Showing papers on "Flow shop scheduling published in 2021"

A Two-Stage Cooperative Evolutionary Algorithm With Problem-Specific Knowledge for Energy-Efficient Scheduling of No-Wait Flow-Shop Problem

Abstract: Green scheduling in the manufacturing industry has attracted increasing attention in academic research and industrial applications with a focus on energy saving. As a typical scheduling problem, the no-wait flow-shop scheduling has been extensively studied due to its wide industrial applications. However, energy consumption is usually ignored in the study of typical scheduling problems. In this article, a two-stage cooperative evolutionary algorithm with problem-specific knowledge called TS-CEA is proposed to address energy-efficient scheduling of the no-wait flow-shop problem (EENWFSP) with the criteria of minimizing both makespan and total energy consumption. In TS-CEA, two constructive heuristics are designed to generate a desirable initial solution after analyzing the properties of the problem. In the first stage of TS-CEA, an iterative local search strategy (ILS) is employed to explore potential extreme solutions. Moreover, a hybrid neighborhood structure is designed to improve the quality of the solution. In the second stage of TS-CEA, a mutation strategy based on critical path knowledge is proposed to extend the extreme solutions to the Pareto front. Moreover, a co-evolutionary closed-loop system is generated with ILS and mutation strategies in the iteration process. Numerical results demonstrate the effectiveness and efficiency of TS-CEA in solving the EENWFSP.

Iterated Greedy Algorithms for Flow-Shop Scheduling Problems: A Tutorial

Abstract: An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems. IGA was first developed to solve an FSP in 2007. Since then, various FSPs have been tackled by using IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. Up until now, over 100 articles related to this field have been published. However, to the best of our knowledge, there is no existing tutorial or review paper of IGA. Thus, we focus on FSPs and provide a tutorial and comprehensive literature review of IGA-based methods. First, we introduce a framework of basic IGA and give an example to clearly show its procedure. To help researchers and engineers learn and apply IGA to their FSPs, we provide an open platform to collect and share related materials. Then, we make classifications of the solved FSPs according to their scheduling scenarios, objective functions, and constraints. Next, we classify and introduce the specific methods and strategies used in each phase of IGA for FSPs. Besides, we summarize IGA variants and hybrid algorithms with IGA integrated, respectively. Finally, we discuss the current IGA-based methods and already-solved FSP instances, as well as some important future research directions according to their deficiency and open issues.

Energy-Efficient Scheduling of Distributed Flow Shop With Heterogeneous Factories: A Real-World Case From Automobile Industry in China

Abstract: Distributed flow shop scheduling of a camshaft machining is an important optimization problem in the automobile industry. The previous studies on distributed flow shop scheduling problem mainly emphasized homogeneous factories (shop types are identical from factory to factory) and economic criterion (e.g., makespan and tardiness). Nevertheless, heterogeneous factories (shop types are varied in different factories) and environment criterion (e.g., energy consumption and carbon emission) are inevitable because of the requirement of practical production and life. In this article, we address this energy-efficient scheduling of distributed flow shop with heterogeneous factories for the first time, where contains permutation and hybrid flow shops. First, a new mathematical model of this problem with objectives of minimization makespan and total energy consumption is formulated. Then, a hybrid multiobjective optimization algorithm, which integrates the iterated greedy (IG) and an efficient local search, is designed to provide a set of tradeoff solutions for this problem. Furthermore, the parameter setting of the proposed algorithm is calibrated by using a Taguchi approach of design-of-experiment. Finally, to verify the effectiveness of the proposed algorithm, it is compared against other well-known multiobjective optimization algorithms including MOEA/D, NSGA-II, MMOIG, SPEA2, AdaW, and MO-LR in an automobile plant of China. Experimental results demonstrate that the proposed algorithm outperforms these six state-of-the-art multiobjective optimization algorithms in this real-world instance.

A Hybrid Iterated Greedy Algorithm for a Crane Transportation Flexible Job Shop Problem

Abstract: In this study, we propose an efficient optimization algorithm that is a hybrid of the iterated greedy and simulated annealing algorithms (hereinafter, referred to as IGSA) to solve the flexible job shop scheduling problem with crane transportation processes (CFJSP). Two objectives are simultaneously considered, namely, the minimization of the maximum completion time and the energy consumptions during machine processing and crane transportation. Different from the methods in the literature, crane lift operations have been investigated for the first time to consider the processing time and energy consumptions involved during the crane lift process. The IGSA algorithm is then developed to solve the CFJSPs considered. In the proposed IGSA algorithm, first, each solution is represented by a 2-D vector, where one vector represents the scheduling sequence and the other vector shows the assignment of machines. Subsequently, an improved construction heuristic considering the problem features is proposed, which can decrease the number of replicated insertion positions for the destruction operations. Furthermore, to balance the exploration abilities and time complexity of the proposed algorithm, a problem-specific exploration heuristic is developed. Finally, a set of randomly generated instances based on realistic industrial processes is tested. Through comprehensive computational comparisons and statistical analyses, the highly effective performance of the proposed algorithm is favorably compared against several efficient algorithms.

Field-synchronized Digital Twin framework for production scheduling with uncertainty

Abstract: Research on scheduling problems is an evergreen challenge for industrial engineers. The growth of digital technologies opens the possibility to collect and analyze great amount of field data in real-time, representing a precious opportunity for an improved scheduling activity. Thus, scheduling under uncertain scenarios may benefit from the possibility to grasp the current operating conditions of the industrial equipment in real-time and take them into account when elaborating the best production schedules. To this end, the article proposes a proof-of-concept of a simheuristics framework for robust scheduling applied to a Flow Shop Scheduling Problem. The framework is composed of genetic algorithms for schedule optimization and discrete event simulation and is synchronized with the field through a Digital Twin (DT) that employs an Equipment Prognostics and Health Management (EPHM) module. The contribution of the EPHM module inside the DT-based framework is the real time computation of the failure probability of the equipment, with data-driven statistical models that take sensor data from the field as input. The viability of the framework is demonstrated in a flow shop application in a laboratory environment.

Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm

Abstract: With the development of economic globalization and sustainable manufacturing, sustainable scheduling of distributed manufacturing has attracted increasing concern. However, distributed manufacturing with the non-identical factory is rarely researched. Meanwhile, scheduling can affect the sustainability of manufacturing. Thus, this paper is the first attempt to investigate a sustainable distributed permutation flow-shop scheduling problem with a non-identical factory (DPFSP-NF). We formulate a novel mathematical model of this DPFSP-NF with objectives of minimizing makespan, negative social impact (NSI), and total energy consumption (TEC). A knowledge-based multi-objective memetic optimization algorithm (KMMOA) is presented to address this DPFSP-NF. First, a new energy conservation strategy is designed and embedded in the model to reduce TEC criterion. Second, a cooperative initialization mechanism is presented to yield initial solutions with good diversity and convergence. Third, several properties of DPFSP-NF are investigated and utilized to develop the knowledge-based local search operator. The impact of parameter configuration on KMMOA is studied by the Taguchi method. Finally, we compare KMMOA to its variants and other well-known multi-objective optimization algorithms by performing a number of experiments. Experiment results demonstrate the effectiveness of each improvement component of the KMMOA, and verify that KMMOA is an effective approach to deal with the DPFSP-NF.

Energy-efficient distributed heterogeneous welding flow shop scheduling problem using a modified MOEA/D

Abstract: In this study, a multi-objective evolutionary algorithm based on decomposition (MOEA/D) is proposed for energy-efficient scheduling of distributed heterogeneous welding flow shop (DHWFSP). This problem is extended from distributed flow shop with different amounts of machines in different factories. In addition, it is allowed that multiple machines could operate one job simultaneously. Considering energy efficiency and productivity, this problem could be treated as three sub-problems: job assignment among factories, job scheduling within each factory and deciding the amount of multi-machines upon each job. A multi-objective mathematical model and modified MOEA/D are proposed to minimize the total energy consumption and makespan simultaneously. In modified MOEA/D, various genetic operators and problem-specific local search strategies are designed for multi-level optimization. The comparison experiment with some well-known algorithms shows the effectiveness of the proposed MOEA/D in optimizing and balancing two contradictory objectives.

A shuffled frog-leaping algorithm with memeplex quality for bi-objective distributed scheduling in hybrid flow shop

Abstract: Hybrid flow shop scheduling problem has been extensively considered in single factory; however, distributed hybrid flow shop scheduling problem (DHFSP) is seldom investigated in multiple factories

A Greedy Cooperative Co-evolution ary Algorithm with Problem-specific Knowledge for Multi-objective Flowshop Group Scheduling Problems

Abstract: The flowshop sequence-dependent group scheduling problem (FSDGSP) with the production efficiency measures has been extensively studied due to its wide industrial applications. However, energy efficiency indicators are often ignored in the literature. This paper considers the FSDGSP to minimize makespan, total flow time and total energy consumption, simultaneously. After the problem-specific knowledge is extracted, a mixed-integer linear programming model and a critical path-based accelerated evaluation method are proposed. Since the FSDGSP includes multiple coupled sub-problems, a greedy cooperative co-evolutionary algorithm (GCCEA) is designed to explore the solution space in depth. Meanwhile, a random mutation operator and a greedy energy-saving strategy are employed to adjust the processing speeds of machines to obtain a potential non-dominated solution. A large number of experimental results show that the proposed algorithm significantly outperforms the existing classic multi-objective optimization algorithms, which is due to the usage of problem-related knowledge.

A genetic programming hyper-heuristic for the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times

Abstract: In this paper, a genetic programming hyper heuristic (GP-HH) algorithm is proposed to solve the distributed assembly permutation flow-shop scheduling problem with sequence dependent setup times (DAPFSP-SDST) and the objective of makespan minimization. The main idea is to use genetic programming (GP) as the high level strategy to generate heuristic sequences from a pre-designed low-level heuristics (LLHs) set. In each generation, the heuristic sequences are evolved by GP and then successively operated on the solution space for better solutions. Additionally, simulated annealing is embedded into each LLH to improve the local search ability. An effective encoding and decoding pair is also presented for the algorithm to obtain feasible schedules. Finally, computational simulation and comparison are both carried out on a benchmark set and the results demonstrate the effectiveness of the proposed GP-HH. The best-known solutions are updated for 333 out of the 540 benchmark instances.

Multi-objective evolutionary algorithm based on multiple neighborhoods local search for multi-objective distributed hybrid flow shop scheduling problem

Abstract: In order to be competitive in today’s rapidly changing business world, enterprises have transformed a centralized to a decentralized structure in many areas of decision. It brings a critical problem that is how to schedule the production resources efficiently among these decentralized production centers. This paper studies a multi-objective distributed hybrid flow shop scheduling problem (MDHFSP) with the objectives of minimizing makespan, total weighted earliness and tardiness, and total workload. In the MDHFSP, a set of jobs have to be assigned to several factories, and each factory contains a hybrid flow shop scheduling problem with several parallel machines in each stage. A multi-objective evolutionary algorithm based on multiple neighborhoods local search (MOEA-LS) is proposed to solve the MDHFSP. In the initialization phase, a weighting mechanism is used to decide which position is the best one for each job when constructing a new sequence. Several multiple neighborhoods local search operators based on the three objectives are designed to generate offsprings. Some worse neighboring solutions are replaced by the solutions in the achieve set with a simulated annealing probability. In order to avoid trapping into local optimum, an adaptive weight updating mechanism is utilized when the achieve set has no change. The comprehensive comparison with other classic multi-objective optimization algorithms shows the proposed algorithm is very efficient for the MDHFSP.

Coordinated optimization of equipment operations in a container terminal

Abstract: Increasing international maritime transport drives the need for efficient container terminals. The speed at which containers can be processed through a terminal is an important performance indicator. In particular, the productivity of the quay cranes (QCs) determines the performance of a container terminal; hence QC scheduling has received considerable attention. This article develops a comprehensive model to represent the waterside operations of a container terminal. Waterside operations comprise single and twinlift handling of containers by QCs, automated guided vehi- cles and yard cranes. In common practice, an uncoordinated scheduling heuristic is used to dispatch the equipment operating on a terminal. Here, uncoordinated means that the different machines that operate in the container terminal seek optimal productivity solely considering their own respective stage. By contrast, our model provides a coordinated schedule in which operations of all terminal equipment can be considered at once to achieve productivity closer to the QC optimal. The model takes the form of a hybrid flow shop (HFS) with novel features for bi-directional flows and job pairing. The former enables jobs to move freely through the HFS in both directions; the latter constrains certain jobs to be performed simultaneously by a single machine. We solve the coordinated model by means of a tailored simulated annealing (SA) algorithm that balances solution quality and computational time. We empirically study time-bounded variants of SA and compare them with a branch- and-bound algorithm. We show that our approach can produce coordinated sched- ules for a terminal with up to eight QCs in near real time.

An improved multi-objective whale optimization algorithm for the hybrid flow shop scheduling problem considering device dynamic reconfiguration processes

Abstract: Manufacturing industries frequently encounter production scheduling problems containing device dynamic reconfiguration processes (DRP). DRP refers to dynamic device adjustments (such as replacement of tools), leading to changes in the devices’ actual processing time. It has a severe impact on the production schedule. Nevertheless, there is scarcely research upon hybrid flow shop scheduling problem (HFSP) with DRP. Besides, it is necessary to consider multiple conflict objectives in the HFSP. Thus, the multi-objective HFSP with DRP (MOHFSP-DRP) is significant in both theoretical research and application. This paper first proposes a multi-objective mathematical model (MOHFSP-DRP) that simultaneously considers the DRP and devices’ adjustable processing modes. The bi-objective of this model is to minimize both the makespan and the whole device’s energy consumption. This study then proposes an improved multi-objective whale optimization algorithm (IMOWOA) to solve the MOHFSP-DRP and obtain the Pareto-based optimal solution set. After that, to verify the proposed method’s effectiveness, numerical experiments are implemented based on the real-world cases in a Chinese company’s digital hot-rolling workshop. Results denote that the presented IMOWOA is superior to SPEA2 and NSGA-II. Finally, the MOHFSP-DRP model and IMOWOA are applied to a real-world hot-rolling shop successfully. The real-world cases verify the proposed IMOWOA can tackle the presented MOHFSP-DRP very well.

A matrix-cube-based estimation of distribution algorithm for the distributed assembly permutation flow-shop scheduling problem

Abstract: The distributed assembly permutation flow-shop scheduling problem (DAPFSP) is a typical NP-hard combinatorial optimization problem that has wide applications in advanced manufacturing systems and modern supply chains. In this work, an innovative three-dimensional matrix-cube-based estimation of distribution algorithm (MCEDA) is first proposed for the DAPFSP to minimize the maximum completion time. Firstly, a matrix cube is designed to learn the valuable information from elites. Secondly, a matrix-cube-based probabilistic model with an effective sampling mechanism is developed to estimate the probability distribution of superior solutions and to perform the global exploration for finding promising regions. Thirdly, a problem-dependent variable neighborhood descent method is proposed to perform the local exploitation around these promising regions, and several speedup strategies for evaluating neighboring solutions are utilized to enhance the computational efficiency. Furthermore, the influence of the parameters setting is analyzed by using design-of-experiment technique, and the suitable parameters are suggested for different scale problems. Finally, a comprehensive computational campaign against the state-of-the-art algorithms in the literature, together with statistical analyses, demonstrates that the proposed MCEDA produces better results than the existing algorithms by a significant margin. Moreover, the new best-known solutions for 214 instances are improved.

An Adaptive Iterated Greedy algorithm for distributed mixed no-idle permutation flowshop scheduling problems

Abstract: Distributed flow shop scheduling is a very interesting research topic. This paper studies the distributed permutation flow shop scheduling problem with mixed no-idle constraints, which have important applications in practice. The optimization goal is to minimize total flowtime. A mixed-integer linear programming model is presented and an Adaptive Iterated Greedy (AIG) algorithm with the sample length changing according to the search process is designed. A restart strategy is also introduced to escape from local optima. Additionally, to further improve the performance of the algorithm, swap-based local search methods and acceleration algorithms for swap neighborhoods are proposed. Referenced Local Search (RLS), which shows better performance in solving scheduling problems, is also used in our algorithm. In the destruction stage, the job to be removed is selected according to the degree of influence on the total flowtime. In the initialization and construction phase, when a job is inserted, the jobs before and after the insertion position are removed and re-inserted into a better position to improve the algorithm search performance. A detailed design experiment is carried out to determine the best parameter configuration. Finally, large-scale experiments show that the proposed AIG algorithm is the best-performing one among all the algorithms in comparison.

A cooperated shuffled frog-leaping algorithm for distributed energy-efficient hybrid flow shop scheduling with fuzzy processing time

Abstract: Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention; however, DHFSP with uncertainty and energy-related element is seldom studied. In this paper, distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with fuzzy processing time is considered and a cooperated shuffled frog-leaping algorithm (CSFLA) is presented to optimize fuzzy makespan, total agreement index and fuzzy total energy consumption simultaneously. Iterated greedy, variable neighborhood search and global search are designed using problem-related features; memeplex evaluation based on three quality indices is presented, an effective cooperation process between the best memeplex and the worst memeplex is developed according to evaluation results and performed by exchanging search times and search ability, and an adaptive population shuffling is adopted to improve search efficiency. Extensive experiments are conducted and the computational results validate that CSFLA has promising advantages on solving the considered DEHFSP.

A discrete whale swarm algorithm for hybrid flow-shop scheduling problem with limited buffers

Abstract: This paper studies a hybrid flow-shop scheduling problem with limited buffers and two process routes that comes from an engine hot-test production line in a diesel engine assembly plant. It extends the classical hybrid flow-shop scheduling problem by considering practical constraints on buffer area resources and alternative process routes. Because of its NP-hardness and large scale, traditional optimization methods and heuristic rules cannot obtain satisfactory solutions. A discrete whale swarm algorithm (DWSA) is proposed to identify near-optimal solutions efficiently. The proposed algorithm adopts an encoding method based on the problem characteristic and a greedy delayed decoding strategy to avoid infeasible solutions. A hybrid initialization is used to ensure the quality of the initial population and diversity. A new way of computing distances and a movement rule between individuals are designed. Five mutation operators and a deduplication strategy are proposed to improve the population diversity. The effectiveness of the proposed DWSA is validated on three groups of instances and a real-world industrial case.

Multi-objective evolutionary algorithms with heuristic decoding for hybrid flow shop scheduling problem with worker constraint

Abstract: The classical hybrid flow shop scheduling problem (HFSSP) considers the operation and machine constraints but not the worker constraint. Acknowledging the influence and potential of human factors as a key element in improving production efficiency in a real hybrid flow shop, we consider a new realistic HFSSP with worker constraint (HFSSPW) and construct its mixed integer linear programming model. Seven multi-objective evolutionary algorithms with heuristic decoding (HD) (MOEAHs) are proposed to solve the HFSSPW. According to list scheduling, we first present four HD methods for four MOEAHs, and these methods incorporate four priority rules of machine and worker assignments. The earliest due date (EDD) rule is further introduced into the HD methods for the other three MOEAHs. The developed model is solved using CPLEX based on 20 loose instances under a time limit, and the four proposed MOEAHs are evaluated by comparing them with the results from CPLEX and two best-performing algorithms in the literature. The computational results reveal that the proposed MOEAHs perform excellently in terms of the makespan objective. Additionally, comprehensive experiments, including 150 tight instances, are conducted. In terms of solution quality and efficiency, the computational results show that the proposed MOEAHs demonstrate highly effective performance, and integrating EDD into the HD can substantially enhance algorithm performance. Finally, a real-life problem of the foundry plant is solved by MOEAHs and the scheduling solutions totally meet the delivery requirement.

Electricity cost minimisation for optimal makespan solution in flow shop scheduling under time-of-use tariffs

Abstract: 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 i...

Ranking dispatching rules in multi-objective dynamic flow shop scheduling: a multi-faceted perspective

Abstract: 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 t...

Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment

Abstract: 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 perfor...

An improved iterated greedy algorithm for the energy-efficient blocking hybrid flow shop scheduling problem

Abstract: With the continuous development of national economies, problems of various energy consumption levels and pollution emissions in manufacturing have attracted attention from researchers. Most existing research has focused on reducing economic costs and energy consumption. However, the Hybrid Flow Shop Scheduling Problem with energy-efficient criteria has not yet been well studied, especially with blocking constraints. This paper is the first to present a mathematical model of the blocking hybrid flow shop problem with an energy-efficient criterion and a modified Iterative Greedy algorithm based on a swap strategy designed to optimize the constructed model. In the proposed algorithm, first, a heuristic is adopted to generate the initial solution. Second, a local perturbation strategy based on a swap operator is designed to ensure the convergence of the algorithm. Third, a simple global perturbation strategy based on a half-swap operator is proposed as a means to further search for the potentially best solution with the traditional simulated annealing criterion. The proposed algorithm is applied to 150 test instances at different scales and compared to state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms and can obtain a better solution.

A virtual commissioning based methodology to integrate digital twins into manufacturing systems

Abstract: Digital Twin (DT) is considered a key approach to enhance the system reactivity to uncertain events due to its ability to getting data from the field and triggering actions on the physical asset. Given the modern technological and rapidly changing work environment, it is likely that in the next years companies will need to retrofit their manufacturing systems by integrating DTs. In this context, it is fundamental to define the necessary steps for the development of DTs and for their integration into manufacturing systems through a DT architecture. In response to this issue, a methodology based on Virtual Commissioning is proposed. A stepwise approach is illustrated in which the DT is designed, integrated and verified using a virtual environment. The methodology is validated through the integration of a DT into a flow shop for the implementation of a scheduling reactive to machine breakdown. By following the steps of the proposed methodology, a DT architecture able to improve the makespan of the studied flow shop is developed, suggesting the potential applicability of the approach to industrial manufacturing systems.

An effective multi-objective whale swarm algorithm for energy-efficient scheduling of distributed welding flow shop

Abstract: Distributed welding flow shop scheduling problem is an extension of distributed permutation flow shop scheduling problem, which possesses a set of identical factories of welding flow shop. On account of several machines can process one job simultaneously in welding shop, increasing the amount of machines can short the processing time of operation while waste more energy consumption at the same time. Thus, energy-efficient is of great significance to take total energy consumption into account in scheduling. A multi-objective mixed integer programming model for energy-efficient scheduling of distributed welding flow shop is presented based on three sub-problems with allocating jobs among factories, scheduling the jobs in each factory and determining the amount of machines upon each job. A multi-objective whale swarm algorithm is proposed to optimize the total energy consumption and makespan simultaneously. In the proposed algorithm, a new initialization method is designed to improve the quality of the initial solution. And various update operators, as well as local search, are designed according to the feature of the problem. To conduct the experiment, diversified indicators are applied to evaluate the proposed algorithm and other MOEAs performance. And the experiment results demonstrate the effectiveness of the proposed method. The proposed algorithm is applied in the real-life case with great performance compared with other MOEAs.