Production Scheduling Research Articles

Hybrid Renewable Production Scheduling for a PV–Wind-EV-Battery Architecture Using Sequential Quadratic Programming and Long Short-Term Memory–K-Nearest Neighbors Learning for Smart Buildings

Electricity demand in residential areas is generally met by the local low-voltage grid or, alternatively, the national grid, which produces electricity using thermal power stations based on conventional sources. These generators are holding back the revolution and the transition to a green planet, being unable to cope with climatic constraints. In the residential context, to ensure a smooth transition to an ecological green city, the idea of using alternative sources will offer the solution. These alternatives must be renewable and naturally available on the planet. This requires a generation that is very responsive to the constraints of the 21st century. However, these sources are intermittent and require a hybrid solution known as Hybrid Renewable Energy Systems (HRESs). To this end, we have designed a hybrid system based on PV-, wind-turbine- and grid-supported battery storage and an electric vehicle connected to a residential building. We proposed an energy management system based on nonlinear programming. This optimization was solved using sequential quadrature programming. The data were then processed using a long short-term memory (LSTM) model to predict, with the contribution and cooperation of each source, how to meet the energy needs of each home. The prediction was ensured with an accuracy of around 95%. These prediction results have been injected into K-nearest neighbors (KNN), random forest (RF) and gradient boost (GRU) repressors to predict the storage collaboration rates handled by the local battery and the electric vehicle. Results have shown an R2_score of 0.6953, 0.8381, and 0.739, respectively. This combination permitted an efficient prediction of the potential consumption from the grid with a value of an R²-score of around 0.9834 using LSTM. This methodology is effective in allowing us to know in advance the amount of energy of each source, storage, and excess grid injection and to propose the switching control of the hybrid architecture.

A fusion gas load prediction model with three-way residual error amendment

Accurately predicting gas load is crucial for optimal planning and scheduling of natural gas production. Existing machine learning or deep learning-based prediction methods primarily aim to improve accuracy. However, these methods face challenges such as overlapped features, significant background noise, and residual errors. In response to these challenges, we proposed a fusion prediction model, known as the Double Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) with Three-way Residual Error Amendment (DT-LGBM-3WREA). To address the overlapped feature issue in time series data, we utilize the STL module for data decomposition. To handle background noise, the network incorporates a double TCN module specifically designed for trend and seasonal components. Thus, it can better avoid the transmission of noise between modules. To address residual error, we introduce three-way decision methodology and develop the 3WREA module, which aims to avoid local optima and random factors from perturbing the prediction accuracy. This approach mitigates the influence of remainder item on overall prediction performance. Experimental results demonstrate that the DT-LGBM-3WREA model excels in gas load prediction scenarios, surpassing state-of-the-art methods.

Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems

This paper addresses the challenges associated with nesting and production scheduling in additive manufacturing (AM). The problem studied consists of grouping a set of parts into batches, which are then assigned to and sequenced across the available machines, guaranteeing the production of all parts. This work stands out by proposing exact methods for the AM nesting and scheduling problem considering irregular-shaped parts with specific release dates, processing times, and due dates, with the aim of minimizing the cumulative tardiness. The proposed approaches include two logic-based Benders decompositions: one combining Mixed Integer Programming (MIP) and Constraint Programming (CP), and the other relying solely on CP. To deal with the sub-problems, a strategic procedure was developed to reduce the solution space while maintaining low resolution times per iteration. Problem-specific cuts are also generated to improve the efficiency of these approaches. Computational experiments show that both decompositions significantly outperform a prior monolithic CP model, with the decomposition based solely on CP yielding the best results. Moreover, the results show that this approach has the potential to achieve similar computational performance of non-exact approaches that are currently considered state-of-the-art. A set of instances is provided to serve as a benchmark for future studies.

Workforce and task optimization to guarantee oxygen bottling under a COVID-19 pandemic scenario: A Chilean case study

This work describes an optimization model to analyze the response of a Chilean oxygen-bottling plant to disruptions induced by large external events such as the COVID-19 pandemic. The model addresses employee shift scheduling, machine scheduling with setup times, production scheduling and inventory management, thus being non-linear, although resolved in reasonable computation times. The workers are assumed to have the same training and skills for a specific task, and to behave homogeneously from the psychological and physical perspectives. The objective function minimizes the cost of bottling oxygen, using monthly average of deaths as a proxy variable to model oxygen demand. The results obtained with this model suggest optimizing task assignments as a short-term solution to satisfy an increased oxygen demand representative of a pandemic scenario, while an additional long-term solution is installing a second cryogenic tank. Considering the latter solution, the model allows to quantify the number of workers necessary to operate an enlarged plant. An updated qualifications matrix coupled with an optimized schedule shows that the number of workers required under these conditions does not have to be doubled, but would have to be increased by only one worker.

Flexible job-shop scheduling problem with parallel batch machines based on an enhanced multi-population genetic algorithm

AbstractThe flexible job-shop scheduling problem (FJSP) with parallel batch processing machine (PBM) is one of those long-standing issues that needs cutting-edge approaches. It is a recent extension of standard flexible job shop scheduling problems. Despite their wide application and prevalence in practical production, it seems that current research on these types of combinatorial optimization problems remains limited and uninvestigated. More specifically, existing research mainly concentrates on the flow shop scenarios in parallel batch machines for job shop scheduling but few literature emphasis on the flexible job shop integration in these contexts. To directly address the above mentioned problems, this paper establishes an optimization model considering parallel batch processing machines, aiming to minimize the maximum completion time in operating and production environments. The proposed solution merges variable neighborhood search with multi-population genetic algorithms, conducting a neighborhood search on the elite population to reduce the likelihood of falling into local optima. Subsequently, its applicability was evaluated in computational experiments using real production scenarios from a partnering enterprise and extended datasets. The findings from the analyses indicate that the enhanced algorithm can decrease the objective value by as much as 15% compared to other standard algorithms. Importantly, the proposed approach effectively resolves flexible job shop scheduling problems involving parallel batch processing machines. The contribution of the research is providing substantial theoretical support for enterprise production scheduling.

A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving

Abstract Energy saving has always been a concern in production scheduling, especially in distributed hybrid flow shop scheduling problems. This study proposes a shuffled frog leaping algorithm with Q-learning (QSFLA) to solve distributed hybrid flow shop scheduling problems with energy-saving(DEHFSP) for minimizing the maximum completion time and total energy consumption simultaneously. The mathematical model is provided, and the lower bounds of two optimization objectives are given and proved. A Q-learning process is embedded in the memeplex search of QSFLA. The state of the population is calculated based on the lower bound. Sixteen search strategy combinations are designed according to the four kinds of global search and four kinds of neighborhood structure. One combination is selected to be used in the memeplex search according to the population state. An energy-saving operator is presented to reduce total energy consumption without increasing the processing time. One hundred forty instances with different scales are tested, and the computational results show that QSFLA is a very competitive algorithm for solving DEHFSP.

Carbohydrates Are Associated with the Flowering Ability of Oncidesa Gower Ramsey ‘Honey Angel’

Oncidesa Gower Ramsey ‘Honey Angel’ is a cut flower crop of high economic value worldwide. The regulation of flowering is important for cut flower production scheduling. However, its flowering transition mechanism is still unclear. Oncidesa usually flowers at the end of the growth cycle for each pseudobulb; this timing is probably related to carbohydrate accumulation. During this study, we investigated the carbohydrates in the pseudobulbs from juvenile plants to adult plants and compared the carbohydrates in flowering and nonflowering adult plants. The current pseudobulb and back pseudobulbs of the plants at 0, 0.5, 1.0, 1.5, and 2.0 years after having been moved out of the tissue culture flask were collected. The first pseudobulb formed at 0.5 years, and plants had fulfilled four growth cycles and flowered at 2.0 years. Each successive current shoot grew larger and the back shoot number progressively increased after each new growth cycle. The concentration of total soluble sugars in the current shoot increased from 5.5% of dry weight at 0.5 years to 20.2% of dry weight at 1.5 years. Conversely, the starch concentration decreased in the current pseudobulb as the plants matured. The starch concentration in the back pseudobulbs did not change when the plant grew a new shoot. The starch concentrations in the back pseudobulbs ranged from 33.2% to 57.5% of dry weight, but the combined content of starch in all of the back pseudobulbs increased significantly from 168 mg at 0.5 years to 4608 mg at 2.0 years because of the increasing number of back shoots. The starch in the first back pseudobulb of the nonflowering adult plants accounted for 18.0% of dry weight, which was lower than that of the flowering plants (48.3%). There was no significant difference in total soluble sugars in the current pseudobulb of the nonflowering and flowering plants. Overall, we revealed that the increase in the back shoot number increased the total amount of reserve carbohydrates as the plant reached reproductive maturity. A low starch level was observed in nonflowering adult plants. In both cases, flowering plants had higher starch storage in the back pseudobulbs, suggesting that carbohydrates might regulate the flowering of Oncidesa Gower Ramsey ‘Honey Angel’.

AUTOMATION WORK ORDER AND MASTER PRODUCTION SCHEDULE IN ENTERPRISE RESOURCES PLANNING FOR PT SFP

<p><strong>In today's dynamic business landscape, manufacturing enterprises like PT SFP face the constant challenge of optimizing their production processes to remain competitive. The Enterprise Resources Planning (ERP) architecture will be used to develop an integrated solution to automate work order administration and master production scheduling. The production and planning division is a very important process and is interconnected with one another. Since manufacturing is the most demanding business process in a company's activities, the significance of the planning system is greatly raised. Even though the company already has an ERP system that is quite integrated, there are still many processes that are still carried out offline which causes manual inefficiency, this not only increases waiting time but can also cause anomalies. This project provides ERP development ideas, especially in the Production and Planning module for PT SFP by implementing automation and integration in the work order planning workflow to improve business processes. It includes the business process of creating Order Confirmation (OC) Receiving Date Input, and automation of planning work order creation, usage standards in the work order and the work order itself. This system is expected to be more effective and efficient in the field of production planning using ERP web-based that can be accessed online.</strong></p>

Energy-Efficient Permutation Flow Shop Scheduling Problem: A Systematic Literature Review

Energy-efficient scheduling is an important problem in the industrial world because improper scheduling can increase energy consumption. One of the production scheduling problems is the Permutation Flow Shop Scheduling Problem (PFSP). This article tries to review research on energy-efficient PFSP to provide a comprehensive review of trends and research gaps in energy-efficient PFSP. A systematic literature review (SLR) was used to conduct this research survey, and 123 articles were collected from 2011-2023. This SLR classifies articles based on publication year, type, objective function, and optimization method used. The results of the literature review analysis show a significant increase every year for energy-efficient PFSP, and multi-objective problems are the dominant problems studied. Based on the analysis of solutions to solve energy efficient PFSP, metaheuristic procedures are the procedures that contribute the largest to solving energy efficient PFSP, and the objective function that is popularly used is to minimize total energy consumption. In addition to this, this study presents a gap research analysis and future research directions for energy-efficient PFSP.

Unlocking Operational Efficiency: The Influence of Advanced B2B Software Solutions on Supply Chain Processes

In this dynamic and fast-paced modern world, operational efficiency has become necessary for businesses that desire to attain and retain their competitive edge. A comprehensive B2B supply chain solution provides real-time visibility into the supply chain, including your inventory and production schedules, enabling prompt responses, informed decisions, and improved customer relationships. This article discusses the influence that advanced B2B Software Solutions have on Supply Chain Processes. It substantiates the absolute need for businesses to employ modern technology for achieving operational excellence in the highly dynamic modern world where beating the competition is the only way forward and application of sophisticated technology is increasingly becoming a necessity and not a choice.

Investigation of Workload Control Methods for Shops with Re-Entrant Flows

Abstract The re-entrant flow with an unpredictable nature of arrival would apparently harm production plans and schedules in flow type of shops. The re-entrant flow with varied arrival frequencies in rotor blade manufacturing is quite complicated and results in disproportionate workloads. Hence, an attempt has been made to study the significant influence of disproportionate workloads and research on an innovative order release method to enhance performance. The manufacturing process was observed thoroughly to incorporate the uncertain events that cause disturbance in the production. A simulation model was developed on a discrete event simulation platform by analysing problem phenomena right from the conceptualization phase. The model has been verified and validated to ensure the accuracy. The model was subjected to 288 experiments representing different scenarios that a flow shop undergoes in reality. The factors considered in the experimentation were re-entrant frequency, re-entrant proportions, order release methods and priority dispatching rules. A refined load release policy for disproportionate loads has been proposed to judge its effectiveness in terms of profit computation by comparing it with other relevant policies. Results of the experiment revealed that the order release methods contribute 95.93% to throughput performance, in addition, the use of the new re-entrant method policy in the above scenario was productive in improving the overall shop performance.

Real-Time Production Scheduling and Industrial Sonar and Their Application in Autonomous Mobile Robots

In real-time production planning, there are exceptional events that can cause problems and deviations in the production schedule. These circumstances can be solved with real-time production planning, which is able to quickly reschedule the operations at each work centre. Mobile autonomous robots are a key element in this real-time planning and are a fundamental link between production centres. Work centres in Industry 4.0 environments can use current technology, i.e., a biomimetic strategy that emulates echolocation, with the aim of establishing bidirectional communication with other work centres through the application of agile algorithms. Taking advantage of these communication capabilities, the basic idea is to distribute the execution of the algorithm among different work centres that interact like a parasympathetic system that makes automatic movements to reorder the production schedule. The aim is to use algorithms with an optimal solution based on the simplicity of the task distribution, trying to avoid heuristic algorithms or heavy computations. This paper presents the following result: the development of an Industrial Sonar algorithm which allows real-time scheduling and obtains the optimal solution at all times. The objective of this is to reduce the makespan, reduce energy costs and carbon footprint, and reduce the waiting and transport times for autonomous mobile robots using the Internet of Things, cloud computing and machine learning technologies to emulate echolocation.

Production planning in an indoor farm: Using time and space requirements to define an efficient production schedule and farm size

Abstract Indoor agriculture is an innovative and environmentally sustainable approach to high-quality food production, utilizing advanced technology to reduce water usage by 95% and achieve a 100-fold increase in production per unit of land compared to conventional farming systems. These enclosed systems provide year-round production of pesticide-free fresh food, even in cities with less favourable climates, addressing food deserts and creating employment opportunities in urban areas. However, the industry faces significant challenges, primarily stemming from substantial investment and operating costs, exacerbated by a limited understanding of the input-output relationship within these systems. This study employs a bioeconomic framework to establish a foundational production function based on growth cycle duration (time) and required growing area (space). Through a partial budget analysis, a 19-day production schedule was identified to provide the highest contribution margin to profits. Results set the minimum size of this hypothetical lettuce indoor farm at 273 m2, rendering it suitable for installation in urban areas. The farm harvests 118 kg per day, within an 800 m2 growing area distributed across four vertically stacked shelves. Estimates of economic output sensitivity to exogenous factors in the US context are also presented, along with a comparison between cost-minimizing and revenue-maximizing strategies.

Multi-period reverse logistics network design for water resource management in hydraulic fracturing

This paper addresses the effective management of water resources during the hydraulic fracturing process used for natural gas extraction. With increasing demand and scrutiny on shale gas production due to economic, environmental, and social factors, effective water management strategies are needed to address the challenges of flow-back water usage during hydraulic fracturing. This research contributes to the existing literature from a multi-period reverse logistics network design perspective with realistic modeling approaches. A mathematical formulation is developed for optimizing the design of a multi-period reverse logistics network for effective water resource management that incorporates production scheduling of a hydraulic fracturing process. This mixed integer programming model determines the optimal locations and capacities of impoundments, treatment facilities, and disposal sites while minimizing fixed and operational costs under constraints such as freshwater withdrawal limits, production schedules, inventory balance, and treatment and disposal options. A two-stage stochastic programming formulation is further developed as a means to address the uncertainty associated with water flows during the fracturing operation. The strategic location and capacity decisions are given in the first stage of this formulation, whereas operational decisions such as water flows are handled in the second stage under independently sampled scenarios. A sample average approximation algorithm is proposed to solve the stochastic formulation. The model is tested on a case study from the United States that explores potential impacts on water resource management due to cost variations, changes in operational parameters, and uncertainty in demands and supplies. The base case setup delivers a 12.8% reduction in freshwater usage. The benefit of multi-period reverse logistics network design is further demonstrated through variations in water demand and equipment degradation. The managerial insights derived through sensitivity analysis highlight the values of the proposed multi-period formulation, capacity expansions, and parameter estimations.

A genetic algorithm for fuzzy identical parallel machine scheduling of minimising total weighted tardiness under resource constraint

Due to the severity of resource consumption and uncertainty in orders, new challenges have arisen for production scheduling in enterprises. This paper studies the scheduling problem of minimising the total weighted tardiness for jobs with fuzzy processing times and due dates on identical parallel machines with resource constraint. To address this research problem, we first propose methods to calculate the upper bound of resource consumption and the maximum number of machines that can be used, effectively reducing the search space and improving the algorithm's efficiency. Secondly, a local search algorithm based on job swapping is proposed to enhance the algorithm's performance. Then, repair algorithms based on job removal and job swapping are designed to repair infeasible solutions. Finally, we propose a fuzzy genetic algorithm (FGALS) to solve the problem based on the above elements. Through extensive simulation experiments, the effectiveness and efficiency of the FGALS algorithm are verified by comparing it with commercial solver Gurobi and several meta-heuristic algorithms.

A Heuristic Based on LPT rule and a restricted B&B Method to Minimizing the Expected Makespan on a Single Machine

Considering the production and maintenance schedule simultaneously has a great attention for more than two decades. At the beginning, most of these researches treated maintenance activities as a fixed hole in the study period. Later, in the majority of them, the maintenance periods starting time were fixed but their intervals may variate. In the non-preemptive case, only few of articles that deals with the maintenance activities starting time as a decision variable. In this article, the time to starting the planed maintenance activities (PM) is a decision variable and the expected repair time is estimated according to nonhomogeneous Poisson process (NHPP). The failure function is Weibully distributed and the repair due to failure is minimal while the PM repair is perfect. Two cases were considered for the model which is aiming to minimizing the expected makespan (EMS), first, the preemptive case which was solved optimally. Secondly, a heuristic based on longest processing time rule (LPT) and a restricted branch and bound method (B&B) are provided to solve the problem for the non-preemptive case. Each of the proposed procedures uses the optimal solution properties of the preemptive case. The optimal solution for the non-preemptive model can be reached using LPT in case of . Finally, the results showed the robustness for B&B which their lower bound obtained by the optimal solution of the preemptive case and the upper bound is the first solution obtained by LPT.

An Assortment–Quantity Optimization Problem in Printing Industry Using Simulation Modelling

This paper presents a method for assortment–quantity production scheduling in a printing company. The company uses specialized machinery to make prints on clothing. The method is based on a study of the company’s practical operations and the production technologies used. It involves the construction of simulation and optimization models of the process. The simulation models reflect the technical aspects of the production process and the business requirements. Optimization models provide solutions that balance product sales revenue with appropriate production schedules. On this basis, managers can make resource-balanced decisions on the implementation of selected production plans, taking into account the current economic conditions of the company. The experiments used the FlexSim simulation program (by FlexSim Software Products, Inc., Orem, UT 84097 USA; v. 20.1.3.1) and the OptQuest optimization package (embedded in FlexSim), resulting in a cost-effective solution in a short time. The proposed method, thanks to the optimization of the production program, provides savings in the use of materials for production, as well as water and energy savings in the production process. Thanks to the possibility of analyzing the process without interfering with it, provided by simulation modelling, the method practically eliminates the costs and time needed to prepare the execution of new production orders. This contributes to the sustainable development of the company and provides an opportunity to assess the impact of potential business decisions in the company prior to their implementation. The method has been directly applied in a company to improve its performance. The method is scalable and can be applied to problems of varying complexity and production systems of different types and sizes. This is especially true for small- and medium-sized companies that use discrete manufacturing in the textile, metal, and furniture industries.

Model for Hydrogen Production Scheduling Optimisation

This scientific article presents a developed model for optimising the scheduling of hydrogen production processes, addressing the growing demand for efficient and sustainable energy sources. The study focuses on the integration of advanced scheduling techniques to improve the overall performance of the hydrogen electrolyser. The proposed model leverages constraint programming and satisfiability (CP-SAT) techniques to systematically analyse complex production schedules, considering factors such as production unit capacities, resource availability and energy costs. By incorporating real-world constraints, such as fluctuating energy prices and the availability of renewable energy, the optimisation model aims to improve overall operational efficiency and reduce production costs. The CP-SAT was applied to achieve more efficient control of the electrolysis process. The optimisation of the scheduling task was set for a 24 h time period with time resolutions of 1 h and 15 min. The performance of the proposed CP-SAT model in this study was then compared with the Monte Carlo Tree Search (MCTS)-based model (developed in our previous work). The CP-SAT was proven to perform better but has several limitations. The model response to the input parameter change has been analysed.

Multi-objective hybrid job-shop scheduling with multiprocessor task (HJSMT) problem with cooperative effect

In order to solve the problem of discrete manufacturing customization and personalized production scheduling, considering the influence of manual labor on processing time, we propose a multi-objective Hybrid Job-shop Scheduling with Multiprocessor Task(HJSMT) problem with cooperative effect model. Based on the actual production, two optimization objectives are set, i. e. minimizing the maximum completion time and the total tardiness. Firstly, considering the situation where workers’ cooperation reduces job processing time, the cooperative effect of workers co-processing is considered by referring to the learning effect curve in the model. Subsequently, we develop an Improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) to solve the multi-objective HJSMT problem by improving Precedence Operation Crossover (POX) and Multiple Mutations (MM) operations. Finally, the scheduling results and the C values are compared with other algorithms to verify the effectiveness of the algorithm. Simultaneously, the multi-objective HJSMT problem with the cooperative effect is solved by the INSGA-II algorithm, and the experimental results also demonstrate the superior performance of the algorithm.

Innovation of production scheduling and service models for cloud manufacturing of tourism equipment based on artificial intelligence

Innovation of production scheduling and service models for cloud manufacturing of tourism equipment based on artificial intelligence