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The spinning machine manufacturing shop scheduling problem is a flexible Job-Shop scheduling problem with complex process constraints and sequence-dependent setup times. This paper proposed a multi-action deep reinforcement learning algorithm with the optimization objective of minimizing the maximum completion time to ensure the quality of the scheduling solution and improve the on-time order delivery capability of the enterprise. Firstly, this paper modeled the scheduling problem as a multi-Markov decision process. Then, in order to predict the probability distribution of selecting different processes and machines, this paper designed two encoders for the two sub-problems of workpiece selection and machine selection of spinning machine manufacturing plant scheduling, for defining the process selection policy and machine selection policy, respectively. In the process selection encoder, it used a graphical neural network to encode the disjunctive graph to reduce the impact of problem size on the quality of the solution. Based on this, the paper designed a reinforcement learning training algorithm with multiple action spaces for the two substrategies. Finally, it validated the proposed method on a real production case of a spinning machine manufacturing company. The results show that the method exhibits good performance on problems of different scales, is able to obtain higher quality scheduling solutions compared with other comparative algorithms, and the model has better generalization ability and stability. [ABSTRACT FROM AUTHOR]

Semiconductor manufacturing systems consist of highly complex and constrained processes owing to the requirement for high-quality wafers and dynamic customer orders. This study focuses on waiting time constraints to achieve high-quality wafers, with missing operations, to manufacture different types of orders, which are common scheduling requirements in semiconductor manufacturing systems. Waiting time constraints limit the waiting times between consecutive machines. Although sequential steps are modelled as a flow shop, some jobs can skip some steps in the flow shop. Such jobs can accordingly skip certain machines in the flow shop, which is called 'missing operations'. This research investigates a scheduling problem for a flow shop with waiting time constraints and missing operations. A mathematical programming formulation and scheduling algorithms are proposed to operate such a flow shop efficiently. Through computational experiments, the proposed algorithms were evaluated, and analysis of various parameters for waiting time constraints and missing operations was conducted. [ABSTRACT FROM AUTHOR]

Lean manufacturing is a corporate strategy that focuses on continually improving all processes in the production environment by reducing waste and costs. The implementation of lean manufacturing systems will always begin within the existing facility layout. Therefore, the arrangement of the facility has a tremendous impact on the company’s development and space utilization. This research addresses possible research gaps related to the industry's requirement for successful lean manufacturing and space utilization. In this research, an integration of lean techniques and facility layout planning has been used to optimize the workshop operation by implementing 5S and Systematic Layout Planning to rearrange machines, which eliminated waste caused by unnecessary motion. As a result, out of two alternatives of facility layout proposed, the process efficiency is improved from 53% to 66%. An efficient facility layout helps production schedules run more smoothly while minimizing costs and enhancing space utilization. It has contributed to create a safe and healthy work environment. The research is limited to optimizing space utilization and attempts to eliminate excessive motion of employees and equipment in the machining shop of a mechanical workshop. The need to close the implementation gap for lean and space utilization will make a substantial contribution to long-term industrial sustainability. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop. [ABSTRACT FROM AUTHOR]

In order to reduce carbon emission and energy consumption in the process of raw material distribution, the workshop material distribution management model was established based on the business compass model; it can help guide enterprises to manage workshop production. Based on the raw material distribution equipment, a path calculation model considering the carbon emission and energy consumption in the process of raw material distribution was established. The dung beetle optimizer was selected for the optimization calculation. The dung beetle optimizer has the characteristics of fast convergence and high solution accuracy. The material distribution of an engine assembly workshop was taken as an example; the results showed that the optimized scheduling model could effectively optimize the material distribution route and reduce energy consumption and carbon emission in the distribution process on the basis of meeting the distribution demand. [ABSTRACT FROM AUTHOR]

In this paper, the two-machine no-wait proportionate flow shop problem is analysed with the objective of minimising the total absolute deviation of job completion times. The optimality of V-shaped sequences is extended to problems with 12 jobs and it is shown that an optimal sequence may not be V-shaped with 13 jobs. For problems of larger size, the best V-shaped sequence is determined in linear time without resorting to dynamic programming. For larger size problems, it is shown that the number of non-dominated potentially optimal semi-V-shaped sequences increases rapidly. This indicates that derivation of optimal solutions in low order polynomial time is unlikely with a large number of jobs. Finally, the findings are extended to the corresponding problem with the objective of minimising the total absolute differences in waiting times. [ABSTRACT FROM AUTHOR]

Multi-factory production planning and scheduling problems have been increasingly studied by scholars recently due to market uncertainty, technological trends like Industry 4.0 and increasing collaboration. Geographically dispersed factories may provide cost-saving potential and increase efficiency while also being subjected to varying capabilities and restrictions such as capacity constraints and labour costs. Traditional approaches in production planning and scheduling focus on the allocation of demand to a single factory and obtain sequences of operations on machines in this factory. In the multi-factory or distributed setting, an additional task includes assigning orders to potential factories beforehand. Starting with the first case studies in the late 1990s, research has increasingly been devoted to this research field and has considered numerous variations of the problem. We review 128 articles on multi-factory production planning and scheduling problems in this contribution and classify the literature according to shop configuration, network structure, objectives, and solution methods. Bibliometric analysis and network analysis are utilised to generate new findings. Research opportunities identified include integration with other planning stages, an investigation of key real-life objectives such as due date compliance and examining dynamic characteristics in the context of Industry 4.0. Besides, empirical studies are necessary to gain new practical insights. [ABSTRACT FROM AUTHOR]

The industrial sector consumes half of the world delivered energy and is responsible for a third of carbon dioxide emissions which cause severe environmental pollution. The industry has to change its behaviour concerning the energy consumption. Since two-machine flow shop scheduling ( F 2 | p e r m | C m a x ) is one of the typical problems of the manufacturing industry, this paper aims to build an energy-cost-aware scheduling plan. This work tackles the joint optimisation of makespan and electricity cost in two-machine flow shop scheduling problem under electricity pricing. We enhance the financial aspect of the optimal solution of F 2 | p e r m | C m a x by minimising the electricity cost without increasing the makespan. Firstly, we show the contribution of the generation of several optimal equivalent solutions of F 2 | p e r m | C m a x . The optimal equivalent solutions have different electricity costs but present the same makespan. Then, we determine the optimal starting time of jobs on several equivalent optimal solutions to get the best production plan. Finally, the numerical tests show that our proposed approach improves the electricity cost significantly under optimal makespan. The results provide good solutions to managers and decision makers to achieve energy cost savings without sacrificing the productivity which can contribute to sustainable development of the manufacturing industry. [ABSTRACT FROM AUTHOR]

With the increased awareness of the market competition and protection of the environment, many studies have examined sustainable manufacturing, which combines lean production and sustainable performance, but there still exist barriers between the theories and the practices. This paper proposes a dynamic scheduling unit (DSU) with the multi-agent system (MAS) to build and formulate a kind of sustainable hybrid flow shop in a ubiquitous environment. The processing time, energy consumption and carbon emission are considered the sustainability indicators; and the machine failure, job inserting and job reworking are considered the disruption events. Then, a GA-based dynamic scheduling optimisation with variable priorities is proposed, including a weighted sum of indicators-genetic algorithm (WSI-GA) and an event-driven priority weights local search (EPW-LS) to dynamically generate the prescheduling and rescheduling solutions of the sustainable hybrid flow shop. Lastly, the proposed theories are applied to a computational case of part machining via the discrete event simulation method to demonstrate their validity and feasibility. The results show that the WSI-GA for prescheduling is superior to the referenced traditional priority-based genetic algorithms in the four different production modes and that EPW-LS for rescheduling can effectively improve the solutions of the preschedulings once disruption events occur. [ABSTRACT FROM AUTHOR]

In this paper, we present a multi-faceted approach for ranking dispatching rules (DRs) in multi-objective dynamic flow shop scheduling systems using data envelopment analysis (DEA). The merits of the proposed DEA-based approach stem in its ability to (1) integrate explicitly, under the same DEA framework, desirable and undesirable performance criteria of DRs without a priori normalisation or aggregation; (2) guarantee that the best DR preserves its benchmarking status regardless of the production scenario; (3) circumvent potential occurrence of multiple efficient DRs through embedding ordered weighted averaging (OWA) under DEA cross evaluation to produce aggregate ranking scores for the DRs. The evaluation of the new ranking approach is conducted using 18 data instances of 20 DRs each. The results reveal that, whatever the OWA optimism level, the preferred DR shifts away from the Shortest Processing Time (SPT) rule to the Cost Over Time (COVERT) rule as due-date tightness becomes relaxed, which appears consistent with known performance expectations of these DRs under such settings. To demonstrate a possible implementation of these results to support decision making in operations scheduling, we present a basic adaptive rule that switches automatically between the preferred rules based on real-time due-date tightness and machine utilisation levels. [ABSTRACT FROM AUTHOR]

This paper studied two-stage permutation flow shop problems with batch processing machines, considering different job sizes and arbitrary arrival times, with the optimisation objective of minimising the makespan. The quantum-inspired ant colony optimisation (QIACO) algorithm was proposed to solve the problem. In the QIACO algorithm, the ants are divided into two groups: one group selects the largest job in terms of job size as the initial job for each batch and the other group selects the smallest job as the initial job for each batch. Each group of ants has its own pheromone matrix. In the computational experiment, our novel algorithm was compared with the hybrid discrete differential evolution (HDDE) algorithm and the batch-based hybrid ant colony optimisation (BHACO) algorithm. Although the HDDE algorithm has a shorter run time, the quality of the solution for large-scale jobs is not good, while the BHACO algorithm always obtains a better solution but requires a longer run time. The computational results show that the QIACO algorithm embedded in the quantum information has advantages in terms of both solution quality and running time. [ABSTRACT FROM AUTHOR]

The traditional flexible job shop scheduling problem (FJSP) considers machine flexibility but not worker flexibility. Given the influence and potential of human factors in improving production efficiency and decreasing the cost in practical production systems, we propose a mathematical model of an extended FJSP with worker flexibility (FJSPW). A hybrid artificial bee colony algorithm (HABCA) is presented to solve the proposed FJSPW. For the HABCA, effective encoding, decoding, crossover and mutation operators are designed, and a new effective local search method is developed to improve the speed and exploitation ability of the algorithm. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the HABCA. Extensive computational experiments carried out to compare the HABCA with some well-performing algorithms from the literature confirm that the proposed HABCA is more effective than these algorithms, especially on large-scale FJSPW instances. [ABSTRACT FROM AUTHOR]

Reducing energy costs has become an important concern for sustainable manufacturing systems, owing to concern for the environment. We present a multi-objective hybrid ant colony optimisation (MHACO) algorithm for a real-world two-stage blocking permutation flow shop scheduling problem to address the trade-off between total energy costs (TEC) and makespan ( C max ) as measures of the service level with the time-of-use (TOU) electricity price. We explore the energy-saving potential of the manufacturing industry in consideration of the differential energy costs generated by variable-speed machines. A mixed integer programming model is developed to formulate this problem. In the MHACO algorithms, the max–min pheromone restriction rules and the local search rules avoid the localisation trap and enhance neighbourhood search capabilities, respectively. The Taguchi method and small-scale pilot experiments are employed to determine the appropriate experimental parameters. Based on three well-known multi-objective optimisation algorithms, viz., NSGAII, SPEA2, and MODEA, six algorithms with different batch-sorting methods are adopted as a comparison in small-, moderate-, and large-scale instances. A four-dimensional performance evaluation system is established to evaluate the obtained Pareto frontier approximations. The computational results show that the proposed MHACO–Johnson algorithm outperforms other algorithms in terms of solution quality, quantity, and distribution, although it is time consuming when dealing with moderate- to large-scale instances. [ABSTRACT FROM AUTHOR]

We propose a trade-off balancing (TOB) heuristic in a no-wait flow shop to minimise the weighted sum of maximum completion time ( C max ) and total completion time (TCT) based on machine idle times. We introduce a factorisation scheme to construct the initial sequence based on current and future idle times at the operational level. In addition, we propose a novel estimation method to establish the mathematical relationship between the objectives min( C max ) and min(TCT) at the production line level. To evaluate the performance of the TOB heuristic, computational experiments are conducted on the classic Taillard's benchmark and one-year historical data from University of Kentucky HealthCare (UKHC). The computational results show that minimisations of C max and TCT yield inconsistent scheduling sequences, and these two sequences are relatively uncorrelated. We also show that our TOB heuristic performs better than the best existing heuristics with the same computational complexity and generates stable performances in balancing trade-offs. [ABSTRACT FROM AUTHOR]

Traditional energy-saving optimization of shop scheduling often separates the coupling relationship between a single machine and the shop system, which not only limits the potential of energy-saving but also leads to a large deviation between the optimized result and the actual application. In practice, cutting-tool degradation during operation is inevitable, which will not only lead to the increase in actual machining power but also the resulting tool change operation will disrupt the rhythm of production scheduling. Therefore, to make the energy consumption calculation in scheduling optimization more consistent with the actual machining conditions and reduce the impact of tool degradation on the manufacturing shop, this paper constructs an integrated optimization model including a flexible job shop scheduling problem (FJSP), machining power prediction, tool life prediction and energy-saving strategy. First, an exponential function is formulated using actual cutting experiment data under certain machining conditions to express cutting-tool degradation. Utilizing this function, a reasonable cutting-tool change schedule is obtained. A hybrid energy-saving strategy that combines a cutting-tool change with machine tool turn-on/off schedules to reduce the difference between the simulated and actual machining power while optimizing the energy savings is then proposed. Second, a multi-objective optimization model was established to reduce the makespan, total machine tool load, number of times machine tools are turned on/off and cutting tools are changed, and the total energy consumption of the workshop and the fast and elitist multi-objective genetic algorithm (NSGA-II) is used to solve the model. Finally, combined with the workshop production cost evaluation indicator, a practical FJSP example is presented to demonstrate the proposed optimization model. The prediction accuracy of the machining power is more than 93%. The hybrid energy-saving strategy can further reduce the energy consumption of the workshop by 4.44% and the production cost by 2.44% on the basis of saving 93.5% of non-processing energy consumption by the machine on/off energy-saving strategy. [ABSTRACT FROM AUTHOR]

This paper addresses a bi-objective welding shop scheduling problem (BWSSP) aiming to minimise the total tardiness and the machine interaction effect. The BWSSP is a special flow-shop scheduling problem (FSP) which is characterised by the fact that more than one machine can process on one job at a certain stage. This study analyses the operation of a structural metal manufacturing plant, and includes various aspects such as job sequence, machine-number-dependent processing time, lifting up time, lifting down time and different delivery time. A novel mixed-integer programming model (MIPM) is established, which can be used to minimise the delayed delivery time and the total machine interaction effect. One machine interaction effect formula is given in this paper. In order to solve this BWSSP, an appropriate non-dominated sorting Genetic Algorithm III (NSGAIII), embedded with a restarted strategy (RNSGAIII), is proposed. The restarted strategy, which can increase the diversity of the solutions, will be triggered with a restart probability. Following the iterative process, an effective strategy is applied to reduce the interaction effect penalty, on the premise that the makespan will remain unchanged. Total five algorithms, namely NSGAII, NSGAIII, harmony search algorithm (HSA), strength Pareto evolutionary algorithm (SPEA2), and RNSGAIII are utilised to solve this engineering problem. Numerical simulations show that the improved RNSGAIII outperforms the other methods, and the Pareto solution distribution and diversity, in particular, are significantly improved. [ABSTRACT FROM AUTHOR]

Energy-efficient scheduling is highly necessary for energy-intensive industries, such as glass, mould or chemical production. Inspired by a real-world glass-ceramics production process, this paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2. The performance measures considered are makespan and total energy consumption. Time-of-use (TOU) electricity prices and different states of machines (working, idle and turnoff) are integrated. To tackle this problem, a mixed integer programming (MIP) is formulated, based on which an augmented ε-constraint (AUGMECON) method is adopted to obtain the exact Pareto front. A problem-tailored constructive heuristic method with local search strategy, a bi-objective tabu search algorithm and a bi-objective ant colony optimisation algorithm are developed to deal with medium- and large-scale problems. Extensive computational experiments are conducted, and a real-world case is solved. The results show effectiveness of the proposed methods, in particular the bi-objective tabu search. [ABSTRACT FROM AUTHOR]

Distributed scheduling becomes popular with the shift of production from single factory to multiple factories and reveals new features and the increasing optimisation difficulties. In this study, distributed unrelated parallel machines scheduling problem with makespan minimisation is considered in the heterogeneous production network, which is directly simplified as an extended machine assignment. A novel imperialist competitive algorithm with memory (MICA) is presented, in which a machine assignment string is adopted and four neighbourhood structures and a global search operator are introduced. In each empire, some best colonies learn from a member of memory or imperialist and other colonies move toward imperialist or one of the best colonies, and revolution is newly implemented by using good solutions. Global search of imperialist is added into imperialist competition to avoid the addition of the weakest colony of the weakest empire into the winning empire. Lower bound is provided. Extensive experiments are conducted to test the performance of MICA and computational results show that MICA is a very competitive method for the considered problem. [ABSTRACT FROM AUTHOR]

Job shop scheduling, as one of the classical scheduling problems, has been widely studied in literatures, and proved to be mostly NP-hard. Although it is extremely difficult to solve job shop scheduling with no-wait constraint to optimality, the two-machine no-wait job shop scheduling to minimise makespan could be solvable in polynomial time when each job has exactly two equal length operations (proportionate job shop). In the present paper, an extension is attempted by considering a proportionate flexible two-stage no-wait job shop scheduling problem with minimum makespan, and a set-covering formulation is put forward which contains a master problem and a pricing problem. To solve this problem, a column generation (CG)-based approach is implemented. In comparison, a mixed integer programming model is constructed and optimised by Cplex. A series of randomly generated numerical instances are calculated. And the testing result shows that the mixed integer model handled by Cplex can only solve small scale cases, while the proposed CG-based method can conquer larger size problems in acceptable time. [ABSTRACT FROM AUTHOR]

A paint manufacturing firm's customers typically place orders for two or more products simultaneously. Each product belongs to a family that denotes batching compatibility during manufacturing. Further, products can be split into several sublots to allow overlapping production in a two-stage hybrid flow shop wherein various identical, capacitated machines operate in parallel at each stage. We present a mixed-integer linear program (MILP) for this integrated batching and lot streaming problem with variable sublots, incompatible job families, and sequence-dependent setup times. The model determines the number and size of sublots for each product and the production sequencing for each sublot such that the total weighted completion time is minimised. To promote practical implementation, we develop and evaluate heuristics to efficiently solve this problem. [ABSTRACT FROM AUTHOR]

Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper is the first to consider online algorithms to schedule a proportionate flexible flow shop of batching machines (PFFB). The scheduling model is motivated by manufacturing processes of individualized medicaments, which are used in modern medicine to treat some serious illnesses. We provide two different online algorithms, proving also lower bounds for the offline problem to compute their competitive ratios. The first algorithm is an easy-to-implement, general local scheduling heuristic. It is 2-competitive for PFFBs with an arbitrary number of stages and for several natural scheduling objectives. We also show that for total/average flow time, no deterministic algorithm with better competitive ratio exists. For the special case with two stages and the makespan or total completion time objective, we describe an improved algorithm that achieves the best possible competitive ratio φ = 1 + 5 2 , the golden ratio. All our results also hold for proportionate (non-flexible) flow shops of batching machines (PFB) for which this is also the first paper to study online algorithms. [ABSTRACT FROM AUTHOR]

In this study, the dynamic joint scheduling problem for processing machines and transportation robots in a flexible job shop is investigated. The study aims to minimize the order completion time (makespan) of a job shop manufacturing system. Considering breakdowns, order insertion and battery charging maintenance of robots, an event-driven global rescheduling strategy is adopted. A novel memetic algorithm combining genetic algorithm and variable neighborhood search is designed to handle dynamic events and obtain a new scheduling plan. Finally, numerical experiments are conducted to test the effect of the improved operators. For successive multiple rescheduling, the effectiveness of the proposed algorithm is verified by comparing it with three other algorithms under dynamic events, and through statistical analysis, the results verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

To realize the dynamic scheduling of complex workpiece processing in complex workpiece job shop, a hybrid dynamic scheduling method with Digital Twin and improved bacterial foraging algorithm (IBFOA) is proposed to minimize the maximum completion time and machine load. During the actual workshop processing, the flexible job shop scheduling problem (FJSP) is divided into two sub-problems: machine assignment and process sequencing. The initial scheduling scheme is completed using an IBFOA to construct a Digital Twin flexible job shop scheduling model. Digital Twin model is used to solve the impact of workshop emergencies. Based on typical benchmark cases and real data from a machine company's mould shop, the machining shop production scheduling experiments are conducted. The results show that the scheduling scheme using the IBFOA combined with the Digital Twin can optimize the system performance as a whole and effectively deal with the problem of extended production time caused by disruption. The algorithm can obtain the most satisfactory scheduling solution and the effectiveness of solving the multi-objective FJSP are verified. (Received in April 2022, accepted in June 2022. This paper was with the authors 3 weeks for 1 revision.) [ABSTRACT FROM AUTHOR]

In a manufacturing system, production control‐related decision‐making activities occur at different levels. At the process level, one of the main control activities is to tune the parameters of individual manufacturing equipment. At the system level, the main activity is to coordinate production resources and to route parts to appropriate workstations based on their processing requirement, priority indices, and control policy. At the factory level, the goal is to plan and schedule the processing of parts at different operations for the entire system in order to optimize certain objectives. Note that the results of such activities at different levels are closely coupled and affect the overall performance of the manufacturing system as a whole. Therefore, it is important to systematically integrate these control and optimization activities into one unified platform to ensure the goal of each individual activity is aligned with the overall performance of the system. In this paper, we develop a simulation‐based virtual testbed that implements dynamic optimization, automatic information exchange, and decision‐making from the process‐level, system‐level, and factory‐level of a manufacturing system into an integrated computation environment. This is demonstrated by connecting a Python‐based numerical computation program, discrete‐event simulation software (Simul8), and an optimization solver (CPLEX) via a third‐party master program. The application of this simulation‐based virtual testbed is illustrated by a case study in a machining shop. [ABSTRACT FROM AUTHOR]

We investigate the problem to schedule a set of precedence constrained jobs of unit size on an open-shop or on a flow-shop to minimize the makespan. The precedence constraints among the jobs are presented as a directed acyclic graph called the precedence graph. When the number of machines in the shop is part of the input, both problems are strongly NP-hard on general precedence graphs, but were proven polynomial-time solvable for some special precedence graphs such as intrees. In this paper, we characterize improved lower bounds on the minimum makespan in terms of the number of agreeable pairings among certain jobs and propose approximation algorithms based on a maximum matching among these jobs, so that every agreeable pair of jobs can be processed on different machines simultaneously. For open-shop with an arbitrary precedence graph and for flow-shop with a spine precedence graph, both achieved approximation ratios are 2 -- m, where m is the number of machines in the shop. [ABSTRACT FROM AUTHOR]

The past few years have witnessed a resurgence of interest in assembly flow shop scheduling as evidenced by increasing number of published articles in this field. A basic assembly flow shop consists of two types of stages: fabrication or machining stage and assembly stage. Machining and assembly stages are composed of either one or a set of machines that are working in parallel. Final products have hierarchical assembly structure with several components and assembly operation(s). The components need to be processed in the machining stage(s) and then assembled based on hierarchical assembly structure. The goal is to find the sequence of jobs that optimises certain objectives. Assembly flow shop scheduling problem has several interesting derivatives and applications in various manufacturing and service industries. This paper provides a consolidated survey of assembly flow shop models with their solution methodology. Finally, the paper concludes by presenting some problems receiving less attention and proposes several salient research opportunities. [ABSTRACT FROM AUTHOR]

We analyze the 'no-wait' proportionate two-stage super shop scheduling problem with the objective of minimising the maximum job completion time (makespan). The existing simple sorting procedures for the open shop and the job shop do not work on the super shop. We show that the problem is still solvable in time using a procedure based on dynamic programming (DP). A unique feature of this procedure is that the state variable is defined based on the number of jobs; thus, reducing the complexity to polynomial. The same procedure solves the super shop problem with the blocking constraint. We also consider the case of machine dominance and show that the problem can be solved in linear time. Finally, we highlight the differences between the super shop with 'no-wait' and 'synchronous' processing modes via a numerical example. [ABSTRACT FROM AUTHOR]

This paper introduces a case study to improve productivity of a multi-product transmission case machining line at a motorcycle manufacturing plant. First, the manufacturing process is introduced to characterise the production flow. Through structural modelling, such a process is simplified through aggregations and transformed into a two-stage Bernoulli line model with split dedicated machines and finite buffers. Using Markov chain analysis, the system throughput can be estimated. The results are validated by plant data. To improve system productivity, through numerical experiments, we investigate the impacts of increasing machine efficiency, varying demands and implementing different loading policies. Such a study provides a quantitative tool for plant engineers and managers to improve production operations. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately. [ABSTRACT FROM AUTHOR]

This research is aimed at the design of a machining workshop for the repair and maintenance of agricultural machinery in the Alquízar municipality, Artemisa province. Through direct interviews with farmers and directors of cooperatives in the area, the existence of a large number of agricultural equipment in poor technical condition was determined, due to the absence of a mechanical workshop that provides services to the cooperative sector of the municipality. For this reason, the National Association of Small Farmers of Artemisa, requested the setting up of a machining workshop in Alquízar through the Cooperative Support Project (APOCOOP) with the collaboration and willingness of the Oxford Committee for Family Relief (OXFAM), to finance this purpose. With this workshop, the agricultural park of the cooperatives linked to the project guarantees maintenance and repair services without delay, using the land of the Capitan Tomás Agricultural Production Cooperative as a location. [ABSTRACT FROM AUTHOR]

In this article, scheduling flexible open shops with identical machines in each station is studied. A new mathematical model is offered to describe the overall performance of the system. Since the problem enjoys an NP-hard complexity structure, we used two distinct metaheuristic methods to achieve acceptable solutions for minimizing weighted total completion time as the objective function. The first method is customary memetic algorithm (MA). The second one, MPA, is a modified version of memetic algorithm in which the new permutating operation is replaced with the mutation. Furthermore, some predefined feasible solutions were imposed in the initial population of both MA and MPA. According to the results, the latter action caused a remarkable improvement in the performance of algorithms. [ABSTRACT FROM AUTHOR]

Robotic manipulators have become part of manufacturing systems in recent decades. However, in the realm of Industry 4.0, a new type of manufacturing cell has been introduced—the so-called collaborative manufacturing cell. In such collaborative environments, communication between a human operator and robotic manipulators must be flawless, so that smooth collaboration, i.e., human safety, is ensured constantly. Therefore, engineers have focused on the development of suitable human–robot interfaces (HRI) in order to tackle this issue. This research work proposes a closed-loop framework for the human–robot interface based on the utilization of digital technologies, such as Mixed Reality (MR). Concretely, the framework can be realized as a methodology for the remote and safe manipulation of the robotic arm in near real-time, while, simultaneously, safety zones are displayed in the field of view of the shop-floor technician. The method is based on the creation of a Digital Twin of the robotic arm and the setup of a suitable communication framework for continuous and seamless communication between the user interface, the physical robot, and the Digital Twin. The development of the method is based on the utilization of a ROS (Robot Operating System) for the modelling of the Digital Twin, a Cloud database for data handling, and Mixed Reality (MR) for the Human–Machine Interface (HMI). The developed MR application is tested in a laboratory-based machine shop, incorporating collaborative cells. [ABSTRACT FROM AUTHOR]

The longstanding view in US economic history is that the shift in manufacturing in the nineteenth century from the hand labor artisan shop to the machine labor of the mechanized factory led to "labor de-skilling" – the substitution of less skilled workers, such as operatives, for skilled craft workers. Investigating the Department of Labor's 1899 Hand and Machine Labor Study , we show the adoption of inanimate power, which we call "mechanization," did induce de-skilling at the production operation level. However, while the treatment effect of mechanization was economically and statistically significant, it accounted for only 16 percent of the de-skilling on average in the sample, using our preferred IV estimator. Broadening the scope of our inquiry, we find that variations in the division of labor, as captured by the share of production tasks performed by the average worker, accounted for a substantially larger fraction. [ABSTRACT FROM AUTHOR]