Single machine scheduling with general positional deterioration and rate-modifying maintenance

We present polynomial-time algorithms for single machine problems with generalized positional deterioration effects and machine maintenance. The decisions should be taken regarding possible sequences of jobs and on the number of maintenance activities to be included into a schedule in order to minimize the overall makespan. We deal with general non-decreasing functions to represent deterioration rates of job processing times. Another novel extension of existing models is our assumption that a maintenance activity does not necessarily fully restore the machine to its original perfect state. In the resulting schedules, the jobs are split into groups, a particular group to be sequenced after a particular maintenance period, and the actual processing time of a job is affected by the group that job is placed into and its position within the group

Approximation schemes for scheduling on a single machine subject to cumulative deterioration and maintenance

We consider a scheduling problem on a single machine to minimize the makespan. The processing conditions are subject to cumulative deterioration, but can be restored by a single maintenance. We link the problem to the Subset-sum problem (if the duration of maintenance is constant) and to the Half-Product Problem (if the duration of maintenance depends on its start time). For both versions of the problem, we adapt the existing fully polynomial-time approximation schemes to our problems by handling the additive constants

Single machine scheduling with a generalized job-dependent cumulative effect

We consider a single machine scheduling problem with changing processing times. The processing conditions are subject to a general cumulative effect, in which the processing time of a job depends on the sum of certain parameters associated with previously scheduled jobs. In previous papers, these parameters are assumed to be equal to the normal processing times of jobs, which seriously limits the practical application of this model. We further generalize this model by allowing every job to respond differently to these cumulative effects. For the introduced model, we solve the problem of minimizing the makespan, with and without precedence constraints. For the problem without precedence constraints, we also consider a situation in which a maintenance activity is included in the schedule, which can improve the processing conditions of the machine, not necessarily to its original state. The resulting problem is reformulated as a variant of a Boolean programming problem with a quadratic objective, known as a half-product, which allows us to develop a fully polynomial-time approximation scheme with the best possible running time

Combining time and position dependent effects on a single machine subject to rate-modifying activities

We introduce a general model for single machine scheduling problems, in which the actual processing times of jobs are subject to a combination of positional and time-dependent effects, that are job-independent but additionally depend on certain activities that modify the processing rate of the machine, such as, maintenance. We focus on minimizing two classical objectives: the makespan and the sum of the completion times. The traditional classification accepted in this area of scheduling is based on the distinction between the learning and deterioration effects on one hand, and between the positional effects and the start-time dependent effects on the other hand. Our results show that in the framework of the introduced model such a classification is not necessary, as long as the effects are job-independent. The model introduced in this paper covers most of the previously known models. The solution algorithms are developed within the same general framework and their running times are no worse than those available earlier for problems with less general effects

Single machine scheduling with time-dependent linear deterioration and rate-modifying maintenance

We study single machine scheduling problems with linear time-dependent deterioration effects and maintenance activities. Maintenance periods (MPs) are included into the schedule, so that the machine, that gets worse during the processing, can be restored to a better state. We deal with a job-independent version of the deterioration effects, that is, all jobs share a common deterioration rate. However, we introduce a novel extension to such models and allow the deterioration rates to change after every MP. We study several versions of this generalized problem and design a range of polynomial-time solution algorithms that enable the decision-maker to determine possible sequences of jobs and MPs in the schedule, so that the makespan objective can be minimized. We show that all problems reduce to a linear assignment problem with a product matrix and can be solved by methods very similar to those used for solving problems with positional effects

SEMI-SUPERVISED MACHINE LEARNING OF INTENT DATA MODELS BASED ON GROUP BASED POLICY

Techniques are described herein for using semi-supervised machine learning to simplify an intent interface for end users by allowing a user to specify key network features in which they are interested. A continual learning based approach better adapts to a continuously changing intent interface and simplifies the experience for end users. The semi-supervised learning algorithm learns the reverse mapping (stored in an intent cache database) of Group-Based Policy (GBP) policy templates expressed using data models (e.g., as Yang models) as well as user network feature key words given a set of existing network configuration use cases provided as topology network maps, device configurations, and manually crafted GBP policy objects. A new user starts by specifying key intent features of interest, picks the closest mapping GBP template, and configures their network

Jagged Peak: The Case For Going Direct

This case explores the opportunities and challenges confronting Jagged Peak during its first decade in operation. For nearly ten years, Jagged Peak had grown at a rapid pace despite the absence of a defined competitive strategy or formal marketing plan. Today, Jagged Peak management faces strategic decisions that impact the company’s future success and perhaps position the organization as a pioneer in an already saturated e-commerce solutions marketplace. The company does not have a definitive strategy for obtaining or retaining customers. The team that struggled to define their company mission and vision is faced with the challenge of identifying their value proposition and target market. A task that is typically completed during the formation of a business was being re-evaluated nearly ten years after the company’s inception -- to define their position in the market. &nbsp

Mexx - An Attitude, A Lifestyle, A Kiss: A Case Study In Global Strategy

This case explores the opportunities and challenges confronting Mexx in the early 21st century. For more than 20 years, Mexx, an Amsterdam-based global retailer, grew quickly and successfully.  Purchased by the Liz Claiborne organization in 2001, at the turn of the century, Mexx was poised for continued expansion and support to build a powerful, global retail brand. In 2008, Mexx management faces strategic decisions that will impact the company’s future in the highly competitive global fashion arena