A Novel Hybrid Algorithm for Multiobjective Location-Allocation Problem in Emergency Logistics

Increased frequency of disasters keeps reminding us of the importance of effective resource distribution in postdisaster. To reduce the suffering of victims, this paper focuses on how to establish an effective emergency logistics system. We first propose a multiobjective optimization model in which the location and allocation decisions are made for a three-level logistics network. Three objectives, deprivation costs, unsatisfied demand costs, and logistics cost, are adopted in the proposed optimization model. Several cardinality and flow balance constraints are considered simultaneously. Then, we design a novel effective IFA-GA algorithm by combining the firefly algorithm and genetic algorithm to solve this complex model effectively. Furthermore, three schemes are proposed to improve the effectiveness of the IFA-GA algorithm. Finally, the numerical results provide several insights on the theory and practice of relief distribution, which also illustrate the validity of the proposed solution algorithm.

Optimum transportation of relief materials aftermath the disaster

Purpose: Natural disasters disrupt not only the lives of individuals but also the functioning of society. Given the unpredictability of disasters and the uncertainty associated with them, preparation is the best way to mitigate and reduce the effects of the disaster. Design/methodology/approach: The study presents a mathematical model in the form of a multi-objective linear programming problem for the relief distribution from the airports which minimizes the total operational cost as well as travel time. Further, the solution approach and analytical results have also been discussed. Findings: The main aims at the preparedness stage are to identify and build infrastructures that might function as useful operation centres during a disaster. The study also provides decisions that include the type and number of vehicles for each affected location. Research limitations/implications: Airports can function as centres for relief collection and distribution. However, relief operations carried out through airports are often subject to problems such as stockpiling. Further, various modes are available for the transport of relief supplies- air, water, and land transport modes primarily. While aircraft and helicopters are faster, their costs of operation are too high. Instead, trucks are economical but very slow as compared to aircraft. Practical implications: The choice of model depends on many factors including the availability of vehicles, availability of routes, and criticality of situations. The choices made in turn affect the costs and the time of operations. Originality/value: The model converts a disaster scenario into a demand-supply problem with the aim being to decide allocations at specified intervals of time.

Robust Design of Relief Distribution Networks Considering Uncertainty

In the post-disaster response phase, an efficient relief distribution strategy plays a vital role in alleviating suffering in disaster-stricken areas, which sometimes becomes challenging in humanitarian logistics. Most governments pre-located the relief goods at the pre-determined warehouses against possible disasters. Those goods must be shipped to the relief distribution centers (RDCs) to be further distributed to the victims in impacted areas upon the disasters. Secondary disasters can occur due to the first disaster and can occur relatively close in time and location, resulting in more suffering and making the relief distribution activities more challenging. The needs of additional RDCs must be determined as well in response to the secondary disasters. A robust optimization model is proposed to hedge against uncertainties in RDCs’ capacity and relief demand. Its objective is to minimize the sum of transportation cost, additional RDC cost, and shortage of commodities. The computational results are given to demonstrate the effectiveness of the proposed model. The sensitivity analysis gives an insight to the decision-makers.

A Model for Prepositioning Emergency Relief Items Before a Typhoon with an Uncertain Trajectory

Problem definition: We study the problem faced by the Philippine Department of Social Welfare (DSWD) in prepositioning relief items before landfall of an oncoming typhoon whose future outcome (trajectory and wind speed) is uncertain. Academic/practical relevance: The importance of prepositioning was a hard lesson learned from Super Typhoon Haiyan that devastated the Philippines in 2013, when many affected by the typhoon did not have immediate access to food and water. In a typhoon-prone country, it is important to build resilience through an effective prepositioning model. Methodology: By engaging with DSWD, we developed a practically relevant stochastic prepositioning model. The probability models of municipality-level demand and of supply damage are both dependent on the typhoon outcome. A linear mixed effects model is used to estimate the dependence of demand on the typhoon outcome using a large data set that includes the municipality-level impact of West Pacific typhoons during 2008–2019. The model has two objectives motivated from the practical realities of the Philippine network: prioritizing regions with high demand and prepositioning in all affected regions proportional to their total demand. Results: We find that the choice of the demand model significantly impacts the distributed relief items in the Philippine setting where it is challenging to adjust region-level supply after a typhoon. By using the historical data on past typhoons, we show that in this setting, our stochastic demand model provides the best distribution to date of any existing demand models. Managerial implications: There currently exists a gap between theory and practice in the management of relief inventories. We contribute toward bridging this gap by engaging with DSWD to develop a practically relevant relief distribution model. Our work is an effective example of collaboration with government and nongovernment agencies in developing a relief distribution model.