A robust optimization approach for humanitarian needs assessment planning under travel time uncertainty

Balcik, Burcu; Yanıkoğlu, İhsan

doi:10.1016/j.ejor.2019.09.008

Cited by 56 publications

(37 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Balcik (2017) presents a mixed-integer model for the proposed "Selective Assessment Routing Problem" (SARP) which simultaneously addresses site selection and routing decisions and supports the RNA process that involves the purposive sampling method, a method that only selects those sites that carry certain characteristics. Balcik and Yanıkoglu (2020) take the study further by considering the travel time as an uncertain parameter in post-disaster networks and present a robust optimization model to address the uncertainty. The objective function in Balcik (2017) is maximizing the minimum coverage ratio achieved across the community groups, where the coverage ratio for a group is calculated by dividing the number of times that the group is covered by the total number of sites in the network with that group.…”

Section: Related Literaturementioning

confidence: 99%

Evaluation of field visit planning heuristics during rapid needs assessment in an uncertain post-disaster environment

et al. 2021

Self Cite

View full text Add to dashboard Cite

A Rapid Needs Assessment process is carried out immediately after the onset of a disaster to investigate the disaster’s impact on affected communities, usually through field visits. Reviewing practical humanitarian guidelines reveals that there is a great need for decision support for field visit planning in order to utilize resources more efficiently at the time of great need. Furthermore, in practice, there is a tendency to use simple methods, rather than advanced solution methodologies and software; this is due to the lack of available computational tools and resources on the ground, lack of experienced technical staff, and also the chaotic nature of the post-disaster environment. We present simple heuristic algorithms inspired by the general procedure explained in practical humanitarian guidelines for site selection and routing decisions of the assessment teams while planning and executing the field visits. By simple, we mean methods that can be implemented by practitioners in the field using primary resources such as a paper map of the area and accessible software (e.g., Microsoft Excel). We test the performance of proposed heuristic algorithms, within a simulation environment , which enables us to incorporate various uncertain aspects of the post-disaster environment in the field, ranging from travel time and community assessment time to accessibility of sites and availability of community groups. We assess the performance of proposed heuristics based on real-world data from the 2011 Van earthquake in Turkey. Our results show that selecting sites based on an approximate knowledge of community groups’ existence leads to significantly better results than selecting sites randomly. In addition, updating initial routes while receiving more information also positively affects the performance of the field visit plan and leads to higher coverage of community groups than an alternative strategy where inaccessible sites and unavailable community groups are simply skipped and the initial plan is followed. Uncertainties in travel time and community assessment time adversely affect the community group coverage. In general, the performance of more sophisticated methods requiring more information deteriorates more than the performance of simple methods when the level of uncertainty increases.

show abstract

Section: Related Literaturementioning

confidence: 99%

Evaluation of field visit planning heuristics during rapid needs assessment in an uncertain post-disaster environment

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…This paper inspired us to consider the multistage perspective of logistics scheduling under uncertainty and to employ adjustable decisions when part of the uncertain data is realized. Recently transportation time uncertainty [7], [22] has attracted attention as a fundamental factor after a disaster. In [22], a tractable robust optimization formulation and a coaxial box uncertainty set were proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Recently transportation time uncertainty [7], [22] has attracted attention as a fundamental factor after a disaster. In [22], a tractable robust optimization formulation and a coaxial box uncertainty set were proposed. A recent review paper from Govindan et al [23] provided a survey on the uncertainty of supply chain network design, which is helpful for a better understanding of these studies.…”

Section: Related Workmentioning

confidence: 99%

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

View full text Add to dashboard Cite

In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period postdisaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a realworld instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.INDEX TERMS Emergency service, uncertain environment, humanitarian logistics, Pareto optimization, robustness, multiphase scheduling.

show abstract

“…Barbarosoglu and Arda [ 14 ] propose a two-stage stochastic programming model to plan the transportation of vital first-aid commodities to disaster-affected areas during emergency responses. Focusing on rapid needs assessment operations conducted immediately after a disaster, Balcik and Yanıkoğlu [ 15 ] identify the urgent needs of affected community groups, addressing the problem of selecting the sites to be visited by the assessment teams during a fixed assessment period and constructing assessment routes under travel time uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Zhu

Wang

Regan

et al. 2020

IJERPH

View full text Add to dashboard Cite

Awareness of the requested quantity and characteristics of emergency supplies is crucial for facilitating an efficient relief operation. With the aim of focusing on the quantitative study of immediate food supplies, this article estimates the numerical autoregressive integrative moving average (ARIMA) model based on the actual data of 14 key commodities in the Sendai City of Japan during the 2011 Tohoku earthquake. Although the temporal patterns of key food commodity groups are qualitatively similar, the results show that they follow different ARIMA processes, with different autoregressive moving averages and difference order patterns. A key finding is that 3 of the 14 items are significantly related to the number of temporary residents in shelters, revealing that the relatively low number of different items makes it easier to deploy these key supplies or develop regional purchase agreements so as to promptly obtain them from distributors.

show abstract

A robust optimization approach for humanitarian needs assessment planning under travel time uncertainty

Cited by 56 publications

References 52 publications

Evaluation of field visit planning heuristics during rapid needs assessment in an uncertain post-disaster environment

Evaluation of field visit planning heuristics during rapid needs assessment in an uncertain post-disaster environment

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

A Quantitative Study on Crucial Food Supplies after the 2011 Tohoku Earthquake Based on Time Series Analysis

Contact Info

Product

Resources

About