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Concepts in Disaster Management

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Published: 12th Dec 2019

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2.1 Broader Views on Disaster Management

2.1.1 Definition of Disaster

Disaster has been defined in some different ways. Indeed, there is no precise definition for a disaster (Eshghi & Larson, 2008).

In complete form, Emergency Events Database (EM-DAT) defines disasters as “A situation or event which overwhelms local capacity, necessitating a request to the national or international level for external assistance, or is recognized as such by a multilateral agency or by at least two sources, such as national, regional or international assistance groups and the media” (Centre for Research on the Epidemiology of Disasters (CRED), 2004). Below et al. (2007) propose “an accumulation of widespread losses over multiple economic sectors, associated with a natural hazard event, that overwhelms the ability of the affected population to cope” as a definition of a disaster. International Federation on Red Cross and Red Crescent (IFRC) defines a disaster as “a sudden, calamitous event that seriously disrupts the functioning of a community or society and causes human, material, and economic or environmental losses that exceed the community’s or society’s ability to cope using its own resources…” (IFRC, 2008). van Wassenhove (2006) proposes “a disruption that physically affects a system as a whole and threatens its priorities and goals” as a definition of disaster, while Asian Disaster Reduction Center (ADRC, 2008) defines disaster as “a serious disruption of the functioning of society, causing widespread human, material or environmental losses which exceed the ability of affected society to cope using only its own resources”, which is similar with Reliefweb’s (2008) definition. Emergency Management Australia (EMA, 2008) defines disaster as “a serious disruption to community life which threatens or causes death or injury in that community and/or damage to property which is beyond the day-today capacity of the prescribed statutory authorities and which requires special mobilization and organization of resources other than those normally available to those authorities”, while emergency is defined as ‘An event, actual or imminent, which endangers or threatens to endanger life, property or the environment, and which requires a significant and coordinated response.’ (EMA, 2008).

2.1.2 Disaster Types

With a wide variability of disaster definition, it is understandable to have different initial classifications for disasters (Eshghi & Larson, 2008; Shaluf 2007a, b). Canadian Disaster Database (2008) categorises disasters into five different types as summarized in Table 1.

Table 1. Disaster types

(Source: Canadian Disaster Database, 2008)

Disaster types



Epidemic, infestation


Earthquake, landslide, tsunami

Meteorological and hydrological

Cold wave, drought, flood, hail/ thunderstorm, heat wave, hurricane/ typhoon, snow avalanche, storm surges, storm-freezing rain, storm-unspecified/ other, storm-winter, tornado, wildfire


Terrorism, civil unrest


Accident-industrial, accident-other, accident-transport, fire, hazardous chemicals

van Wassenhove (2006) proposes a metrics (see Table 2) to understand disasters.

Table 2. Categorization of disasters based on van Wassenhove (2006)




Earthquake, hurricane, tornado

Terrorist attack, coup d’etat, chemical leak


Famine, drought, poverty

Political crisis, refugee crisis

In general, Shaluf (2007a, b) categorises disasters into three types:

  1. Natural disasters, which are catastrophic events resulting from natural causes such as volcanic eruptions, tornadoes, earthquakes, etc.
  2. Man made disasters, which are those catastrophic events that result from human decisions.
  3. Hybrid disasters are those disasters that result from both human error and natural forces.

In further detail, Shaluf (2007b) breaks down each type of disasters and gives examples and characteristics, as can be seen in Table 3.

Table 3. Disaster types, taken from Shaluf (2007b)

Disaster type



Name of disasters


A natural disaster is a natural phenomenon;

A natural disaster is an unplanned and socially disruptive event with a sudden and severe disruptive effect;

A natural disaster is single event over which no human has control;

The impact of natural disaster is localized to a geographical region and specific time period;

The consequences of a natural disaster are felt at the place and time of its occurrence;

The disaster can be a high-impact disaster (e.g. a flood) that has a greater direct effect on the community over a longer period;

Rapid onset disasters include earthquakes, flash floods, hurricanes, volcanic eruptions, landslides, tsunamis, slow onset disasters, droughts, floods, and epidemics

Natural phenomena beneath the earth’s surface



Volcanic eruptions

Topographical phenomena



Meteorological/ hydrological phenomena

Windstorms (Cyclones, typhoons, hurricanes)


Hailstorms and snowstorms

Sea surges



Heat waves/ could waves

Biological phenomena

Infestations (locust swarms, mealy bug)

Epidemics (cholera, dengue, ebola, malaria, measles, meningitis, yellow fever, HIV/ AIDS, tuberculosis)


Characteristics of socio-technical disasters:

A socio-technical disaster is a man-made event;

A socio-technical disaster occurs in an organisation due to the interaction between internal factors and external factors;

It arises suddenly: when the disaster occurs it does so as a shock;

A socio-technical disaster is a complex system of interdependence;

The impact of a socio-technical disaster sometimes transcends geographical boundaries and can even have trans-generational effects (e.g. Three Mile Island, Bhopal, Chernobyl);

Socio-technical disasters do not always have their worst consequences at the point of occurrence; the worst effects can occur long after the event;

Socio-technical disasters are characterized by a low probability/ high consequences event;

Sudden-impact disasters (e.g. air/road/rail accident) are usually of short duration and have a limited direct effect on the local community;

Socio-technical disasters arise not because of a single factor but of accumulated unnoticed events;

Disaster involves management procedures which must be maintained, and management problems must be coped with under the conditions of a major technical emergency involving threats of injury and loss of life;

Rapid onset disasters include fires, technological disasters, industrial accidents, and transportation accidents;

An inquiry report is required


Technological disasters


Explotions (munitions explosions, chemical explosions, nuclear explosions, mine explosions)


Toxic release

Pollutions (pollution, acid rain, chemical pollution, atmospheric pollution)

Structural collapse of physical assets

Transportation disasters

Air disasters

Land disasters

Sea disasters

Stadia or other public places failures


Structural collapse

Crowd stampede

Production failure

Computer system breakdown

Distribution of defective products



Civil war between armed groups in the same country

Civil strikes

Civil disorder

Bomb threats/ terrorist attack


Conventional war

War between two armies from different countries



Non-conventional war





The characteristics of a hybrid disaster can be the characteristics of both man-made and natural disasters

Natural and man-made events

Floods ravage community built on known floodplain

Location of residential premises, factories, etc., at the foot of an active volcano, or in an avalanche area


Slightly different from those, EM-DAT (2008a) classifies disasters into three groups:

  1. Natural disasters
  2. Technological disasters
  3. Complex emergencies

Regarding its scope in terms of sufferer number and/ or geographic areas affected, Gad-el-Hak (2008) distinguishes disasters into five categories as can be seen in Table 4.

Table 4. Disaster scope in terms of number of victims and/ or geographic area affected

(Source: Gad-el-Hak, 2008)



No. of sufferers

Geographic areas affected

Scope I

Small disaster

< 10 persons


< 1 km2

Scope II

Medium disaster

10-100 persons


1-10 km2

Scope III

Large disaster

100-1,000 persons


10-100 km2

Scope IV

Enormous disaster

1,000-104 persons


100-1,000 km2

Scope V

Gargantuan disaster

> 104 persons


> 1,000 km2

While the definition of natural disasters and technological disasters are principally the same as those proposed by Shaluf (2007a, b), complex emergencies need a further exploration. Alballa-Bertrand (see Alballa-Bertrand, 2000) proposes the following definition for a complex humanitarian emergency or, in short, complex emergency:

‘A purposeful and unlikely neutral response, intended mostly to counteract the worse effects of the massive human destitution that derive from an overt political phenomenon, which takes the form of a violent, entrenched and long-lasting factionalist conflict or imposition with ultimate institutional aims’.

On the other hand, ReliefWeb (2008) defines a complex emergency as “A multifaceted humanitarian crisis in a country, region or society where there is a total or considerable breakdown of authority resulting from internal or external conflict and which requires a multi-sectoral, international response that goes beyond the mandate or capacity of any single agency and/or the ongoing UN country program. Such emergencies have, in particular, a devastating effect on children and women, and call for a complex range of responses.” While Complex Emergency Database (CE-DAT) (2008) defines complex emergency as all crises characterized by extreme vulnerability that display the following features:

  1. There exist the unwillingness or incapability of the government to give effective response, leading call for external assistance;
  2. Political oppression or armed conflict;
  3. Displacement;
  4. Increased mortality.

2.1.3 The Increasing Trend of Disaster Occurrences

Lichterman (1999) predicts that the frequency of disasters and their effects seem to be increasing. By reviewing various related published sources from 1900-2005, Eshghi and Larson (2008) confirm Lichterman’s prediction. A disaster leads to a severe trouble of society, including extensive human misery and physical loss or damage (Davis & Lambert, 2002). Both natural and man-made disasters are likely to raise another five-fold over the next fifty years (from the year 2005) due to environmental degradation, rapid urbanization and the spread of HIV/AIDS in less developed world (Thomas & Kopczak, 2005). More than 250 million people in the world are affected by disasters every year (IFRC, 2008). In the sense of natural disasters – which are then divided into biological, geophysical, climatological, hydrological, and meteorological disasters -, CRED (see Scheuren et al., 2008) reports that there were 414 natural disaster occurrences (excluding biological disasters) in year 2007 which killed 16847 persons, affected more than 211 million others and caused over 74.9 US$ billion in economic damages. Until year 2004, over 90 percent of natural disasters occurred in developing countries (United Nations ISDR, 2004).

By including biological disasters and regrouping natural disasters into three different categories, as follows:

  1. Hydro-meteorological disasters: comprising floods and wave surges, storms, droughts and related disasters (extreme temperatures and forest/scrub fires), and landslides & avalanches;
  2. Geophysical disasters: earthquakes & tsunamis and volcanic eruptions fall into this category;
  3. Biological disasters: consisting of epidemics and insect infestations;

International Strategy for Disaster Reduction (ISDR) (2008) provides data which shows that there is an increasing trend on the occurrences of natural disasters from 1900 to 2005, as can be seen in Table 5.

Table 5. Distribution of natural disasters: by origin

(1900-2005, by decades*)

*) 2000-2005, six year period

The increasing trends of the occurrences of natural disasters between 1900-June 2008 is also documented in EM-DAT (2008b).

Regarding the victims, there were 3,470,162,961 people affected by natural disasters for the period of 1991-2005 with a total of 960,502 deaths. Most of the victims (98.1% of people affected and 92.1% of people killed) were located in developing countries and least-developed countries (IFRC, 2008).

2.1.4 Disaster Management

Disaster management – also known as emergency management (Reliefweb, 2008) – is defined as comprehensive approach and activities to reduce the adverse impacts of disasters (Reliefweb, 2008), while disaster operations could be considered as the set of activities that are performed before, during, and after a disaster which are aimed at preventing loss of human life, reducing its impact on the economy, and returning to a normal situation (Altay & Green III, 2006). Using the terminology of disaster relief operations (DRO) as substitute to disaster operations, Pujawan et al. (2009) state that DRO consists of a variety of activities such as assessing demands, acquiring commodities, finding out priorities as well as receiving, classifying, storing, tracing and tracking deliveries. Regarding its phases, disaster management could be divided into four phases (Altay & Green III, 2006): disaster mitigation, disaster preparedness, disaster response, and disaster recovery.

2.1.5 The Importance of Logistics in Disaster Management

Logistics could be defined as follows (see Sheu, 2007a: 655):

“Logistics is the process of planning, implementing, and controlling the efficient, effective flow and storage of goods, services and related information from the point of origin to the point of consumption for the purpose of conforming to customers[‘] requirements at the lowest total cost.”

Its system operation consists of network design, information, transportation, inventory, warehousing, material handling, and packaging (see Wu & Huang, 2007: 429). There are several Operational Research (OR) techniques utilised in logistics context, including the use of transportation model to determine the location of warehouses and the use of assignment/ allocation model to locate production facilities (Slats et al., 1995: 12), to name a few.

In particular, humanitarian logistics could be defined as “the process of planning, implementing and control­ling the efficient, cost-effective flow and storage of goods and materials, as well as related information, from point of origin to point of consump­tion for the purpose of meeting the end beneficiary’s requirements” (Thomas & Mizushima, January 2005). Similarly, Thomas and Kopczak (2005) define it as “the process of planning, implementing and controlling the efficient, cost-effective flow and storage of goods and materials, as well as related information, from the point of origin to the point of consumption for the purpose of alleviating the suffering of vulnerable people”. Whereas Sheu (2007a) proposes ‘‘a process of planning, managing and controlling the efficient flows of relief, information, and services from the points of origin to the points of destination to meet the urgent needs of the affected people under emergency conditions” as a definition of emergency logistics.

Moreover, disaster relief is usually put aside for sudden upheavals such as natural disasters (earthquakes, avalanches, hurricanes, floods, fires, volcano eruptions, etc.) and very few man-made disasters such as terrorist acts or nuclear disasters (Kovács & Spens, 2007). Relief itself could be understood as “assistance and/or intervention during or after disaster to meet the life preservation and basic subsistence needs. It can be of emergency or protracted duration” (Reliefweb, 2008).

It has been already generally well-known that logistics play a vital role in emergency management. Sheu (2007a) declares that, due to the possibility of disasters’ occurrences anytime around the world with huge effects, emergency logistics management had appeared as a worldwide-noticeable subject matter. People which are affected by disasters and are uprooted from their rights for food, housing, livelihood and other means of supporting themselves need the delivery of food, medicine, tents, sanitation equipment, tools and other necessities (Whybark, 2007). The science of logistics and supply chain management is becoming more vital for humanitarians (van Wassenhove, 2006), and “the subject of disaster management is an absolutely fascinating one that is growing in importance” (van Wassenhove, 2003: 19). Oloruntoba (2005) states that, regarding the Indian Ocean tsunami context, the scale of damage and subsequent response lead to problems of coordination, transportation and distribution among responding groups. In other affected areas of the Indian Ocean tsunami, Thomas (summer/fall 2006) reports that, at the 60-day point, regardless of the enormous relief efforts, only 60% of the families reported receiving well-timed and sufficient aid. It is therefore acceptable to conclude that good logistics planning plays an important role to the success of an emergency program (Davis & Lambert, 2002: 109).

Humanitarian logistics is essential to disaster relief for some reasons (Thomas & Kopczak, 2005):

  1. It is crucial to the effectiveness and speed of response for main humanitarian programs, such as health, food, shelter, water, and sanitation;
  2. It can be one of the most expensive elements of a relief effort as it includes procurement and transportation;
  3. Since the logistics department handles tracking of commodities through the supply chain, it is often the repository of data that can be analyzed to offer post-event knowledge.

In his paper, McEntire (1999) states that the disaster studies must discover ways to improve the provision of relief after certain catastrophe hits. This statement is in line with Perry’s (2007) finding which accentuates the availability of logistician cadres as a key element of disaster response, as part of needs assessment and for procuring, transporting, and distributing the relief provisions. Regarding the relief of the Indian Ocean tsunami, the humanitarian organizations providing those relieves acknowledged that relief can and needs to be faster and more efficient (Thomas, 2005). Together with hurricane “Katrina” disaster, the Indian Ocean tsunami lead to the gap of “the inability to connect the aid provided with the aid received” (Thomas, 2005) in spite of the unprecedented giving during those two misfortunes. It is also pointed out by Tolentino Jr. (2007) that the Indian Ocean tsunami has provided the will to radically improve disaster management and planning, an issue Trim’s (2004: 224) research agrees with, in a broader disaster relief context. Furthermore, the development of new technology for track/trace and disaster relief supply chains is proposed as one of ways to improve the delivery of humanitarian relief (Baluch, 2007). In the context of the participation of non-governmental organizations (NGOs) in worldwide emergencies (e.g. volcanic eruptions, earthquakes, floods, war), Beamon and Kotleba (2006) point out that the capability of an NGO’s supply chain and logistics operations directly influences the success of a relief effort. Whereas Pujawan et al. (2009) propose information visibility, coordination, accountability, and professionalism as successful requirements of logistics for DRO.

2.2 Some Previous Works in Logistics Management

The following paragraphs will give a short overview on several aspects in logistics management, especially those which are perceived as having relevance with the current research. They include distribution network design problem, location-allocation problem (LAP), vehicle routing problem (VRP), and location-routing problem (LRP), respectively.

2.2.1 Distribution Network Design Problem

Citing Chopra (2003), distribution can be seen as “the steps taken to move and store a product from the supplier stage to a customer stage in the supply chain”. While distribution networks can be defined as “networks that carry the flow of some commodity or entity, using a routing rule that is intended to be effective and even optimal” (Whittle, 2007), and distribution network itself could be viewed as similar with the terminology producer network (Ambrosino & Scutellà, 2005: 611).

Distribution network design problem tackles the issues of optimizing the flows of commodities through an existing distribution network as well as improving the performance of the existing network by selecting the most appropriate setting of the facilities in the network aimed at satisfying the company’s goal at one hand and minimising the overall costs at the other hand (Ambrosino & Scutellà, 2005: 611). It involves facility location, transportation and inventory decisions (Ambrosino & Scutellà, 2005: 611). In other words, the aim of distribution network design problem is on deciding the best way of moving goods or products from resource/ supply points to destination/ demand points which is performed by determining the structure of the network, in a such a way that the customer demands are satisfied and the total distribution costs are minimized (Ambrosino et al., 2009: 442). In Amiri’s (2006: 567-568) paper, distribution network design is stated as involving the simultaneous decisions on the best settings of both plants and warehouses and on the best strategy in the sense of product distribution from the plants to the warehouses and from the warehouses to the customers, respectively.

Meanwhile, the term “distribution system design” refers to “the strategic design of the logistics infrastructure and logistics strategy to deliver products from one or more sources to the customers” (Goetschalckx, 2008: 13-1) and – similar to Ambrosino et al.’s (2009) statement on distribution network design problem – focuses on five phases of interconnected decisions, as follows (Goetschalckx, 2008: 13-2):

  1. Establishing the appropriate quantity of distribution centers (DCs);
  2. Setting up the location of each DC;
  3. Allocating customers to each DC;
  4. Allocating appropriate commodities to each DC; and
  5. Determining the throughput and storage capacity of each DC.

Various models and approaches that have been built for designing distribution system or distribution network, to name a few, are (Goetschalckx, 2008: 13-8-13-15; Lapierre et al., 2004): K-median model, location-allocation model, warehouse location model, Geoffrion and Graves distribution system design model, models that focus on mathematical description of cost functions on each route in order to incorporate returns to scale, models of which concentration are in shipments on hub-to-hub routes regarding discounts, and models that aim at solving the freight transportation problem precisely.

2.2.2 Location-Allocation Problem (LAP)

As previously stated in Goetschalckx (2008), LAP could be seen as part of distribution network design problems. Given the place of a set of customers with different demands, LAP is concerned with the selection of supply centres’ positions dedicated for serving the customers as well as the decision of the allocation of the customers to supply centres, with both of them are aimed at optimizing a given criterion (Hsieh & Tien, 2004: 1017). It is also assumed that there is no interaction among supply centres. The criterion could be single such as transportation costs (see, for example, Goetschalckx, 2008; Zhou & Liu, 2003; Manzini & Gebennini, 2008) or it may comprises several aspects (see, for example, Mitropoulos et al., 2006).

The following paragraphs provide some previous researches on LAP.

The un-capacitated-type LAP with rectilinear distances could be found in Hsieh and Tien (2004). In this paper, the authors propose a heuristic method which is based on Kohonen self-organising feature maps (SOFMs).

Sometimes distribution networks are built in hierarchies, where high-level distribution channels are constructed in straight lines from which low-level channels stem. Furthermore, destinations are allocated to branching facilities in high-level channels through low-level channels. Due to cost considerations, the number and locations of branching facilities as well as the allocation of the destinations to the aforementioned branching facilities need to be determined correctly. Eben-Chaime et al.’s (2002) paper addresses this type of problem by formulating appropriate mathematical optimisation models and subsequently proposing heuristic solution methods.

Capacitated LAP with stochastic demands is addressed by Zhou and Liu (2003). More specifically, they propose three types of stochastic programming models: (1) expected value model (EVM), (2) chance-constrained programming (CCP), and (3) dependent-chance programming (DCP). To solve these models efficiently, the authors develop a hybrid intelligent algorithm within which three type stochastic simulations are used. The proposed algorithm integrates the network simplex algorithm, stochastic simulation and genetic algorithm.

In more recent paper, Zhou and Liu (2007) address the LAP with fuzzy demands by developing three types of fuzzy programming models – fuzzy expected cost minimisation model, fuzzy -cost minimisation model, and credibility maximisation model with respect to different decision criterion. To solve these models, the authors apply a hybrid intelligent algorithm developed previously (see Zhou and Liu, 2003). Nonetheless, instead of using stochastic simulations, they are developing and employing fuzzy simulations.

Similar with the abovementioned paper, Wen and Imamura (2008) also address LAP with fuzzy demands. For this type of problem, they build a fuzzy -cost model under the Hurwicz criterion. The problem is subsequently solved using the same algorithm as in Zhou and Liu (2007).

The establishment of mixed integer programming optimisation models for multi-period, multi-stage LAPs could be found in Manzini and Gebennini (2008). In their paper, the authors develop optimisation models each for the following classes of multi-period, multi-stage LAPs: (1) single-commodity, multi-period, two-stage LAPs, (2) multi-commodity, multi-period, two-stage LAPs, (3) single-commodity, multi-period, two-stage open/ closed LAPs, and single-commodity, multi-period, three-stage LAPs.

The application of various search methods to a generalised class of LAPs known as multi-facility location problem with generalised objects (MFLPO) is presented by Bischoff and Dächert (2009). The end of the paper gives comparison of the involved search methods for various sizes of test problem.

Research on LAP in health service context could be found in Harper et al. (2005) and Mitropoulos et al. (2006). The former addresses the need to plan health services which takes geographical aspects into consideration. The problem is formulated as a stochastic LAP. The latter paper, on the other hand, develops a bi-objective model to solve the LAP arise in determining the location of hospitals and health centres and the allocation of the patients to those facilities.

2.2.3 Vehicle Routing Problem (VRP)

In its most basic form (e.g. Bulbul et al., 2008; Laporte, 2007), VRP is concerned with the optimal delivery or collection routes for a limited number of identical vehicles with limited capacities from a central depot/ warehouse to a set of geographically scattered customers. It assumes that the vehicles are at the central depot/ warehouse initially. It also requires the existence of the routes that connect the central depot/ warehouse to customers and customers to customers as well. In this type of VRP, a route must start and finish at the depot and a customer is visited by exactly one vehicle. The total demand of customers served by one vehicle could not exceed the vehicle’s capacity, and the ultimate goal is to minimise the total routing costs.

Since its introduction by Dantzig and Ramser in 1959 (Bulbul et al., 2008), it has given rise to a rich body of works (Laporte, 2007). In 2008, searching the words vehicle routing problem by using Google scholar search results more than 21,700 entries (Golden et al. (eds), 2008).

Laporte’s (1992) paper provides various exact methods and heuristics developed to solve the VRP. Several meta-heuristics intended to solve the classical VRP could be traced from his more recent paper (2007), while Toth and Vigo’s (2002) paper presents various existing exact algorithms for the solution of classical VRP. The comparison of descent heuristics, simulated annealing, and tabu search in solving VRP is addressed by Van Breedam (2001). Jozefowiez et al. (2008), on the other hand, give a survey on works that have been carried out on multi-objective VRP.

A range of VRP variants can be seen in Crainic and Laporte (eds., 1998), Bulbul et al. (2008), and Golden et al. (eds., 2008). Other variants also exist: VRP with stochastic demands and VRP with backhaul. Different classification of VRP could be found in Pisinger and Ropke’s (2007) paper. The following sub-sections mention examples of works on some of them, while new directions in modelling and algorithms for various types of LRP could be found in Part II of Golden et al.’s (eds., 2008) edited book. VRP with Time Windows

In this type of VRP, customer i may only be visited within a time window [ai, bi] (see, e.g., Kontoravdis & Bard, 1995; Badeau et al., 1997; Bouthillier & Crainic, 2005; Fügenschuh, 2006; Hsu et al., 2007; Kim, et al., 2006; Dondo & Cerdá, 2007; Kallehauge et al., 2007). VRP with Pickup and Delivery

When the vehicles need to deliver commodities to customers and collect items – for example, defective products – from them as well, then this is called a VRP with pickup and deliveries. Research papers by Nagy & Salhi (2005), Wassan et al. (2008), Wassan et al. (2008), Gribkovskaia et al. (2008), Hoff et al. (2009), and Ai & Kachitvichyanukul (2009) are several examples on it. VRP with Backhaul

In this type of VRP, the customers are separated into two mutually exclusive subsets so that the first subset of customers receives commodities whereas the second one sends back the products. Additionally, the second subset of customers are only served after the first one. The first subset is called line-haul customers and the second one is named backhaul customers. The f

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