Manufacturing Scheduling Research Articles

Optimizing job release and scheduling jointly in a reentrant hybrid flow shop

In a reentrant manufacturing system, job release and scheduling are of great importance. However, the two decisions have been made separately in the past. This study mainly aims at optimizing job release and scheduling jointly in a reentrant hybrid flow shop (RHFS-OJRSJ) with unrelated parallel machines. To promote energy conservation and emission reduction, a mathematical model for minimizing the makespan and total energy consumption is proposed. Due to the frequent reentrances of jobs and the complexity of the RHFS-OJRSJ problem, it is a strongly NP-hard problem. To effectively solve the RHFS-OJRSJ problem, an improved multi-objective evolutionary algorithm based on inverse optimization and decomposition (IMOEA/IOD) is proposed. First, the improved multi-objective evolutionary algorithm based on decomposition is employed to search the solution space. Second, a genetic algorithm is integrated to determine an initially best job release plan, based on which the scheduling problem can be encoded. Third, in the decoding, inverse optimization is introduced to further optimize the job release plan. Finally, to avoid falling into a local optimum and ensure population diversity, an adaptive neighborhood updating strategy is proposed. The experimental results demonstrate that the IMOEA/IOD algorithm bridges the gap of the job release and scheduling problem and the proposed algorithm can effectively solve the RHFS-OJRSJ problem.

Optimization of integrated production scheduling and vehicle routing problem with batch delivery to multiple customers in supply chain

In supply chain literature, production coordination and vehicle routing have received a lot of attention. Even though all functions in the supply chain are interrelated, they are normally handled independently. This disconnected approach might lead to less-than-ideal outcomes. Increasing total efficiency by integrating manufacturing and delivery scheduling processes is popular. This study focuses on synchronic production–distribution scheduling difficulties, particularly permutation flow shop scheduling in production and sequence-dependent setup time (SDST) and vehicle routing alternatives in distribution. To create a cost-effective distribution among the placement of geographically separated clients and hence to minimize delivery costs, batch delivery to customers employing a succession of homogenized capacity limitation vehicles is examined here. However, this might result in the failure to complete multiple client orders before their deadlines, raising the cost of lateness. As a result, the goal of this study is to lower the overall cost of tardiness and batch distribution in the supply chain. To accomplish so, a mixed-integer nonlinear programming model is developed, and the model is solved using a suggested genetic algorithm (GA). Because there is no established benchmark for this issue, a set of genuine problem scenarios is created in order to assess the proposed GA in a viable and difficult environment. Ruiz's benchmark data, which is derived from Taillard's benchmark cases of permutation flow shops, was supplemented with SDSTs in the production of test examples. In comparison to an exact method, the results show that the proposed GA can rapidly seek solutions to optimality for most small-sized instances. Furthermore, for medium and large-scale cases, the proposed GA continues to work well and produces solutions in a fair amount of time in comparison to GA without the local search.

Process Stability and Reproducibility of the Dieless Drawing Process for AZ31 Magnesium Wires

Magnesium (Mg)-based wires are in the focus of interest for numerous applications like micro-forming technologies or medical engineering. Manufacturing thin Mg-based wires is widely realized by applying a conventional multiple pass cold wire drawing process. This requires a complex manufacturing schedule of multiple passes with intermediate heat treatments to overcome work hardening, because of the cold forming process. Especially Mg and its alloys are known for their rather low formability at room temperature associated with the hexagonal close-packed lattice structure. The dieless drawing process uses local heating to initialize a localized plastic zone under an external tensile load to achieve higher reductions in diameter in a single wire drawing pass. It can therefore present a solution for a more efficient warm manufacturing process of Mg-based wires. In this study, the stability of the steady state material flow during a dieless wire drawing process and its reproducibility was investigated. For this purpose, a variation of process parameters was selected and wire manufacturing was carried out using magnesium alloy AZ31. A single and double dieless drawing process was applied. Additionally, a conventional cold wire drawing process including a die with the same forming schedule was executed as a benchmark experiment. The results of this study show, that the dieless drawing process is not only a stable process after reaching the steady state, but it is also a reproducible and accurately adjustable process. Moreover, the dieless drawing process maintains the property profile of the starting material to a large extend.

A Statistical Comparison of Metaheuristics for Unrelated Parallel Machine Scheduling Problems with Setup Times

Manufacturing scheduling aims to optimize one or more performance measures by allocating a set of resources to a set of jobs or tasks over a given period of time. It is an area that considers a very important decision-making process for manufacturing and production systems. In this paper, the unrelated parallel machine scheduling problem with machine-dependent and job-sequence-dependent setup times is addressed. This problem involves the scheduling of tasks on unrelated machines with setup times in order to minimize the makespan. The genetic algorithm is used to solve small and large instances of this problem when processing and setup times are balanced (Balanced problems), when processing times are dominant (Dominant P problems), and when setup times are dominant (Dominant S problems). For small instances, most of the values achieved the optimal makespan value, and, when compared to the metaheuristic ant colony optimization (ACOII) algorithm referred to in the literature, it was found that there were no significant differences between the two methods. However, in terms of large instances, there were significant differences between the optimal makespan obtained by the two methods, revealing overall better performance by the genetic algorithm for Dominant S and Dominant P problems.

A Hyper-Heuristic for the Preemptive Single Machine Scheduling Problem to Minimize the Total Weighted Tardiness

Abstract A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal schedule whenever finding the exact solution is practically intractable. The three heuristics are based on the well-known rules, whereas the 21 heuristics are introduced first. Therefore, the hyper-heuristic selects the best heuristic schedule among 24 schedule versions, whose total weighted tardiness is minimal. Each of the 24 heuristics can solely produce a schedule which is the best one for a given scheduling problem. Despite the percentage of zero gap instances decreases as the greater number of jobs is scheduled, the average and maximal gaps decrease as well. In particular, the percentage is not less than 80 % when up to 10 jobs are scheduled. The average gap calculated over nonzero gaps does not exceed 4 % in the case of scheduling 7 jobs. When manufacturing consists of hundreds of jobs, the hyper-heuristic is made an online scheduling algorithm by applying it only to a starting part of the manufacturing process.

An investigation of the indoor environment and its influence on manufacturing applications via computational fluid dynamics simulation

The prevalence of precision manufacturing equipment in industrial facilities continues increasing with advances in manufacturing science and technology. However, challenges with the built environment for manufacturing applications are becoming more apparent. For example, traditional heating, ventilation, and air conditioning (HVAC) systems cannot accurately detect environmental changes in large manufacturing spaces. This results in the untimely activation of the HVAC system and, subsequently, wasted energy, which is not conducive to sustainable development. Accordingly, a new approach is needed that can accurately monitor environmental variations around equipment to change the indoor environment with improved efficiency.This research uses computational fluid dynamics (CFD) to simulate the ambient conditions of a manufacturing environment during computer numerical control (CNC) machining scenarios under varied conditions: manned vs. unmanned operations, machining vs. no machining, and an open vs. closed door. The simulations were validated with sensor data, resulting in an average error of 0.1% and an error range of 0 m/s to 0.65 m/s for the temperature and airflow simulations, respectively. The opening of the laboratory door led to the largest change in ambient conditions with an increase in temperature of 3.81K (vs. the closed-door scenario). Under these conditions, the associated thermal deformation in the machine tool frame was estimated to reach up to 0.085 mm, which adversely affects the range of precision CNC machining tolerances (±0.05 mm). Future research is recommended to scale-up the simulations to a factory to inform production scheduling for precision manufacturing in order to reduce the load on the HVAC system.

One-of-a-kind production (OKP) planning and control: a comprehensive review and future research directions

PurposeThe aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted.Design/methodology/approachThe following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis.FindingsThe results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP.Originality/valueThis paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process.

Variable neighbourhood search for job scheduling with position-dependent deteriorating processing times

This article studies the problem of job scheduling in hand-intensive manufacturing with processing times that deteriorate depending on the sequence in order to minimize the maximum delay of the set of jobs. In the literature, only a small number of academic works has considered the assumption of non-stationary workers during the planning horizon. This problem is NP-hard since it is as an extension of the parallel machine scheduling problem with makespan minimization, which is itself NP-hard. A variable neighbourhood search (VNS) algorithm is presented to solve this problem; its efficiency is evaluated through an extended set of numerical experiments with random-generated datasets. Results are compared with an exact method based on mathematical programming. The results show that the VNS algorithm provides good solutions in reasonable computing times even for large problems.

Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective

Based on a scientific literature review in the conceptual domain defined by smart manufacturing scheduling (SMS), this article identifies the benefits and limitations of the reviewed contributions, establishes and discusses a set of criteria with which to collect and structure its main synergistic attributes, and devises a conceptual framework that models SMS around three axes: a semantic ontology context, a hierarchical agent structure, and the deep reinforcement learning (DRL) method. The main purpose of such a modelling research is to establish a conceptual and structured relationship framework to improve the efficiency of the job shop scheduling process using the approach defined by SMS. The presented model orients the job shop scheduling process towards greater flexibility, through enhanced rescheduling capability, and towards autonomous operation, mainly supported by the use of machine learning technology. To the best of our knowledge, there are no other similar conceptual models in the literature that synergistically combine the potential of the specific set of Industry 4.0 principles and technologies that model SMS. This research can provide guidance for practitioners and researchers’ efforts to move toward the digital transformation of job shops.

Adjusted Iterated Greedy for the optimization of additive manufacturing scheduling problems

As a disruptive technology, additive manufacturing (AM) is revolutionizing manufacturing supply chains. AM consists of producing 3-dimensional objects through layer-by-layer addition of compound material based on digital models. The scheduling of AM operations differs from traditional (i.e., subtractive and injection molding) manufacturing with a single production run involving several parts/geometries; this makes the jobs heterogeneous. Limited studies have investigated the Additive Manufacturing Scheduling Problems (AMSP). This study extends the Iterated Greedy algorithm to solve the AMSPs considering a single-machine production setting. For this purpose, several computational mechanisms are customized to account for AM-specific characteristics of production scheduling. Numerical analysis shows that the vast majority of the best-found solutions are yielded by the Adjusted Iterated Greedy (AIG) algorithm considering both solution quality and stability; the outperformance becomes more significant with an increase in problem size. Statistical analysis confirms that AIG’s performance is notably better than that of the existing solution algorithm in terms of solution quality and stability. This study is concluded by providing directions for future development of AM and AMSPs to extend the industrial reach of 3D printing technology.

Prioritizing Scheduling Parameters in the Automotive Industry Using Fuzzy TOPSIS-DEMATEL Model

The Automotive industry is one of the biggest emerging sectors in terms of revenue. Every automotive industry has an indispensable need for optimum manufacturing scheduling systems for generating good revenues and profits. This need can be pulled off by identifying and prioritizing the scheduling parameters. Multi-criteria decision making (MCDM) is one of the best techniques of operation research in selecting the best parameters or factors among the various alternatives. This study includes the identification and prioritization of the various important scheduling parameters (SPs) in the Indian automotive industry. The twelve scheduling parameters have been identified in this study and these parameters are prioritized by the fuzzy- Technique for order preference by similarity to ideal solution (TOPSIS) and Decision Making Trial and Evaluation Laboratory Model (DEMATEL). The expert’s views are gathered from the five automobile industries. Makespan, energy consumption, due date, and travel time are the crucial parameters obtained using Fuzzy TOPSIS. The least important parameters obtained using Fuzzy TOPSIS are work in process, flow time, and release date. The most influential parameters identified using the DEMATEL method are completion time and processing time. This study is valuable for every industry and research organization in the field of the automobile industry.

Rule-Based Genetic Algorithms for Integrated Scheduling of Parallel Batch Additive Manufacturing and Batch Delivery Problem

Rule-Based Genetic Algorithms for Integrated Scheduling of Parallel Batch Additive Manufacturing and Batch Delivery Problem

Toward smart manufacturing scheduling from an ontological approach of job-shop uncertainty sources

An integral application of the enabling technologies of Industry 4.0 in the job-shop scheduling problem (JSSP) must contemplate the automation and autonomy of the involved decision-making processes as a goal, which is the main purpose of the smart manufacturing scheduling (SMS) paradigm. In a real production context, uncertainty acts as a barrier that hinders this goal being met and, therefore, any SMS model should integrate uncertainty generators in one way or another. This paper proposes an ontological framework that identifies and structures the entities shaping the joint domain formed by the job-shop scheduling process in its itinerary toward the SMS paradigm, the sources of uncertainty that it faces, and the interrelationship type that link these entities. This ontological framework will serve in future research as a conceptual basis to design new quantitative models that, from a holistic perspective, will address the stochasticity of manufacturing environments and incorporate the management of disturbances into the realtime resolution of automatic and autonomous job-shop scheduling.

Collective impact of scrap, random breakdown, overtime and discontinuous issuing on batch production planning in a supply-chain environment

This research explores the collective impact of overtime, random breakdown, discontinuous issuing rule, and scrap on batch production planning in a supply-chain environment. In today’s global business environment, manufacturing firms encounter numerous operational challenges. Externally, they must promptly satisfy the customers’ various requests, while internally, they must cautiously manage several inevitable issues in the fabrication process. These issues might be concerned with scrap, random breakdown, etc. Resolving such issues is crucial for meeting the due dates of customers’ orders, adhering to the expected manufacturing schedules, product quality, and minimizing the total fabrication-transportation-inventory costs. The study develops a model to characterize the system’s features mentioned above and assist the manufacturers with batch fabrication planning. The model proposes a solution process with an algorithm seeking an optimal runtime for the system. Additionally, it gives a numerical illustration depicting the collective and individual impacts of these special features on the operating policy and other performance indices. This model and the research findings can facilitate manufacturers’ decision-making for green batch fabrication and enhance competitive advantage.

A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

As the critical component of manufacturing systems, production scheduling aims to optimize objectives in terms of profit, efficiency, and energy consumption by reasonably determining the main factors including processing path, machine assignment, execute time and so on. Due to the large scale and strongly coupled constraints nature, as well as the real-time solving requirement in certain scenarios, it faces great challenges in solving the manufacturing scheduling problems. With the development of machine learning, Reinforcement Learning (RL) has made breakthroughs in a variety of decision-making problems. For manufacturing scheduling problems, in this paper we summarize the designs of state and action, tease out RL-based algorithm for scheduling, review the applications of RL for different types of scheduling problems, and then discuss the fusion modes of reinforcement learning and meta-heuristics. Finally, we analyze the existing problems in current research, and point out the future research direction and significant contents to promote the research and applications of RL-based scheduling optimization.

Optimal scheduling for simultaneous refinery manufacturing and multi oil-product pipeline distribution

Refinery manufacturing and the downstream oil-product distribution should be highly integrated in the refinery supply chain system. However, systematic studies dealing with simultaneous optimization of these two sectors are still lacking. In this paper, a general scheduling model for simultaneous optimization of refinery manufacturing and the downstream multi oil-product pipeline distribution (RM&MOPD) has been developed. It consists of three sub-models: (i) a refinery manufacturing (RM) sub-model covering all major refinery processing units such as crude distillation, hydrocracking, hydrotreating, and blending operations; (ii) a new multi oil-product pipeline distribution (MOPD) sub-model that considers comprehensive handling measures for oil transmix in the pipeline including downgrading, blending, and distillation operations; and (iii) a joint sub-model coupling the upstream refinery manufacturing and the downstream oil-product pipeline distribution. The developed RM&MOPD is a large-scale continuous-time and continuous-volume based MINLP model, where the objective is to minimize the total operating cost subject to various constraints such as operation and product specifications, inventory limits, delivery constraints, and oil-product demands at each oil depot. It can simultaneously provide optimal solutions for refinery unit operations, oil-product inventory management at refinery and depots, oil slug movement profiles inside pipeline, as well as oil-product feeding and discharging strategies from pipeline to multiple tanks at different depots.

A genetic algorithm for order acceptance and scheduling in additive manufacturing

ABSTRACT We consider the problem of order acceptance and scheduling faced by an additive manufacturing facility consisting of multiple build chambers and postprocessing operations for support removal and surface finishing. We model each build chamber as a batch processing machine with processing times determined by the nesting and orientation of parts within the chamber. Due to the difficulty of developing an explicit functional relation between part batching, batch processing time, and postprocessing requirements we develop random-keys based genetic algorithms to select orders for complete or partial acceptance and produce a high-quality schedule satisfying all technological constraints, including part orientation and rotation within the build chamber. Extensive computational experiments show that the proposed approaches yield significant improvements in profit over the situation where all orders must be accepted, and produce solutions that compare favourably to statistically estimated bounds.

Semiconductor final testing scheduling using Q-learning based hyper-heuristic

Semiconductor final testing scheduling problem (SFTSP) has extensively been studied in advanced manufacturing and intelligent scheduling fields. This paper presents a Q-learning based hyper-heuristic (QHH) algorithm to address the SFTSP with makespan criterion. The structure of QHH employs the Q-learning algorithm as the high-level strategy to autonomously select a heuristic from a pre-designed low-level heuristic set. The selected heuristic in different stages of the optimization process is recognized as the executable action and performed on the solution space for better results. An efficient encoding and decoding pair is presented to generate feasible schedules, and a left-shift scheme is embedded into the decoding process for improving resources utilization. Additionally, the design-of-experiment method is implemented to investigate the effect of parameters setting. Both computational simulation and comparison are finally carried out on a benchmark set and the results demonstrate the effectiveness and efficiency of the proposed QHH.

Green scheduling to achieve green manufacturing: Pursuing a research agenda by mapping science

The strengthening of measures to reduce greenhouse gas emissions meant that manufacturing scheduling had to acquire a green approach. The need to reduce energy consumption becomes necessary for companies to achieve sustainable development. Therefore, a new challenge for the scientific community was foreseen, researching new algorithms or knowledge hubs to achieve green scheduling. Green scheduling may be considered one of the principles of green manufacturing, aimed at minimizing environmental damage and energy waste. A review of the literature shows that there are no research works that analyze the scientific development carried out in “green scheduling” through methodologies based on bibliometric analysis, thus the need and the novelty of this research. Based on a dataset formed by 420 scientific documents published from 2006 to 2020 a bibliometric and network analysis is carried out to find the scientific trends, the main relationships according to collaborations and intermediaries, and the research hubs that help to establish the research agenda. The results show that “green scheduling” is a growing research area in the scientific community and in recent years the number of new research topics has experienced considerable growth. This research is developed in Asia, Europe and America, but China stands out as the most productive, collaborative, intermediary, influential and active country at present, through its organizations which are mainly universities, such as Huanzhong University of Science and Technology and Tongji University. However, research development related to green scheduling is carried out in a collaborative environment between institutions located in different countries, allowing countries that are not scientific powerhouses to develop research in the area. The network analysis makes it possible to define the research framework through the clustering of the dataset's research keywords, highlighting that the main areas of research focus on the development of new methods through algorithms aimed at improving energy efficiency in production environments, in areas of computational development such as cloud computing, and in transportation. The most cited research papers, considered the main drivers of knowledge, are published in high-quality research journals, and are mainly developments in scheduling algorithms for different work environments with a green approach. Research findings can provide the academic community with relevant information about green scheduling to make appropriate decisions and become a research agenda for future research.

A parallel genetic algorithm for multi-objective flexible flowshop scheduling in pasta manufacturing

Among the potential road maps to sustainable production, efficient manufacturing scheduling is a promising direction. This paper addresses the lack of knowledge in the scheduling theory by introducing a generalized flexible flow shop model with unrelated parallel machines in each stage. A mixed-integer programming formulation is proposed for such model, solved by a two-phase genetic algorithm (GA), tackling job sequencing and machine allocation in each phase. The algorithm is parallelized with a specialized island model, where the evaluated chromosomes of all generations are preserved to provide the final Pareto-Optimal solutions. The feasibility of our method is demonstrated with a small example from literature, followed with the investigation of the premature convergence issue. Afterwards, the algorithm is applied to a real-sized instance from a Belgium pasta manufacturer. We illustrate how the algorithm converges over iterations to trade-off near-optimal solutions (with 8.50% shorter makespan, 5.24% cheaper energy cost and 6.02% lower labor cost), and how the evaluated candidates distribute in the objective space. A comparison with a NSGA-II implementation is further performed using hypothesis testing, having 5.43%, 0.95% and 2.07% improvement in three sub-objectives mentioned above. Although this paper focuses on scheduling issues, the proposed GA can serve as an efficient method for other multi-objective optimization problems.