No-wait Flow Shop Research Articles

An objective review and perspectives on the permutation flowshop scheduling with no-wait constraint and setup times

One of the most important decisions in any manufacturing company is how to schedule the operations on the available machines. In several industries, the nature of the job imposes certain constraints to operations scheduling. In a no-wait flowshop, once a job starts on the first machine, it has to continue being processed on the next ones, without any interruptions. As an extension of the flowshop scheduling problem, the no-wait version is also very difficult to be solved to optimality within a reasonable time, and many heuristics have been proposed for it. This paper aims to classify existing solution algorithms proposed to solve the no-wait flowshop scheduling problem with setup times and some of its variants. Our classification is based on the type of setup considered; we also review all available performance measures in the literature. We show how combining a heuristic to generate a good initial solution, local search procedures, insertion and swapping of job positions and techniques developed originally to solve transportation problems are among the popular and efficient techniques for this problem. We identify the main available benchmark instance sets and propose several promising avenues to guide future research.

Algorithms for a two-machine no-wait flow shop scheduling problem with two competing agents

Algorithms for a two-machine no-wait flow shop scheduling problem with two competing agents

Hybrid meta-heuristic solving no-wait flow shop scheduling minimizing maximum tardiness

Hybrid meta-heuristic solving no-wait flow shop scheduling minimizing maximum tardiness

Active learning based hyper-heuristic for the integration of production and Transportation: A third-party logistics perspective

The integration problem of production and transportation (IPPT) is one of the most important decision issues in real-life manufacturing and flow industries. To illustrate its potential for improving difficult production and transportation processes, we focus on a challenging case of IPPT, namely the distributed no-wait flow-shop and transportation integrated scheduling problem (DNWFSTISP) considering third party logistics (3PL), which occurs in many practical industries. DNWFSTISP consists of integrating the well-known distributed no-wait flow-shop scheduling and dedicated vehicle routing in the sense of 3PL while minimizing the total cost. First, we investigate the integration approach of the production and transportation of DNWFSTISP and present three problem-specific loading heuristics, which account for both a make-to-order strategy and 3PL providers to improve customer satisfaction. Second, we propose an active learning based hyper-heuristic algorithm (ALHHA) to solve the problem. ALHHA is unique in that it relies on an active learning based high-level exploration to discover promising search regions and a neighborhood search based low-level exploitation to intensively examine given search regions. Third, we assess the composing ingredients of the proposed ALHHA on 40 instances with up to 200 jobs and 7 factories to shed light on their impacts on the performance of the algorithm. Moreover, we make the 40 instances and solved upper bounds publicly available, which would be valuable for future research on DNWFSTISP.

A Novel Evolutionary Algorithm for Scheduling Distributed No-Wait Flow Shop Problems

A Novel Evolutionary Algorithm for Scheduling Distributed No-Wait Flow Shop Problems

Q-learning guided algorithms for bi-criteria minimization of total flow time and makespan in no-wait permutation flowshops

Combining Deep Reinforcement Learning and meta-heuristic techniques represents a new research direction for enhancing the search capabilities of meta-heuristic methods in the context of production scheduling. Q-learning is a prominent reinforcement learning in which its utilization aims to direct the selection of actions, thus preventing the necessity for a random exploration in the iterative process of the metaheuristics. In this study, we provide Q-learning guided algorithms for the Bi-Criteria No-Wait Flowshop Scheduling Problem (NWFSP). The problem is treated as a bi-criteria combinatorial optimization problem where total flow time and makespan are optimized simultaneously. Firstly, a deterministic mixed-integer linear programming (MILP) model is provided. Then, Q-learning guided algorithms are developed: Bi-Criteria Iterated Greedy Algorithm with Q-Learning (BC-IGQL). Bi-Criteria Block Insertion Heuristic Algorithm with Q-Learning (BC-BIHQL). Moreover, the performance of the proposed Q-learning guided algorithms is compared over a collection of Bi-Criteria Genetic Local Search Algorithms (BC-GLS), Bi-Criteria Iterated Greedy Algorithm (BC-IG), Bi-Criteria Iterated Greedy Algorithm with a Local Search (BC-IGALL) and Bi-Criteria Variable Block Insertion Heuristic Algorithm (BC-VBIH). The complete computational experiment, performed on 480 problem instances of Vallada et al. (2015), which is known as the VRF benchmark set, indicates that the BC-BIHQL and the BC-IGQL algorithms outperform the BC-GLS, BC-IG, BC-IGALL, and BC-VBIH algorithms in comparative performance metrics. More specifically, the proposed BC-BIHQL and BC-IGQL algorithms can yield more non-dominated bi-criteria solutions with the most substantial competitiveness than the remaining algorithms. At the same time, both are competitive with each other on the benchmark problems. Moreover, the BC-IGQL algorithm dominates almost 97% and 99% of the solutions reached by the BC-IG, BC-IGALL, and BC-VBIH algorithms in small and large datasets. Similarly, The BC-BIHQL algorithm dominates almost 98% and 99% of the solutions reached by the BC-IG, BC-IGALL, and BC-VBIH algorithms in small and large datasets, respectively. This means that, among all the features that have been compared, the Q-learning-guided algorithms demonstrate the highest level of competitiveness. The outcomes of this study encourage us to discover many more bi-criteria NWFSPs to reveal the trade-off between other conflicting objectives, such as makespan & the number of early jobs, to overcome various industries' problems.

An enhanced estimation of distribution algorithm with problem-specific knowledge for distributed no-wait flowshop group scheduling problems

With the trend of economic globalization, distributed manufacturing widely exists in modern manufacturing systems. As an extension of the distributed flowshop scheduling problem, the distributed no-wait flowshop group scheduling problem with sequence-dependent setup times (DNFGSP_SDSTs) is investigated in this article. To address DNFGSP_SDSTs with the criterion of minimizing makespan, this study proposes an enhanced estimation of distribution algorithm·(EEDA) with problem-specific knowledge. First, a mixed integer linear programming(MILP) model of DNFGSP_SDSTs is established. Second, based on the characteristics of DNFGSP_SDSTs, five problem-specific properties about local search operators are derived as prior knowledge to reduce computational cost. Third, two NEH-based two-stage heuristics are presented to construct a high-quality population with diversity. Fourth, a probability model with problem-specific knowledge and a family-based updating mechanism are developed to accumulate valuable pattern information from high-quality solutions, while a sampling strategy is designed to generate new populations with the accumulated information. Fifth, several local search operators are devised to refine the obtained solutions. Furthermore, perturbation and reinitialization methods are developed to avoid premature convergence. Finally, the validity of the MILP model is verified by using the Gurobi solver. The parameters of EEDA are tuned through a design of experiments. The effectiveness of key components in EEDA is confirmed through extensive experiments, and the computational comparisons with the state-of-the-art algorithms indicate the effectiveness of the proposed EEDA for solving DNFGSP_SDSTs.

An ALNS to optimize makespan subject to total completion time for no-wait flow shops with sequence-dependent setup times

An ALNS to optimize makespan subject to total completion time for no-wait flow shops with sequence-dependent setup times

An asymptotically optimal solution for the minimization of the variation of job completion times in two-stage proportionate no-wait flow shops

We propose an asymptotically optimal solution for the two-stage no-wait proportionate flow shop scheduling problem with the objective of minimizing the total absolute deviation of job completion times. The proposed solution is within 1% of the optimum for problems with more than 20 jobs. We also show that the problem is solved optimally in constant time for the special case where the two smallest jobs have equal processing times. Our results provide answers to most of the outstanding research questions for this problem.

No-Wait Flow Shop scheduling problem: a systematic literature review and bibliometric analysis

One of the most widely studied problems in flow shop scheduling is not allowing jobs to wait to be processed at the next stage. This constraint causes the job to be processed immediately at the next stage without waiting, so this problem is popularly called the No-Wait Flow Shop. This article aims to provide a comprehensive review of the No-Wait Flow Shop Scheduling (NWFS) problem based on a survey of published articles from 1999 to 2023. The article review is based on a systematic literature review, and bibliometric analysis is also presented based on the network processed using VOSviewer. One hundred twenty articles were collected from the Scopus database, which was reviewed based on NWFS variants, objective functions, and optimization procedures. The no-wait permutation flow shop scheduling (NWPFS) problem is a variant that researchers have widely investigated. Meta-heuristic procedures are widely applied to solve NWFS problems. In addition, the objective function of minimizing makespan is an objective function that researchers often apply. NWFS research gaps and future research trends are also presented in this paper.

An effective approach for total completion time minimization subject to makespan constraint in permutation flowshops

This article addresses the permutation flowshop scheduling problem with the objective of minimizing the total completion time subject to a makespan constraint. The makespan is related to system utilization while the total completion time is related to the waiting time, and hence to the work in process (WIP); in real contexts, focusing on both total completion time and makespan allows a good trade-off between WIP and utilization to be found. Two local search algorithms are developed in the paper and, by using an extensive computational experience on literature benchmark instances, they are proved to be able to find good solutions both for regular and no-wait flowshops.

A hybridization of evolution strategies with iterated greedy algorithm for no-wait flow shop scheduling problems

This study investigates the no-wait flow shop scheduling problem and proposes a hybrid (HES-IG) algorithm that utilizes makespan as the objective function. To address the complexity of this NP-hard problem, the HES-IG algorithm combines evolution strategies (ES) and iterated greedy (IG) algorithm, as hybridizing algorithms helps different algorithms mitigate their weaknesses and leverage their respective strengths. The ES algorithm begins with a random initial solution and uses an insertion mutation to optimize the solution. Reproduction is carried out using (1 + 5)-ES, generating five offspring from one parent randomly. The selection process employs (µ + λ)-ES, allowing excellent parent solutions to survive multiple generations until a better offspring surpasses them. The IG algorithm’s straightforward search mechanism aids in further improving the solution and avoiding local minima. The destruction operator randomly removes d-jobs, which are then inserted one by one using a construction operator. The local search operator employs a single insertion approach, while the acceptance–rejection criteria are based on a constant temperature. Parameters of both ES and IG algorithms are calibrated using the Multifactor analysis of variance technique. The performance of the HES-IG algorithm is calibrated with other algorithms using the Wilcoxon signed test. The HES-IG algorithm is tested on 21 Nos. Reeves and 30 Nos. Taillard benchmark problems. The HES-IG algorithm has found 15 lower bound values for Reeves benchmark problems. Similarly, the HES-IG algorithm has found 30 lower bound values for the Taillard benchmark problems. Computational results indicate that the HES-IG algorithm outperforms other available techniques in the literature for all problem sizes.

Effective Population-Based Meta-heuristics with NEH and GRASP Heuristics Minimizing Total Weighted flow Time in No-Wait Flow Shop Scheduling Problem Under Sequence-Dependent Setup Time Constraint

Effective Population-Based Meta-heuristics with NEH and GRASP Heuristics Minimizing Total Weighted flow Time in No-Wait Flow Shop Scheduling Problem Under Sequence-Dependent Setup Time Constraint

An attempt to resolve no-wait flow shop scheduling problems using hybrid ant colony and whale optimization algorithms

The incentive for many developments and scientific progresses within the field of scheduling has emerged from industrial environments, and naturally, it could be utilized in expressing the scheduling concepts regarding terms used in the industry. Generally speaking, scheduling problems are known as limited optimization issues through which decisions related to the machines’ assignment and works processing sequence are probed. Thus, following a review of the related literature, the major goal of this research is to design a mathematical model and to solve it through a meta-heuristic for no-wait flow shop scheduling problem using different machines for the purpose of minimizing the time required to complete the work using whale and ant colony optimization (ACO) algorithms in Sanat-Gostar-e-Hamgam Shoe Company. The ACO and whale algorithm methods are used to compare and predict scheduling activities in manufacturing line of shoe industry. The results showed an ACO algorithm with two stages in mean ideal distance (MID) end amounting to 76.65 and 77.38, respectively. Also, regarding the amounts of standard error mean squares, it could be claimed that the model designed using the improved whale algorithm has a better prediction, and the minimum time required to complete works using the whale algorithm is estimated to be equal to 86.1071. This could lead to an optimal state in achieving the predetermined goals.

A reactive iterated greedy algorithm for the no-wait flowshop to minimize total tardiness

A reactive iterated greedy algorithm for the no-wait flowshop to minimize total tardiness

Research On the No-Wait Flow Shop Scheduling Problem Based on Simulated Annealing Algorithm.

This paper focuses on the No-Wait Flow Shop Scheduling Problem and conducts research based on an improved Simulated Annealing Algorithm. The No-Wait Flow Shop Scheduling Problem is of great significance in practical production, but its solution complexity is high, requiring the search for appropriate optimization methods. In this study, a solution based on an improved Simulated Annealing Algorithm is proposed. Simulated Annealing Algorithm, as a global optimization method, introduces random perturbations to the current solution and accepts or rejects the new solution based on Metropolis criterion to escape local minima and search for better solutions. In this paper, the termination condition of the improved Simulated Annealing Algorithm is enhanced. Specifically, if no better solution is obtained after a certain number of consecutive temperature calculations or when the maximum number of outer loop iterations is reached, the algorithm terminates and outputs the result. This improvement aims to increase efficiency and reduce unnecessary computations.By comparing the results with other heuristic algorithms, it is found that the solution based on the improved Simulated Annealing Algorithm can effectively provide better scheduling solutions for the No-Wait Flow Shop Scheduling Problem, thereby improving production efficiency, reducing costs, and optimizing resource utilization.

A Reinforcement Learning Driven Artificial Bee Colony Algorithm for Distributed Heterogeneous No-Wait Flowshop Scheduling Problem With Sequence-Dependent Setup Times

The distributed heterogeneous factory system is a typical scenario in the manufacturing industry. A distributed heterogeneous no-wait flowshop scheduling problem with sequence-dependent setup times (DHNWFSP-SDST) is studied in this paper. The differences in factory configuration and transportation time are considered in DHNWFSP-SDST. A mixed-integer linear programming (MILP) model is constructed and an artificial bee colony algorithm (ABC) with Q-learning (QABC) is proposed to address the DHNWFSP-SDST. Heuristic methods named NEH_H and DHHS are designed to construct potential initial candidates for the population. The neighborhood structures based on the job blocks are introduced in QABC to explore the solution space during the evolution processes. The Q-learning mechanism is employed to select neighborhood structures via empirical knowledge in the operation processes. The speed-up methods to accelerate the evaluation of the obtained neighborhood are designed to reduce the computation time of the QABC. The experimental results show that the QABC is a potential algorithm to address the DHNWFSP-SDST. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Distributed manufacturing under the heterogeneous environment generally exists in real manufacturing systems. The scheduling problem refers to the reasonable arrangement of production orders to optimize certain indicators under limited time, resources, and computing costs. Distributed heterogeneous no-wait flowshop scheduling problem with setup times is an important industrial scheduling problem in distributed heterogeneous manufacturing systems. The problem takes into account the differences in factory configuration and transportation time in distributed regions. The problem is a kind of NP-hard problem as the solution space is huge. A reinforcement learning driven artificial bee colony algorithm is designed to address the problem. Heuristic methods are utilized to construct potential initial solutions. Neighborhood structures are designed to further optimize the initial solution. The effective empirical guidance is provided by Q-learning for the selection of the neighborhood structure of the algorithm to avoid invalid search. The experimental results show that the QABC obtains a high-quality scheduling scheme in a reasonable time.

Dispatching Rules-based Optimization of the No-wait Flow Shop Scheduling Problem

To improve the productivity of the flow shop, this paper proposes a new mathematical model to describe the scheduling problem of the no-wait flow shop (NWFS) with the minimization makespan as the scheduling objective. In addition, a new dispatching rule PCA-AS is created using the principal component analysis (PCA) algorithm, and PCA-AS is applied to the process of solving the scheduling problem to gain a scheduling strategy with excellent performance. Finally, the paper numerically verifies that PCA-AS outperforms the shortest processing time (SPT) dispatching rule and the first-in-first-out (FIFO) dispatching rule in minimizing the makespan.

An improved iterative greedy athm for energy-efficient distributed assembly no-wait flow-shop scheduling problem

An improved iterative greedy athm for energy-efficient distributed assembly no-wait flow-shop scheduling problem

No-Wait Flowshop Permutation Scheduling Problem : Fire Hawk Optimizer Vs Beluga Whale Optimization Algorithm

No-Wait Flowshop Permutation Scheduling Problem (NWPFSP) is a scheduling problem that states that every job completed on machine n must be processed immediately on the next machine. The NWPFSP problem is an extension of the flowshop problem. This article proposes two new algorithms fire hawk optimization and beluga whale optimization, to solve the NWPFSP problem and minimize makespan. The two new algorithms developed to solve the NWPFSP problem are tested on three different cases. Each algorithm was run 30 times and was compared using an independent sample t-test. The results were also compared with the Campbell Dudek Smtih algorithm. In addition, the effectiveness of the FHO and BWO algorithms was assessed against the CDS algorithm using the Relative Error Percentage (REP) method. The results show that the FHO and BWO algorithms are better at solving NWPFSP problems when compared to the CDS algorithm. However, the BWO algorithm is more recommended in cases with large data because it can provide better results.