Production Scheduling Research Articles

A data-driven robust optimization method based on scenario clustering for PVC production scheduling under uncertainty

In this paper, a data-driven robust optimization method based on scenario clustering is proposed for addressing energy consumption uncertainty characterized by correlation and multi-peaked distribution within the PVC production process. Firstly, principal component analysis (PCA) and kernel density estimation (KDE) methods are used to capture the correlation and distribution information effectively across multidimensional uncertain parameters; then a modified K-means clustering method based on density peaks is applied to cluster energy consumption scenarios and the flexible uncertainty subsets is established. A two-stage robust optimization model for the vinyl chloride production section is then proposed, and the column and constraint generation algorithm is applied to solved. Finally, the effectiveness of the proposed method is validated through a PVC production case study. Comparative results demonstrate that the proposed model reduces energy consumption under uncertainty and improves the robustness of PVC production scheduling.

Dynamic flexible scheduling with transportation constraints by multi-agent reinforcement learning

Reinforcement learning-based methods have addressed production scheduling problems with flexible processing constraints. However, delayed rewards arise due to the dynamic arrival of jobs and transportation constraints between two successive operations. The flow time of operations can only be determined after processing due to the possibility that the solution for job sequencing may change if new operations are inserted in dynamic environments. Job sequencing is often overlooked in single-agent-based scheduling methods. The lack of information sharing between multiple agents necessitates that researchers manually design reward functions to fit the relationship between optimization objectives and rewards, thereby reducing the accuracy of the learned policies. Thus, this paper proposes a multi-agent-based scheduling optimization framework that facilitates collaboration between the agents of both machines and jobs to address dynamic flexible job-shop scheduling problems (DFJSP) with transportation time constraints. Then, this paper formulates the Partial Observation Markov Decision Process and constructs a reward-sharing mechanism to tackle the delayed reward issue and facilitate policy learning. Finally, we develop an improved multi-agent dueling double deep Q network algorithm to optimize scheduling policy during long-term training. The results show that, compared with the state-of-the-art methods, the proposed method efficiently shortens the weighted flow time under the trained and unseen scenarios. Additionally, the case study results demonstrate its efficiency and responsiveness. It indicates that the proposed method efficiently addresses production scheduling problems with complex constraints, including the insertion of jobs, transportation time constraints, and flexible processing routes.

Optimal seasonal schedule for the production of isoprene, a highly volatile biogenic VOC

The leaves of many trees emit volatile organic compounds (abbreviated as BVOCs), which protect them from various damages, such as herbivory, pathogens, and heat stress. For example, isoprene is highly volatile and is known to enhance the resistance to heat stress. In this study, we analyze the optimal seasonal schedule for producing isoprene in leaves to mitigate damage. We assume that photosynthetic rate, heat stress, and the stress-suppressing effect of isoprene may vary throughout the season. We seek the seasonal schedule of isoprene production that maximizes the total net photosynthesis using Pontryagin’s maximum principle. The isoprene production rate is determined by the changing balance between the cost and benefit of enhanced leaf protection over time. If heat stress peaks in midsummer, isoprene production can reach its highest levels during the summer. However, if a large portion of leaves is lost due to heat stress in a short period, the optimal schedule involves peaking isoprene production after the peak of heat stress. Both high photosynthetic rate and high isoprene volatility in midsummer make the peak of isoprene production in spring. These results can be clearly understood by distinguishing immediate impacts and the impacts of future expectations.

Uncertain delayed production risk process and its application in the shortage for a production process system

Classical production scheduling based on stochastic process cannot deal with the cases that the estimated distribution function is not close enough to the real frequency. Unfortunately, since uncertainty is common for a production process system with machine breakdowns or innovative products, uncertainty theory proposed by Professor Baoding Liu in 2007 should be applied. To analyze the effect of delayed time existing in a production process, this paper first introduces uncertain delayed production risk process and proposes the concepts of delayed shortage index as well as delayed shortage time for measuring the function and its failure of the production process system. Moreover, some numerical examples are documented to illustrate the calculation for the delayed shortage index and the uncertainty distribution of the delayed shortage time.

Optimization of heat management and utilization in batch processes

Batch plants boast operational flexibility in manufacturing process and resource sharing, making batch processes suitable to fluctuating-demand or small-scale production. However, given time dependence of operational behavior, batch processes are more demanding in energy integration than continuous processes, a task which is especially important amid the global net zero-emission current. It is, therefore, imperative to develop sustainable design strategies for intermittent processes. The study introduced a targeting method and developed an innovative over-time heat integration technique to optimize energy utilization in batch processes, involving use of heat transfer media for heat recovery over various time intervals and a heat makeup strategy to heat the media over different periods, in order to enhance heat-energy utilization efficiency. The technique is applicable to both fixed and unspecified batch schedules, the latter of which involves simultaneous design of production schedule and heat recovery systems, increasing flexibility and improving heat energy utilization. Case studies are conducted to demonstrate the proposed method. With the heat makeup technique, energy efficiency can be significantly enhanced, achieving energy savings of 51% and 93.1% in hot and cold utility, respectively. Meanwhile, the simultaneous design of production schedule and heat integration can boost savings up to 53% and 99%. Results have proven the benefits of the technique, including significant reduction of energy consumption and carbon emission, contributing to clean production, environmental protection, and sustainable development.

An integrated optimization model for procurement and production lot sizing and scheduling problems

Abstract Lot sizing is a prevalent issue within manufacturing companies, where determining the optimal procurement and production lot sizes is crucial for maximizing profits. This problem has become more complex, given that numerous suppliers can provide the same raw materials with different prices and quantity discount schemes. A company should also determine optimal carriers to deliver materials to the company’s warehouse. In a manufacturing process, the company should determine the optimal production lot size and its schedules. In this paper, a model was developed to solve simultaneously procurement and production lot sizing, as well as production scheduling problems. The model encompasses multiple suppliers offering quantity discounts, aiming to maximize company profit by accounting for various costs, including procurement, production, inventory, and quality costs. A case study is taken from a company producing noodles and its related derivative products to illustrate the application of the model. Based on the optimization results, the company obtained a total profit of IDR. 14,656,550,000 or $950,921.30 (the exchange rate of $1 at IDR. 15,413). The sensitivity analysis results show that the objective function is sensitive to changes in the purchase cost, sale revenue, and discount rate parameters. The decision variables for accepted product demand, product quantity, and the starting and completion time of product family are only sensitive to changes in certain parameters. Meanwhile, the decision variables for product inventory, product backlog, raw material inventory, and purchased raw material quantity are sensitive to the changes in all the analyzed parameters.

A production and transport scheduling strategy of energy and resources of pelagic clustering islands based on generalized movable energy storage

At present, most pelagic islands rely on the mainland for resource replenishment. And due to the limited island area, it is not sufficient to simultaneously place enough basic load facilities and new energy generation and consumption facilities on a single island. Considering pelagic islands are distributed in groups, vigorously developing renewable energy around islands can be considered, and the islands can be divided into resource islands and load center island. Resource islands can use renewable energy to produce and transport electric power, fresh water and hydrogen which are needed for load center island. The whole islands group forms a cleaner and green integrated energy system. However, this kind of system will face the coexistence challenge of intra-island independent optimization and inter-island collaborative optimization, continuity of energy production and discreteness of transportation, production scheduling and route planning, which is less studied. To ensure reasonable operation of the energy system, according to multi-agent theory, the inter-island energy and resource production and transportation scheduling model based on generalized movable energy storage can be divided into two parts: load center island agent scheduling model and resource islands agent scheduling model. And scheduling objectives and constraints of each agent in two scenarios are determined. The simulation results show that the new model can comprehensively take into account the production efficiency of equipment, production effectiveness and transportation cost and realize the rational production scheduling and transportation of energy and resources under the premise of meeting the demand of load center island.

Synchronisation of operations and maintenance in distributed unrelated parallel machine systems: a hierarchical optimisation framework

ABSTRACT Synchronisation of operations and maintenance means that production and maintenance departments work together effectively to achieve integrated optimisation of production scheduling and maintenance activities. This paper addresses such an integrated optimisation problem towards distributed unrelated parallel machine systems, where deteriorating processing time and machine failures are considered. For the trade-off between production and maintenance to obtain a minimal makespan in the worst-case scenario, a hierarchical optimisation framework with reinforcement learning-based encoder and knowledge-based intelligent optimiser (HOFRK) is developed. Experimental results show that HOFRK can be treated as an excellent optimiser for enterprise information systems to solve the proposed problem.

A Study on the Man-Hour Prediction in Structural Steel Fabrication

Longitudinal cutting is the most common process in steel structure manufacturing, and the man-hours of the process provide an important basis for enterprises to generate production schedules. However, currently, the man-hours in factories are mainly estimated by experts, and the accuracy of this method is relatively low. In this study, we propose a system that predicts man-hours with history data in the manufacturing process and that can be applied in practical structural steel fabrication. The system addresses the data inconsistency problem by one-hot encoding and data normalization techniques, Pearson correlation coefficient for feature selection, and the Random Forest Regression (RFR) for prediction. Compared with the other three Machine-Learning (ML) algorithms, the Random Forest algorithm has the best performance. The results demonstrate that the proposed system outperforms the conventional approach and has better forecast accuracy so it is suitable for man-hours prediction.

Scheduling redundancy optimization for precast production to enhance disruption management in prefabricated construction

Redundancy is a highly effective approach for minimizing losses in precast production when faced with inevitable disruptions. However, existing studies have primarily focused on enhancing production redundancy in order to mitigate losses caused by disruptions, without considering the added production costs resulting from decreased efficiency. Besides, the cost-effectiveness of redundancy design may be influenced by the disruption types and their associated risk levels, which has not been thoroughly investigated. In this paper, a redundancy optimization model for precast production is proposed to achieve the trade-off between the redundancy benefits and the additional costs involved. The concept of production scheduling redundancy is firstly defined, and the benefits derived from its implementation are quantified. Additionally, different scenarios with high, moderate, and low occurrence levels of disruptions are designed to optimize the production redundancy in different situations. On this basis, the effectiveness and cost-benefits of redundancy are compared subject to various types and levels of disruptions. Finally, a case study is conducted to illustrate the model. The findings show that the machine breakdown is the most critical disruption in the precast production, and implementing redundancy measures prove to be more cost-effective in situations characterized by higher levels of risk. Besides, the optimal degree of redundancy is related to both the levels and the types of disruptions. The methodology of this paper can provide insights for disruption management in precast production and give full play to the benefits of prefabricated construction.

The Use of Artificial Intelligence and Machine Learning in Forecasting the Financial Growth of Automobile Industries

The automotive industry's financial forecasting is vital for growth and competitiveness in today's rapidly evolving market landscape. Accurate and reliable forecasting is essential for strategic planning, resource allocation, and decision-making processes. However, traditional forecasting methods often struggle to adapt to the dynamic nature of the automotive sector, characterized by fluctuating consumer demands, evolving market trends, and complex supply chain dynamics. The integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies has revolutionized financial forecasting in the automotive industry, transforming forecasting accuracy, efficiency, and decision-making processes. By leveraging AI algorithms and ML models, automotive companies can gain deeper insights into market dynamics, predict consumer behavior more accurately, optimize production schedules, and streamline distribution processes. This paper explores the transformative impact of AI and ML technologies on financial forecasting in the automotive industry, delving into key applications, benefits, challenges, and future directions of integrating these advanced technologies into forecasting processes. The adoption of AI and ML empowers automotive companies to make data-driven decisions with greater precision and agility, driving innovation, growth, and competitiveness in this dynamic sector. Key Words: Artificial Intelligence, Machine Learning, Financial Forecasting, Automotive Industry, Predictive Maintenance, Hybrid Decision Support Systems, Production Planning and Control Systems, Intelligent Transport Logistics.

Digital twin-enabled robust production scheduling for equipment in degraded state

Technological advancements are leading to a world where digital twins will become integral to manufacturing operations management. While wide-ranging applications of digital twins are being researched, robust production scheduling remains an enduring challenge, especially considering the numerous sources of uncertainty in a complex manufacturing system that can affect the validity of the obtained solution. Thus, the article proposes a Prognostics and Health Management (PHM)-enabled digital twin framework to perform job scheduling for a flow shop scheduling problem considering real-time equipment health state. The framework combines the adoption of a Genetic Algorithm optimizer with data-driven modelling leveraging algorithms like Principal Component Analysis and models like Discrete Event Simulation with the purpose to solve the engineering task of scheduling at hand. Building on such a technological blend, the framework incorporates the degradation and fault detection and diagnosis of failure modes across multiple components. The effect of degradation and faults on job processing times is learned as a distribution from the field data. The proposed framework is then validated in a laboratory environment where degradation is produced by inducing degradation and faults in the equipment. By means of various experiments, the optimized makespan is compared for the output schedules in different configurations. When equipment is degraded, production scheduling with PHM-enabled field-synchronized digital twin results in better makespan estimation, if compared to the digital twin without PHM. This shows the superiority of the framework in terms of more realistic makespan estimations, which finally corresponds to improved production schedule optimization, sensitive also to degraded states.

Robust-optimization-guiding deep reinforcement learning for chemical material production scheduling

This study presents a novel guiding framework embedded with robust optimization (RO) for the training phase of reinforcement learning (RL), specifically tailored for dynamic scheduling of a single-stage multi-product chemical reactor in an uncertain environment. The proposed framework addresses the challenge of local optima in policy gradient methods by integrating optimization methods, enhancing both the objective value and learning stability of the RL. We further enhance the robustness of the proposed model against parameter distortions by incorporating RO as a guiding engine. A numerical study of chemical material production scheduling is conducted to validate the proposed model and the results demonstrate the effectiveness to address demand volatility with several metrics including sensitivity analysis, solution quality analysis, computational time, compared to a typical actor-critic RL.

Leveraging Digital Transformation and ERP for Enhanced Operational Efficiency in Manufacturing Enterprises

Purpose: The process of making the most of digital tools in the workplace to support an organization's success is known as "leveraging on technology." By automating manual tasks more quickly, modern ERP systems free up time and funds for other important projects. Data can be aggregated by contemporary ERP systems for simpler management, access, and analysis. Method: Systems for enterprise resource planning, or ERPs, have become a vital component of contemporary manufacturing processes. These integrated software solutions are essential for improving, streamlining, and controlling many aspects of manufacturing, including financial control, customer service, production and inventory management. Manufacturers can effectively manage vital resources like labor, equipment, and materials with the help of ERP systems. ERP helps eliminate waste, minimize production delays, and maximize resource utilization by offering real-time visibility into resource allocation and availability. Result and Conclusion: Manufacturing process synchronization and automation are made easier by ERP software. Work orders, production schedules, and quality control can all be managed by it, resulting in seamless and effective manufacturing processes. In manufacturing, keeping the proper amount of inventory on hand is a difficult task. ERP systems give producers insights into demand trends, order lead times, and reorder points, which helps them optimize inventory levels. In addition to lowering carrying costs and preventing stockouts, this minimizes superfluous inventory. Implication of Research: An organization's operational model is altered by digital transformation. This process involves systems, workflow, processes, and culture. Every level of an organization is impacted by this transition, which unifies data from different departments to collaborate more successfully. Originality/Value: Enhanced Efficiency: Businesses are able to allocate resources more effectively thanks to automation and streamlined processes, which also lower operational costs and increase productivity.

Online Integrated Production and Distribution Scheduling: Review and Extensions

As a growing number of manufacturers adopt the make-to-order business mode and a growing number of retailers sell online, we are seeing numerous decision problems that can be modeled as what are known in the literature as integrated production and distribution scheduling (IPDS) problems. In such problems, order processing and delivery must be scheduled jointly in order to achieve an optimal balance between total operational costs and overall customer service. Offline IPDS problems, in which the information about every order is known in advance with certainty, are extensively studied. However, research on online IPDS problems, in which orders arrive randomly with their information unknown until they arrive, is relatively recent but is growing rapidly. In this paper, we first describe several real-world applications to illustrate the importance of studying online IPDS problems from a practical point of view. We then review the existing literature on online IPDS problems with a focus on existing online algorithms for these problems and their theoretical performance. We also derive some new results to fill several gaps left in the literature and discuss possible topics for future research. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0305 .

Economic evaluation of mineral deposit: A bottom-up approach

Proper economic evaluation of a mineral deposit is critical to effective investment decision-making in a mineral project. However, this often requires detailed mine production scheduling to produce a schedule that reflects the actual cash flow when the project comes on stream. Because of the dexterity required for this task many mine planners and explorationist attempt to use statistical formulas that approximate the mine scheduling and value of the project. The mathematical model developed for production scheduling often produces a constant production rate schedule over an approximate life span of the project. In this paper, we have attempted to apply a bottom-up approach that begins with geometrical modelling and equipment deployment pattern to define the number of equipment required for each sequence of operation based on available workfront in the development of each bench. Then, based on the number of equipment and the production rate at each sequence of operations, a production schedule is developed. This production schedule therefore will reflect annual cash flow since it is based on the sequence of operation. Application of this method on the west pit of Itakpe mine shows a considerable net present value and internal rate of return of the deposit compared with the evaluation made using statistical models. The NPV of the west pit was found to be USD621 million as against USD122.41 million using Nwosu’s formula and USD123.85 million Taylor’s formula. The above value of NPV using the proposed method shows the maximum expected NPV of the mineral project-based technical restrictions. An understanding of this value can guide the mineral property owner in decision-making.

Simultaneous stochastic optimisation of mining complexes: Integrating progressive reclamation and waste management with contextual bandits

ABSTRACT Managing environmental performance of waste dump facilities in mining complexes is an integral part of long-term production planning. Sustainable long-term production scheduling solutions are desired to mitigate risk and return the environment to a productive post-mining state. A simultaneous stochastic optimisation framework for long-term production scheduling in mining complexes is developed that integrates waste management and progressive reclamation. The waste dump placement schedule is jointly optimised with the extraction sequence, destination policy, and stockpiling decisions in a single stochastic mathematical programming framework. This includes the timing of progressive reclamation activities in parallel with production to enhance waste dump rehabilitation. Uncertainty related to the production of acid rock drainage is quantified by simulating geochemical properties of waste and managing the blending of uncertain waste properties within the optimisation framework. With respect to the framework for simultaneous stochastic optimisation, contextual bandits are explored to improve the metaheuristic solution approach and solve the corresponding large-scale optimisation model. The framework is tested in a multi-mine copper-gold mining complex leading to improved environmental performance. Risk of acid rock drainage is decreased by 52.5% in the waste dump facilities. Reclamation planning activities for meeting environmental requirements are scheduled prior to closure. The solution approach more effectively improves the objective function with contextual bandits leading to a 24% improvement in the study presented.

Research on Multi-Objective Flexible Job Shop Scheduling Problem with Setup and Handling Based on an Improved Shuffled Frog Leaping Algorithm

Flexible job shop scheduling problem (FJSP), widely prevalent in many intelligent manufacturing industries, is one of the most classic problems of production scheduling and combinatorial optimization. In actual manufacturing enterprises, the setup of machines and the handling of jobs have an important impact on the scheduling plan. Furthermore, there is a trend for a cluster of machines with similar functionalities to form a work center. Considering the above constraints, a new order-driven multi-equipment work center FJSP model with setup and handling including multiple objectives encompassing the minimization of the makespan, the number of machine shutdowns, and the number of handling batches is established. An improved shuffled frog leading algorithm is designed to solve it through the optimization of the initial solution population, the improvement of evolutionary operations, and the incorporation of Pareto sorting. The algorithm also combines the speed calculation method in the gravity search algorithm to enhance the stability of the solution search. Some standard FJSP data benchmarks have been selected to evaluate the effectiveness of the algorithm, and the experimental results confirm the satisfactory performance of the proposed algorithm. Finally, a problem example is designed to demonstrate the algorithm’s capability to generate an excellent scheduling plan.

Automatic Design of Energy-Efficient Dispatching Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling Based on Dual Feature Weight Sets

Considering the requirements of the actual production scheduling process, the utilization of the genetic programming hyper-heuristic (GPHH) approach to automatically design dispatching rules (DRs) has recently emerged as a popular optimization approach. However, the decision objects and decision environments for routing and sequencing decisions are different in the dynamic flexible job shop scheduling problem (DFJSSP), leading to different required feature information. Traditional algorithms that allow these two types of scheduling decisions to share one common feature set are not conducive to the further optimization of the evolved DRs, but instead introduce redundant and unnecessary search attempts for algorithm optimization. To address this, some related studies have focused on customizing the feature sets for both routing and sequencing decisions through feature selection when solving single-objective problems. While being effective in reducing the search space, the selected feature sets also diminish the diversity of the obtained DRs, ultimately impacting the optimization performance. Consequently, this paper proposes an improved GPHH with dual feature weight sets for the multi-objective energy-efficient DFJSSP, which includes two novel feature weight measures and one novel hybrid population adjustment strategy. Instead of selecting suitable features, the proposed algorithm assigns appropriate weights to the features based on their multi-objective contribution, which could provide directional guidance to the GPHH while ensuring the search space. Experimental results demonstrate that, compared to existing studies, the proposed algorithm can significantly enhance the optimization performance and interpretability of energy-efficient DRs.

Perencanaan Produksi UMKM dengan Menggunakan Pendekatan Metode Transportasi

Micro, Small and Medium Enterprises (MSMEs) are an important part of the Indonesian economy. Many micro, small and medium enterprises (MSMEs) are making new innovations in the fields of handicrafts, food and drinks, among others. The development of MSMEs faces demands that change every month. To meet this demand, they must carry out optimal production planning. Production planning and control can be used as a reference for determining production quantities and forecasting product demand. By carrying out good production planning and control, MSMEs can reduce or anticipate risks, thereby increasing their profits. Coconut jelly MSMEs are one of the businesses that are also affected by poor production planning. On average, in 2023 there will be an excess production of 150 pieces per month. For small and medium businesses (MSMEs), fluctuations of this magnitude are very disruptive to company finances. Effective production planning should be able to optimize the use of all organizational resources, including raw materials, materials, labor and finance. This article carries out production planning using a transportation method approach. Initial planning is carried out following a pattern of forecasting the number of requests in the future. Forecasting is carried out using 3 methods, namely single moving average, double exponential smoothing and trend analysis. Of these three methods, the one with the smallest Mean Square Error (MSE) value will be selected. Production planning will be outlined in the Master Production Schedule (JIP). Determination of JIP is based on the results of production planning through 2 alternatives, namely permanent and changing workforce. From the results of the transportation method approach, it was found that the minimum production cost was found in the variable labor alternative, namely IDR 178,326,000, there was a cost difference from the fixed labor alternative of IDR 1,451,200. The approach method used in this article still needs a lot of improvement, especially in terms of determining the cost of each production component which can be more dynamic. The approach that can be taken can use heuristic methods such as genetic algorithms. So, it is hoped that the results obtained can be closer to reality.