Flow Shop Production Research Articles

Negotiation-based scheduling considering agent emotion

Scheduling in decentralized manufacturing systems is being expanded and transformed to a smart level where manufacturing tasks are executed autonomously. However, achieving self-organized manufacturing resources that can make collaborative decisions remains challenging. This paper presents a novel negotiation model for decentralized scheduling in an autonomous manufacturing system. A variety of emotions represent the willingness of autonomous intelligent agents (IAs) to negotiate for a consensus despite their diverse goals. The proposed communication protocol considers the emotional aspects of the autonomous machine agents in decentralized decision-making. The negotiation model focuses on analyzing the effects of two key emotions: selfishness and cooperation. This study constitutes the basis for experiments on multi-goal machine tasks and artificial emotion mechanism in a flow shop production system. The complexities of negotiation dynamics between IAs, where the interplay of selfish and cooperative strategies influences the outcomes and efficiency of reaching agreements, are explored. The experimental results show that selfish IAs eventually reach agreements through gradual compromise, but cooperative IAs, with their flexibility, can quickly achieve resolutions, especially when bargaining power is balanced. Aligning with the dual concern theory, this study reveals that while selfish IAs can dominate cooperative ones, the cooperative approach often leads to faster and more effective outcomes, regardless of the level of selfishness involved.

A novel fuzzy finite-horizon economic lot and delivery scheduling model with sequence-dependent setups

This paper addresses the economic lot and delivery scheduling problem (ELDSP) within three-echelon supply chains, focusing on the complexities of demand uncertainty, limited shelf-life of products, and sequence-dependency of setups. We develop a novel mixed-integer non-linear programming (MINLP) model for a supply chain comprising one supplier, multiple manufacturers with flexible flow shop (FFS) production systems, and multiple retailers, all operating over a finite planning horizon. The common cycle (CC) strategy is adopted as the synchronization policy. Our model employs fuzzy set theory, particularly the “Me measure,” to effectively handle the retailers’ demand uncertainty. Our findings indicate that total supply chain costs escalate with an increase in demand, final components’ holding costs, and sequence-dependent setup costs, but decrease with increasing production rates. Furthermore, while total costs are significantly sensitive to changes in demand, they are relatively insensitive to fluctuations in sequence-dependent setup times. The models developed offer valuable managerial insights for optimizing costs in synchronized multi-stage supply chains, aiding managers in making informed decisions about production lot sizes and delivery schedules under both deterministic and fuzzy demand scenarios. Additionally, the proposed models bridge key research gaps and provide robust decision-making tools for cost optimization, enhancing supply chain synchronization in practical settings.

Incorporating uncertain human behavior in production scheduling for enhanced productivity in Industry 5.0 context

Human-centered production systems are of increasing interest to researchers, especially with the advent of the Industry 5.0 paradigm. Most research into production scheduling has long neglected human workers’ specific roles and unpredictable behavior in a production system, treating them as machines with deterministic behavior. This work studies the impact of human operational behavior on the performance of a production system and proposes an optimization model to allocate workers’ profiles to workstations. We modeled the punctuality profile as a Markov chain representing a worker’s productive and non-productive states. We developed a simulation process based on the multi-agent system (MAS) paradigm to test the effectiveness of the proposed model and to measure the impact of workers’ behaviors and their assignments to different workstations on the productivity of the workshop. A non-linear programming model is also proposed to provide the optimal assignment of workers to workstations while maximizing the throughput of a dual-resource-constrained flow-shop production system for a given mix of production. The results obtained highlight the significant impact of human operator behavior on the performance of a production system. The findings demonstrate the importance of incorporating human behavior models into the decision-making process for assigning workers to workstations based on their operational profiles.

An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops

This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times.

Optimization of Scheduling using Heuristic Approach with Campbell Dudek Smith Algorithm (CDS) at PT OSIN

PT OSIN is a company that runs a business in the automotive manufacturing sector. The company applies a flow shop production model whose production pattern is line to line in series using the First Come First Serve production scheduling method. The problem with the FCFS method which is considered to have not reached the optimal enough in meeting uncertain product demands, causing delays in the production schedule, which has an impact on the flow or distribution of goods and can lead to increased production in activities. The proposed improvement method is made to optimize production scheduling, focusing on minimizing the makespan values in the scheduling system's order. This study uses a heuristic approach to the Campbell, Dudek, Smith (CDS) algorithm in overcoming scheduling problems. The CDS method is a production scheduling heuristic method developed from the Johnson algorithm in obtaining several sequences of 6 types of work processes at the work center which aims to analyze the total time for each processing process (makespan). Comparing the two methods using the FCFS method by sorting the scheduling according to the order of the initial order time produces a total value of 2,408.75 minutes, while using the CDS algorithm which uses 5 iterations produces a minor matter of 881.3 minutes. Comparing the results obtained by applying the scheduling system with the CDS method can be used as a proposed improvement in overcoming the delay in delivery and the production scheduling process.

A digital-twin-based adaptive multi-objective Harris Hawks Optimizer for dynamic hybrid flow green scheduling problem with dynamic events

In many hybrid flow shop production environments, dynamic events (DEs) often occur and seriously impact production schedules. However, the traditional model rarely considers DEs, which makes the model seriously inconsistent with the actual situation. In addition, to reduce carbon emissions, there is an urgent need to carry out green optimization scheduling of production workshops, especially in emerging digital workshops. First, this paper proposes a dynamic digital-twin-based Multi-Objective Hybrid Flowshop Green Scheduling Model with DEs (MOHFGSM-DEs) to fill the above gaps. The MOHFGSM-DEs model’s bi-objectives are the makespan minimization and digital workshop total energy consumption minimization. In addition, the MOHFGSM-DEs model takes account of representative DEs in the actual dynamic production process, including controlled processing time, device dynamic reconfiguration events, and workpiece reworking events, which makes it more adaptive in practical production. Secondly, an Adaptive Multi-Objective Dynamic Harris Hawks Optimizer (AMODHHO) is presented with DEs parameters perceived by the digital twin of the workshop, adjusting the scheduling scheme adaptively in real time to address the MOHFGSM-DEs model efficiently. AMODHHO combines a nonlinear optimization strategy on balancing the exploration and exploitation of Harris Hawks Optimizer (HHO) and introduces the crossover operator of Genetic Algorithm (GA) to improve its optimal global ability. Moreover, a digital-twin-driven dynamic encoding methodology containing many optimization strategies is designed based on problem-specific characteristics. Finally, numerical experiments and application case comparisons are performed among AMODHHO, SPEA2, and NSGA-II. Results show that AMODHHO is superior to SPEA2 and NSGA-II regarding comprehensive performance. Therefore, the proposed model and AMODHHO are feasible to solve the real-world MOHFGSM-DEs adaptively. Furthermore, it performs dynamic adaptive adjustment for the actual scheduling schemes according to the real-time DEs the digital twin perceives.

Integrated scheduling optimization of production and transportation for precast component with delivery time window

PurposeProduction and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy.Design/methodology/approachIn this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data.FindingsThe optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%.Originality/valueThis study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components.

Event-Triggered Hybrid Energy-aware Scheduling in Manufacturing Systems

Incorporating renewable energy sources (RESs) into manufacturing systems has been an active research area in order to address many challenges originating from the unpredictable nature of RESs such as photovoltaics. In the energy-aware scheduling for manufacturing systems, the traditional off-line scheduling techniques cannot always work well due to their lack of robustness with respect to uncertainties coming from imprecise models or unexpected situations. On the other hand, on-line scheduling or rescheduling, which can improve the robustness by using the model and the latest measurements simultaneously, suffer from a high computational cost. This work proposes a hybrid scheduling framework, which combines the advantages of both off-line scheduling and on-line scheduling, to provide a balanced solution between robustness and computational cost. A novel concept of partially-dispatchable state is introduced. It can be treated as a constant in scheduling when the model works well. When the model does not work well, it is triggered as the variable to tune to improve the performance. Such an event-triggered structure can reduce the number of rescheduling and computational costs while achieving a reasonable performance and enhancing system robustness. Moreover, the choice of partially-dispatchable state also provides an extra design freedom in achieving green manufacturing. Simulation examples on a manufacturing system, which consists of a 100-kW solar photovoltaic system, a 10-machine flow shop production line, a 50-kWh energy storage system, a 100-kW gas turbine, and the grid for power supply, demonstrate the validity and applicability of this event-triggered hybrid scheduling (ETHS) framework.

Modified multi-objective simulated annealing algorithm for scheduling a flow shop production system with setup times

The presence of setup times and multiple objectives makes the scheduling problem complex. This paper presents two algorithms namely: multi-objective simulated annealing (MOSA) and modified MOSA for the simultaneous minimisation of makespan and mean tardiness in scheduling a flow shop production system wherein setup times depend on processing sequence. The modified MOSA algorithm is developed by hybridising MOSA with variable neighbourhood search. Benchmark problems are used to conduct computational experiments. The analysis of the algorithms reveals that modified MOSA outperforms MOSA for the measures considered. Hybridisation improves the performance of MOSA. The proposed algorithms are applied on scheduling a real-world SDST flow shop and the results indicate superior performance of modified MOSA.

An improved memetic algorithm for integrated production scheduling and vehicle routing decisions

Motivated by traditional Chinese medicine decoction and delivery problems in our collaborative Chinese medicine decoction company, this study addresses integrated production scheduling and vehicle routing decisions. To deal with this problem, we first propose a mixed integer linear programming model considering hybrid flow shop production and multi-trip multi-vehicle delivery, which is challenging to solve for medium- and large-size instances. We then propose an improved memetic algorithm (MA) that combines a genetic algorithm (GA) with education operators, including local search procedures. To improve the performance of the MA, we consider the contribution of diversity in the fitness function to enhance the exploratory ability, propose a parent selection operator by considering the softmax function to balance exploitation and exploration, and design three customized crossover operators and five education operators with local search procedures. Numerical experiments show that the integrated scheduling method shortens the total makespan by 11.19% compared with separated production scheduling and vehicle routing methods. We compare the proposed MA with Gurobi for small-size instances, and with the GA for special cases (single-stage production) for medium- or large-size instances. The results of numerical experiments for 360 instances show that the MA can find solutions with a gap of no more than 2% from the optimal solution within 3 s for small-size instances, and can improve the solutions of the GA and the adaptive large neighborhood search (ALNS) algorithm by more than 30% and 10%.

A user-induced proactive-reactive scheduling tool for dynamic flow-shop environments

This paper proposes a flexible scheduling tool for dynamic flow-shop production systems that is based on a non-preemptive version of the overlapping load adjustment approach. The developed tool adopts a novel two-phase proactive-reactive dynamic scheduling mechanism. The first phase characterises a set of possible scheduling alternatives without actually building any detailed schedule. This phase follows the earliest loading approach via exploiting the jobs’ slack times across all machines. During the second phase, the dynamic build-up of schedules takes place in a progressive manner through a user-induced module that utilises graphical displays capturing in real-time the impact of unforeseen events on the ongoing schedule. Extensive experiments involving450 randomly generated test instances of three different sizes are carried out to assess the performance of the scheduling tool. The flexibility of the tool is assessed by the number of possible scheduling alternatives offered to the end-user. The results unveiled a high number of scheduling possibilities ranging from 155 schedules for the small size instances to 96,202 schedules for the large size instances. The relatively low number of tardy jobs (0.79 jobs on average) clearly exemplifies the robustness and quality of the scheduling alternatives despite the high number of potentially available schedules.

Joint optimization of integrated mixed maintenance and distributed two-stage hybrid flow-shop production for multi-site maintenance requirements

Similar to the synergy of machines and workers in the production process, a perfect operation and maintenance (O&M) service can be implemented only when spare parts and service workers are available at the same time. Considering the production of spare parts and the limited number of workers, a collaborative optimization problem of spare parts production and worker arrangement driven by O&M is proposed in this paper. Specifically, in view of the characteristics that distributed hybrid flow-shops are often encountered in the industrial environment and one O&M activity can be completed by one worker or two workers jointly, spare parts are produced in distributed two-stage hybrid flow shop, and worker service adopts a mixed mode of one worker and two workers. To obtain a satisfactory joint optimization scheme of spare parts and workers, this paper first establishes a mixed integer programming model with dual objectives: the total weighted earliness/tardiness penalty and the number of lost orders. Then, an improved Non-dominated Sorting Genetic Algorithm-II (INSGA-II) is proposed to solve it. In INSGA-II, an initialization rule, an initialization optimization strategy and three local search operators are designed. Next, to give full play to the best performance of the algorithm, the key parameters are set by a full factor experiment. Finally, extensive experiments are carried out to verify that INSGA-II has good advantages in solving the problem studied.

Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill

With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling.

A deep reinforcement learning based approach for dynamic distributed blocking flowshop scheduling with job insertions

The distributed blocking flowshop scheduling problem (DBFSP) with new job insertions is studied. Rescheduling all remaining jobs after a dynamic event like a new job insertion is unreasonable to an actual distributed blocking flowshop production process. A deep reinforcement learning (DRL) algorithm is proposed to optimise the job selection model, and local modifications are made on the basis of the original scheduling plan when new jobs arrive. The objective is to minimise the total completion time deviation of all products so that all jobs can be finished on time to reduce the cost of storage. First, according to the definitions of the dynamic DBFSP problem, a DRL framework based on multi-agent deep deterministic policy gradient (MADDPG) is proposed. In this framework, a full schedule is generated by the variable neighbourhood descent algorithm before a dynamic event occurs. Meanwhile, all newly added jobs are reordered before the agents make decisions to select the one that needs to be scheduled most urgently. This study defines the observations, actions and reward calculation methods and applies centralised training and distributed execution in MADDPG. Finally, a comprehensive computational experiment is carried out to compare the proposed method with the closely related and well-performing methods. The results indicate that the proposed method can solve the dynamic DBFSP effectively and efficiently.

Multi-objective Q-learning-based hyper-heuristic with Bi-criteria selection for energy-aware mixed shop scheduling

Owning to diverse customer demands and enormous product varieties, mixed shop production systems are applied in practice to improve responsiveness and productivity along with energy-saving. This work addresses a mixture of job-shop and flow-shop production scheduling problem with a speed-scaling policy and no-idle time strategy. A mixed-integer linear programming model is formulated to determine the speed level of operations and the sequence of job-shop and flow-shop products, aiming at the simultaneous optimization of production efficiency and energy consumption. Then, a multi-objective Q-learning-based hyper-heuristic with Bi-criteria selection (QHH-BS) is developed to obtain a set of high-quality Pareto frontier solutions. In this algorithm, a new three-layer encoding is designed to represent the production sequence of job-shop and flow-shop products; the Pareto-based and indicator-based selection criteria are sequentially implemented to encourage diversity and convergence; Q-learning with a multi-objective metric-based reward mechanism is applied to select an optimizer from three prominent optimizers in each iteration for better exploration and exploitation. Three conclusions are drawn from extensive experiments: (1) Bi-criteria selection is superior to single-criterion selections; (2) Q-learning-based hyper-heuristic is more effective and robust than single optimizer-based algorithms and simple hyper-heuristics; (3) QHH-BS outperforms the existing state-of-the-art multi-objective algorithms in convergence and diversity.

Adaptive Simulation-Based Optimization for Production Scheduling: A Comparative Study

Production planning and control is an essential activity for all production systems, as its performance directly affects important factors that are fundamental for business competitiveness. The flow shop scheduling problem is one of the most common problems involved in the planning of production systems, being often applied in the real world. The solution to this problem cannot be easily found due to its complexity, which makes it difficult to use methods that return optimal results due to their high computational execution time. Thus, Adaptive Simulation-Based Optimization is a tool with potential application as it considers the stochasticity of the problem, seeking solutions that are aligned with the real scenario through a simulation and optimizing the desired variables. The simulation-based optimization approach consists in a loop between an optimization heuristic/meta-heuristic and a simulation. The optimization inputs the simulation with a parameter set, while the simulation tests all the models generated by the optimization and returns the fitness function values. However, the literature presents several different heuristics, simulation approaches and optimization methodologies to solve the problem. The general objective of the present work is to comparatively evaluate different adaptive simulation-based optimization models for flow shop production systems. Two methods for comparison were considered, which apply a genetic algorithm integrated with a discrete events simulation. The first method seeks to find the best dispatching rule for each machine in the production system, while the second seeks to directly find the best sequencing of production orders without a predetermined rule. The results show that the rule-free sequencing of production orders returns a result 3.56% better than annual makespan and 5.62% better in execution time compared to the model based on dispatching rules.

Joint Optimization of Integrated Mixed Maintenance and Distributed Two-Stage Hybrid Flow-Shop Production for Multi-Site Maintenance Requirements

Joint Optimization of Integrated Mixed Maintenance and Distributed Two-Stage Hybrid Flow-Shop Production for Multi-Site Maintenance Requirements

Development of a heuristic for no-wait flowshop production process to minimise makespan

Development of a heuristic for no-wait flowshop production process to minimise makespan

A two-stage discrete water wave optimization algorithm for the flowshop lot-streaming scheduling problem with intermingling and variable lot sizes

Considering lot-streaming of different orders in the actual production system, this paper examines the lot-streaming scheduling problem in flowshop production system. Given production sublots of the different jobs allowed to be intermingled in the production process, we take a deeper dive into the flowshop lot-streaming scheduling problem with detached setup and transfer batches for production sublots of jobs. And the mathematical model is presented with the objective of makespan. To tackle the complexity of the problem at hand, a two-stage discrete water wave optimization (TSDWWO) algorithm is proposed to optimize the sublots and sequencing. In the algorithm, a double-wave parallel encoding scheme is designed for two kinds of water waves, which are splitting wave and sequencing wave. Besides, considering the operation effect and time of the algorithm, the corresponding operations during the process of wave propagation are improved for these two kinds of waves respectively. Afterward, the parameters of the proposed TSDWWO are calibrated by the design of experiments approach. To evaluate the performance of the proposed TSDWWO, two different types of test sets are utilized in the experiments, which are the test sets in the literature and test sets randomly generated containing instances of variant scales. Compared with other existing algorithms in the literature, the results show the efficiency of this approach for the flowshop lot-streaming scheduling problem.

A Machine-Learning-Assisted Simulation Approach for Incorporating Predictive Maintenance in Dynamic Flow-Shop Scheduling

In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a discrete event simulation tool is used to generate the dynamic schedule. A case study for a pharmaceutical company by the name of Factory X is investigated to validate the proposed framework while taking into consideration the change in forecast demand. The proposed approach uses Microsoft Azure to calculate the predictive maintenance slots and include it in the scheduling process to simplify the process of applying machine-learning techniques with no need for hard coding. Several machine-learning algorithms are tested and compared to see which one provides the highest accuracy. To gather the required dataset, multiple sensors were designed and deployed across machines to collect their vitals that allow the prediction of whether and when they require maintenance. The proposed framework with discrete event simulation generates optimized schedule with minimum makespan while taking into consideration predictive maintenance parameters. Boosted Decision Tree and Neural Network algorithms showed the best results in estimating the predictive maintenance slots. Furthermore, the Earliest Due Date (EDD) model produced the minimum makespan with 76.82 h while scheduling 25 products using 18 machines.