Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Unreliable numbers: error and harm induced by bad design can be reduced by better design

Number entry is a ubiquitous activity and is often performed in safety- and mission-critical procedures, such as healthcare, science, finance, aviation and in many other areas. We show that Monte Carlo methods can quickly and easily compare the reliability of different number entry systems. A surprising finding is that many common, widely used systems are defective, and induce unnecessary human error. We show that Monte Carlo methods enable designers to explore the implications of normal and unexpected operator behaviour, and to design systems to be more resilient to use error. We demonstrate novel designs with improved resilience, implying that the common problems identified and the errors they induce are avoidable.     

Removal of imidacloprid and acetamiprid from tea infusions by microfiltration membrane

Removal of imidacloprid and acetamiprid in tea infusions by microfiltration membrane using dead‐end model was investigated in the present study. The results showed that microfiltration significantly promoted the removal of both pesticides (P < 0.05) in tea infusions. Furthermore, the extent of removal was strongly influenced by the pore size of membrane, operational pressure and the concentrations of tea infusions. The initial concentration of imidacloprid and acetamiprid showed no significant effect on their removal rates. The maximum removal rates were 79.7% for imidacloprid and 81.9% acetamiprid. The changes in major chemical components of tea infusions after microfiltration were evaluated. The results indicated that microfiltration caused no considerable changes in total polyphenols and total free amino acids, and small but statistically significant losses (6.3–18.0%) of eight catechins and three methylxanthines when filtration volume reached to 200 mL. The present study validated the application of microfiltration as a potentially feasible and promising method for the removal of imidacloprid and acetamiprid residues from tea infusions.