Production Scheduling Research Articles

A double-layer Q-learning driven memetic algorithm for integrated scheduling of procurement, production and maintenance with distributed resources

The existing research on integrated production scheduling typically focuses on activities related to both production and post-production (e.g., operation and maintenance), with limited consideration for simultaneously integrating pre-production activities (e.g., raw material procurement). However, achieving the equilibrium between the manufacturer and the demander for intelligent manufacturing systems requires the optimal scheduling solution that integrates both pre-production and post-production activities. Inspired by this, we investigate a novel integrated scheduling problem that concurrently considers raw material procurement, production scheduling and equipment maintenance (abbreviated as SIPPM). A mixed-integer linear programming model is developed to simultaneously minimize the total costs for the manufacturer and the demander. Furthermore, a double-layer Q-learning driven memetic algorithm (DQMA) is proposed to solve the SIPPM. In DQMA, a well-tailored three-layer hybrid encoding method is presented for chromosome representation. The global search of DQMA employs three crossover and three mutation operators. Moreover, a knowledge-based local search operator with six methods, guided by an effective double-layer Q-learning structure, is devised to enhance local exploitation capabilities. The superiority of DQMA is verified through comparison with three popular multi-objective optimization algorithms on 108 newly established benchmark instances. The proposed integrated scheduling mode is proven to be more effective than two separated scheduling modes without considering raw material procurement.

An application of simultaneous stochastic optimisation on an open-pit copper mining complex with supply, recovery, and market uncertainties

ABSTRACT A mining complex is an integrated value chain that consists of multiple mines, stockpiles, processors, waste dumps, and tailing facilities, and where the in-situ materials mined are transformed into products sold to customers. Simultaneous stochastic optimisation provides a strategic mine planning framework that incorporates all components of a mining complex into a single mathematical representation, capitalising on the synergies between components while considering uncertainty. An application of this optimisation framework at a coper mining complex demonstrates that the incorporation of material supply, market demand, and processing recovery uncertainties into the modelling leads to production schedules that yield a higher NPV and metal recovery.

Matheuristic algorithm for dock planning problems considering tandem and parallel methods in the shipbuilding industry

Docks are crucial production resources that significantly impact shipyard productivity. This study addresses a real-world dock planning problem (DPP) focused on ship production scheduling within docks. Unlike traditional scheduling issues, the DPP involves managing multiple concurrent tasks due to the use of tandem and parallel methods common in the shipbuilding industry. This distinctive feature introduces unique considerations and opportunities for optimizing production processes within shipyards. Despite its importance, research on DPPs is limited, with dock planning typically depending on the expertise of planners. This study formally defines a DPP as a mixed-integer programming (MIP) model, incorporating tandem and parallel methods. It also introduces MathLNS, a matheuristic algorithm that combines MIP-based large neighborhood search with heuristic methods, as an efficient solution approach to this problem. The effectiveness of the proposed model and algorithm is evaluated using real-world data from one of the largest shipbuilders globally. The results demonstrate significant improvements over the planner-driven approach. Notably, the developed algorithm has been integrated into the company’s production planning system, resulting in improved dock planning efficiency. Furthermore, the proposed approach offers potential benefits for other shipbuilding companies, enhancing operational efficiency in dock planning.

Shaping of the risk of a series-parallel manufacturing structure maintenance according to quasi-coherence and the [formula omitted]-th survival value

The paper presents the authors’ model for determining the probability value of the survival function of the difference of two random variables: TP – the amount of time of correct operation and TB – the amount of time of failure. For this purpose, the concept of K-th survival value is introduced. The K-th survival value defines the probability that the total time of correct operation of the production system will be longer by at least k time units (for k≥0) than the total amount of time of failures. The model was exemplified by a real production system with a series–parallel structure. In the nesting manufacturing structure, the availability of machines determines the number and the type of flow streams of the processed material. To formalise the chains of relations at a given Δt, this paper utilises the quasi-coherence property. The paper identifies four possible cases of system operation: 1) a fully operational system in which every machine is capable of meeting the assumed production schedule; 2) a system which has lost the property of quasi-coherence (certain system objects are in an inoperable state) but it is possible to meet the assumed production schedule; 3) a system which has lost the property of quasi-coherence but the assumed task schedule is possible to meet after redefining the material flow streams; 4) a system which has lost the property of quasi-coherence and it is impossible to meet the assumed production schedule. Based on the definition of the survival function [1] S of the random variable T: ST:=PT>τ=1-FTτ,τ∈R, the concept of K-th survival value was introduced. For possible cases, K-th survival value was defined as: KT1,Ti=PT1-Ti≥k=1-FT1-Tik (for i=2,3,4), where T1 and Ti are random variables with given probability distributions. In turn, k is a deterministic value that determines the total number of time units of correct system operation in relation to the total failure time. The application of the developed algorithm enables the system to be analysed in terms of three aspects: (1) to determine the probability of on-time execution of tasks, taking into account historical values of machine failure and correct operation durations; (2) to determine the stability of the system based on empirical data; (3) to monitor the level of risk in real time. The paper validates the model by determining the stability of the analysed system for real machine failure data. The introduced criterion of stochastic stability of the production system has been verified for data in which distributions of correct operation times and failure times are random variables from the family of Gamma distributions. The presented model can be included in the group of multifactorial risk assessment methods.

Integrated Production and Outbound Distribution Scheduling: a hybrid framework

Integrated Production and Outbound Distribution Scheduling: a hybrid framework

Step feature line extraction from large-scale point clouds of open-pit mine based on structural characteristics

High-precision step feature lines play a crucial role in open-pit mine design, production scheduling, mining volume calculations, road network planning, and slope maintenance. Compared with the feature lines of the geometric model, step feature lines are more irregular, complex, higher in density, and richer in detail. In this study, a novel technique for extracting step feature line from large-scale point clouds of open-pit mine by leveraging structural attributes, that is, SFLE_OPM (Step Feature Line Extraction for Open-Pit Mine), is proposed. First, we adopt the k-dimensional tree (KD-tree) resampling method to reduce the point-cloud density while retaining point-cloud features and utilize bilateral filtering for denoising. Second, we use Point Cloud Properties Network (PCPNET) to estimate the normal, calculate the slope and aspect, and then filter them. We then apply morphological operations to the step surface and obtain more continuous and smoother slope lines. In addition, we construct an Open-Pit Mine Step Feature Line (OPMSFL) dataset and benchmarked SFLE_OPM, achieving an accuracy score of 89.31% and true positive rate score of 80.18%. The results demonstrate that our method yields a higher extraction accuracy and precision than most of the existing methods. Our dataset is available at https://github.com/OPMDataSets/OPMSFL.

A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective

In this study, a systematic review on production scheduling based on reinforcement learning (RL) techniques using especially bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of pertinent publications contained in Scopus, ScienceDirect, and Google Scholar databases. The study focuses on a wide spectrum of publications spanning the years between 1996 and 2024. The findings of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.

Software analysis and its importance in the food industry: a review

Software is being increasingly explored and is of utmost importance in the food industry. It has emerged to make processes more flexible and improve industrial bottlenecks after the rise of Industry 4.0. The food industry is no exception; software to predict food quality, cold chains, and food safety, among others, is being used more and more every day. This review, in its scarcity, aims to portray the urgency and necessity of implementing software in processes and its significant improvement in the food industry. It was observed that software is a computational tool of high reliability, flexibility, and importance, evaluating various points within the food industry, such as production scheduling, inventory management, inputs, raw materials, time and stops, and even people, machinery, food safety, and quality. Therefore, software is not just essential, but a critical component for the evolution of Industry 4.0.

Optimasi Penjadwalan Mesin Bor Manual pada Produksi Side Chair Menggunakan Metode Shortest Processing Time (SPT)

CV Mandiri Abadi is a manufacturing industry that operates on a make-to-order system, specifically for side chair products, necessitating a carefully considered scheduling system. Issues with the use of manual drilling machines causing delays in component deliveries to the warehouse prompted an evaluation of optimal production scheduling using the shortest processing time (SPT) method. This study includes detailed calculations to determine average completion time, utilization, average job time, and average job delay. Analysis results show that using manual drilling machines achieved an average completion time of 854.28 minutes, a proposed utilization improvement of 28%, an average job time of 3.46 minutes, and an average delay of 614.14 minutes. This approach is more effective in optimizing processing time and reducing delivery delays. Therefore, the use of the shortest processing time (SPT) method is recommended to improve production performance within the company.

Research on the Production Scheduling System for Prefabricated Component Factories in Smart Construction

Research on the Production Scheduling System for Prefabricated Component Factories in Smart Construction

Distributed assembly shop scheduling problem for complex products considering multiskilled worker assignment and transportation time

The distributed assembly hybrid flow shop scheduling problem (DAHFSP) is a type of distributed shop scheduling problem, and each distributed shop can be regarded as a hybrid flow shop. In distributed assembly processes for complex products such as satellites and missiles, transportation time and worker assignment have important effects on production scheduling. Based on real production situations, this study proposes a novel DAHFSP considering worker assignment and transportation time, aiming to minimise the makespan and the imbalance degree of worker workloads. First, to solve these problems, we construct a mathematical model and design a two-layer chromosome coding scheme including worker assignment and task sequence. Then, in the local search stage, we propose a mutation-based search method and an elite search method. On that basis, we propose a multi-objective evolutionary algorithm with reinforced elite retention strategy (MOEA-RERS). Finally, based on a set of 12 test instances generated by actual enterprise production data, we compare the MOEA-RERS algorithm with five multi-objective evolutionary algorithms. The results show that the MOEA-RERS algorithm is superior to other algorithms in terms of solution quality and distribution.

Energy-saving scheduling strategy for variable-speed flexible job-shop problem considering operation-dependent energy consumption

The rapid growth of industrial production signifies its CO2 footprint and climate impact, highlighting the demand for efficient and energy-saving production scheduling. Generally, a single production job comprises multiple operations on various machines. Existing studies estimate job energy consumption from a broader perspective, often disregarding the detailed scheduling of operations. This simplification may result in inaccurate evaluations of energy consumption and savings, as energy consumption can differ significantly among operations, with each requiring specific levels of energy on various machines. Proposing a new scheduling framework to replace the classical strategy is challenging yet necessary due to these variations. This study aims to investigate the multiobjective flexible job-shop scheduling problem by considering operation-dependent energy consumption and to improve the accuracy and thoroughness of the energy-consumption evaluation framework. First, a core metamodel for energy assessment is established and a mathematical model is introduced to minimize the manufacturing span and total energy consumption. Subsequently, a dynamic diffusion weight adjustment for a multiobjective evolutionary algorithm based on decomposition (MOEA/D-DDWA) is proposed. Operation-dependent energy consumption and processing quality are discussed, and a superior unified energy-saving strategy is designed to facilitate the selection of the processing-speed range, balancing energy consumption and saving. Finally, numerical experiments are conducted based on datasets of various scales, demonstrating that the core metamodel for energy assessment can achieve significant energy savings. Compared with other classical multiobjective optimization algorithms, the MOEA/D-DDWA is more energy efficient.

Large-scale off-grid wind power hydrogen production multi-tank combination operation law and scheduling strategy taking into account alkaline electrolyzer characteristics

This paper proposes a multi-electrolyzer switching scheduling strategy based on the Pelican Optimization Algorithm (POA) to enhance the efficiency of large-scale wind power hydrogen production systems. To validate the effectiveness of the proposed strategy, we analyzed wind power data from three typical days in northern Hebei, China, with a 2.5 MW wind turbine output. We designed three strategies for comparative analysis: a simple start-stop strategy, a rule-based multi-electrolyzer switching strategy, and a POA-based multi-electrolyzer switching strategy. The study results demonstrate that the POA-based strategy exhibits higher hydrogen production efficiency and system stability under various wind conditions. Particularly, in extreme wind scenarios, this strategy significantly reduces the start-stop cycles of electrolyzers, thereby lowering operational costs and improving overall system performance. The main contribution of this study lies in the novel optimization algorithm and its validation through real-world data, demonstrating its effectiveness in large-scale wind power hydrogen production systems. Our findings provide new insights for enhancing the utilization of renewable energy and the economics of hydrogen production systems.

Research on production scheduling method of cement plants considering demand response and carbon trading

Research on production scheduling method of cement plants considering demand response and carbon trading

Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent’s chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.

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.

Collaborative Optimization Algorithm Based on Chameleon Swarm Algorithm Designed for the Problem of Beer Production Scheduling

Aiming at finding a better way to solve the problem of beer production scheduling, a new collaborative optimization based on the Manhattan Distance and Chameleon Swarm Algorithm is proposed. Firstly, a dynamic relaxation factor is introduced to the constraints at the system level, which combines the changing trend of the inconsistency information and the optimal solution of the discipline level. Additionally, the Manhattan Distance is used to replace the square of the Euclidean Distance at the system level. Thirdly, the Chameleon Swarm Algorithm is used to improve the update rule during the process of iteration. As these improvements are applied to the collaborative optimization, the steps of this new algorithm are given. Through the test case of a multivariate function, it can be found that the algorithm has been improved compared to the original algorithm. Then, a model for beer production scheduling is proposed, and the results of the optimization show that the improved collaborative optimization has better optima effectiveness and fewer iterations and is not sensitive to initial points, which proves that the improved collaborative optimization has a better ability to solve the problem of beer production scheduling than normal collaborative optimization and collaborative optimization with fixed relaxation factors.

Two-sided resource-constrained assembly line balancing problem: a new mathematical model and an improved genetic algorithm

Two-sided assembly lines are typically employed in the production of medium and large-sized products with the aim of reducing the length of the assembly line, enhancing assembly efficiency and consequently reducing the time required for product assembly. However, traditional Two-sided assembly lines lack effective resource scheduling management methods in production scheduling, which results in low productivity and high resource costs. In order to address this issue, we propose a new two-sided resource-constrained assembly line balancing problem (TRCLBP) model. The model takes the minimum number of workstations and the minimum assembly cost as its objective function and proposes an improved genetic algorithm (I-GA) to solve it. A three-layer chromosome initialization method is proposed for the assembly tasks and resource decisions, which effectively improves the diversity and quality of the initial population. Furthermore, the algorithm employs strategies such as matching crossover and redistributing variants to ensure rapid convergence of the populations and to prevent them from falling into local optimums. Finally, the efficacy of the model and algorithm proposed in this paper is validated through a comprehensive analysis of arithmetic case studies and enterprise engineering examples. This analysis reveals a reduction of approximately 18 % in the total cost of assembly. Furthermore, the model enables enterprises to make informed decisions regarding the optimal allocation of resources, thereby reducing production costs and improving the efficiency of assembly operations during periods of expansion.

Selection of well opening schedules in pre-salt reservoirs using WAG-CO2

In field development projects involving many wells, the limitation of available rigs can increase the need for investments. In our literature research about the definition of well opening schedules, we found some proposals but could not find studies that deep dive into this topic, nor any that propose an approach that could be applied to the Brazilian pre-salt fields. Therefore, this research focuses on evaluating the influence in the NPV of well opening schedule of a project using WAG-CO2, inspired in pre-salt fields, and we propose a simple procedure to specify the well opening schedule without having to perform many simulations or complex analysis. We used the UNISIM-II-D-BO benchmark (synthetic simulation model with characteristics of a Brazilian pre-salt field) and a well configuration proposed in a previous study, with 11 producers and 10 injectors. First, we calculated the distance between the producer and injector wells and evaluated the well economic indicator (WEI) of each well by opening all at the same time at the beginning of the production schedule. We then evaluated 252 combinations of those injector wells, where five of them were injecting water and the other five were injecting gas, from where we defined three reference alternatives to check the repeatability of the results. With all these data, we tested predefined schedules: (1) with the 1st injector well injecting only gas or participating in the WAG cycle; (2) the producers' opening sequence based on their WEI; (3) the injectors’ opening based on their WEI or distance to producers; and (4) testing the relationship between producers and injectors (number of producers to open before the injectors), making sure that all the gas could be reinjected. For comparison purposes, we then run an optimization algorithm to select the schedule, using 2,660 simulations. At the end, we verified that the difference between the optimization with the algorithm (demanding thousands of simulations runs) and the proposed schedules (demanding few simulations) in the NPV was about 2%. Thus, to speed-up decision making, it is possible to have an opening schedule with good performance considering a simple procedure, as follows: 1st injector well collaborating with the WAG; producer wells opening from the highest to the lowest WEI; injector wells opening from the closest to the furthest distance to the producers; and the relationship between producers and injectors has to be investigated, not only aiming to find the best economic return, but also because of the environmental restriction (the need to reinject all the produced gas). This procedure reduces the number of simulations that must be performed if an engineer decides to use an optimization technique.

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.