Minimizing latency in post-disaster road clearance operations

Ajam, Meraj; Akbari, Vahid; Salman, Serdar

doi:10.1016/j.ejor.2019.03.024

Cited by 43 publications

(29 citation statements)

References 34 publications

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“…The rst model minimizes the total time to restore the connectivity of disconnected components of the network while the second maximizes the total components connected in a given time limit. Ajam et al (2019) adapted the models proposed by Kasaei and Salman (2016) to minimize the latency of critical nodes, where the latency of a node is dened as the travel time from the depot to that node, including the repair time of the traversed damaged roads. The authors developed a metaheuristic based on GRASP and variable neighborhood search.…”

Section: Background Literaturementioning

confidence: 99%

The heterogeneous multicrew scheduling and routing problem in road restoration

Moreno

Alem

Gendreau

et al. 2020

Transportation Research Part B: Methodological

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This paper introduces the heterogeneous multicrew scheduling and routing problem (MCSRP) in road restoration. The MCSRP consists of identifying the schedule and route of heterogeneous crews that must perform the restoration of damaged nodes used in the paths to connect a source node to demand nodes in a network aected by extreme events. The objective is to minimize the accessibility time dened as the time that the demand nodes remain unconnected from the source node. The main contributions of the paper include three novel mathematical formulations that dier in the way of modeling the scheduling decisions and the synchronization of the crews, and the development of valid inequalities based on some particular properties of the problem. Additionally, we prove that the MCSRP is NP-hard. Extensive numerical experiments with randomly generated instances and a case study based on oods and landslides disasters in Rio de Janeiro, Brazil, are performed to assess the eciency and applicability of our approach. In particular, we show that the valid inequalities signicantly improve the solvability of the mathematical models. In terms of managerial implications, our results suggest that the incorporation of multiple crews helps to reduce the worst-case accessibility times across the demand nodes, thus providing more equitable solutions.

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Section: Background Literaturementioning

confidence: 99%

The heterogeneous multicrew scheduling and routing problem in road restoration

Moreno

Alem

Gendreau

et al. 2020

Transportation Research Part B: Methodological

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“…In comparison with various road networks that are generated from real data in the literature (see (Rawls & Turnquist, 2010), (Ajam et al, 2019), (Akbari & Salman, 2017a), and (Akbari & Salman, 2017b)) where the average degree of the nodes in all of the cases is less than five, these networks are relatively dense and make the instances more challenging.…”

Section: Data Generationmentioning

confidence: 99%

Online routing and scheduling of search-and-rescue teams

2020

Self Cite

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We study how to allocate and route search-and-rescue (SAR) teams to areas with trapped victims in a coordinated manner after a disaster. We propose two online strategies for these time-critical decisions considering the uncertainty about the operation times required to rescue the victims and the condition of the roads that may delay the operations. First, we follow the theoretical competitive analysis approach that takes a worst-case perspective and prove lower bounds on the competitive ratio of the two variants of the defined online problem with makespan and weighted latency objectives. Then, we test the proposed online strategies and observe their good performance against the offline optimal solutions on randomly generated instances.

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“…-Salesman 1 visits fast foods (150,118,117,90,85,20,15,131,5,107,18,130,127,79,72,6,143,135,9,27,133,116,125,124,106,59,52,58,51,57,69,68,120,14,149,109,2,110,13,25,139,140,136,35,11). -The salesman 4 visits fast foods (108,50,46,105,113,91,89,45,39,38,42,40,97 ,104,112,30,153,160,144,155,161,142,31,76 ,80,81,36,93,137,138,134,152,17,12,123,15 8,154,33,132,19,22).…”

Section: Practical Examplementioning

confidence: 99%

“…Meraj Ajam et al [25] develops a heuristic that solves a mixed integer program for the post-disaster road clearing problem so as to find the route of a work troop responsible for clearing blocked roads with the aim of minimizing minimize total latency by minimization of makespan. The researchers also develop a metaheuristic based on a combination of Greedy Randomized Adaptive Search Procedure and Variable Neighborhood Search.…”

Section: Introductionmentioning

confidence: 99%

Makespan Minimizing on Multiple Travel Salesman Problem With a Learning Effect of Visiting Time

Colombaroni

Mohammadi

Rahmanifar

2020

Wseas Transactions on Systems and Control

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The multiple traveling salesman problem (MTSP) involves the assignment and sequencing procedure simultaneously. The assignment of a set of nodes to each visitors and determining the sequence of visiting of nodes for each visitor. Since specific range of process is needed to be carried out in nodes in commercial environment, several factors associated with routing problem are required to be taken into account. This research considers visitors’ skill and category of customers which can affect visiting time of visitors in nodes. With regard to learning-by-doing, visiting time in nodes can be reduced. And different class of customers which are determined based on their potential purchasing of power specifies that required time for nodes can be vary. So, a novel optimization model is presented to formulate MTSP, which attempts to ascertain the optimum routes for salesmen by minimizing the makespan to ensure the balance of workload of visitors. Since this problem is an NP-hard problem, for overcoming the restriction of exact methods for solving practical large-scale instances within acceptable computational times. So, Artificial Immune System (AIS) and the Firefly (FA) metaheuristic algorithm are implemented in this paper and algorithms parameters are calibrated by applying Taguchi technique. The solution methodology is assessed by an array of numerical examples and the overall performances of these metaheuristic methods are evaluated by analyzing their results with the optimum solutions to suggested problems. The results of statistical analysis by considering 95% confidence interval for calculating average relative percentage of deviation (ARPD) reveal that the solutions of proposed AIS algorithm has less variation and Its’ confidence interval of closer than to zero with no overlapping with that of FA. Although both proposed metaheuristics are effective and efficient in solving small-scale problems, in medium and large scales problems, AIS had a better performance in a shorter average time. Finally, the applicability of the suggested pattern is implemented in a case study in a specific company, namely Kalleh.

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Minimizing latency in post-disaster road clearance operations

Cited by 43 publications

References 34 publications

The heterogeneous multicrew scheduling and routing problem in road restoration

The heterogeneous multicrew scheduling and routing problem in road restoration

Online routing and scheduling of search-and-rescue teams

Makespan Minimizing on Multiple Travel Salesman Problem With a Learning Effect of Visiting Time

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