Minimizing latency in post-disaster road clearance operations

After a natural disaster, roads and bridges can be damaged or blocked by debris, causing inaccessibility between critical locations such as hospitals, disaster response centers, shelters and disaster-struck areas. We study the post-disaster road clearing problem with the aim of providing a fast and effective method to determine the route of a work troop responsible for clearing blocked roads. The problem is to find a route for the troop that starts at the depot and visits all of the critical locations. The objective is to minimize the total latency of critical nodes, where the latency of a node is defined as the travel time from the depot to that node. A mathematical model for this problem has already been developed in the literature. However, for real-life instances with more than seven critical nodes, this exact formulation cannot solve the problem optimally in a 3-hour limit. To find a near-optimal solution in a short running time, we develop a heuristic that solves a mixed integer program on a transformed network and a lower bounding method to evaluate the optimality gaps. Alternatively, we develop a metaheuristic based on a combination of Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Search (VNS). We test both the matheuristic and the metaheuristic on Istanbul data and show that optimal or near-optimal solutions are obtained within seconds. We also compare our algorithms with existing work in the literature. Finally, we conduct an analysis to observe the trade-off between total and maximum latency

Makespan minimizing on multiple travel salesman problem with a learning effect of visiting time

-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 meta-heuristics 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

Methodology for planning reconstruction activities after a disaster considering interdependencies and priorities

A disaster relief chain can be divided into phases such as pre-disaster, disaster response, and post-disaster. This paper focus on the post-disaster phase, specifically in the recovery activity. After a disaster occurs, the road infrastructure gets compromised as roads can be damaged or blocked by debris. This situation represents a threat for the people affected by the disaster because it severely impacts their accessibility to vital locations such as hospitals, police stations, and fire stations. For efficient planning of reconstruction activities, we develop a two-stage methodology employing Steiner Tree and scheduling algorithms that incorporate the principal characteristics of the real-world situation. The objective was to minimize the total completion time to restore access to essential facilities. The mathematical modeling approach identifies the roads that need to be restored considering dynamic resources, priorities, and interdependencies among the essential facilities that need to be connected. In addition, the optimal schedule for restoring the roads, including the crews' assignment is provided. Considering these aspects in the overall methodology were some of the key challenges that our study has addressed. Hazus, a tool developed by the Federal Emergency Management Agency (FEMA) was used to obtain the data related to the impact of a disaster on facilities and transportation network. We replicated the 1994 Northridge Earthquake to test the applicability of our methodology and models under multiple scenarios