Most of the studies of the operational logistical activities in humanitarian relief logistics focus with the objective of optimising the flow of supplies through existing distribution networks and post-disaster events (Balcik and Beamon, 2008). Tzeng et al. (2007) compared the characteristics of general and relief distribution systems, which are shown in Table 2.7. General physical distribution systems for business consider material items, cost of materials, number of vehicles, modes of transportation, number of depots, demand of materials, transportation networks, vehicle capacity, travel time of the route, and various operational modes (Tzeng et al. 2007). The objectives of the physical distribution systems are to find a combination of those variables that minimises total travelling time, minimises size of vehicle fleet, maximises service capacity, and minimises fixed and variable costs. Similar to general
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physical distribution systems, relief distribution systems also consist of three separate parts: demand, supply, and transportation. The collection points of commodities in non-devastated areas play the role of supply, while the demand points are the devastated areas where relief is provided to victims who play the role of customers. Additionally, large-scale commodities distribution depots near the demand point or the devastated areas serve the role of a distribution centre. The only difference is that the distribution depots are temporary storage points instated of a permanent distribution centre. Another characteristic of disaster relief operations is that, instead of driving for profit in business, the operators of disaster relief are often government agents or non-profit organisations who claim to pursue efficiency and fairness.
Table 2.7 Comparison of general and relief distribution systems
Comparison Items General distribution systems Relief distribution systems
System objectives Maximise profit Fairness and efficiency
Dimensional role Factories
Distribution centres Customers
Collection points for commodities Transfer depots for commodities Demand points of commodities Facility characteristics Regular facilities
Substantial/tangible existence
Temporary facilities
Scheduling plan Long term: location
Median-term: vehicle-fleet size Short-term: scheduling
Urgent decisions based on available information
Trade-offs between algorithm- efficiency and optimisation
Paying attention optimisation Emphasis of algorithm efficiency
Delivery models Round-trip delivery; circulating
delivery
Round-trip delivery
Source: Tzeng et al. (2007)
A summary of the literature relating to disaster relief logistics is given in Table 2.8. Knott (1987) considered a single mode of transportation for last mile food delivery to determine the number of trips to each refugee camps to satisfy demand while minimising the transportation cost or maximising the amount of food delivered. In a later study, Knott (1988) combines operations research heuristics with artificial intelligence techniques to develop a decision support tool to support the previous problem. Meanwhile, Rathi et al. (1993) identify the optimal number of vehicles to be assigned to each route, the problem then becomes an assignment problem. Haghani and Oh (1996) and Oh and Haghani (1997) developed a multi- commodity multimode network flow model to help to organise detailed load plans for moving commodities after an event. Barbarosoğlu et al. (2002) focused on tactical and operational
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scheduling of helicopter activities in a disaster relief operation. They decomposed the problem hierarchically into two sub-problems where tactical decision are made in the top level, and the operational routing and loading decision are made in the second level. Hwang (1999) studied the inventory allocation and vehicle routing alternatives with a model that uses an objective function that minimises starvation instead of distribution costs, thereby providing different but very acceptable solutions. Viswanath and Peeta (2003) formulate a network design model to identify critical routes for earthquake responses. Barbarosoglu and Arda (2004) consider the uncertainties in available supplies, demands and network capacities through definition of a set of scenarios focus in post-event response. Özdamar et al. (2004) generate multi-period vehicle routes and schedules, along with commodity load-unload assignments. Dessouky et al. (2006) used facility location and vehicle routing problems in the pharmaceutical supply chain and found that facility that is close to a demand point provides a better quality of coverage to that demand point than a facility located far from the demand point. Beamon and Kotleba (2006a) used multi-supplier inventory model developing an inventory management strategy for a warehouse supporting a long-term emergency relief operation, which optimises the reorder quantity and reorder level based on the costs of reordering, holding, and back-orders.
Beamon and Kotleba (2006b) compare the performance of three inventory management strategies by developing a simulation model and relief-specific performance measurement system to identify system factors that contribute most significantly to overall performance. Meanwhile, Angelis et al. (2007) considered a multi-depot, multi-vehicle routing, and scheduling problem for air delivery of emergency supply deliveries. Choi et al. (2010) studied the case study of the volatile and fragile supply chain aid in East Africa (post Rwandan Civil War) providing the cost, speed and physical capability to be the most important factors for response. Sheu (2007) describes distribution of emergency supplies through a three-layer supply chain that connects relief suppliers, distribution centres and victims post-disaster situation. Tzeng et al. (2007) provides a multi-criteria deterministic model to distribute commodities to disaster areas considering the cost service time and demand satisfaction. Balcik et al. (2008) considers vehicle-based last mile distribution system that determines delivery schedules for vehicles and equitably allocates resources, based on supply vehicle capacity and delivery time restrictions. Mete and Zabinsky (2010) used the stochastic optimisation approach for distribution problem of medical supplies to be used for disaster management to select storage locations and required inventory levels. Widener and Horner (2010) explore the use of geographic information systems in conjunction with hierarchical
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capacitated-median model in post-hurricane settings to accomplish efficient placements of facilities for distributing relief services. Vitoriano et al. (2011) proposed several criteria for an aid distribution problem and a multi-criteria optimisation model that was developed to deal with these aspects. The criteria attributes used for their model was cost, time, equity, priority, reliability, and security.
Table 2.8 Transportation selection/routing problem in humanitarian relief logistics
Author(s) Contribution
Knott (1987) Determines to satisfy demand while minimising the transportation cost or maximising the amount of food delivered
Knott (1988) Combines heuristics research with artificial intelligence techniques to develop a decision support
Rathi et al. (1993) Identify the optimal number of vehicle to be assigned to each route and the problem becomes an assignment problem
Haghani and Oh (1996)
Oh and Haghani (1997)
Determine routing and scheduling plans for multiple transportation modes carrying various commodities from multiple supply points
Minimise the sum of the vehicular flow costs, commodity flow costs, supply/demand carry-over costs and transfer costs over all time periods
Hwang (1999) Inventory allocation and vehicle routing alternatives were accounted Uses objective function that minimises starvation instead of distribution costs Barbarosoglu et al.
(2002)
Decomposed the problem hierarchically into two sub-problems: Tactical; operational routing and loading Viswanath and Peeta
(2003)
Identify critical routes for earthquake response Barbarosoglu and Arda
(2004)
Includes relief network uncertainties related to supply, route capacities and demand requirements
Özdamar et al. (2004) Distribute multiple commodities from a number of supply centres to distribution centres near the affected areas Minimise the amount of unsatisfied demand over time
Beamon and Kotleba (2006a)
Developed an inventory management strategy for a warehouse supporting a long-term emergency relief operation
Optimise the reorder quantity and level based on the costs of reordering, holding and back-orders Beamon and Kotleba
(2006b)
Compares the performance of three inventory management strategies Identify system factors that contribute most significant to overall performance Relief-specific performance measurement system
Dessouky et al. (2006) Solve facility location and vehicle routing problems in the pharmaceutical supply chain
Shows that a facility that is close to a demand point provides a better quality of coverage to that demand point than a facility located far from the demand point
Angelis et al. (2007) Consider multi-depot, multi-vehicle routing, and scheduling problem for air delivery of emergency supply deliveries
Maximises the total satisfied demand
Sheu (2007) Operation of emergency logistics co-distribution responding to the urgent relief demands in the crucial rescue period
Disaster-affected area grouping and relief co-distribution
Tzeng et al. (2007) Distribute commodities to disaster areas considering the cost, service time and demand satisfaction Balcik et al. (2008) Considers vehicle-based last mile distribution system
Determines delivery schedules for vehicles and equitably allocates resources, based on supply vehicle capacity and delivery time restriction
Ben-Tal et al. (2010) Generate a logistics plan that can mitigate demand uncertainty in humanitarian relief supply chain
Application for dynamically assigning emergency response and evacuation traffic flow problems with time dependent demand uncertainty
Choi et al. (2010) - Case study of the volatile and fragile supply chain in East Africa
- Cost, speed and physical capability considered to be the most important factor Widener and Horner
(2010)
Accomplish efficient placements of facilities for distributing relief services in post-hurricane settings Vitoriano et al. (2011) Developed multi-criteria optimisation model for humanitarian aid relief distribution
Cost, time, equity, priority, reliability, and security were the attributes used for the criteria
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