# Emergency Repair Scheduling Model for Road Network Integrating Rescheduling Feature
## 2 Literature Review
[PERSON] and [PERSON] [33] proposed a repair plan for the road network during recovery phases under scarce resources based on the Greedy randomized adaptive search procedure (GRASP) and Variable Neighborhood Search (VNS) to maximize the communities' accessibility in the affected disaster location. Furthermore, [PERSON] and [PERSON] [12] attempted to restore blocked links in a road network during the first three days of emergency response under resource constraints using an integer programming model in the Debris Clearance Scheduling Model (DCSM). Improved from the previous model [31], [PERSON] et al. [34] developed a framework called Network Repair Crew Scheduling and Routing Problem (NRCSRP) in terms of a road network to minimize the traveling movement of a repair crew in opening the access from disaster location to the relief center. This method is identical to the concept of VRP with utilizing iterated greedy-randomized constructive procedure (IGRCP). Based on their works, [PERSON] et al. [35] utilized the Ant Colony System (ACS) algorithm to develop an optimization model for solving NRCSRP in the disaster response phase for short-term disaster; this model is called network repair crew scheduling for short-term disaster (NRCSRSD). Similar to the works of [PERSON] et al. and [PERSON] et al., [PERSON] and [PERSON] [11] developed a model that is referred to as the network repair work scheduling and relief logistic problem (NRWSRLP). Multi-period bilevel programming problems were applied in this model to minimize the total required time for delivering the relief materials in the lower level and maximize the accessibility of the road network in the upper level. Extending from the previous research, [PERSON] et al. [14] focused on integrated logistics support scheduling (LSS) with repair crew scheduling and routing problems (RCSRP) to accomplish post disaster road network repair (PDRNR). The developed model utilizes non-linear programming and an intertwined two-stage heuristic algorithm (ITSHA) to determine the repair crew scheduling. A bilevel programming model for an emergency recovery problem of the transportation network was proposed by [PERSON] et al. [36]. They developed an emergency relief supply scheduling framework that considers different degrees of relief priority and multiple relief distribution centers. This model adopted the Artificial Bee Colony (ABC) technique that combines bilevel programming with a soft time window problem caused by road network failure. Under insufficient information conditions in large-scale natural disasters, [PERSON] [17] proposed a dynamic relief demand management model for emergency logistics operation using a fuzzy clustering-based model to group affected regions, combine with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify the urgent relief demand associated with each group. [PERSON] et al. [37] developed a model to minimize the total time of delivery relief logistics by deploying a rolling horizon approach with real-time information. [PERSON] and [PERSON] [38] established a method for designing a centralized emergency supply network that supports emergency logistic operations considering the psychological cost. Furthermore, [PERSON] and [PERSON] [29] proposed an optimization model to minimize the impact of disproportionate relief supply-demand on the affected region during the response phase using stochastic dynamic programming. [PERSON] et al. [39] solved a dynamic job shop problem by adopting bridge-road transportation network restoration schedules. A genetic algorithm (GA) job shop problem was formulated to search the minimum recovery time and optimize the recovery trajectory of the whole bridge-road network. Another similar research performed by [PERSON] et al. [40] aimed to solve the optimization problem of the transportation network recovery strategy (TNRS) based on the emergency recovery phase (ERP).