In this work we propose new randomized rounding algorithms for matroid intersection and matroid base polytopes. We prove concentration inequalities for polynomial objective functions and constraints that has numerous applications and can be used in approximation algorithms for Minimum Quadratic Spanning Tree, Unrelated Parallel Machines Scheduling and Scheduling with Time Windows and Nonlinear ...

We consider the unrelated parallel machines scheduling problem where jobs have earliness and tardiness penalties and a common due date. We formulate this problem and some of its variants as 0-1 programs with quadratic objective function subject to linear constraints. The main contribution of this paper is to use and develop different convex reformulations to solve exactly the quadratic programs...

In this paper, we study a planning and scheduling problem for unrelated parallel machines. There are n jobs that have to be assigned and sequenced on m unrelated parallel machines. Each job has a weight that represents the priority of the corresponding customer order, a given due date, and a release date. An Automated Guided Vehicle is used to transport at maximum jobs into a storage space in f...

This research deals with the problem of scheduling N jobs on M unrelated parallel machines. Each job has a due date and requires a single operation. A setup that includes detaching one die and attaching another from the appropriate die type is incurred if the type of the job scheduled is different from the last job on that machine. Due to the mechanical structure of machines and the fitness of ...

In this paper, we consider scheduling problems with general truncated job-dependent learning effect on unrelated parallel-machine. The objective functions are to minimize total machine load, total completion (waiting) time, total absolute differences in completion (waiting) times respectively. If the number of machines is fixed, these problems can be solved in m O n 2 ( ) + time respectively, w...

This work deals with the problem of scheduling n independent jobs on m unrelated parallel machines with the objective of minimizing the makespan (the total elapsed time from the start of execution until all jobs have been completed). Preemption is not allowed. This way a job, once assigned to a particular machine, may not be removed until it has finished its execution. All the machines start wo...

The paper suggests a stochastic approximate solution to scheduling problems with unrelated parallel machines. The presented method is based on neurodynamic programming (reinforcement learning and feed-forward artificial neural networks). For various scheduling environments (static-dynamic, deterministicstochastic) different variants of episodic Q-learning rules are proposed. A way to improve th...