Deep Reinforcement Learning Techniques For Solving Hybrid Flow Shop Scheduling Problems: Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C)

Well-studied scheduling practices are fundamental for the successful support of core business processes in any manufacturing environment. Particularly, the Hybrid Flow Shop (HFS) scheduling problems are present in many manufacturing environments. The current advances in the field of Deep Reinforcement Learning (DRL) attracted the attention of both practitioners and academics to investigate their adoption beyond synthetic game-like applications. Therefore, we present an approach that is based on DRL techniques in conjunction with a discrete event simulation model to solve a real-world four-stage HFS scheduling problem. The main narrative behind the presented concepts is to expose a DRL agent to a game-like environment using an indirect encoding. Two types of DRL techniques namely, Proximal Policy Optimization (PPO) and Asynchronous Advantage Actor-Critic (A3C), are evaluated for solving problems of different complexity. The computational results suggest that the DRL agents successfully learn appropriate policies for solving the investigated problem. In addition, the investigation shows that the agent can adjust their policies when we expose them to a different problem. We further evaluate the approach to solving problem instances published in the literature to establish a comparison

Investigating different optimization criteria for a hybrid job scheduling approach based on heuristics and metaheuristics

The Information Technology industry has revolutionized through the advent of cloud computing as the cloud offers dynamic computing utilities to global users. The performance of cloud computing services depends on the process of job scheduling. There has been a great research focus on the different amalgamation of heuristics with meta-heuristics (hybrid scheduling approaches) in the cloud computing scheduling context with the aim of optimizing several performance metrics. This paper discusses a hybrid job scheduling approach that intends to optimize the performance metrics namely makespan, average flow time, average waiting time, and throughput. The main focus of this paper is to evaluate this hybrid job scheduling approach based on different optimization criteria which includes single-objective and multi-objectives functions based on the aforementioned performance metrics on different large-scale problem instances. This helps us to investigate and identify the best optimization criteria for the hybrid job scheduling approach

A Hybrid Job Scheduling Approach on Cloud Computing Environments on the Usage of Heuristics and Metaheuristics Methods

The Information Technology Industry has been revolutionized through Cloud Computing by offering dynamic computing services to users through its on-demand provisioning of scalable and virtualized resources over the internet on a pay-per-use measured basis. Performance improvements in task scheduling can have a great impact on the efficiency of cloud computing. This paper proposes a hybrid task scheduling approach which employs the metaheuristic optimization technique, genetic algorithm to produce a certain combination of scheduling heuristics for processing cloud workloads. This approach is developed to optimize the performance metrics namely makespan, average flow time, throughput, and average waiting time. The developed approach is evaluated on the CloudSimPlus simulation framework using large-scale benchmarks against other heuristics in terms of the stated performance metrics. The results indicate that the proposed hybrid approach consistently outperforms the baseline individual heuristics in terms of the stated metrics irrespective of the scale of the workload. It is also observed that the optimization potential tends to increase as the workload scale becomes heavier and optimizing flow time produces complementary effects on the other metrics

Evolving Neural Networks to Solve a Two-Stage Hybrid Flow Shop Scheduling Problem with Family Setup Times

We present a novel strategy to solve a two-stage hybrid flow shop scheduling problem with family setup times. The problem is derived from an industrial case. Our strategy involves the application of NeuroEvolution of Augmenting Topologies - a genetic algorithm, which generates arbitrary neural networks being able to estimate job sequences. The algorithm is coupled with a discrete-event simulation model, which evaluates different network configurations and provides training signals. We compare the performance and computational efficiency of the proposed concept with other solution approaches. Our investigations indicate that NeuroEvolution of Augmenting Topologies can possibly compete with state-of-the-art approaches in terms of solution quality and outperform them in terms of computational efficiency

Automation of Customer Initiated Back Office Processes: A Design Science Research Approach to link Robotic Process Automation and Chatbots

While the emerging technology of robotic process automation is primarily suitable for back office processes, companies use traditional chatbots to support customer interaction in the front office. However, customer requests that require more than written information usually demand an employee to execute an internal process. This paper summarizes the results of a technical design process for a combination of both technologies. After an introduction on both topics, the findings of a literature review regarding existing approaches are outlined. The development of the IT artefact is then carried out according to the design science research methodology. In particular, the research focuses on the constitution of a design theory in consideration of criteria that are found to be important for a purposeful appearance to the external user. After a proof of concept by testing the developed artefact and a summary of the results, an outlook on possible future developments is provided

An adaptive scheduling framework for solving multi-objective hybrid flow shop scheduling problems

The proposed new technologies in the context of industry 4.0 challenge the current practices of scheduling in industry and their associated research in academia. The conventional optimization techniques that are employed for solving scheduling problems are either computationally expensive or lack the required quality. Therefore, in this paper, we propose an adaptive scheduling framework to address scheduling problems taking into account multi-objective optimality measures. The framework is motivated by a hybrid design to combine the use of heuristic and metaheuristic approaches. The main idea behind the presented concept is to achieve an acceptable tradeoff between the quality of the suggested solutions for a problem and the required computational effort to obtain them. The perused narrative in such implementation is combining some advantages of heuristic and metaheuristic approaches such as: the light execution time of heuristics and the robustness as well as the quality of metaheuristic approaches. The framework is evaluated for solving hybrid flow shop scheduling problems that are derived from a real use case

An adaptive scheduling framework for the dynamic virtual machines placement to reduce energy consumption in cloud data centers

Cloud computing has revolutionized the IT industry through its on-demand provisioning of virtualized resources through the internet. Although it relies on sharing of resources to improve the performance of datacenters, it has increased the complexity of IT systems in recent years. To meet the market requirements, cloud providers are expanding their datacenters with a large number of servers leading to high energy consumption and therefore, increasing the carbon footprint. Environmental impact and rapidly surging energy costs have become a major concern for both the government bodies and the IT service providers. In this paper, we propose a genetic algorithm based hybrid load management strategy which uses multiple existing VM allocation policies to minimize the energy consumption, Service Level Agreement (SLA) violations and number of VM migrations. The presented solution approach is evaluated on CloudSim Plus simulation framework using the well known PlanetLab workload. The results obtained from the experiments show substantial improvement in energy consumption in comparison to the individual approaches while maintaining the performance constraints

Scheduling Approach for the Simulation of a Sustainable Resource Supply Chain

The general goal of waste management is to conserve resources and avoid negative environmental impacts. This paper deals with the optimization of logistics processes at an underground waste storage site by means of solving scheduling issues and reducing setup times, with the help of a simulation model. Specific to underground waste storage is the fact that it is often only a side business to actual mining. With limited capacity and resources, all legal requirements must be met, while the business should still be profitable. This paper discusses the improvement of a logistical system’s performance using machine scheduling approaches with the support of a plant simulation model. The process sequence is determined by means of a priority index. Genetic algorithms are then applied to improve the priority index to further increase performance. Results of the simulation model show that the performance of the logistics system can be increased by up to 400 percent, ensuring adequate system performance for current as well as future demand without changes to the system’s capacities and resources

Toward A Lifecycle for Data Science: A Literature Review of Data Science Process Models

Data Science projects aim to methodologically extract knowledge and value from data to help organizations to improve performance. Dedicated process models are applied to support the management of these endeavors. However, high failure rates in the execution highlight the need for improvements in Data Science project management. Therefore, in this paper, stages and activities, functional roles, and artifacts of 28 Data Science process models are analyzed in a literature review. Based on the findings, a Data Science Lifecycle, consisting of six phases, is derived. Additionally, a corresponding Data Science process map provides an overview regarding the involved team roles and required deliverables of the individual activities in a Data Science undertaking. Accordingly, this artifact aims to mitigate the current project management issues in Data Science. For future research, the results of this study can serve as the foundation for a holistic process model for Data Science project management

A Holistic View of Adaptive Supply Chain in Retailing Industry

The retailing industry witnessed a significant shift from conventional retailing to online marketplaces, inducing many challenges on the common practices of supply chain. Supply chains are normally subject to a wide range of disruptions, that are caused by certain events. Still, few organizations are relying on appropriate data streams and required technologies to detect and report on potential disruptions in supply chain. As a result, current supply chains remain marginally adaptive and lack the ability to react to the dynamic nature of markets on strategic as well as operative levels, which leads to loss of optimization potential. Therefore, in this study, we investigate data sources that might be beneficial to develop adaptive supply chain management (A-SCM) practices. This paper presents a holistic view of A-SCM that includes a thorough analysis of the problem domain and nature, often used data sources, employed solution techniques and finally the adopted objective function