A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Lot Streaming in Different Types of Production Processes: A PRISMA Systematic Review

At present, any industry that wanted to be considered a vanguard must be willing to improve itself, developing innovative techniques to generate a competitive advantage against its direct competitors. Hence, many methods are employed to optimize production processes, such as Lot Streaming, which consists of partitioning the productive lots into overlapping small batches to reduce the overall operating times known as Makespan, reducing the delivery time to the final customer. This work proposes carrying out a systematic review following the PRISMA methodology to the existing literature in indexed databases that demonstrates the application of Lot Streaming in the different production systems, giving the scientific community a strong consultation tool, useful to validate the different important elements in the definition of the Makespan reduction objectives and their applicability in the industry. Two hundred papers were identified on the subject of this study. After applying a group of eligibility criteria, 63 articles were analyzed, concluding that Lot Streaming can be applied in different types of industrial processes, always keeping the main objective of reducing Makespan, becoming an excellent improvement tool, thanks to the use of different optimization algorithms, attached to the reality of each industry.This work was supported by the Universidad Tecnica de Ambato (UTA) and their Research and Development Department (DIDE) under project CONIN-P-256-2019, and SENESCYT by grants “Convocatoria Abierta 2011” and “Convocatoria Abierta 2013”

Industrial engineering applications in metrology: Job scheduling, calibration interval and average outgoing quality

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThis research deals with the optimization of metrology and calibration problems. The optimization involved here is the application scientifically sound operations research techniques to help in solving the problem intended optimally or semi-optimally with a practical time frame. The research starts by exploring the subject of measurement science known as metrology. This involves defining all the constituents of metrology facilities along with their various components. The definitions include the SI units’ history and structure as well as their characteristics. After that, a comprehensive description of most of the operations and parameters encountered in metrology is presented. This involves all sources of uncertainties in most of the parameters that affect the measurements. From the background presented and using all the information within it; an identification of the most important and critical general problems is attempted. In this treatment a number of potential optimization problems are identified along with their description, problem statement definition, impact on the system and possible treatment method. After that, a detailed treatment of the scheduling problem, the calibration interval determination problem and the average outgoing quality problem is presented. The scheduling problem is formulated and modelled as a mixed integer program then solved using LINGO program. A heuristic algorithm is then developed to solve the problem near optimally but in much quicker time, and solution is packaged in a computer program. The calibration interval problem treatment deals with the determination of the optimal CI. Four methods are developed to deal with different cases. The cases considered are the reliability target case, the CI with call cost and failure cost of both first failure and all failures and the case of large number of similar TMDEs. The average out going quality (AOQ) treatment involves the development two methods to assess the AOQ of a calibration facility that uses a certain multistage inspection policy. The two methods are mathematically derived and verified using a simulation model that compares them with an actual failure rate of a virtual calibration facility

Shop-floor scheduling as a competitive advantage:A study on the relevance of cyber-physical systems in different manufacturing contexts

The aim of this paper is to analyse the relevance of cyber-physical systems (CPS) in different manufacturing contexts and to study whether CPS could provide companies with competitive advantage by carrying out a better scheduling task. This paper is developed under the umbrella of contingency theory which states that certain technologies and practices are not universally applicable or relevant in every context; thus, only certain companies will benefit from using particular technologies or practices. The conclusion of this paper, developed through deductive reasoning and supported by preliminary simulation experiments and statistical tests, is that factories with an uncertain and demanding market environment as well as a complex production process could benefit the most from implementing a CPS at shop-floor level since a cyber-physical shop-floor will provide all the capabilities needed to carry out the complex scheduling task associated with this type of context. On the other hand, an increase in scheduling performance due to a CPS implementation in factories with simple production flows and stable demand could not be substantial enough to overcome the high cost of installing a fully operational CPS

Impact of Preventive Maintenance and Machine Breakdown on Performance of Stochastic Flexible Job Shop Scheduling with Setup Time

Real-time scheduling problems increase the practical implementation of the manufacturing system. In this study, using a single objective performance measure i.e., Number of Tardy Jobs (NTJ), the influence of 5 input constraints, i.e., reliability level (R_L), percentage of machine failure (%McF), mean time to repair for random machine breakdown (MTR_RMcB), due date tightness factor (Ғ), and routing flexibility level (R_FL) were evaluated for considered stochastic Flexible Job Shop Scheduling Problem (FJSSP). The study integrated reliability-centered preventive maintenance (PMRC) and random machine breakdown (RMcB) environment with sequence-dependent setup time in the considered problem. A statistical response surface methodology was used to assesses NTJ. A second-order regression model was obtained to compute correlation between input constraints and NOTJ at 95% confidence level. The results demonstrate that main effects of R_L, %McF, Ғ, and R_FL; the interaction effects of R_L and Ғ, %McF and R_FL, MTR_RMcB and R­_FL, and Ғ and R_FL; and quadratic effects of Ғ and R_FL, have significant impact on NTJ performance measure. Ғ has emerged as the major factor affecting NTJ. The confirmatory data demonstrate that error is less than 5%, confirming model can be used for future computations. Further, the novelties of the work are shown by the fact that it takes into account the uncertainties in the scheduling issue, as well as the dynamic tasks arrival environment. The aforementioned findings will assist production managers in planning and scheduling flexible job shops in order to satisfy customer demand on time

Modeling and Analysis of Scheduling Problems Containing Renewable Energy Decisions

With globally increasing energy demands, world citizens are facing one of society\u27s most critical issues: protecting the environment. To reduce the emission of greenhouse gases (GHG), which are by-products of conventional energy resources, people are reducing the consumption of oil, gas, and coal collectively. In the meanwhile, interest in renewable energy resources has grown in recent years. Renewable generators can be installed both on the power grid side and end-use customer side of power systems. Energy management in power systems with multiple microgrids containing renewable energy resources has been a focus of industry and researchers as of late. Further, on-site renewable energy provides great opportunities for manufacturing plants to reduce energy costs when faced with time-varying electricity prices. To efficiently utilize on-site renewable energy generation, production schedules and energy supply decisions need to be coordinated. As renewable energy resources like solar and wind energy typically fluctuate with weather variations, the inherent stochastic nature of renewable energy resources makes the decision making of utilizing renewable generation complex. In this dissertation, we study a power system with one main grid (arbiter) and multiple microgrids (agents). The microgrids (MGs) are equipped to control their local generation and demand in the presence of uncertain renewable generation and heterogeneous energy management settings. We propose an extension to the classical two-stage stochastic programming model to capture these interactions by modeling the arbiter\u27s problem as the first-stage master problem and the agent decision problems as second-stage subproblems. To tackle this problem formulation, we propose a sequential sampling-based optimization algorithm that does not require a priori knowledge of probability distribution functions or selection of samples for renewable generation. The subproblems capture the details of different energy management settings employed at the agent MGs to control heating, ventilation and air conditioning systems; home appliances; industrial production; plug-in electrical vehicles; and storage devices. Computational experiments conducted on the US western interconnect (WECC-240) data set illustrate that the proposed algorithm is scalable and our solutions are statistically verifiable. Our results also show that the proposed framework can be used as a systematic tool to gauge (a) the impact of energy management settings in efficiently utilizing renewable generation and (b) the role of flexible demands in reducing system costs. Next, we present a two-stage, multi-objective stochastic program for flow shops with sequence-dependent setups in order to meet production schedules while managing energy costs. The first stage provides optimal schedules to minimize the total completion time, while the second stage makes energy supply decisions to minimize energy costs under a time-of-use electricity pricing scheme. Power demand for production is met by on-site renewable generation, supply from the main grid, and an energy storage system. An ε-constraint algorithm integrated with an L-shaped method is proposed to analyze the problem. Sets of Pareto optimal solutions are provided for decision-makers and our results show that the energy cost of setup operations is relatively high such that it cannot be ignored. Further, using solar or wind energy can save significant energy costs with solar energy being the more viable option of the two for reducing costs. Finally, we extend the flow shop scheduling problem to a job shop environment under hour-ahead real-time electricity pricing schemes. The objectives of interest are to minimize total weighted completion time and energy costs simultaneously. Besides renewable generation, hour-ahead real-time electricity pricing is another source of uncertainty in this study as electricity prices are released to customers only hours in advance of consumption. A mathematical model is presented and an ε-constraint algorithm is used to tackle the bi-objective problem. Further, to improve computational efficiency and generate solutions in a practically acceptable amount of time, a hybrid multi-objective evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed. Five methods are developed to calculate chromosome fitness values. Computational tests show that both mathematical modeling and our proposed algorithm are comparable, while our algorithm produces solutions much quicker. Using a single method (rather than five) to generate schedules can further reduce computational time without significantly degrading solution quality

Platooning-based control techniques in transportation and logistic

This thesis explores the integration of autonomous vehicle technology with smart manufacturing systems. At first, essential control methods for autonomous vehicles, including Linear Matrix Inequalities (LMIs), Linear Quadratic Regulation (LQR)/Linear Quadratic Tracking (LQT), PID controllers, and dynamic control logic via flowcharts, are examined. These techniques are adapted for platooning to enhance coordination, safety, and efficiency within vehicle fleets, and various scenarios are analyzed to confirm their effectiveness in achieving predetermined performance goals such as inter-vehicle distance and fuel consumption. A first approach on simplified hardware, yet realistic to model the vehicle's behavior, is treated to further prove the theoretical results. Subsequently, performance improvement in smart manufacturing systems (SMS) is treated. The focus is placed on offline and online scheduling techniques exploiting Mixed Integer Linear Programming (MILP) to model the shop floor and Model Predictive Control (MPC) to adapt scheduling to unforeseen events, in order to understand how optimization algorithms and decision-making frameworks can transform resource allocation and production processes, ultimately improving manufacturing efficiency. In the final part of the work, platooning techniques are employed within SMS. Autonomous Guided Vehicles (AGVs) are reimagined as autonomous vehicles, grouping them within platoon formations according to different criteria, and controlled to avoid collisions while carrying out production orders. This strategic integration applies platooning principles to transform AGV logistics within the SMS. The impact of AGV platooning on key performance metrics, such as makespan, is devised, providing insights into optimizing manufacturing processes. Throughout this work, various research fields are examined, with intersecting future technologies from precise control in autonomous vehicles to the coordination of manufacturing resources. This thesis provides a comprehensive view of how optimization and automation can reshape efficiency and productivity not only in the domain of autonomous vehicles but also in manufacturing