The online machine minimization problem seeks to design a preemptive scheduling algorithm on multiple machines — each job j arrives at its release time rj, has to be processed for pj time units, and must be completed by its deadline dj.
This paper focuses on the minimization of the machining time by optimizing the values of the cutting parameters.
Structural risk minimization is a machine-learning framework that addresses the issue of over-fitting by providing a backbone for analysis as well as an impetus for the design of better learning algorithms.
Based on structural risk minimization principle, support vector machine (SVM) was first proposed by Cortes and Vapnik [10] in the 1990s.
The paper considers makespan minimization on a single machine subject to release dates in the relocation problem, originated from a resource-constrained redevelopment project in Boston.
Kacem, A. Ridha Mahjoub, Fully polynomial time approximation scheme for the weighted flow-time minimization on a single machine with a fixed non-availability interval, Computers and Industrial Engineering 56 (2009) 1708 1712].
Kacem, Approximation algorithm for the weighted flow-time minimization on a single machine with a fixed non-availability interval, Computers & Industrial Engineering 54 (2008) 401 410], this paper is the first successful attempt to develop a constant ratio approximation algorithm for this problem.
For the third objective function, that is, Eq. (6), deals with minimization workload variance on machines in different cells.
In this paper, U-line rebalancing problem is formalized with respect to minimization the moving cost of machines and labor cost.
This paper is the first attempt to successfully design efficient approximation algorithms for the single-machine maximum lateness minimization problem when jobs have different release dates and tails (or delivery times) under the no idle time assumption (i.e., the schedule cannot contain any idle time between two consecutive jobs on the machine).
