This paper addresses to the Cell Formation Problem (CFP) in which Automated Guided Vehicles (AGVs) have been employed to transfer the jobs which may need to visit one or more cells. Because of added constraints to problem such as AGVs’ conflict and excessive cessation on one place, it is possible that AGVs select the different paths from one cell to another over the time. This means that the ti...

This paper breaks new ground by modelling lot sizing and scheduling in a flexible flow line (FFL) simultaneously instead of separately. This problem, called the ‘General Lot sizing and Scheduling Problem in a Flexible Flow Line’ (GLSP-FFL), optimizes the lot sizing and scheduling of multiple products at multiple stages, each stage having multiple machines in parallel. The objective is to satisf...

  These days, data mining one of the most significant issues. One field data mining is a mixture of computer science and statistics which is considerably limited due to increase in digital data and growth of computational power of computer. One of the domains of data mining is the software cost estimation category. In this article, classifying techniques of learning algorithm of machine ...

This paper addresses a production and outbound distribution scheduling problem in which a set of jobs have to be process on a single machine for delivery to customers or to other machines for further processing. We assume that there is a sufficient number of vehicles and the delivery costs is independent of batch size but it is dependent on each trip. In this paper, we present an Artificial Imm...

We study a scheduling problem that belongs to the yard operations component of the railroad planning problems, namely the hump sequencing problem. The scheduling problem is characterized as a single-machine problem with stepwise tardiness cost objectives. This is a new scheduling criterion which is also relevant in the context of traditional machine scheduling problems. We produce complexity re...

We investigate in this paper the problem of scheduling n jobs on a single machine with general stochastic cost functions, which involves the effects of stochastic due times, and with the stochastic processing times in a class of distributions, which covers the family of exponential distributions, the family of geometrical distributions and some other important distribution families as its speci...

Results: A total of 2,274 individual patients met our inclusion criteria, 1,090 patients for the year 2011 and 1,184 for 2012. The most important variables predicting cost and LOS were consistent across all models and included the Charlson and Van Walraven comorbidity scores. The best performing model predicting total cost was Support Vector Machine with Linear Kernel with root mean square erro...

In many business and science applications, it is important to track trends over historical data, for example, measuring the monthly prevalence of influenza incidents at a hospital. In situations where a machine learning classifier is needed to identify the relevant incidents from among all cases in the database, anything less than perfect classification accuracy will result in a consistent and ...

Supply chain scheduling is a new emerging area of research. We study batch arrival scheduling problems at the manufacturer in a multi-level customer-centric supply chain, where promised job due dates are considered constraints which must be satisfied. We analyze the tradeoff between inventory holding costs and batch delivery costs. We show that the problems are closely related to batch scheduli...

The optimal scheduling or unit commitment of power generation systems to meet a random demand involves the solution of a class of dynamic programming inequalities for the optimal cost and control law. We study the behavior of this optimality system in terms of two parameters: (i) a scheduling delay, e.g., the startup time of a generation unit; and (ii) the relative magnitudes of the costs (oper...