We study a problem of scheduling a maintenance activity on a single machine. Following several recent papers, the maintenance is assumed to be deteriorating, that is, delaying the maintenance increases the time required to perform it. The following objective functions are considered: makespan, flowtime, maximum lateness, total earliness, tardiness and due-date cost, and number of tardy jobs. We...

This paper addresses a bi-criteria two-machine flowshop scheduling problem when the learning effect is present. The objective is to find a sequence that minimizes a weighted sum of the total completion time and the maximum tardiness. In this article, a branch-and-bound method, incorporating several dominance properties and a lower bound, is presented to search for the exact solution for small j...

This paper presents a new mathematical model for a hybrid flow shop scheduling problem with multiprocessor tasks in which sequence dependent set up times and preemption are considered. The objective is to minimize the weighted sum of makespan and maximum tardiness. Three meta-heuristic methods based on genetic algorithm (GA), imperialist competitive algorithm (ICA) and a hybrid approach of GA a...

This paper addresses a practical scheduling problem arising in the packaging department of a pharmaceutical industrial plant. The problem is modeled as a parallel machine scheduling problem with setups, release and due dates and additional constraints related to the scarce availability of tools and human operators. The objective functions are minimization of makespan and maximum tardiness in le...

Existing reinforcement learning paradigms proposed in the literature are guided by two performance criteria; namely: the expected cumulativereward, and the average reward criteria. Both of these criteria assume an inherently present cumulative or additivity of the rewards. However, such inherent cumulative of the rewards is not a definite necessity in some contexts. Two possible scenarios are p...

Maximum margin criterion (MMC) is a well-known method for feature extraction and dimensionality reduction. However, MMC is based on vector data and fails to exploit local characteristics of image data. In this paper, we propose a two-dimensional generalized framework based on a block-wise approach for MMC, to deal with matrix representation data, that is, images. The proposed method, namely, bl...

Text classification is a very important task in information retrieval and data mining. In vector space model (VSM), document is represented as a high dimensional vector, and a feature extraction phase is usually needed to reduce the dimensionality of the document. In this paper, we propose a feature extraction method, named Local Relevance Weighted Maximum Margin Criterion (LRWMMC). It aims to ...