Scheduling N variable-time interdependent tasks across M workers
This can been seen as a variation of the job shop problem where you want to find the policy that yields the minimum makespan (time taken for all machines to process all jobs); as well as a variation of the assignment problem (find the optimal pairing of workers to jobs that minimizes cost). The variation in both cases is an added dependency between jobs (Job A must complete before Job C) etc. This collection of combinatorial optimization problems is NP-Hard. Most of what you'd find in the literature is approximation heuristics to find a near optimal policy (sequence of tasks per worker).

This paper is one such approach: "Approximation Algorithms for Multiprocessor Scheduling under Uncertainty" 

A quick way to solve your problem (quick as in prototype a solution, not necessarily runtime, or necessarily near optimal), is to construct a topological sort of your job dependency graph, use breadth first search to batch all jobs of a given height and put that batch on a stack (assumes directed edge represents a depends on relationship, with zero out degree nodes having no dependencies). Afterwards, Until the stack is empty, pop a batch and treat it as a traditional assignment problem and solve using the Hungarian algorithm, then enqueue the tasks to each assigned worker's task queue. Afterwards you'd have a policy. (When you go to execute the policy, you'd have to have some signalling mechanism that a task completed so that workers don't start on a task whose dependencies haven't finished yet.)