Production Scheduling Research Articles

LIVE MRP (Material Requirements Planning) Impact on Supply Chain

Live Material Requirements Planning (Live MRP) refers to an advanced, real-time approach to managing and optimizing inventory, production, and procurement processes. Unlike traditional MRP systems that update periodically (e.g., daily, weekly), Live MRP integrates dynamic data from across the supply chain and production processes, providing instantaneous updates to material needs, production schedules, and procurement activities. This capability allows companies to respond more swiftly to market fluctuations, production disruptions, and changing customer demands, enhancing operational efficiency, reducing lead times, and improving decision-making

Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via Adaptive cooperative co-evolutionary algorithm

In human-centred manufacturing, deploying collaborative robots (cobots) is recognized as a promising strategy to enhance the inclusiveness and resilience of production systems. Despite notable progress, current production scheduling methods for human-robot collaboration (HRC) still fail to adequately accommodate workforce heterogeneity, significantly reducing their adoption and implementation. To address this gap, we introduce a novel model for the Assembly Line Worker Integration and Balancing Problem considering Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC). This model aims to optimize task scheduling between semi-skilled workers and cobots, aiming to maximize productivity and minimize costs. It features a multi-skilled human-robot collaboration (mHRC) task assignment scheme that selects the optimal assembly/collaboration mode from seven scenarios, based on specific task requirements and resource-skill availability, thus maximizing resource-skill complementarity. To tackle the complexities of this problem, we propose an adaptive multi-objective cooperative co-evolutionary algorithm (a-MOCC) that incorporates a sub-problem decomposition and decoding framework tailored for ALWIBP-mHRC, enhanced by an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). Experimental tests demonstrate the superior performance of the proposed method compared to other established metaheuristic algorithms across various instance sizes, underscoring its effectiveness in enhancing the productivity of production systems for semi-skilled workers. The findings are significant for investment decision-making and resource planning, as they highlight the strategic value of integrating cobots in large-scale heterogeneous workforce production. This work underscores the potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.

Low-carbon economic optimization strategy for industrial loads in parks considering source-load-price multivariate uncertainty

A low-carbon economic optimization method is proposed for industrial loads in parks considering multivariate uncertainty. Model of dynamic load node carbon intensity is proposed considering the uncertainty of clean energy and dynamic conventional units carbon intensity based on system currents. The robustness is improved based on multi-interval uncertainty set. The gap is filled by hybrid copula model with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) prices in the uncertainty model. The GARCH is used to built the marginal distribution function of prices, and a hybrid copula model is proposed to obtain the joint probability density function based on the weights calculated by Euclidean distance. The LNCI probability distributions of the regulation potential of loads are proposed with the virtual energy storage and the virtual power model to improve the descriptive ability of uncertainty based on the expansion and contraction functions. Finally, the typical scenarios are extracted based on Monte Carlo and Wasserstein measures. The Column sum Constraint Generation algorithm and strong duality theory are used to get the robust-stochastic optimization. The method proposed in the paper has a positive effect on enterprises to rationalize their production schedules, reduce electricity and carbon emissions costs and increase the revenue from participating in grid regulation.

Computational analysis of the Logistics Supply Chain from a global trade perspective

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

A multi-objective production scheduling model and dynamic dispatching rules for unrelated parallel machines with sequence-dependent set-up times

This study presents a production scheduling for unrelated parallel machines with machine and job sequence-dependent setup times. The system performance measures to minimize include makespan, total tardiness, and number of tardy jobs. The aim of the study is to develop a solution methodology that can solve the problem for the large scale. First, the problem is formulated as a mixed-integer linear programming model. The augmented ɛ-constraint method is applied to find Pareto solutions for small problem instances. The purpose is to demonstrate that Pareto solutions, which balance the trade-offs among three measures of performance, can be found for these instances. Dispatching rule-based heuristics is developed to solve large problem instances. The heuristics feature three dispatching rules that are designed to handle the dependent setup time. In addition, these rules are combined into six variants using a time-based rule-switching mechanism. The heuristics are tested with 18 problem instances, containing 244 to 298 jobs, in two demand scenarios derived from the monthly demand data from an industrial user. Under each demand scenario, a set of heuristics that provides the best performance with respect to the three measures is identified. The heuristics include combinations of the shortest completion time and due date-based rules. Finally, a multi-criteria decision-making analysis is performed to determine the conditions specified by the weight given to each measure, with which one heuristic is preferred over the others.

Joint scheduling of hybrid flow-shop with limited automatic guided vehicles: A hierarchical learning-based swarm optimizer

Transportation system in workshop is essential for high-efficient production scheduling. Due to the limited transportation resources, the joint scheduling of production and transportation has emerged as a pivotal issue in modern manufacturing. This paper investigates a joint scheduling of hybrid flow-shop with limited automatic guided vehicles (HFSP-LAGV), which extends the classical hybrid flow-shop scheduling by considering the limited number of the AGVs on the transportation resources. To solve such problem, a mixed integer linear programming (MILP) model is firstly built to formulate HFSP-LAGV. Then, a hierarchical learning-based swarm optimizer (HLSO) is proposed. An encoding and decoding method based on three dispatch rules is proposed. The framework of HLSO comprises a pyramid-based layering strategy, an inter-layer learning and an intra-layer learning. The pyramid-based layering strategy divides the swarm into several layers. In the inter-layer learning, the individuals in higher layers guide the evolution of individuals in lower layers to achieve the exploration of global area. In the intra-layer learning, an offline Q-learning-based local search is designed to implement the self-learning of elite individuals in higher layer to intensify the exploitation of the local area. A Q-learning model that has been pre-trained offline is used to guide the selection of appropriate operator of local search. Experimental results reveal the effectiveness of the designs and the superiority of HLSO over several well-performing methods on solving HFSP-LAGV.

Optimal seasonal schedule for producing biogenic volatile organic compounds for tree defense

The leaves of many trees emit biogenic volatile organic compounds (BVOCs) that protect them from various threats, including herbivory, pathogens, and heat stress. In a previous study, we analyzed the optimal seasonal schedule for producing isoprene, a highly volatile BVOC, in leaves to mitigate heat damage and maximize net carbon gain. In this paper, we investigate the seasonal production schedule of BVOCs stored in leaves, such as monoterpenes and sesquiterpenes, which decay slowly. When the leaves are bitten, these chemicals are emitted and help to prevent further herbivory. The optimal seasonal schedule, analyzed using Pontryagin’s maximum principle, includes a period of singular control. Producing BVOCs for defense is advantageous if their decay rate is slow and the photosynthetic rate is fast. The amount of BVOCs produced increases with slower decay rate and faster photosynthetic rate. But it does not increase monotonically with the magnitude of the threat. BVOCs are produced earlier than the peak period of the threat for which the chemicals are intended. Based on the results of the model, we discuss the reported variations in BVOC production among different chemical species and tree species, as well as the seasonal patterns of gene expression in different pathways for BVOC production.

Convergence of Hybrid Grey Wolf Optimization with Heuristic Approaches for Enhanced Job Shop Scheduling

This scholarly inquiry examines the utilization of the Hybrid Grey Wolf Optimization Algorithm (HGWOA) in addressing the Job Shop Scheduling Problem (JSSP), a combinatorial optimization problem commonly encountered within production management. The central aim is to minimize makespan, defined as the cumulative duration necessary to finalize all tasks on a designated set of machines while observing precedence constraints. Conventional Optimization methodologies frequently encounter difficulties with intricate instances of JSSP owing to its NP-hard classification. We introduce a ground-breaking method the Grey Wolf Optimization Algorithm (GWOA) with various meta-heuristic strategies to augment its fruitfulness in resolving JSSP. The multiple findings underscore the usefulness of HGWOA, highlighting its prospective applicability in real-world contexts of production scheduling and management.

The Impact of Lead Time Variability on Supply Chain Management

Purpose: The impact of lead time variability on supply chain management (SCM) is a critical factor affecting operational efficiency, cost management, and service delivery. This study examines how variations in lead time affect key aspects of supply chain performance, including inventory management, production scheduling, and order fulfillment. Methodology: A mixed-methods approach is adopted, combining quantitative analysis through simulation modeling and qualitative insights derived from case studies of companies in the manufacturing and retail sectors. The simulation model accounts for different levels of lead time variability and its influence on stockouts, excess inventory, and overall supply chain responsiveness. Case studies are used to illustrate real-world challenges and strategies employed by businesses to mitigate the effects of lead time fluctuations. Findings: Findings indicate that higher lead time variability leads to increased inventory costs, stockouts, and delays in product deliveries. Companies that rely heavily on just-in-time (JIT) systems are particularly vulnerable to these fluctuations, whereas firms with flexible inventory strategies or buffer stock perform better in managing variability. Additionally, the study reveals that advanced demand forecasting and more resilient supplier relationships can significantly reduce the negative impact of lead time uncertainty. The analysis also highlights the importance of integrating lead time variability into the broader risk management framework of supply chains. Unique Contribution to Theory, Practice and Policy: Based on these findings, recommendations include implementing more robust demand forecasting systems, optimizing safety stock levels, diversifying suppliers, and using advanced inventory management technologies. Moreover, it is advised that businesses foster stronger communication and collaboration with suppliers to reduce variability and improve lead time predictability. Future research should explore the potential of real-time data and AI-driven supply chain management systems to further mitigate lead time variability's impact on SCM.

Method for Increasing the Energy Efficiency of the Gear Teeth Cutting Process by Smoothing the Cutting Force Variation

The energy efficiency in metal cutting is, sometimes, very low. Recent studies show that the energy consumed for detaching chips represents only about 15% of the total energy involved in material machining. The available solutions for energy optimization in cutting processes mentioned are the improvement of manufacturing equipment, the optimization of processes, and appropriate production scheduling. Already-performed research shows that to increase energy efficiency, the cutting force must be reduced, for example, by reducing the depth of the cut and by increasing the feed rate. However, this is applicable when the cutting force is quasi-constant during the process, which is not the case for gear teeth cutting when the cutting force significantly varies. The present paper proposes a new solution for energy efficiency increase in gear teeth cutting, namely, the smoothing of the cutting force variation during the machining process, by keeping the area of the detached chip section quasi-constant during the cutting process. This can be reached by replacing the constant rolling feed, currently used in gear teeth cutting, with a rolling feed that varies after an appropriate law. An algorithm dedicated to implementing this solution has been developed. It uses graphical modeling in CATIA to find the variation in the detached chip area. Original MatLab R2018a applications were developed (i) to identify the analytical form of the law that approximates this variation and (ii) to find the variation law of the feed during the rolling motion, if imposing a constant area of the detached chip. The algorithm was successfully applied in the cases of toothing with a rack tool (by slotting) and with a pinion cutter (by slotting and turning). A technical solution for method implementation in practice is also presented.

Including Lifetime Hydraulic Turbine Cost into Short-Term Hybrid Scheduling of Hydro and Solar

In traditional short-term hydropower scheduling problems, which usually determine the optimal power generation schedules within one week, the off-design zone of a hydraulic turbine is modeled as a forbidden zone due to the significantly increased risk of turbine damage when operating within this zone. However, it is still plausible to occasionally operate within this zone for short durations under real-world circumstances. With the integration of Variable Renewable Energy (VRE) into the power system, hydropower, as a dispatchable energy resource, operates complementarily with VRE to smooth overall power generation and enhance system performance. The rapid and frequent adjustments in output power make it inevitable for the hydraulic turbine to operate in the off-design zone. This paper introduces the operating zones associated with various production costs derived from fatigue analysis of the hydraulic turbine and calculated based on the turbine replacement cost. These costs are incorporated into a short-term hybrid scheduling tool based on Mixed Integer Linear Programming (MILP). Including production costs in the optimization problem shifts the turbine’s working point from a high-cost zone to a low-cost zone. The resulting production schedule for a Hydro-Solar hybrid power system considers not only short-term economic factors such as day-ahead market prices and water value but also lifetime hydraulic turbine cost, leading to a more comprehensive calculation of the production plan. This research provides valuable insights into the sustainable operation of hydropower plants, balancing short-term profits with lifetime hydraulic turbine costs.

Bi-objective optimization modeling of a three-level supply chain in production planning and scheduling considering price-dependent demand: a case study of a soap factory

Bi-objective optimization modeling of a three-level supply chain in production planning and scheduling considering price-dependent demand: a case study of a soap factory

Identical Parallel Machine Scheduling to Minimize Makespan Using Suggested Algorithm Method at XYZ Company

XYZ company produce the various shape of motor spare parts product. The company has threeidentical parallel spot welding machines that use a random method of production scheduling, basedon machine capacity without any sequence of jobs, and only use daily production targets given tooperators. Based on the data, the actual scheduling of the machines has a very large completion timedifference between each machine, or the machine loading is uneven. As a result, the makespanbecomes longer with a value of 440000 seconds (26 days). This research aims to minimize the existingmakespan by giving proposed scheduling, using the suggested algorithm method, which has a smallnumber of iterations and has an optimal result. The method begins with the longest processing timesequence rule which is used as the upper bound for the first iteration, then continued to calculate thelower bound and machine workload. The calculation stops at the 15th iteration because the completiontime value exceeds the lower and upper bound so that the optimal scheduling taken is scheduled inthe 14th iteration with a makespan value of 914412 seconds (16 days). The proposed scheduling canminimize the makespan from the actual schedule by 38%.

HVLV-Motor-KC: Production Efficiency of HVLV Motor Classification using K-means Clustering

This paper aims to introduce the K-means clustering algorithm to complement the Group Technology (GT) methodology as part of a multi-product, low-volume production system. This challenge aims to overcome the limitations of the GT methodology and optimize the production schedule to increase efficiency. We propose a high-variation, low-volume K-means clustering (HVLV-Motor-KC) algorithm, which is a K-means clustering algorithm that focuses on high-variety, low-volume data. This algorithm helps to optimize production by placing motors with similar characteristics in the same cluster.

Construction of a Digital Twin System and Dynamic Scheduling Simulation Analysis of a Flexible Assembly Workshops with Island Layout

Assembly Workshops with Island Layout (AWIL) possess flexible production capabilities that realize product diversification. To cope with the complex scheduling challenges in flexible workshops, improve resource utilization, reduce waste, and enhance production efficiency, this paper proposes a production scheduling method for flexible assembly workshops with an island layout based on digital twin technology. A digital twin model of the workshop is established according to production demands to simulate scheduling operations and deal with complex scheduling issues. A workshop monitoring system is developed to quickly identify abnormal events. By employing an event-driven rolling-window rescheduling technique, a dynamic scheduling service system is constructed. The rolling window decomposes scheduling problems into consecutive static scheduling intervals based on abnormal events, and a genetic algorithm is used to optimize each interval in real time. This approach provides accurate, real-time scheduling decisions to manage disturbances in workshop production, which can enhance flexibility in the production process, and allows rapid adjustments to production plans. Therefore, the digital twin system improves the sustainability of the production system, which will provide a theoretical research foundation for the real-time and unmanned production scheduling process, thereby achieving sustainable development of production.

Reinforcement learning-based alpha-list iterated greedy for production scheduling

Metaheuristics can benefit from analyzing patterns and regularities in data to perform more effective searches in the solution space. In line with the emerging trend in the optimization literature, this study introduces the Reinforcement-learning-based Alpha-List Iterated Greedy (RAIG) algorithm to contribute to the advances in machine learning-based optimization, notably for solving combinatorial problems. RAIG uses an N-List mechanism for solution initialization and its solution improvement procedure is enhanced by Reinforcement Learning and an Alpha-List mechanism for more effective searches. A classic engineering optimization problem, the Permutation Flowshop Scheduling Problem (PFSP), is considered for numerical experiments to evaluate RAIG's performance. Highly competitive solutions to the classic scheduling problem are identified, with up to 9% improvement compared to the baseline, when solving large-size instances. Experimental results also show that the RAIG algorithm performs more robustly than the baseline algorithm. Statistical tests confirm that RAIG is superior and hence can be introduced as a strong benchmark for future studies.

Multi-Objective Production Rescheduling: A Systematic Literature Review

Production rescheduling involves re-optimizing production schedules in response to disruptions that render the initial schedule inefficient or unfeasible. This process requires simultaneous consideration of multiple objectives to develop new schedules that are both efficient and stable. However, existing review papers have paid limited attention to the multi-objective optimization techniques employed in this context. To address this gap, this paper presents a systematic literature review on multi-objective production rescheduling, examining diverse shop-floor environments. Adhering to the PRISMA guidelines, a total of 291 papers were identified. From this pool, studies meeting the inclusion criteria were selected and analyzed to provide a comprehensive overview of the problems tackled, dynamic events managed, objectives considered, and optimization approaches discussed in the literature. This review highlights the primary multi-objective optimization methods used in relation to rescheduling strategies and the dynamic disruptive events studied. Findings reveal a growing interest in this research area, with “a priori” and “a posteriori” optimization methods being the most commonly implemented and a notable rise in the use of the latter. Hybridized algorithms have shown superior performance compared to standalone algorithms by leveraging combined strengths and mitigating individual weaknesses. Additionally, “interactive” and “Pareto pruning” methods, as well as the consideration of human factors in flexible production systems, remain under-explored.

Thermo-hydro-mechanical fully coupled model for enhanced geothermal system and numerical solution method based on finite volume method

Enhanced geothermal system (EGS) has been widely used to improve the production performance of geothermal reservoirs. Extracting the heat energy from geothermal reservoirs involves complex multi-physical process, referred to as the thermal–hydro–mechanical (T–H–M) coupling. To optimize the production schedule, numerical simulator is an important tool for EGS development. However, the numerical solution method based on FVM is rare in the literature. To address this gap, in this study, we derive the mathematical equations of fully coupled T–H–M model. Then, the solution method based on FVM is established. Next, the its accuracy is verified against two cases. Last, the proposed method is applied to an EGS model to simulate the heat extraction process. The evolutions of pressure, temperature, and fracture aperture are analyzed. The effects of controlling parameters of the extraction efficiency are discussed. Results indicate that increasing the injection rate will reduce the lifespan of EGS but accelerate the energy extraction. Increasing the injection temperature can promote the heat extraction to limited extent. Increasing the matrix permeability can significantly improve the extraction efficiency. Enlarging the fracture spacing can enlarge the heat recovery rate. In addition, reducing the fracture permeability can avoid thermal shortcut and improve the production performance.

FROM PLANNING TO PERFORMANCE: ASSESSING THE ROLE OF MILESTONE SCHEDULING IN GARMENT ENTERPRISES IN ELDORET TOWN, KENYA

The performance of garment enterprise projects is a critical indicator of the economic progress and sustainability of any country. With respect to small garment enterprises in Eldoret town, data has shown suboptimal performance in the industry, resulting in some SMEs ceasing their operations. The aim of this paper is to assess the effect of scheduling milestones on the performance of small-scale garment enterprise projects in Eldoret town. The study was anchored on the Goal Setting Theory. An explanatory research design was used, and the target population consisted of 386 small-scale garment enterprise owners. A sample size of 196 small-scale garment enterprise projects in Eldoret town was selected. The respondents were selected using a stratified sampling method, and data was collected using questionnaires. The analysis was done using both descriptive and inferential statistics. With respect to findings, mapping risks was found to have a significant positive effect on the performance of garment enterprise projects (β 0.968, p<0.05). Milestone scheduling has a significant positive impact on the performance of garment enterprises in Eldoret town. The study recommends that small-scale garment enterprise projects prioritize the efficient use of their available resources and develop detailed production schedules.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0773/a.php" alt="Hit counter" /></p>

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines