Optimisation of waste clean-up after large-scale disasters.

Disaster waste clean-up after large disasters is one of the core activities at the recovery stage of disaster management, which aims to restoring the normal functioning of the disaster affected area. In this paper we considered a waste clean-up system consists of (i) demolition operation, (ii) collection of waste from customer nodes to temporary disaster waste management sites (TDWMSs), (iii) processing at TDWMSs, and (iv) transportation of the waste to final disposal sites in the recovery of disasters. A multi-objective mixed integer programming model is developed to minimise the total clean-up cost and time. Three different approaches are developed to solve the problem, which are tested with artificial instances and a real case study. Results of artificial instances indicate that the models developed can be used to obtain close to optimal solutions within an acceptable computing time. Results of the case study can facilitate the decision-makers to develop the waste clean-up with minimised total cost and clean-up time by selecting the right location of TDWMSs and setting up the proper waste clean-up schedule.

Reliability analysis of road networks in disaster waste management.

The condition of road networks which connect disaster affected areas and final disposal sites has a critical role in the management of disaster waste. In this paper, we present a two-stage framework to estimate the overall reliability and failure modes of a disaster waste management system considering the reliability of each route involved in the road network. In the first stage, first order reliability method and Ditlevsen bounds are applied to estimate system reliability. In the second stage, an event tree approach is implemented to analyse the failure modes of the system. The methodology is demonstrated with an artificial case study considering three different scales of disasters. The results obtained from the case study can provide information to decision makers regarding the priority of the routes in the system and the most likely failure mode.

Reliability analysis for disaster waste management systems.

The management of disaster waste is one of the most critical tasks associated with recovery after a disaster. Having a general idea of the required capacity, cost and target clean-up time while considering the uncertainties involved in the system before the detailed plan of a disaster waste clean-up system is significant. Reliability analysis is a method to judge the performance of a system and deal with uncertainties in the system. Evaluating the reliability of the system, which can indicate the possibility to complete the clean-up within the target time and cost, and optimising the system to maximise the reliability to provide information to decision-makers regarding the capacity, cost and time required to finish the clean-up is the purpose of this paper. A mathematical model is developed applying the First Order Reliability Method (FORM) to address the problem. Additionally, a non-linear optimisation model is developed to improve the reliability of the disaster waste clean-up system with consideration of the total cost and clean-up time constraints, and solved using a Genetic Algorithm. The proposed models are implemented to solve a case study in Queensland, Australia. It is shown that the models have the capability of maximising the reliability and minimising the total clean-up costs by optimising the arrangement of vehicles during the clean-up process.