A three-stage stochastic facility routing model for disaster response planning

Rennemo, Sigrid Johansen; Rø, Kristina Fougner; Hvattum, Lars Magnus; Tirado, Gregorio

doi:10.1016/j.tre.2013.12.006

Cited by 168 publications

(77 citation statements)

References 25 publications

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“…A further increase in supply (A5) yields higher EVPI and VSS, but by assuming that an increase in budget that is sufficient to supply most demands (A6) decreases EVPI and VSS; similar results were found in Rennemo et al (2014).…”

Section: Numerical Results and Managerial Insightssupporting

confidence: 56%

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Stochastic network models for logistics planning in disaster relief

Alem

Clark

Moreno

2016

European Journal of Operational Research

218

102

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Emergency logistics in disasters is fraught with planning and operational challenges, such as uncertainty about the exact nature and magnitude of the disaster, a lack of reliable information about the location and needs of victims, possible random supplies and donations, precarious transport links, scarcity of resources, and so on. This paper develops a new two-stage stochastic network flow model to help decide how to rapidly supply humanitarian aid to victims of a disaster within this context. The model takes into account practical characteristics that have been neglected by the literature so far, such as budget allocation, fleet sizing of multiple types of vehicles, procurement, and varying lead times over a dynamic multiperiod horizon. Attempting to improve demand fulfillment policy, we present some extensions of the model via state-of-art risk measures, such as semideviation and conditional value-at-risk. A simple two-phase heuristic to solve the problem within a reasonable amount of computing time is also suggested.Numerical tests based on the floods and landslides in Rio de Janeiro state, Brazil, show that the model can help plan and organise relief to provide good service levels in most scenarios, and how this depends on the type of disaster and resources. Moreover, we demonstrate that our heuristic performs well for real and random instances.

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Section: Numerical Results and Managerial Insightssupporting

confidence: 56%

“…The integration between location and routing in emergency logistics is also tackled in Rennemo et al (2014) and Ahmadi et al (2015). The former authors have developed a first multi-stage stochastic programming model to deal with a facility routing model in humanitarian logistics.…”

Section: Related Literaturementioning

confidence: 99%

Stochastic network models for logistics planning in disaster relief

Alem

Clark

Moreno

2016

European Journal of Operational Research

218

102

View full text Add to dashboard Cite

show abstract

“…Researchers investigated new evaluation and measurement methods and tools to improve the design methods including integrated facility location and routing problems [33,34], development of strategic planning framework [35,36], or stochastic facility routing [37]. Third-party logistics (3PL) providers allow the management to outsource processes of supply chain functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

2018

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Supply chain management applies more and more Industry 4.0 innovations to increase their availability, elasticity, sustainability, and efficiency. In interconnected logistics networks, operations are integrated from suppliers through 3rd party logistics providers to customers. There are different delivery models depending on the time and cost. In the last few years, a wide range of customers is willing to pay an extra fee for the same delivery or instant delivery. This fact led to the increased importance of the optimized design and control of first mile/last mile (FMLM) delivery solutions. Cyberphysical system-based service innovations make it possible to enhance the productivity of FMLM delivery in the big data environment. The design and operation problems can be described as NP-hard optimization problems. These problems can be solved using sophisticated models and methods based on heuristic and metaheuristic algorithms. This research proposes an integrated supply model of FMLM delivery. After a careful literature review, this paper introduces a mathematical model to formulate the problem of realtime smart scheduling of FMLM delivery. The integrated model includes the assignment of first mile and last mile delivery tasks to the available resources and the optimization of operations costs, while constraints like capacity, time window, and availability are taken into consideration. Next, a black hole optimization-(BHO-) based algorithm dealing with a multiobjective supply chain model is presented. The sensitivity of the enhanced algorithm is tested with benchmark functions. Numerical results with different datasets demonstrate the efficiency of the proposed model and validate the usage of Industry 4.0 inventions in FMLM delivery.

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“…A multi-mode transportation eet (i.e., ground, marine, and air) made the model more e cient and exible in the real world. Therefore, Barbasoglu [19], Rennemo et al [24], Hu [25], Naja et al [26], Adivar and Mert [27], and Ozdamar [28] followed this issue in separate studies.…”

Section: Related Workmentioning

confidence: 99%

A cluster-based emergency vehicle routing problem in disaster with reliability

et al. 2017

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Abstract. In the event of natural disasters, relief distribution is the most challenging problem in emergency transportation. What is important in response to disaster is victims' relief in disaster areas with the quick distribution of vital commodity. In this regard, damage to infrastructure (e.g., roads) can make trouble in designing a distribution network. Therefore, this paper considers a problem using a three-stage approach. In the rst stage, pre-processing of model inputs is done through an Arti cial Neural Fuzzy Inference System (ANFIS) followed by investigating the safest route for each cluster using decision-making techniques and graph theory. In the second stage, a heterogeneous multi-depot multimode vehicle routing problem is formulated for minimizing the transportation time and maximizing the reliability. Finally, since the routing problem is NP-hard, 2 multi-objective meta-heuristic algorithms, namely, Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Fire y Algorithm (MOFA), are proposed to obtain the optimal solution and their performances are compared through a set of randomly generated test problems. The results show that for this routing problem, the MOFF gives better solutions than NSGA-II does, and it performs well in terms of accuracy and solution time.

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A three-stage stochastic facility routing model for disaster response planning

Cited by 168 publications

References 25 publications

Stochastic network models for logistics planning in disaster relief

Stochastic network models for logistics planning in disaster relief

Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach

A cluster-based emergency vehicle routing problem in disaster with reliability

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