Tricoire et al. (2012), Fetter and Rakes (2011) Recovery Office of the demand in known (based on time serial analysis and causal models) while some other is variable given sure probabilities or distributions that depend on the operational risks nowadays in the region
Demand uncertainty and medical supply insufficiency Jia et al. (2007), Lee et al. (2009), Murali et al. (2011), Zhan and Liu (2011), Murali et al. (2012) Preparedness and Response Each demand signal is covered by multiple facilities, located at unlike distances and then that the overall coverage is achieved based on expected values of demand. Muroli and Zhan and Liu also used gamble constraints to include the probability distribution of need at each demand betoken

Demand location Demand incertitude, number of possible sites for temporary centers and resources bachelor Yi and Özdamar (2007), Balcik and Beamon (2008), Beraldi and Bruni (2009), Görmez et al. (2011), Günneç and Salman (2011), Zhan and Liu (2011), Chen and Miller-Hooks (2012) Preparedness and Response Apply of different scenarios, each with a known probability of touch and location, to cope with uncertainties in need and disaster location for the model
Demand location Chang and Hsueh (2007), Chang et al. (2007), Lei (2007), Van Hentenryck, Bent, and Coffrin (2010), Rawls and Turnquist (2010), Li et al. (2011), Rawls and Turnquist (2012) Preparedness and Response Employ of multiple disaster scenarios, with probabilities for each one, to establish possible demand locations based on GIS and other analysis functions of disaster potential/hazard
Dubiety in the location of an entity and the probability of detection of the entity Jotshi and Batta (2008) Response The entity is considered to exist uniformly distributed in an Eulerian graph

Man behavior Characteristics and possible decisions taken by an agent involved in an evacuation Massaguer et al. (2006), Kailiponi (2010) Preparedness and Response Defining different agents past providing a hateful and a variance for each of the agent's profile parameters and evaluating their behavior using multiple scenarios or by doing a probability assessment of the possible behaviors
Uncertainty in criteria weights Levy and Taji (2007) Response Use of ordinal information in group decision making to constitute the preferences of the criteria

Supply Supply (resource levels, vehicle availability) and need uncertainty Balcik et al. (2008) Response Use a rolling-horizon framework to capture the multiperiodicity of the problem, too equally its inherent supply and need uncertainties

Site or infrastructure damage Probability of impairment from an attack or disaster Zhuang and Bier (2007), Ezell et al. (2010) Mitigation Level of attacker effort is represented as a continuous variable, allowing the probability of damage to be modeled every bit a function of the levels of both assaulter try/disaster magnitude, vulnerability and defensive investment
Uncertainty due to possible inaccuracy in determining the consequences (in terms of emergency response) of bridge collapse. Bana e Costa et al. (2008) Mitigation Develop a sensitivity analysis introducing in the model an interval of dubiousness of 0.5   h effectually the consequence estimated for each construction
Level of damage and probability of accuracy in information on route weather condition Jotshi et al. (2009) Response Each road is given a level of damage based on information given by civilians, police officers, fire-fighters etc., and a probability of accurateness of this data, based on the source and previous information of the bespeak
Level of harm of infrastructure Frey and Butenuth (2010) Response Use of a Bayesian network to model the behavior of the system later on a disaster, and establish its trafficability
Survival probability of an structure (bridges, highways, etc.) Peeta, Sibel Salman, Gunnec, and Viswanath (2010), Rawls and Turnquist (2010), Cimellaro et al. (2010), Hassin et al. (2010) Response The survival probability is obtained from experts opinions or analysis taking into account the intensity of the issue and loss estimation functions, with the possibility of increasing the survival probability or resilience by investing more resource on given structures
Damage level of a structure (bridges, highways, etc.) Mardiyono et al. (2012) Response Evolution of a backpropagation bogus neural network model to constitute the level of impairment of a structure given a certain disaster

Trafficability of transportation network Reliability of a route Vitoriano et al. (2011), Nolz et al. (2011), Liberatore et al. (2013), Yi and Kumar (2007), Ukkusuri and Yushimito (2008), Ren et al. (2012), Song, He and Zhang (2009) Response Establish an attribute of reliability which in this example is given by the probability to cross completely an arc in the route, based on damage expected in each region

Trafficability of transportation network Emergency calls and travelling times Beraldi and Bruni (2009), Huang and Fan (2011), Noyan (2012) Preparedness and Response Incertitude is handled past including in the traditional two phase framework stochastic constraints or their probabilistic counterparts allowing the system manager to evaluate different solutions by varying reliability levels
Occurrence of traffic events that may cause filibuster in evacuation Fonseca et al. (2009) Preparedness and Response Time of arrival, final destination exit and accident proneness are assigned based on cumulative probability distributions based on empirical data collected in data collection phase of the project. Other variables equally accident cistron and accident delay time are generated every bit user-defined probability distributions given by expert officials and the entry of entities is treated as a stochastic process with Poisson or Binomial distributions
Probabilities of bundle loss after the event and routing probabilities Heegaard and Trivedi (2009) Response Routing probabilities are imported from ns-two simulations earlier and after a failure, rerouting and repair. The routing probabilities tin can likewise be obtained from operational networks
Congestion and time delays on route links and blockage probabilities Heegaard and Trivedi (2009), Chen et al. (2012) Response Utilize of M/1000/c/c state dependent queuing models to cope with congestion and time delays on road links and stochastic constraints to control blockage probabilities

Taking into account these results, it can be seen that most of the mitigation inquiry is developed with the objective of reducing the uncertainties in disaster run a risk or occurrence, by developing models to forecast disaster's bear on or models that could explain in a better way the behavior of the phenomena. The use of probability functions from known international codes or studies, or the employ of ARIMA, logistic regression and Artificial Network models and Markov bondage are very pop in this stage. In the preparedness and response phases it tin can be stated that the demand quantity and location uncertainty is a major concern, given the large number of papers that accept this gene into business relationship. Withal it tin can be seen how uncertainty factors such as infrastructure reliability and transportation complications are acquiring more popularity. The apply of multiple scenario models, undirected or directed graphs, stochastic programming and robust optimization assay are among the near popular techniques to model the uncertainties in the preparedness and response stages.

A cistron that is still in its early stages is the modeling of human beliefs in mail service-disaster events, an area that may provide interesting and useful information for many DOM models, and and then is seen as a very promising future research management. Finally it can be said that as in the previous analysis the recovery stage has not been very popular, however the methodologies used in prior stages, mainly in the response stage, can be used to tackle the stochastic components of the recovery stage.

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Procurement in humanitarian organizations: Body of knowledge and practitioner'southward challenges

Mohammad Moshtari , ... Paulo Gonçalves , in International Periodical of Product Economics, 2021

Appendix A

Count of Papers Reviewed by Journal of Publication.

Journals Number of publications
Periodical of Humanitarian Logistics and Supply Chain Direction eleven
Computers & Industrial Engineering 3
International Periodical of Disaster Risk Reduction 3
International Journal of Concrete Distribution & Logistics Management iii
Periodical of Public Procurement 3
Production and Operations Management 3
Annals of Operations Inquiry ii
European Journal of Operational Research 2
International Journal of Procurement Management ii
International Periodical of Production Economics 2
Socio-Economical Planning Sciences 2
Determination Sciences 1
Disaster Prevention and Direction 1
International Journal of Disaster Resilience in the Built Environment 1
International Periodical of Operations & Production Management 1
International Periodical of Production Inquiry 1
International Journal of Services and Operations Direction 1
Journal of Business Logistics i
Journal of Homeland Security and Emergency Management i
Periodical of Purchasing and Supply Management 1
Omega 1
SAGE Open up one
Supply Chain Management: An International Journal 1
Transportation Journal 1
Transportation Research Part E-Logistics 1
World Development 1

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Emergency management systems later disastrous earthquakes using optimization methods: A comprehensive review

A. Kaveh , ... R. Mahdipour Moghanni , in Advances in Applied science Software, 2020

3 Emergency response planning

A disaster is a sudden, calamitous event that seriously disrupts the functioning of a customs or society and causes human, material, and economic or ecology losses that exceed the community's or club's power to cope using its resources (IFRC: http://www.ifrc.org; EMDAT; IRDR Data working group)

(VULNERABILITY+ Chance) / CAPACITY = DISASTER

A sample disaster nomenclature is shown in Fig. three. It is worth noting that in most cases, a disaster involves multiple hazards.

Fig 3

Fig. 3. Disasters nomenclature.

Humanitarian logistics refers to the processes and systems involving the mobilization of people, resources, and expertise to aid vulnerable communities affected past natural disasters and complex emergencies, reducing the loss of lives and relieving human suffering [59]. Accordingly, EM is classified into 2 phases: Pre-disaster operations, and Post-disaster operations.

Pre-disaster operations deal with the demand to identify, inventory, dispatch, mobilize, ship, recover, demobilize, relief requirements, and accurately runway human and cloth resources [60]. In Table 4, a summary of the representative Emergency logistics scheduling problems (ELSP) papers solved by different techniques is listed.

Table 4. Summary of the representative ELSP papers.

Method Research group, publishing year Method Research group, publishing yr
Dynamic integer linear programming (Sheu, 2007) [61] Metaheuristic algorithms (Peng et al., 2009 [62]; Yi & Kumar, 2007 [63]; Yuan & Wang, 2009 [64]; Hu, 2011 [65]; Zhang, 2013 [66])
Goal programming (Vitoriano et al., 2011) [67] Game theory (Reyes, 2005 [68])
Greedy method (Ozdamar & Yi, 2008) [69] Fuzzy multi-objective programming (Sheu, 2007 [61]; Yang et al., 2007 [70])

Post-disaster operations deal with management, allocation, and distribution of emergency rescue materials such every bit shelters, food, water, tablets, protective equipment, and and so forth [71, 72].

Equally can be noticed, response to a wide range of requirements is a tightly interwoven human relationship. Fig. iv presents a sample response framework in the United States based on Greer research [73].

Fig 4

Fig. 4. A response framework in the United states of america.

An overview of the researches in the field of optimal emergency response planning including the Bailiwick of investigation, optimization method, and example studies used is provided in Table 5.

Tabular array 5. Characteristics of inquiry in optimal emergency response planning.

Inquiry grouping, publication yr Subject area of investigation Applied methods Case study
(Narzisi et al., 2006) [74] Emergency response planning NSGA-Ii, PAES –
(Levy & Taji, 2007) [75] Improving controlling transparency, emergency direction effectiveness, and user satisfaction Quadratic Mathematical Programming Brandon, Manitoba
(Rolland et al., 2010) [76] Proposing a conclusion-back up system for disaster response and recovery Forward Loading (FL) heuristic –
(Georgiadou et al., 2010) [77] Proposing a methodology for multi-objective optimization of emergency response planning in case of a major blow SPEA Two West Attica, Greece
(Caunhye et al., 2012) [78] Reviewing optimization models used in the field of emergency logistics – –
(Barzinpour & Esmaeili, 2014) [79] Maximizing coverage of urban populations and minimize logistics costs past developing a multi-objective model Multi-objective Mixed-Integer Linear Programming (MILP) Tehran, Iran
(Zheng et al., 2015) [80] Comparing Several state-of-art methods for the emergency transportation trouble EAs –
(Naser, 2019) [81] To minimize disruptions to supply chain operations and/or evacuations during an emergency. Bio-Inspired Machine Learning Algorithms –
(Mohammadi et al., 2016) [82] Prepositioning emergency earthquake response supplies Multi-objective PSO (MOPSO) Tehran, Iran

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Sustainable supply chain management towards disruption and organizational ambidexterity: A information driven analysis

Tat-Dat Bui , ... Ming Yard. Lim , in Sustainable Production and Consumption, 2021

Appendix I. FDM round 1 questionnaire

Please evaluate the performance/importance level of each indicator below to Sustainable supply chain direction towards disruption and organizational ambidexterity by marking the blank
Indicators Extreme Demonstrated Stiff Moderate Equal
one Adaptability
2 Additive Manufacturing
3 Agility
4 Ambidexterity
5 Artificial Intelligence
6 Asymmetric Information
seven Backup Supplier
viii Benders Decomposition
ix Bifurcation
10 Big Information
xi Blockchain Technology
12 Bounded Rationality
thirteen Bullwhip Outcome
14 Business Continuity
15 Heir-apparent-Supplier Relationships
xvi Anarchy Control
17 Climatic change
18 Closed-Loop Supply Chain
nineteen Cloud Calculating
xx Collaboration
21 Contest
22 Competitive Reward
23 Complexity
24 Conditional Value-At-Risk
25 Contingency Planning
26 Coordination
27 Corporate Social Responsibleness
28 Crunch Direction
29 Critical Infrastructure
xxx Decision Making
31 Demand Disruption
32 Demand Dubiousness
33 Design
34 Disaster Management
35 Disaster Recovery
36 Disaster Response
37 Disruption
38 Disruption Management
39 Confusing Innovation
40 Disruptive Technology
41 Distributed Ledger Engineering science
42 Distribution
43 Dual Sourcing
44 Dual-Aqueduct
45 Dynamic Capabilities
46 East-Commerce
47 Economic Crisis
48 Emergency Management
49 Emergency Response
fifty Energy Security
51 Exploitation
52 Exploration
53 Facility Location
54 Fiscal Crisis
55 Financial Performance
56 Flexibility
57 Global Supply Chain
58 Global Value Chain
59 Globalization
60 Governance
61 Green Supply Chain
62 Humanitarian Logistics
63 Humanitarian Supply Chain
64 Industry four.0
65 Information Asymmetry
66 Data Sharing
67 Data Systems
68 Information Technology
(connected on side by side page)
Please evaluate the performance/importance level of each indicator beneath to Sustainable supply chain direction towards disruption and organizational ambidexterity by marking the blank
Indicators Extreme Demonstrated Strong Moderate Equal
69 Infrastructure
seventy Innovation
71 International Trade
72 Cyberspace of Things
73 Interpretive Structural Modelling
74 Inventory
75 Knowledge Direction
76 Lagrangian Relaxation
77 Lean
78 Life Cycle Assessment
79 Logistics
eighty Machine Learning
81 Manufacturing
82 Market place Disruption
83 Marketing
84 Multi-Agent System
85 Natural Disasters
86 Network Design
87 Operations Management
88 Optimization
89 Outsourcing
90 Performance
91 Port Resilience
92 Product Disruption
93 Purchasing
94 Quality
95 Quantity Discount
96 Recovery
97 Recycling
98 Reliability
99 Remanufacturing
100 Resilience
101 Resilient Supply Concatenation
102 Responsiveness
103 Acquirement Sharing Contract
104 Reverse Logistics
105 Ripple Effect
106 Run a risk Management
107 Prophylactic
108 Safety Stock
109 Scenario Planning
110 Security
111 Service Level
112 Smart Contracts
113 Social Responsibility
114 Sourcing Strategy
115 Strategic Planning
116 Supplier Selection
117 Supply Chain Agility
118 Supply Chain Ambidexterity
119 Supply Chain Collaboration
120 Supply Chain Coordination
121 Supply Chain Pattern
122 Supply Chain Disruption
123 Supply Chain Disruptions
124 Supply Chain Dynamics
125 Supply Concatenation Engineering
126 Supply Chain Finance
127 Supply Concatenation Flexibility
128 Supply Concatenation Integration
129 Supply Chain Network Design
130 Supply Chain Performance
131 Supply Chain Resilience
132 Supply Chain Chance
133 Supply Chain Risk Management
134 Supply Chain Risks
135 Supply Chain Security
136 Supply Chain Vulnerability
137 Supply Disruption
138 Supply Disruptions
139 Sustainability
140 Sustainable Development
141 Sustainable Supply Chain
(continued on adjacent page)
Please evaluate the performance/importance level of each indicator beneath to Sustainable supply concatenation direction towards disruption and organizational ambidexterity past mark the bare
Indicators Extreme Demonstrated Strong Moderate Equal
142 Sustainable Supply Concatenation Management
143 System Dynamics
144 Engineering science
145 Terrorism
146 Traceability
147 Trade
148 Merchandise Credit
149 Transportation
150 Trust
151 Uncertain Need
152 Uncertainty
153 Value Concatenation
154 Variational Inequalities
155 Vulnerability

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