Flow Shop Scheduling Research Articles

An ordered precedence constrained flow shop scheduling problem with machine specific preventive maintenance

In reality, the machines may interrupt because of the nature of deterioration of the machines. Thus, it is inevitable to perform maintenance alongside production planning. The preventive maintenance is a schedule of strategic operations that are performed prior to the failure occurring, to retain the system operating at the preferred level of consistency. Thus, preventive maintenance plays a significant role in flow shop scheduling models. With its practical significance, this study addresses a practical three-machine n jobs flow shop-scheduling problem (FSSP) in which machine specific preventive maintenance, where each machine is given with a maintenance schedule is considered. In addition, a practical ordered precedence constraint in which some set of jobs has to process in the specified order irrespective of their processing times is also considered. The problem’s goal is to establish the optimal job sequence and preventive maintenance such that the overall cost of tardiness and preventive maintenance is as minimum as possible. An efficient heuristic approach is designed to tackle the present model, resulting in total cost savings. A comparative analysis is not conducted due to absence of studies on the current problem in the literature. However, Computational experiments are carried out on some test instances and results are reported. The reported results may be useful for future studies.

Flowshop scheduling problem with objective of minimizing TCT

In manufacturing industries, scheduling problems are the origin. Particularly flowshop scheduling problem occurs in time table algorithm, operatory theory and combinatorial optimization problem with the total completion time (TCT) minimizing is a herculean task to obtain near optimal solution. Researchers are interested in solving these types of problems with the objective being that TCT is quite fashionable. Comparing the solution with existing one is quite logical to arrive the conclusion which algorithm performance better than the other. The heuristic approach developed by us, Jayakumar and Vasudevan performs better compare with other algorithms found in the literature.

Multi-stage hybrid evolutionary algorithm for multiobjective distributed fuzzy flow-shop scheduling problem.

In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of fuzzy completion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages. In the first stage, the hybrid sampling strategy makes the population rapidly converge toward the Pareto front (PF) in multiple directions. In the second stage, the sequence difference-based differential evolution (SDDE) is used to speed up the convergence speed to improve the convergence performance. In the last stage, the evolutional direction of SDDE is changed to guide individuals to search the local area of the PF, thereby further improving the convergence and distribution performance. The results of experiments show that the performance of MSHEA-SDDE is superior to the classical comparison algorithms in terms of solving the DFFSP.

Minimizing Total Tardiness in a Two-Machine Flowshop Scheduling Problem with Availability Constraints

In this paper, we consider the problem of minimizing the total tardiness in a deterministic two-machine permutation flowshop scheduling problem subject to release dates of jobs and known unavailability periods of machines. The theoretical and practical importance of minimizing tardiness in flowshop scheduling environment has motivated us to investigate and solve this interested two-machine scheduling problem. Methods that solve this important optimality criterion in flowshop environment are mainly heuristics. In fact, despite the -hardness in the strong sense of the studied problem, to the best of our knowledge there are no approximate algorithms (constructive heuristics or metaheuristics) or an algorithm with worst case behavior bounds proposed to solve this problem. Thus, the design of new promising algorithms is desirable. We develop five metaheuristics for the problem under consideration. These metaheuristics are: the Particle Swarm Optimization (PSO), the Differential Evolution (DE), the Genetic Algorithm (GA), the Ant Colony Optimization (ACO) and the Imperialist Competitive Algorithm (ICA). All the proposed metaheuristics are population-based approaches. These metaheuristics have been improved by integrating different local search procedures in order to provide more satisfactory, especially in term of quality solutions. Computational experiments carried out on a large set of randomly generated instances provide evidence that the Imperialist Competitive Algorithm (ICA) records the best performances.

Elucidating two-stage flowshop multiprocessor scheduling problems using a hybrid genetic algorithm

In recent decades, genetic algorithms (GAs) have often been applied as heuristic techniques at various settings entailing production scheduling. However, early convergence is one of the problems associated with this approach. This study develops an efficient local search rule for the target-oriented rule in traditional GAs. It also addresses the problem of two-stage multiprocessor flowshop scheduling (FSP) by viewing the due window and sequence-dependent setup times as constraints faced by common flowshops with multiprocessor scheduling suites in the actual production scenario. Using the simulated data, this study verifies the effectiveness and robustness of the proposed algorithm. The results of data testing demonstrate that the proposed method may outperform other algorithms, including a significant hybrid algorithm, in addressing the problems considered.

Online scheduling of two-machine flowshop with lookahead and incompatible job families

Online scheduling of two-machine flowshop with lookahead and incompatible job families

Green Hybrid Flow Shop Scheduling Problem Considering Sequence Dependent Setup Times and Transportation Times

For the green hybrid flow shop scheduling problem considering sequence dependent setup times (SDST) and transportation times, a mixed integer programming model is established with the objectives of minimizing the maximum completion time (makespan) and total energy consumption simultaneously, and an improved memetic algorithm is proposed with the problem characteristics. First of all, an encoding method combining the jobs sequence code at the first stage with the machine allocation code is designed to ensure that the algorithm can search the entire solution space to the greatest extent; then, a mixed population initialization method is designed to improve the quality of the initial population solution; Thirdly, the crossover and mutation operators, and four neighborhood search strategies are designed to balance the global search and local search capabilities of the algorithm; Finally, the effectiveness of the algorithm is verified by numerical experiments.

Improved Meta-Heuristics for Solving Distributed Lot-Streaming Permutation Flow Shop Scheduling Problems

This paper addresses a distributed lot-streaming permutation flow shop scheduling problem that has various applications in real-life manufacturing systems. We aim to optimally assign jobs to multiple distributed factories and sequence them to minimize the maximum completion time (Makespan). A mathematic model is first developed to describe the considered problem. Then, five meta-heuristics are executed to solve it, including particle swarm optimization, genetic algorithm, harmony search, artificial bee colony, and Jaya algorithm. To improve the performance of these meta-heuristics, we employ Nawaz-Enscore-Ham (NEH) heuristic to initialize populations and propose improved strategies based on the problem’s feature. Finally, experiments are carried out based on 120 instances. The performance of improved strategies is verified. Comparisons and discussions show that the artificial bee colony algorithm with improved strategies has the best competitiveness for solving the proposed problem with makespan criteria. Note to Practitioners—In contemporary manufacturing industry, the traditional single-factory environment is being replaced by a distributed multi-factory environment, as a distributed pattern can effectively improve the production efficiency through the reasonable resource allocation strategies. The distributed lot-streaming permutation flow shop scheduling problem in such a pattern is of significance to practitioners. Although intelligent optimization can provide an effective tool to solve such problems, most of the algorithms are parameter-sensitive. A challenge for engineers is parameter selection, which greatly impacts the algorithm performance. To ensure the robustness of the algorithms, we develop five improved meta-heuristics by employing some strategies. Furthermore, parameter setting test is carried out to select the appropriate parameter values. As a result, the proposed algorithms can obtain resource allocation schemes with high-quality. It is shown that the artificial bee colony algorithm with improved strategies outperforms other algorithms well. The proposed methodology can be readily applied to real distributed scheduling problems.

Bi-criteria two-machine flow shop scheduling problem involving sequence-dependent setup times and pieces multiplicity

Bi-criteria two-machine flow shop scheduling problem involving sequence-dependent setup times and pieces multiplicity

An Evaluation of Mathematical Programming and Lower-Bound Methods for Hybrid Flow Shop Problems With a Makespan Criterion

This paper considers the hybrid flow shop scheduling problem, where jobs are processed in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> stages with the same route of the stage. Each stage has identical parallel machines for processing jobs. Some mathematical programming formulations and lower bound calculations have been proposed in the literature for such cases. Nevertheless, there is a lack of complete comparisons of these mathematical programming formulations and lower bounds in the hybrid flow shop literature. In this paper, we propose a new mixed integer programming model and two new lower bounds based on the bin-packing concept for the considered problem. To evaluate the proposed model, two sets of small and small-to-medium problems are used to compare our model with the existing models. Moreover, two propositions are discussed for lower bounds. The experimental results show that the proposed mixed integer programming model efficiently found optimal solutions because it needs a smaller number of binary variables and a smaller number of constraints, and the proposed lower bound can also serve as a strong indicator to evaluate the distances between the solutions obtained by heuristic algorithms and the optimal solution.

An Improved Hyperplane Assisted Multiobjective Optimization for Distributed Hybrid Flow Shop Scheduling Problem in Glass Manufacturing Systems

To solve the distributed hybrid flow shop scheduling problem (DHFS) in raw glass manufacturing systems, we investigated an improved hyperplane assisted evolutionary algorithm (IhpaEA). Two objectives are simultaneously considered, namely, the maximum completion time and the total energy consumptions. Firstly, each solution is encoded by a three-dimensional vector, i.e., factory assignment, scheduling, and machine assignment. Subsequently, an efficient initialization strategy embeds two heuristics are developed, which can increase the diversity of the population. Then, to improve the global search abilities, a Pareto-based crossover operator is designed to take more advantage of non-dominated solutions. Furthermore, a local search heuristic based on three parts encoding is embedded to enhance the searching performance. To enhance the local search abilities, the cooperation of the search operator is designed to obtain better non-dominated solutions. Finally, the experimental results demonstrate that the proposed algorithm is more efficient than the other three state-of-the-art algorithms. The results show that the Pareto optimal solution set obtained by the improved algorithm is superior to that of the traditional multiobjective algorithm in terms of diversity and convergence of the solution.

On employing pythagorean fuzzy processing time to minimize machine rental cost

&lt;abstract&gt; &lt;p&gt;The aim of this paper is to obtain the minimal rental cost of the three-phases flow shop scheduling problems. A novel strategy to tackle this issue using Pythagorean fuzzy processing time is introduced. It depends on converting the three stages machine into two stages when the minimum value of processing time of the first machine is greater than the maximum value of processing time of the second machine. The vague processing time does not convert to its crisp form. The jobs sequencing in machines is obtained using Johnson procedure. The zero element of the Pythagorean set is defined as, $ {\widetilde{\mathrm{O}}}^{\mathrm{p}} = \left(\mathrm{0, 1}\right) $ i.e., it has zero membership and one nonmembership values. A numerical example include Pythagorean rental cost is delivered to demonstrate the reliability of the suggested strategy. The idle time, utilization time, and the overall cost are calculated. The idle time of all machines is zero, which minimize the required time and hence, minimize the total rental cost.&lt;/p&gt; &lt;/abstract&gt;

An Effective Solution Space Clipping-Based Algorithm for Large-Scale Permutation Flow Shop Scheduling Problem

The permutation flow shop scheduling problem (PFSP) is one of the most important scheduling types in the mass customization production with many real-world applications. It is also a well-known NP-hard problem, when the number of jobs increases, the difficulty of solving the problem exponentially increases. However, most of the reported algorithms have not analyzed the solution space and may search many useless solution spaces, which impedes these algorithms from effectively optimizing the large-scale PFSPs in reasonable computation time. To address large-scale PFSPs with more than 100 jobs, this article proposes a solution space clipping-based improved simulated annealing (SA) algorithm. First, inspired by Johnson’s rule, this article explores its essential principle and generalizes it to the general situation. Before the optimization algorithm is used to find the optimal solution, a preordering combination is performed on the processed jobs according to this extended rule to considerably clip the solution space. Second, a hybrid release strategy based on the Palmer algorithm is developed for the proposed algorithm. Then, some key operators of the SA algorithm are also improved. Finally, to verify the performance of the proposed algorithm, this work performs a set of comparative experiments with the-state-of-art methods on the part of the TA benchmark and VRF benchmark with more than 100 jobs. The experimental results show that the proposed method can achieve superior results compared to other algorithms. Furthermore, the performance of the algorithm is comprehensively analyzed, which confirms the effectiveness of the proposed method.

A modified beluga whale optimization for optimizing energy-efficient no-idle permutation flow shop scheduling problem

Attention to energy-efficient permutation flow shop scheduling problems is increasing in the context of globalization and environmental awareness. However, the energy-efficient no-idle permutation flow shop scheduling problem (NIPFSP) has rarely been studied. This study proposes a procedure using Beluga Whale Optimization (BWO) to solve the energy-efficient NIPFSP issue to reduce total energy consumption (TEC). The offered procedure is tested using four job and machine variations and compared with the PSO algorithm. An independent sample t-test was performed to test the optimization results of the BWO and PSO in 4 cases. The results indicate that the offered procedure produces lower total energy consumption than the PSO algorithm. In addition, the algorithm also provides more competitive results when solving the energy-efficient NIPFSP problem with a larger number of jobs. The implications of this academic research show that the proposed procedure can be applied to solve the problem of energy-efficient NIPFSP, which is indicated by low energy consumption.

Environmentally friendly strategy for hybrid flow shop scheduling with the unrelated parallel machines

With the increasing environmental pressure, reducing the environmental impact on hybrid flow shops (HFS) has attracted extensive attention because of its broad industrial applications. The selection of machines in the implementation of HFS is a complex decision-making process that hinders the optimization efficiency in shops. However, this issue has not been addressed thoroughly. In light of this, a multi-objective mathematical model is formulated for the minimization of makespan and energy consumption of the hybrid flow shop scheduling problem (HFSP). The environmental performance of machines is calculated by the proposed evaluation index and is ranked on the basis of the integrated entropy and fuzzy technique for order performance by similarity to ideal solution method. Moreover, to solve the multi-objective model of HFSP, an improved differential evolution algorithm with a heuristic active decoding rule that incorporates the ranking of environmental performance of machines into the iterative process of the algorithm is presented. Finally, a case study is presented to evaluate the effectiveness of the proposed method and to prove the feasibility of the ranking-based differential evolution algorithm (RBDE). The result shows that the proposed RBDE outperforms RBNSGA-II and RBPSO in searching for non-dominated solutions that can solve the environmental production of HFSP effectively.

Distributed shop scheduling: A comprehensive review on classifications, models and algorithms.

In the intelligent manufacturing environment, modern industry is developing at a faster pace, and there is an urgent need for reasonable production scheduling to ensure an organized production order and a dependable production guarantee for enterprises. Additionally, production cooperation between enterprises and different branches of enterprises is increasingly common, and distributed manufacturing has become a prevalent production model. In light of these developments, this paper presents the research background and current state of distributed shop scheduling. It summarizes relevant research on issues that align with the new manufacturing model, explores hot topics and concerns and focuses on the classification of distributed parallel machine scheduling, distributed flow shop scheduling, distributed job shop scheduling and distributed assembly shop scheduling. The paper investigates these scheduling problems in terms of single-objective and multi-objective optimization, as well as processing constraints. It also summarizes the relevant optimization algorithms and their limitations. It also provides an overview of research methods and objects, highlighting the development of solution methods and research trends for new problems. Finally, the paper analyzes future research directions in this field.

Permutation flow shop scheduling with early and late penalty costs using the Jaya algorithm

Permutation flow shop scheduling with early and late penalty costs using the Jaya algorithm

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

A multi-machine bicriteria flow shop scheduling with sequence dependent setup time

A multi-machine bicriteria flow shop scheduling with sequence dependent setup time

Optimising order picking efficiency in a warehouse and distribution centre: A flexible flow shop scheduling approach

The scheduling of order-picking problems is a critical aspect of warehouse and distribution centre operations. The efficient execution of order picking requires determining the sequence in which it is needed to pick items from storage locations, as well as the assignment of employees to different picking tasks. In this paper, we present an approach to optimise the process using the Flexible Flow Shop Scheduling Problem (FFSSP), which encompasses multiple order fulfilment stages performing a set of operations on various machines (groups of employees) assigned to the picking stages (e.g., order creating, product collection, quality control, packing, shipment). The aim of the study was to determine the sequence of operations on the machines to minimise the order completion time while taking into account the available resources. We used IBM'sCP Optimizer. The computational experiments showed, on the one hand, the advantages of solver usage for relatively small instances providing optimal solutions, while on the other hand, it demonstrated its disadvantages as for 11 large instances from 24, optimal solutions were not found. Moreover, in 6 cases from 24 ones, the solution which was found was 94% worse than the optimal one.