Multi-objective decision analytics for short-notice bushfire evacuation: An Australian case study

This paper develops a multi-objective optimisation model to compute resource allocation,shelter assignment and routing options to evacuate late evacuees from affected areas to shelters.Three bushfire scenarios are analysed to incorporate constraints of restricted time-window and potential road disruptions.Capacity and number of rescue vehicles and shelters are other constraints that are identical in all scenarios.The proposed mathematical model is solved by ?-constraint approach.Objective functions are simultaneously optimised to maximise the total number of evacuees and assigned rescue vehicles and shelters.We argue that this model provides a scenario-based decision-making platform to aid minimise resource utilisation and maximise coverage of late evacuees

Основы творческой деятельности веб-журналиста. Учебная программа учреждения высшего образования по учебной дисциплине для специальности: 1-23 01 08 Журналистика (по направлениям) направления специальности 1-23 01 08-03 Журналистика (веб-журналистика)

Wildfires pose a serious threat to life in many countries. For police, fire and emergency services authorities in most jurisdictions in North America and Australia evacuation is now the option that is preferred overwhelmingly. Wildfire evacuation modeling can assist authorities in planning evacuation responses to future threats. Understanding residents' behavior under wildfire threat may assist in wildfire evacuation modeling. This paper reviews North American and Australian research into wildfire evacuation behavior published between January 2005 and June 2017. Wildfire evacuation policies differ across the two regions: in North America mandatory evacuations are favored, in Australia most are advisory. Research from both regions indicates that following a wildfire evacuation warning some threatened residents will wish to remain on their property in order to protect it, many will delay evacuating, and some residents who are not on their property when an evacuation warning is issued may seek to return. Mandatory evacuation is likely to result in greater compliance, enforcement policies are also likely to be influential. Self-delayed evacuation is likely if warnings are not sufficiently informative: residents are likely to engage in information search rather than initiating evacuation actions. The wildfire warning and threat histories of a location may influence residents' decisions and actions. The complexities of behavioral factors influencing residents' actions following an evacuation warning pose challenges for wildfire evacuation modeling. Suggestions are offered for ways in which authorities might reduce the numbers of residents who delay evacuating following a wildfire warning. © 2018 Springer Science+Business Media, LLC, part of Springer Natur

Risk reduction for distribution of the perishable rescue items; A possibilistic programming approach

The expedient transportation of relief supplies plays an undeniable role in minimizing human suffering and maximizing the survival rate in disaster-affected areas. Particularly during the 2009 Black Saturday bushfires in Australia, an investigation by the Victorian Bushfires Royal Commission revealed that resources such as medical teams and medical supplies were poorly coordinated during the initial response phase. Therefore, the aim of this study is to develop a mixed integer programming model to support tactical decision making in allocating emergency relief resources in the context of the Black Saturday bushfires. The proposed model uses historical data to determine the rescue vehicles' delivery loads and schedules based on vehicle capacity utilization, the supply of relief items and strict delivery time windows. Furthermore, a possibilistic programming approach has been employed to minimize the transportation disruption risk under uncertainty in the parameters and solve the model in a complex and unpredictable environment. To evaluate the reliability of the model, various sensitivity analyses have been applied while considering the priority level of the defined objectives. The results show that it would be possible to efficiently manage this emergency distribution context, even if one or two resources have very restricted delivery time constraints. However, disruption risk and priorities to the decision makers prove to impact resource utilization. The modeling outputs will be useful in the development of emergency plans and distribution coordination strategies to enhance rapid response to emergency relief distribution in disaster zones

Robust stochastic vehicle routing and scheduling for bushfire emergency evacuation: An Australian case study

This study proposes a stochastic modeling approach as an evacuation decision support system to determine the required vehicles, scheduling and routes under uncertainties in evacuee population, time windows and bushfire propagation. The proposed model also considers road availability and disruptions. A greedy solution method is developed to cope with the complex nature of vehicle routing problem. Furthermore, the effectiveness of the proposed solution is evaluated by comparison with a designed genetic algorithm on sets of various numerical examples. The model is then applied on the real case study of the 2009 Black Saturday bushfires in Victoria, Australia. Several plausible evacuation scenarios are generated, utilizing the historical data of Black Saturday. The results are analyzed using the frequency approach to determine the optimal evacuation plan. The results show that it would have been possible to evacuate the late evacuees on Black Saturday, even within hard time windows and a maximum population

Designing an integrated multi-objective supply chain network considering volume flexibility

This paper investigates the problem of designing an integrated production-distribution system which supports strategic and tactical decision levels in supply chain management. An important aspect of this problem is consideration of volume flexibility to increase the system ability to change the level of aggregated output. The problem is formulated as a mixed integer linear programming. The objective functions are to minimize the total cost of production, location of DCs, transportation, inventory holding and backorders while maximizing flexibility level simultaneously. Since the problem under study is NP-hard, a multi-objective differential evolution (MOEM) framework is developed to solve this problem. To prove the efficiency and reliability of the proposed algorithm, the results obtained from extensive experiments are compared with the well-known multi-objective genetic algorithms in the literature, i.e. NSGA-II based on some comparison metrics. Computational experiments indicate the superiority of the MODE compared to this algorithm

Possibilistic scheduling routing for short-notice bushfire emergency evacuation under uncertainties: An Australian case study

This paper aims to develop a capacitated vehicle routing solution to evacuate short-notice evacuees with time windows and disruption risks under uncertainties during a bushfire. A heuristic solution technique is applied to solve the triangular possibilistic model to optimise emergency delivery service. The effectiveness of the proposed algorithm is evaluated by comparing it with a designed genetic algorithm on sets of 20 numerical examples. The model is then applied to the real case study of 2009 Black Saturday bushfires in Victoria, Australia. The results show that it is possible to transfer the last-minute evacuees during the Black Saturday bushfires under the hard time window constraint. Network disruptions however have impact on resource utilisation. The modelling outputs will be useful in the development of emergency plans and evacuation strategies to enhance rapid response to last-minute evacuation in a bushfire emergency

Enhancing emergency evacuation response of late evacuees: Revisiting the case of Australian Black Saturday bushfire

This paper develops a multi-objective integer programming model to support tactical planning decision-making during a short-notice evacuation using the situated context of the 2009 Black Saturday bushfires in Victoria. Various bushfire scenarios and sensitivity analysis considering short time windows, availability of resources and road disruptions were implemented to demonstrate the robustness and reliability of the model. The ε-constraint technique was applied to solve the problem. Results showed that it would be possible to evacuate all late evacuees during the Black Saturday bushfire events, even if one or two resources are disrupted within the hard time window constraint

Variable fleet size and mix VRP with fleet heterogeneity in Integrated Solid Waste Management

The Integrated Solid Waste Management (ISWM) is a recent effective tool to manage with the growing challenge of Municipal Solid Waste (MSW). The ISWM integrates all the system components (i.e. transfer, treatment, recycling and disposal of wastes) to enhance the sustainable waste management whilst reducing operational costs. In this paper, we investigate an integrated framework of the Fleet Size and Mix Vehicle Routing Problem (VRP) to optimize the cost-effective ISWM system. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is developed to concurrently minimize the transportation cost in the entire waste management system and total deviation from the fair load allocation to transfer stations. A complete ISWM system with all interdependent facilities and multiple technologies, is developed to address a tri-echelon Fleet Size and Mix VRP with a heterogeneous fleet of vehicles under multiple technologies and waste compatibility constraints. The model was solved for both the Preemptive and Non-Preemptive conditions using Lexicographic and Goal Programming optimization approaches. The model was tested on a case of ISWM in the Southern part of Tehran, Iran

Mathematical modelling and heuristic approaches to the location-routing problem of a cost-effective integrated solid waste management

Integrated solid waste management (ISWM) comprises activities and processes to collect, transport, treat, recycle and dispose municipal solid wastes. This paper addresses the ISWM location-routing problem in which different types of municipal solid wastes are factored concurrently into an integrated system with all interrelated facilities. To support a cost-effective ISWM system, the number of locations of the system's components (i.e. transfer stations; recycling, treatment and disposal centres) and truck routing within the system's components need to be optimized. A mixed-integer linear programming (MILP) model is presented to minimise the total cost of the ISWM system including transportation costs and facility establishment costs. To tackle the non-deterministic polynomial-time hardness of the problem, a stepwise heuristic method is proposed within the frames of two meta-heuristic approaches: (i) variable neighbourhood search (VNS) and (ii) a hybrid VNS and simulated annealing algorithm (VNS + SA). A real-life case study from an existing ISWM system in Tehran, Iran is utilized to apply the proposed model and algorithms. Then the presented MILP model is implemented in CPLEX environment to evaluate the effectiveness of the proposed algorithms for multiple test problems in different scales. The results show that, while both proposed algorithms can effectively solve the problem within practical computing time, the proposed hybrid method efficiently has produced near-optimal solutions with gaps of < 4%, compared to the exact results. In comparison with the current cost of the existing ISWM system in the study area, the presented MILP model and proposed heuristic methods effectively reduce the total costs by 20-22%