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This paper develops a robust stochastic optimization model for reverse logistics in closed-loop supply chains. By determining the optimal flow of products using a Chance Constrained Robust Stochastic Programming (CCRSP), it is highlighted how the number of plant openings is influenced by the changes in carbon credit price. To assess the model performance, a set of numerical experiments in different sizes are developed and conducted. The effectiveness of the results are then compared to a proposed Heuristic Hybrid Taguchi PSO (HTPSO) solution algorithm, which underlines the effectiveness of the model. A sensitivity analysis on the carbon emission rate is carried out which underlines the role of Carbon Tax Policy. Finally, a real-life case study within the automotive manufacturing industry is carried out by applying the developed robust stochastic model. From a practical standpoint, the model can potentially be employed to meet the carbon credits that are used for handling the different carbon prices and trade scenarios. Also, it provides insights on how to better manage uncertainties, as well as to reduce the overall emissions in supply chains.

Resource scheduling for emergency relief operations is complex as it has many constraints. However, an effective allocation and sequencing of resources are crucial for the minimization of the compl...

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.

An effective and efficient suppression of a bushfire is predicated on synchronised collaborative planning, information sharing, scheduling and tasks coordination between multiple agencies. This paper aims to develop a bushfire suppression optimisation model, which minimises the completion times and total costs within resource constraints. An algorithm is developed to tackle the highly complex operational response to bushfire suppression via aerial firefighting. A real case study of 2009 Black Saturday bushfires in Victoria is used to create the situational context and to derive model parameters. Three scenarios are designed to incorporate time and resource constraints to reflect a highly uncertain and dynamic situated bushfire environment. This study has generated bushfire suppression plans in emergency situations by improving resource utilisation efficiency and time scheduling. In the baseline scenario, the model indicates the ability of fire agencies to complete all operational activities, but in the scenarios where resource shortage and time limitations exist, the cost of unmet demands fell slightly. The outcomes of the proposed approach will help to enhance multi-agencies coordination in fire suppression evacuation plans to improve operational response and maximise geographic coverage of service delivery.

Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations.