Rojee Pradhananga

An integrated resource allocation and distribution model for pre-disaster planning

We model a disaster preparedness problem for the supply of a commodity.We consider an integrated pre- and post-disaster preparedness planning model.The use of multiple supply points reduces supply deprivations at the demand points.Deprivation cost structure significantly affect the proportion of pre- and post-disaster allocations. In this paper, a three-echelon network model is proposed for integrated emergency preparedness and response planning for the distribution of emergency supplies. The model minimizes the social cost to identify a set of potential supply points (SPs) at the highest echelon, where supply items are consolidated and sent to the prepositioning facilities. The sum of logistics and deprivation costs incurred by the population due to the lack of access to goods or services, is considered as the social cost in this model. The deprivation cost is assumed to increase exponentially with the deprivation time. The model also considers pre-disaster and post-disaster purchasing decisions at the SPs, and allows direct shipments from SPs and prepositioned facilities to the demand points. Numerical analysis shows that multiple supply sources can ensure efficient distribution of the supplies and reduce the deprivation costs. The results also indicate that partial prepositioning and post-disaster purchasing can reduce the shortage in emergency supplies.

Optimisation model for hazardous material transport routing in Thailand

This paper introduces an optimisation model for transportation of the hazardous material (Hazmat). The focus is on application to developing countries like Thailand. The objective of the proposed Vehicle Routing Problem with Time Window (VRPTW) model for Hazmat routing is to minimise the economic and risk costs using available data and information for the case study. Genetic Algorithm (GA) is used to solve the model. Results in application to the case study showed its effective performance both in economic as well as safety aspects providing a useful approach for number of companies involved in Hazmat transport activities in developing countries.

Creating a Risk-Based Network for HazMat Logistics by Route Prioritization with AHP: - Case Study: Gasoline Logistics in Rayong, Thailand -

This article presents a model for route optimization of hazardous material (HazMat). It uses a risk-based route network for use as a risk-concern shortest-path network by using multiple criteria analysis called Analytic Hierarchy Process (AHP). This method finds the optimum route by taking into account the risk potential for the whole network and considering road elements that are factors that contribute to accidents. Data was collected from HazMat shippers in Thailand through a questionnaire and focus groups. The article includes a description of the AHP structure and criteria, as well as a detailed description of the development of the model with the methods for route prioritization and selection. The authors conclude that due to the weighting system of their approach, the results are appropriate for complex and sensitive issues. They also note that new criteria would need to be built for other types of shipping.

Risk of Traffic Incident Delay in Routing and Scheduling of Hazardous Materials

Unlike normal traffic incidents, incidents involving hazardous material are associated with significant traffic delays. The model of the hazardous material routing and scheduling problem presented in this paper considers such potential effect of a hazardous material incident, in addition to the traditionally considered risk to exposed population. Probable loss due to congestion created by the probable incident is used as its measure. The objective is to minimize sum of the population-based and congestion-based risk cost. The model was used to explore routing and scheduling in an ITS data-based hazardous material logistics instance derived from the road network of Osaka City, Japan. Comparison of the results showed that the model provides a better alternative to the conventional population-based model as it gives balanced optimal solution avoiding paths that causes large increase of the congestion-based cost.