Dynamic adjustment of dispatching rule parameters in flow shops with sequence dependent setup times

Decentralized scheduling with dispatching rules is applied in many fields of production and logistics, especially in highly complex manufacturing systems. Since dispatching rules are restricted to their local information horizon, there is no rule that outperforms other rules across various objectives, scenarios and system conditions. In this paper, we present an approach to dynamically adjust the parameters of a dispatching rule depending on the current system conditions. The influence of different parameter settings of the chosen rule on system performance is estimated by a machine learning method, whose learning data is generated by preliminary simulation runs. Using a dynamic flow shop scenario with sequence dependent setup times, we demonstrate that our approach is capable of significantly reducing the mean tardiness of jobs

Data Mining for Production Scheduling

Data mining is a fast growing field and many industrial engineering applications generate large amounts of data to which data mining techniques can be applied. In this paper we develop a data mining framework for production scheduling. This involves preprocessing of historic schedules into an appropriate data file, discovery of key scheduling concepts, and representation of the data mining results in a way that enables its use for job scheduling

Learning Technology in Scheduling Based on the Mixed Graphs

We propose the adaptive algorithm for solving a set of similar scheduling problems using learning technology. It is devised to combine the merits of an exact algorithm based on the mixed graph model and heuristics oriented on the real-world scheduling problems. The former may ensure high quality of the solution by means of an implicit exhausting enumeration of the feasible schedules. The latter may be developed for certain type of problems using their peculiarities. The main idea of the learning technology is to produce effective (in performance measure) and efficient (in computational time) heuristics by adapting local decisions for the scheduling problems under consideration. Adaptation is realized at the stage of learning while solving a set of sample scheduling problems using a branch-and-bound algorithm and structuring knowledge using pattern recognition apparatus

Learning-based scheduling of flexible manufacturing systems using ensemble methods

Dispatching rules are commonly applied to schedule jobs in Flexible Manufacturing Systems (FMSs). However, the suitability of these rules relies heavily on the state of the system; hence, there is no single rule that always outperforms the others. In this scenario, machine learning techniques, such as support vector machines (SVMs), inductive learning-based decision trees (DTs), backpropagation neural networks (BPNs), and case based-reasoning (CBR), offer a powerful approach for dynamic scheduling, as they help managers identify the most appropriate rule in each moment. Nonetheless, different machine learning algorithms may provide different recommendations. In this research, we take the analysis one step further by employing ensemble methods, which are designed to select the most reliable recommendations over time. Specifically, we compare the behaviour of the bagging, boosting, and stacking methods. Building on the aforementioned machine learning algorithms, our results reveal that ensemble methods enhance the dynamic performance of the FMS. Through a simulation study, we show that this new approach results in an improvement of key performance metrics (namely, mean tardiness and mean flow time) over existing dispatching rules and the individual use of each machine learning algorithm

Machine Learning Applications for Sustainable Manufacturing: A Bibliometric-based Review for Future Research

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). In the present study, we use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing. The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices. A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originalityN/

Application of data mining in scheduling of single machine system

The rapidly growing field of data mining has the potential of improving performance of existing scheduling systems. Such systems generate large amounts of data, which is often not utilized to its potential. The problem is whether it is possible to discover the implicit knowledge behind scheduling practice and then, with this knowledge, we could improve current scheduling practice. In this dissertation, we propose a novel methodology for generating scheduling rules using a data-driven approach. We show how to use data mining to discover previously unknown dispatching rules by applying the learning algorithms directly to production data. We also consider how by using this new approach unexpected knowledge and insights can be obtained, in a manner that would not be possible if an explicit model of the system or the basic scheduling rules had to be obtained beforehand. However, direct data mining of production data can at least mimic scheduling practices. The problem is whether scheduling practice could be improved with the knowledge discovered by data mining. We propose to combine data mining with optimization for effective production. In this approach, we use a genetic algorithm to find a heuristic solution to the optimal instances selection problem, and then induce a decision tree from this subset of instances. The optimal instance selection can be viewed as determining the best practices from what has been done in the past, and the data mining can then learn new dispatching rules from those best practices

Application of Reinforcement Learning to Multi-Agent Production Scheduling

Reinforcement learning (RL) has received attention in recent years from agent-based researchers because it can be applied to problems where autonomous agents learn to select proper actions for achieving their goals based on interactions with their environment. Each time an agent performs an action, the environment¡Šs response, as indicated by its new state, is used by the agent to reward or penalize its action. The agent¡Šs goal is to maximize the total amount of reward it receives over the long run. Although there have been several successful examples demonstrating the usefulness of RL, its application to manufacturing systems has not been fully explored. The objective of this research is to develop a set of guidelines for applying the Q-learning algorithm to enable an individual agent to develop a decision making policy for use in agent-based production scheduling applications such as dispatching rule selection and job routing. For the dispatching rule selection problem, a single machine agent employs the Q-learning algorithm to develop a decision-making policy on selecting the appropriate dispatching rule from among three given dispatching rules. In the job routing problem, a simulated job shop system is used for examining the implementation of the Q-learning algorithm for use by job agents when making routing decisions in such an environment. Two factorial experiment designs for studying the settings used to apply Q-learning to the single machine dispatching rule selection problem and the job routing problem are carried out. This study not only investigates the main effects of this Q-learning application but also provides recommendations for factor settings and useful guidelines for future applications of Q-learning to agent-based production scheduling

Essays in Scheduling: Applications in Health Care and Manufacturing

The problem of finding an optimal schedule is ubiquitous in human endeavor. Airlines strive to find the best schedule for their aircrafts and flight-crews, academic scheduling offices at universities schedule classes, and business organizations prioritize their jobs or customers and schedule them accordingly. Even in our day to day lives, whether consciously or not, we decide on our daily schedule and go about our sequence of tasks, prioritizing certain tasks, and procrastinating on others. Not surprisingly, scheduling of people, tasks, and manufacturing jobs constitute a significant proportion of the operations management literature. Despite all the research that has been published on scheduling, we encounter scheduling problems that call for novel approaches, and when treated with appropriate tools and techniques, provide interesting managerial insights. The dissertation consists of three such scheduling problems, two from the health care industry, and one from manufacturing. The first health care paper (Ganguly et al. 2013) employs mixed-integer linear programming to schedule physicians for a medical emergency department (ED). The second paper (Ganguly and Samorani 2013) uses an analytical approach for making real time updates to patient schedules in outpatient clinics that primarily operate on an appointment basis. The paper on manufacturing (Ganguly and Laguna 2013) develops heuristic techniques for a particular type of job-sequencing problem that involves two types of setups