Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Analysis of Job Shop problem through an expert system

[ENG] In the present work it is defined a new methodology, based on Experts Systems, for sequencing in Job Shop Environments. This work is developed in two phases. In the first one, the different techniques used are defined. In the second one, the necessary statistical tests are executed. The results show that the new technique don’t produce an optimal result every single time; but in few seconds, this technique can find sub-optimal solutions with an approximation of 92.95 % and 73.88%, to the optimal solution, in the variables of total process time (makespan) and total idle time, respectively. Finally, the new technique is compared with other similar techniques

A Generic Library of Problem Solving Methods for Scheduling Applications

In this thesis we propose a generic library of scheduling problem-solving methods. As a first approximation, scheduling can be defined as an assignment of jobs and activities to resources and time ranges in accordance with a number of constraints and requirements. In some cases optimisation criteria may also be included in the problem specification. Although, several attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision, which is needed both to obtain a formal account of scheduling problem solving and to provide effective support for development of scheduling applications by reuse. Our library subscribes to the Task-Method-Domain-Application (TMDA) knowledge modelling framework, which provides a structured organisation for the different components of the library. In line with the organisation proposed by TMDA, we first developed a generic scheduling task ontology, which formalises the space of scheduling problems independently of any particular application domain, or problem solving method. Then we constructed a task-specific, but domain independent model of scheduling problem-solving, which generalises from the variety of approaches to scheduling problem-solving, which can be found in literature. The generic nature of this model was demonstrated by constructing seven methods for scheduling, as alternative specialisation of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for the analysis and development of scheduling applications

Oil price forecasting using gene expression programming and artificial neural networks

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models. The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. Further, the GEP model outperforms the NN and the ARIMA models in terms of the mean squared error, the root mean squared error and the mean absolute error. Finally, the GEP model also has the highest explanatory power as measured by the R-squared statistic. The results of this study have important implications for both theory and practice

Assigning patients to healthcare centers using dispatching rules

This study proposes a model for the balanced assignment of patients to healthcare centers in a region. In the suggested model, it is supposed that patients want to go to the nearest center, which causes an imbalance in the workloads of resources between centers. This disproportion is undesirable not only for the centers but also for the patients. Thus, balancing assignments is targeted. This goal is expressed in a model with a multi-objective function. Since balancing is one of the main goals of the sectorization concept, we characterize the model based on it. Unlike studies in the literature, we do sectorization employing dispatching rules. This diminishes the problem's complexity and makes it suitable for solving actual, large, and dynamic problems. We simulated the system using the Rockwell Arena software. We consider the effect of different seasons, days, and hours on the system. The dispatching rule used for sectorization is optimized using the OptQuest software. The numerical results demonstrate that by optimizing the dispatching rule, it is possible to enhance the objective function significantly.info:eu-repo/semantics/publishedVersio