The bin packing problem is a well-studied problem in combinatorial optimization. In the
classical bin packing problem, we are given a list of real numbers in (0, 1] and the goal is to …

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the
best human-level performance in many domains, yet there are few use-cases in industrial or …

We study the d-dimensional vector bin packing problem, a well-studied generalization of bin
packing arising in resource allocation and scheduling problems. Here we are given a set of …

Mixed-integer linear programming (MILP) is at the core of many advanced algorithms for
solving fundamental problems in combinatorial optimization. The complexity of solving …

Multidimensional packing problems generalize the classical packing problems such as Bin
Packing, Multiprocessor Scheduling by allowing the jobs to be d-dimensional vectors. While …

In online load balancing problems, jobs arrive over a list. Upon arrival of a job, the algorithm
is required to assign it immediately and irrevocably to a machine. We consider such a …

Fundamental problems in operational research are vector scheduling and vector bin
packing where a set of vectors or items must be packed into a fixed set of bins or a minimum …

In this paper, we study the single machine vector scheduling problem (SMVS) with general
penalties, in which each job is characterized by ad-dimensional vector and can be accepted …

The bin packing problem has been the corner stone of approximation algorithms and has
been extensively studied starting from the early seventies. In the classical bin packing …

We study randomized algorithms for the online vector bin packing and vector scheduling
problems. For vector bin packing, we achieve a competitive ratio of Õ (d 1/B), where d is the …