As supply chain management is becoming demand driven, logistics service providers need to use real-time information efficiently and integrate new technologies into their business. Synchromodal logistics has emerged recently to improve flexibility in supply chains, cooperation among stakeholders, and utilization of resources. We survey the existing scientific literature and real-life developments on synchromodality. We focus on the critical success factors of synchromodality and six categories of enabling technologies. We identify open research issues and propose the introduction of a new stakeholder, which takes on the role of orchestrator to coordinate and provide services through a technology-based platform.

Synchromodal logistics: An overview of critical success factors, enabling technologies, and open research issues

Through a systematic review of publications in reputed peer-reviewed journals, this paper investigates the role of blockchain technology in sustainable supply chain management. It uses the What, Who, Where, When, How, and Why (5W+1H) pattern to formulate research objectives and questions. The review considers publications since 2015, and it includes 187 papers published in 2017, 2018, 2019, and the early part of 2020, since no significant publications were found in the year 2015 or 2016 on this subject. It proposes a reusable classification framework—emerging technology literature classification level (ETLCL) framework—based on grounded theory and the technology readiness level for conducting literature reviews in various focus areas of an emerging technology. Subsequently, the study uses ETLCL to classify the literature on our focus area. The results show traceability and transparency as the key benefits of applying blockchain technology. They also indicate a heightened interest in blockchain-based information systems for sustainable supply chain management starting since 2017. This paper offers invaluable insights for managers and leaders who envision sustainability as an essential component of their business. The findings demonstrate the disruptive power and role of blockchain-based information systems. Given the relative novelty of the topic and its scattered literature, the paper helps practitioners examining its various aspects by directing them to the right information sources.

https://www.mdpi.com/2071-1050/12/18/7638/pdf

Blockchain Technology for Sustainable Supply Chain Management: A Systematic Literature Review and a Classification Framework

Augmented reality (AR) has proven to be an invaluable interactive medium to reduce cognitive load by bridging the gap between the task-at-hand and relevant information by displaying information wit...

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2020.1859636

Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review

This study focuses on the transformation of logistics centers in Industry 4.0. Aim is to reveal the important criteria for logistics centers in Industry 4.0 by considering link to traditional logistics centers practices and proposing a framework for new logistics centers. Initially, literature review to reveal criteria for logistics centers in Industry 4.0 is conducted. Secondly, fuzzy multi-criteria decision-making methodology is used to present the importance order, and the causal relationship between criteria to make recommendations for future implications. The results may be useful for logistics center’s professionals in transition process, and new research topics may be derived for academics.

Logistics centers in the new industrial era: A proposed framework for logistics center 4.0

In furtherance of emerging research within smart production planning and control (PPC), this paper prescribes a methodology for the design and development of a smart PPC system. A smart PPC system uses emerging technologies such as the internet of things, big-data analytics tools and machine learning running on the cloud or on edge devices to enhance performance of PPC processes. It achieves this by using a wider range of data sources from the production system, capturing and utilizing the experience of production planners, using analytics and machine learning to harness insights from the data and allowing dynamic and near real-time action to the continuously changing production system. The proposed methodology is illustrated with a case study in a sweets and snacks manufacturing company, to highlight the key considerations and challenges production managers might face during its application. The case further demonstrates considerations for scalability and flexibility via a loosely coupled, service-oriented architecture and the selection of fitting algorithms respectively to address a business requirement for a short-term, multi-criteria and event-driven production planning and control solution. Finally, the paper further discusses the challenges of PPC in smart manufacturing and the importance of fitting smart technologies to planning environment characteristics.

/pdf/designing-and-developing-smart-production-planning-and-fic3tso07q.pdf

Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study

Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions.

/pdf/machine-learning-and-optimization-for-production-1ynkp5rfz7.pdf

Machine learning and optimization for production rescheduling in Industry 4.0

Synchro-modal supply chain eco-NET (SYNCHRO-NET) is a Horizon 2020 European research project aimed at overcoming the stress due to the increasing transportation distances, the higher complexity, and the vulnerability of international supply chains. In order to foster sustainability, quality, and reliability of such supply chains, SYNCHRO-NET mainly exploits the impact and the possible benefits coming from slow/smart-steaming and synchro-modality transportation strategies, and the related business models. The aim of this paper is to summarize and disseminate the main developments and scientific contributions coming from the project. In particular, the working core of the SYNCHRO-NET solution is an integrated and cloud-based eco-system of optimization and simulation software modules that supports stakeholders’ decisions in freight transportation and logistics management at strategic, operational, and real-time levels. The platform has achieved a high grade of automation in several processes to overcome all the issues related to human-based operations. The efficiency and effectiveness of the overall platform have been tested on three case studies considering pan-European and regional trade lanes, as well as commercial activities between the Far East and European ports. The project results and outputs also allow us to analyze barriers and opportunities of the approach, industrial and academic developments, and relations with emerging technologies.

https://www.mdpi.com/2071-1050/11/4/1083/pdf

Sustainable and De-Stressed International Supply-Chains Through the SYNCHRO-NET Approach

Online scheduling has been an attractive field of research for over three decades. Some recent developments suggest that Reinforcement Learning (RL) techniques have the potential to deal with online scheduling issues effectively. Driven by an industrial application, in this paper we apply four of the most important RL techniques, namely Q-learning, Sarsa, Watkins’s Q($\lambda$), and Sarsa($\lambda$), to the online single-machine scheduling problem. Our main goal is to provide insights on how such techniques perform. The numerical results show that Watkins’s Q($\lambda$) performs best in minimizing the total tardiness of the scheduling process.

/pdf/reinforcement-learning-algorithms-for-online-single-machine-n4wb3mw1j6.pdf

Reinforcement Learning Algorithms for Online Single-Machine Scheduling

Manufacturing processes are dynamic and intensive. Efficient and effective production scheduling is a crucial step to guarantee the competitiveness of manufacturing companies. While production scheduling has been studied in the literature for many years, an advanced optimization strategy is still in the lack of adoption. In the fourth industrial revolution, a set of technologies brings the possibility to transform traditional scheduling approach to the smarter production scheduling system. Motivated to fill in the gap between literature study and practical usage, we introduce a new approach integrated into the operating system under Industry 4.0 context through a case study. Besides demonstrating the new scheduling centered workflow, we also discuss the correlation between saturation and scheduling performance in the aspect of completion time.

Yuanyuan Li

Papers

Machine learning and optimization for production rescheduling in Industry 4.0

Sustainable and De-Stressed International Supply-Chains Through the SYNCHRO-NET Approach

Reinforcement Learning Algorithms for Online Single-Machine Scheduling

Production Scheduling in Industry 4.0

Seismic performance of steel-PEC spliced frame beam