this paper studies the multi-stage supply chain system (msscm) controlled by the kanban mechanism. in the kanban system, decision making is based on the number of kanbans as well as batch sizes. a kanban mechanism is employed to assist in linking different production processes in a supply chain system in order to implement the scope of just-in-time (jit) philosophy. for a msscm, a mixed-integer...

this paper addresses the mixed integer reliability redundancy allocation problems to determine simultaneous allocation of optimal reliability and redundancy level of components based on three objective goals. system engineering principles suggest that the best design is the design that maximizes the system operational effectiveness and at the same time minimizes the total cost of ownership (tco...

In the rst part of the paper we present a framework for describing basic tech niques to improve the representation of a mixed integer programming problem We elaborate on identi cation of infeasibility and redundancy improvement of bounds and coe cients and xing of binary variables In the second part of the paper we discuss recent extensions to these basic techniques and elaborate on the investi...

A conic integer program is an integer programming problem with conic constraints. Conic integer programming has important applications in finance, engineering, statistical learning, and probabilistic integer programming. Here we study mixed-integer sets defined by second-order conic constraints. We describe general-purpose conic mixed-integer rounding cuts based on polyhedral conic substructure...

Abstract Large Neighborhood Search (LNS) heuristics are among the most powerful but also expensive for mixed integer programs (MIP). Ideally, a solver adaptively concentrates its limited computational budget by learning which LNS work best MIP problem at hand. To this end, introduces Adaptive (ALNS) MIP, primal heuristic that acts as framework eight popular such Local Branching and Relaxation I...

We introduce a mixed integer program (MIP) for assigning importance scores to each neuron in deep neural network architectures which is guided by the impact of their simultaneous pruning on main learning task network. By carefully devising objective function MIP, we drive solver minimize number critical neurons (i.e., with high score) that need be kept maintaining overall accuracy trained Furth...