In this paper, we propose a Pareto-based tabu search algorithm for multi-objective FJSP with Earliness/Tardiness (E/T) penalty. In the hybrid algorithm, several neighboring structure based approaches were proposed to improve the convergence capability of the algorithm while keep population diversity of the last Pareto archive set. In addition, an external Pareto archive was developed to record ...

We consider the Courier Delivery Problem, a variant of the Vehicle Routing Problem with time windows in which customers appear probabilistically and their service times are uncertain. We use scenario-based stochastic programming with recourse to model the uncertainty in customers and robust optimization for the uncertainty in service times. Our proposed model generates a master plan and daily s...

The single machine scheduling problem with a common due date and nonidentical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performanc...

this paper proposes a HMM (Hidden Markov Model) based fraud detection system for credit card fraud detection. The method works on the statistical behavior of user’s transactions. Since the original transactions are not available due to privacy policies of bank we used here synthetically generated data for a credit card user, and then HMM model is trained using different size of sample of genera...

We consider a common due-window assignment scheduling problem jobs with variable job processing times on a single machine, where the processing time of a job is a function of its position in a sequence (i.e., learning effect) or its starting time (i.e., deteriorating effect). The problem is to determine the optimal due-windows, and the processing sequence simultaneously to minimize a cost funct...

Based on theoretical arguments and empirical evidence we advocate the use of the lognormal distribution as a model for activity times. However, raw data on activity times are often subject to rounding and to the Parkinson effect. We address those factors in our statistical tests by using a generalized version of the Parkinson distribution with random censoring of earliness, ultimately validatin...

Field experiments were conducted near Weslaco, Texas (Lat. 26° 8' N) between 1999 and 2001 to evaluate the field performance of preand post-mycorrhizal inoculations of tomato and onion (2001 only) transplants. In 1999 ‘Heatmaster’ tomato (Lycopersicon esculentum Mill.) transplants were inoculated with Glomes intraradices (Reforestation Technologies, Salinas, CA) at transplanting; in 2000 ‘Heatm...

We present Earliness-Aware Deep Convolutional Networks (EA-ConvNets), an end-to-end deep learning framework, for early classification of time series data. Unlike most existing methods for early classification of time series data, that are designed to solve this problem under the assumption of the availability of a good set of pre-defined (often hand-crafted) features, our framework can jointly ...

A single machine scheduling problem is described which covers earliness costs, tardiness costs and setup costs. Setups are needed whenever a switch is made to a job of another product family. All jobs within the same product family have the same processing time. Besides setup costs also setup times are involved. Idle time is allowed, preemption is not allowed. Both a mixed integer programming f...

The main objective of the working paper series of the IIMA is to help faculty members to test out their research findings at the pre-publication stage. Abstract We address the permutation flowshop scheduling problem with earliness and tardiness penalties (E/T) and common due date of jobs. Large number of process and discrete parts industries follow flowshop type of production process. There are...