Invasive weed optimization based scheduling for digital microfluidic biochip operations

Abstract

Digital Microfluidic Biochips (DMFBs) based on electro-wetting-on-dielectric (EWOD) technology are a class of lab-on-a-chip (LOC) devices. DMFBs can efficiently carry out biochemical analysis and have many advantages over the traditional laboratory system. DMFBs offer miniaturization, automation, and programmability. Resource-constrained scheduling is the first and vital step of fluidic-level synthesis of DMFBs while the other two are placement and routing of droplets. Scheduling DMFB operations is a constrained optimization problem which is NP-Complete. We propose an invasive weed optimization (IWO) algorithm based scheduling for the synthesis of DMFBs. The IWO algorithm is a nature-inspired meta-heuristic algorithm. Proposed algorithm can be used for the offline synthesis of DMFBs, where solution quality is more important than execution time. Each weed in the proposed algorithm represents a potential candidate solution for the scheduling problem. To calculate the fitness of individual weeds, we proposed an algorithm based on Heterogeneous Earliest Finish Time (HEFT), which incorporates resource binding, scheduling, and greedy module selection mechanism for bio-assay operations. Weeds (solutions) update their positions (priorities) by colonization behavior of weeds. Simulation results show that proposed IWO outperforms iterative improvement based algorithms and optimal ILP based algorithms which are existing for the offline synthesis of DMFBs.