Optimization of Temporary Debris Management Site Selection and Site Service Regions for Enhancing Postdisaster Debris Removal Operations

Abstract

Temporary debris management sites (TDMSs) are often established after large‐scale natural disaster to expedite waste removal. However, uses of these sites inevitably increase debris removal cost due to double handling of waste. How to select the quantity of TDMSs to optimize the trade‐off between the recycling benefits and debris removal cost is a critical issue in debris management. Previous studies focused primarily on initial geographical analyses of TDMSs or the debris removal optimization given known TDMS locations and quantities. This article proposes a multiobjective mixed integer linear optimization method to optimize debris removal with a particular focus on TDMS selection and to pursue the minimum debris removal cost, total processing time, and environmental influence. A case study with four scenarios was conducted to test the validity of the proposed approach. Numerical results indicate that a maximum reduction of 33.27% in removal time, a maximum reduction of 37.07% in cost, and a maximum increase of 33.60% in recycle profits can be achieved when the time, cost, and recycle profit objective are solely pursued compared to other studied scenarios, respectively. The findings of this study contribute to better decision making in debris removal operations after major natural disasters.

Knowledge-based decision intelligence in street lighting management

“…E-mail: dcarneiro@estg.ipp.pt. tion of digital agents performing autonomous actions to provide a new level of support to the management of resources or services [3,4]. This is paramount in the current scenario of information overload, as Human deciders cannot possibly be expected to deal with the increasing volume and complexity of the data [5].…”

“…Smart Cities also have a general goal, towards which all autonomous agents converge in their actions: to improve the citizens' quality of life [6]. This rather broad and abstract goal can be broken down into more specific and well-defined ones, such as to improve the efficiency of the city management [7], the management of resources and energy [8,9], the management of infrastructures and assets [3,4,10], the response to disasters [11], or the city's sustainable growth [12] including smart mobility [13]. Hence, the challenges posed by smart city projects are socio-technical in nature [14].…”

Joint location and assignment optimization of multi‐type fire vehicles

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“…Li et al (2018) proposed a cooperative maximal covering model with the consideration of financial efficiency and uncertainty for multiple humanitarian relief chain management. Wang et al (2019) proposed a multi-objective mixed integer linear model to optimize temporary debris management sites after large-scale natural disaster. To mitigate congestion caused by uncertain demands, Hajibabai and Saha (2019) presented a station location and patrol route planning model for first responders.…”

Modeling and Simulation of Collaborative Dispatching of Disaster Relief Materials Based on Urgency

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