Industrial Scheduling Research Articles

Metaheuristics for multi-objective scheduling problems in industry 4.0 and 5.0: a state-of-the-arts survey

The advent of Industry 4.0 and the emerging Industry 5.0 have fundamentally transformed manufacturing systems, introducing unprecedented levels of complexity in production scheduling. This complexity is further amplified by the integration of cyber-physical systems, Internet of Things, Artificial Intelligence, and human-centric approaches, necessitating more sophisticated optimization methods. This paper aims to provide a more comprehensive perspective on the application of metaheuristic algorithms in shop scheduling problems within the context of Industry 4.0 and Industry 5.0. Through a systematic review of recent literature (2015–2024), we analyze and categorize various metaheuristic approaches, including Evolutionary Algorithms (EAs), swarm intelligence, and hybrid methods, that have been applied to address complex scheduling challenges in smart manufacturing environments. We specifically examine how these algorithms handle multiple competing objectives such as makespan minimization, energy efficiency, production costs, and human-machine collaboration, which are crucial in modern industrial settings. Our survey reveals several key findings: 1) hybrid metaheuristics demonstrate superior performance in handling multi-objective optimization compared to standalone algorithms; 2) bio-inspired algorithms show promising results in addressing complex scheduling and multi-objective manufacturing environments; 3) tri-objective and higher-order multi-objective optimization problems warrant further in-depth exploration; and 4) there is an emerging trend towards incorporating human factors and sustainability objectives in scheduling optimization, aligned with Industry 5.0 principles. Additionally, we identify research gaps and propose future research directions, particularly in areas such as real-time scheduling adaptation, human-centric optimization, and sustainability-aware scheduling algorithms. This comprehensive review provides insights for researchers and practitioners in the field of industrial scheduling, offering a structured understanding of current methodologies and future challenges in the evolution from Industry 4.0 to 5.0.

Multiset Grammars as a Basic Knowledge Representation Model for Intelligent Systems Enabling Chemical Reactions Engineering

Application of multiset grammars as a knowledge representation model for intelligent systems, enabling analysis, optimization and design of complex chemical reactions, is proposed. This approach makes it possible to implant chemical knowledge bases into knowledge bases of intelligent systems, implementing smart planning and scheduling in cyberphysical industry, and thus to move to integrated end-to-end smart control of vast heterogeneous industrial clusters.

A Hybrid Honey Badger Algorithm to Solve Energy-Efficient Hybrid Flow Shop Scheduling Problems

A well-planned schedule is essential to any organization’s growth. Thus, it is important for the literature to cover a more comprehensive range of scheduling problems. In this paper, energy-efficient hybrid flow shop (EEHFS) scheduling problems are considered. Researchers have developed several techniques to deal with EEHFS scheduling problems. Also, researchers have recently proposed several metaheuristics. Honey Badger Algorithm (HBA) is one of the most recent algorithms proposed to solve various optimization problems. The objective of the present work is to solve EEHFS scheduling problems using the Hybrid Honey Badger Algorithm (HHBA) to reduce the makespan (Cmax) and total energy cost (TEC). In the HHBA, a constructive heuristic known as the NEH heuristic was incorporated with the Honey Badger Algorithm. The suggested algorithm’s performance was verified using an actual industrial scheduling problem. The company’s results are compared with those of the HHBA. The HHBA could potentially result in an 8% decrease in total energy cost. Then, the proposed algorithm was applied to solve 54 random benchmark problems. The results of the proposed HHBA were compared with the FIFO dispatching rule, the NEH heuristic, and other metaheuristics such as the simulated annealing (SA) algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, Honey Badger Algorithm, and the Ant Colony Optimization (ACO) algorithms addressed in the literature. Average percentage deviation (APD) was the performance measure used to compare different algorithms. The APD of the proposed HHBA was zero. This indicates that the proposed HHBA is more effective in solving EEHFS scheduling problems.

Optimizing the Utilization of Generative Artificial Intelligence (AI) in the AEC Industry: ChatGPT Prompt Engineering and Design

Generative Artificial Intelligence (AI) holds significant potential for revolutionizing the Architecture, Engineering, and Construction (AEC) industry by automating complex tasks such as construction scheduling, hazard recognition, resource leveling, information retrieval from BIM, etc. However, realizing this potential requires a strategic approach to ensure effective utilization and maximum benefit. This paper presents guidelines for prompt design and engineering to elicit desired responses from ChatGPT, a Generative AI tool, in AEC applications. Key steps include understanding user intent, leveraging model capabilities, and optimizing prompt structures. By following these guidelines, stakeholders in the AEC industry can harness the power of Generative AI to improve construction scheduling processes, increase project efficiency, and ultimately drive innovation and growth in the industry. Several illustrative examples on construction scheduling and hazard recognition are provided to demonstrate the methodology proposed in this research. It is concluded that Generative AI, when effectively utilized, significantly enhances project scheduling and hazard recognition capability in the AEC industry with minimal error.

Solving Flexible Job-Shop Scheduling Problem with Heterogeneous Graph Neural Network Based on Relation and Deep Reinforcement Learning

Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural Networks based on Relation (HGNNR) with Deep Reinforcement Learning (DRL). The proposed framework models the complex relationships in FJSP using heterogeneous graphs, where operations and machines are represented as nodes, with directed and undirected arcs indicating dependencies and compatibilities. The HGNNR framework comprises four key components: relation-specific subgraph decomposition, data preprocessing, feature extraction through graph convolution, and cross-relation feature fusion using a multi-head attention mechanism. For decision-making, we employ the Proximal Policy Optimization (PPO) algorithm, which iteratively updates policies to maximize cumulative rewards through continuous interaction with the environment. Experimental results on four public benchmark datasets demonstrate that our proposed method outperforms four state-of-the-art DRL-based techniques and three common rule-based heuristic algorithms, achieving superior scheduling efficiency and generalization capabilities. This framework offers a robust and scalable solution for complex industrial scheduling problems, enhancing production efficiency and adaptability.

Industrial multi-resource flexible job shop scheduling with partially necessary resources

This paper is dedicated to the study of industrial extensions of the flexible job shop scheduling problem with multiple resources in order to propose an alternative to expensive optimization software for small to medium-sized manufacturing companies. In this context, we propose a generic model able to tackle some constraints often found in industrial scheduling problems. This model tackles partially necessary resources by decomposing operations into stages. Instances are solved by a simulated annealing metaheuristic which is further improved using efficient conditions to filter non-interesting solutions. We compare our approach to a constraint programming model using a commercial solver. Extensive experiments and statistical analysis show that our method is competitive and of practical use in the industrial context.

Multi-objective sustainable flexible job shop scheduling problem: Balancing economic, ecological, and social criteria

Industry 5.0 makes it imperative to reevaluate the manner of using resources in manufacturing systems to ensure sustainability. In this context, scheduling problems are encountering new environmental and human-related challenges, and the concept of sustainable scheduling has gained importance, aiming to balance economic, environmental, and human factors. In this paper, we propose two multi-objective mathematical models to simultaneously address these three factors as objective functions. In the first model, we consider the operator safety while using the Occupational Repetitive Action (OCRA) index to assess ergonomic risks related to task execution. The second model includes workers’ preferences in terms of machines, shifts and task variety. The objective is to improve the general well-being of workers by proposing a schedule that respects as much as possible their preferences. Both models integrate the travel time of workers and products between machines. To solve these NP-hard scheduling problems, we use the Non-dominated Sorting Genetic Algorithms II and III (NSGA-II and NSGA-III), enhanced with a Q-learning strategy for parameter selection and a variable neighborhood search based on reinforcement learning. The obtained results provide a comprehensive analysis of the interactions between these criteria, demonstrating the capability of such approach to achieve a favorable balance between multiple objectives while addressing the new challenges of production scheduling in Industry 5.0 context.

Augmented reality in intelligent automation: an application to support industrial scheduling

ABSTRACT This paper proposes the application of augmented reality to an intelligent automation platform to support industrial scheduling. A dual-resource scheduling plan is generated for machines and workers, and there is the necessity of coordination between both resources to avoid delays and unexpected errors. The application of augmented reality aims at two focal points: facilitating the workers’ tasks by displaying the step-by-step procedures for each task they are assigned and also monitoring the ongoing work on machines so that workers can be aware when intervention is needed. The main contributions of this work are the fully integration of augmented reality within the cyber-physical systems present in the intelligent automation platform and the enhancement of workers’ capabilities in order to actively follow a scheduling plan. This study includes an experiment that mimics part of a workday and a questionnaire on usability and user experience, which presented excellent results compared to the benchmarks.

An online dynamic dual bin packing with lookahead approach for server-to-cell assignment in computer server industry

Efficient production scheduling in computer server industry remains a critical challenge. This challenge becomes particularly acute during the testing phase, where servers are assigned for testing in a modular environment consisting of test banks. Unique characteristics such as online arrival of servers known only over a short time window, skewed arrival pattern, power and cooling compatibility constraints, and dynamic assignment complicate decision making, a situation we observed at our industry partner’s site, which often leads to missing due dates and loss of customer trust. Motivated by this challenge, we introduce an Online Dynamic Dual Bin Packing with Lookahead problem and propose an integer linear programming model that maximizes the number of assigned servers. To efficiently solve this model, we decompose the problem into a set of subproblems for a given lookahead length. A ‘2-phase’ computational framework is proposed that seamlessly integrates mathematical programming with genetic algorithm. Based on realistic data available from a server manufacturer, our findings suggest that solutions are sensitive to (i) the length of the lookahead window, (ii) testing capacity and server arrival pattern, (iii) test processing time requirement, and (iv) physical allocation of servers.

Integration of MILP and discrete-event simulation for flow shop scheduling using Benders cuts

Optimization-based scheduling in the chemical industry is highly beneficial but also highly difficult due to its combinatorial complexity. Different modeling and optimization techniques exist, each with individual strengths. We propose Benders decomposition to integrate mixed-integer linear programming (MILP) and discrete-event simulation (DES) to solve flow shop scheduling problems with makespan minimization objective. The basic idea is to generate valid Benders cuts based on sensitivity information of the DES sub problem, which can be found in the critical paths of DES solutions. For scaled literature flow shops, our approach requires at least an order of magnitude fewer iterations than a genetic algorithm and provides optimality gap information. For a real-world case study, our approach finds good solutions very quickly, making it a powerful alternative to established methods. We conclude that the Benders-DES algorithm is a promising approach to combine rigorous MILP optimization capabilities with high-fidelity DES modeling capabilities.

Hybrid Residual Multiexpert Reinforcement Learning for Spatial Scheduling of High-Density Parking Lots.

Industries, such as manufacturing, are accelerating their embrace of the metaverse to achieve higher productivity, especially in complex industrial scheduling. In view of the growing parking challenges in large cities, high-density vehicle spatial scheduling is one of the potential solutions. Stack-based parking lots utilize parking robots to densely park vehicles in the vertical stacks like container stacking, which greatly reduces the aisle area in the parking lot, but requires complex scheduling algorithms to park and take out the vehicles. The existing high-density parking (HDP) scheduling algorithms are mainly heuristic methods, which only contain simple logic and are difficult to utilize information effectively. We propose a hybrid residual multiexpert (HIRE) reinforcement learning (RL) approach, a method for interactive learning in the digital industrial metaverse, which efficiently solves the HDP batch space scheduling problem. In our proposed framework, each heuristic scheduling method is considered as an expert. The neural network trained by RL assigns the expert strategy according to the current parking lot state. Furthermore, to avoid being limited by heuristic expert performance, the proposed hierarchical network framework also sets up a residual output channel. Experiments show that our proposed algorithm outperforms various advanced heuristic methods and the end-to-end RL method in the number of vehicle maneuvers, and has good robustness to the parking lot size and the estimation accuracy of vehicle exit time. We believe that the proposed HIRE RL method can be effectively and conveniently applied to practical application scenarios, which can be regarded as a key step for RL to enter the practical application stage of the industrial metaverse.

Data-driven real-time predictive control for industrial heating loads

Uncertainties and computational complexity are two growing challenges in scheduling industrial heating loads. In this paper, a data-driven real-time predictive control approach is proposed to deal with these challenges in the industrial scheduling of bitumen tanks. Specifically, predictive control technology is utilized to leverage the updated information to mitigate the negative impact of past uncertainties in equipment parameters and external environmental factors, which may lead to temperature constraint violations in the bitumen tank operation processes. Meanwhile, a data-driven method using artificial neural networks (ANN) is developed to ensure efficient computation for real-time predictive control. Moreover, a two-layer control method is devised to reduce the calculation time for day-ahead optimal scheduling of a large scale of bitumen tanks, aiming to generate sufficient high-quality data for training ANN. In the two-layer control method, the clustered temperature transfer processes of bitumen tanks are analyzed and modeled for the first time. Simulation results indicate that the two-layer control method can significantly reduce the computational time required for the day-ahead optimal scheduling of bitumen tanks, facilitating the generation of a large amount of high-quality data for training ANN. Subsequently, the application of ANN enables real-time predictive control, helping to eliminate the negative impact of uncertainties.

Towards solving the airport ground workforce dilemma: A literature review on hiring, scheduling, retention, and digitalization in the airport industry

The airport ground workforce is critical for a smooth operation of the entire aviation industry. This fact has been embarrassingly exposed during the recovery from the COVID-19 pandemic in Summer 2022, when various airlines and airports had to cut flights to reduce excessive delays, lost luggage, and other downstream effects, in absence of ground workforce sufficient to handle the rising travel demands. Our study discusses the challenges inherent to solving this airport ground workforce dilemma, partially caused and amplified by excessive layoffs during the peak of COVID-19. These industry challenges are dissected into four categories: Hiring new talents, workforce scheduling, staff retention, and digitalization. For each challenge, we review the existing literature on the subject and discuss potential avenues for solving the existing airport ground workforce dilemma. Our review covers more than 100 papers from the highly scattered literature on the subject and, accordingly, we believe that our study may serve as a starting point for coordinated efforts by the scientific community to tackle this challenging problem.

Adoption of irrigation scheduling in the South African sugarcane industry: the use of a demonstration trial to bring about change

In the South African sugarcane industry, as in many countries, poor adoption of irrigation scheduling is common, leading to ineffective use of irrigation water and loss in yield and income. The reasons for the lack of adoption include economic constraints, difficulty with implementation and human perceptions. To demonstrate the benefits of effective irrigation scheduling under local conditions, a demonstration trial was conducted over 5 years at Pongola, South Africa. Here, a fixed irrigation schedule (control) was compared with three scheduling treatments: (1) the MyCanesim® computer model with data from an automatic weather station; (2) soil-water data from a capacitance probe; and (3) a combination treatment, where MyCanesim® was integrated with a capacitance probe. The number of stress days from either under- or over-irrigation, along with the RV (recoverable value) yield, irrigation applied, irrigation water productivity (IWP) and financial benefits was used to assess each treatment. The adoption of irrigation scheduling was also evaluated through farmer surveys before and after the trial. When compared to the fixed-cycle control, the use of irrigation-scheduling techniques resulted in an increase of up to 13% in RV yield, a reduction of up to 58% in irrigation water applied, improved IWP of up to 23.9 t cane/100 mm of irrigation and financial benefits of up to ZAR10,487/(ha · a) (USD698/(ha · a)). The combination treatment incurred the least water-stress days, 79% lower than the control. Farmer surveys showed an increase of 45% in the adoption of irrigation scheduling, from 11% (n = 111) in 2014 to 56% (n = 100) in 2020. Although site-specific, the trial results show substantial benefits that can be realised by scheduling irrigation. The trial results compare well to other similar studies, showing that the results can be used in other areas to promote the adoption of irrigation scheduling.

Mathematical scheduling model of complex industrial process combining swarm intelligence algorithm and swarm dimension reduction technology

The swarm intelligence algorithm is widely used in scheduling problems due to its superior searchability and adaptability, but it faces high computational costs and low efficiency when dealing with high-dimensional complex data. To enhance the handling of large-scale and intricate data, as well as enhance the accuracy and efficiency of scheduling, this study develops a mathematical model for industrial scheduling. The model is based on swarm intelligence algorithms such as symbiotic evolution algorithms, grey wolf algorithms, cuckoo algorithms, population dimensionality reduction techniques, and resource optimal allocation calculation methods. The final test results showed that the accuracy of the model in calculating the objective function was about 80 % in cases of high complexity and about 90 % in cases of low complexity. Compared with other algorithms, its accuracy was improved by 7 %, error rate was reduced by 50 %, time cost was reduced by about 30 %, and conversion rate was fast and more stable on average after 200 generations. The experimental results indicate that the proposed mathematical scheduling model, which integrates multiple intelligent algorithms and population dimensionality reduction technologies, has certain practices and feasibility in large-scale and multi-dimensional production for enterprises. This study combines swarm intelligence algorithms with population dimensionality reduction techniques, which is an innovative attempt at complex industrial scheduling problems.

Minimizing Carbon Emission in Hybrid Flow Shop Scheduling: A Comparative Analysis of Flow-based and Set Partitioning Formulations

This article addresses the Hybrid Flow Shop Green Scheduling Problem with Sequence and Machine Dependent Setup Times (HFSGSP-SMDST) in the context of Industry 4.0. The objective is to minimize carbon emission while optimizing scheduling. Two mathematical formulations, a flow-based positional formulation (FB) and a set partitioning positional formulation (HCG), are proposed to provide tighter lower bounds. The FB formulation is solved using a commercial solver, while the HCG algorithm incorporates an Iterated Local Search (ILS) to generate initial solutions and an upper bound for the problem. Computational experiments demonstrate that HCG achieves better lower bounds, although it requires more time compared to FB. The proposed algorithms offer valuable solutions for carbon emission optimization and scheduling in Industry 4.0, with a trade-off between lower bounds and convergence speed.

Innovative Optimization Algorithms for Large-Sized Industrial Scheduling Problems

Innovative Optimization Algorithms for Large-Sized Industrial Scheduling Problems

A hybrid genetic algorithm based on reinforcement learning for the energy-aware production scheduling in the photovoltaic glass industry

Recently, the growing solar energy capacity has played a significant role in developing a clean energy supply system in China. However, the resulting rapid expansion of photovoltaic component (e.g., glass) manufacturing intensifies the energy demand in the locality of the plant. Therefore, this paper considers the energy-aware production scheduling of a deep-processing line in the photovoltaic glass plant, whose layout is a hybrid flow shop with batch and non-batch machines. Firstly, we establish a mixed integer programming model with the minimization of the energy consumption and the penalty for excess of the due date. Then, we propose a hybrid genetic algorithm (GA) based on reinforcement learning to solve the problem. Specifically, the expected Sarsa is used to extract critical knowledge about algorithmic parameters during the population evolution to guide the exploration of the GA. Finally, we conduct extensive numerical experiments to validate the effectiveness of the proposed algorithm by comparing it with a commercial optimization solver and other metaheuristics. The numerical results show that the average gap between the solver and the proposed algorithm is around 4% in small-sized instances. Compared with the heuristic used in the plant, the improvements of this paper are about 16%∼18% and 17%∼21% in practical-sized instances for the delay penalty and energy consumption objectives, respectively. In addition, the computational results provide managerial insights for managers in further pursuing energy efficiency from higher-level decision-making, e.g., planning over multiple periods from a tactical perspective, and changing production line configurations and introducing new processing techniques from a strategic perspective.

Data-driven distributionally robust optimization under combined ambiguity for cracking production scheduling

Distributionally robust optimization has garnered significant attention for its effectiveness in decision-making under uncertainty. However, employing this strategy faces hurdles posed by intractable models and the difficulty in parameter determination while tackling production scheduling issues under uncertainty. This work presents a novel data-driven distributionally robust optimization framework to address these challenges. A data-driven combined ambiguity set, which incorporates Wasserstein distance and moment information, is devised to yield less conservative solutions. Additionally, a data-driven support set established based on an improved kernel technique is introduced to help identify and exclude potential outliers. The relevant ambiguous parameters are determined through bi-level cross-validation. Subsequently, the data-driven distributionally robust optimization model under combined ambiguity is reformulated into tractable by dual theory. The application to industrial scheduling shows that the proposed method can effectively utilize data information and better hedge against uncertainties while obtaining higher profits.

Design and Implementation of Vehicle Scheduling Optimization for Smart Logistics Platform Powered by Hadoop Big Data

E-commerce has become the mainstream consumption mode for people, and with the continuous increase of online shopping business volume, controlling logistics costs has become an urgent problem to be solved. In order to solve the challenges in vehicle scheduling and achieve a scientific and reasonable vehicle scheduling scheme, this research is based on Hadoop Big data platform, introduces the concept of time axis, and builds a vehicle scheduling model based on Hadoop intelligent platform. Based on quantum genetic algorithm and combined with MapReduce model, a quantum improved genetic algorithm is constructed to solve vehicle scheduling optimization problems. The results show that the traditional quantum genetic algorithm converges after 20 iterations, achieving an optimal value of 575 and taking 360 seconds. The improved quantum genetic algorithm converged after 10 iterations and achieved an optimal value of 675, taking 200 seconds. Compared with quantum genetic algorithm, the improved quantum genetic algorithm reduces the time spent by 44.4%. Selecting customer data from a certain logistics company for testing, the improved algorithm shortens the delivery time and achieves the design of the optimal path scheduling plan. This study optimized transportation routes and resource scheduling, reduced transportation costs, and played an important role in optimizing vehicle scheduling in the logistics industry.