Distributed Assembly Permutation Flowshop Scheduling Problem Research Articles

Effective social spider optimization algorithms for distributed assembly permutation flowshop scheduling problem in automobile manufacturing supply chain

This paper presents a novel distributed assembly permutation flowshop scheduling problem (DAPFSP) based on practical problems in automobile production. Different from the existing research on DAPFSP, this study considers that each component of the final product is composed of more than one part. Components are processed in a set of identical components manufacturing factories and are assembled into products in the assembly factory. The integration of manufacturing processes is an important objective of Industry 4.0. For solving this problem with the minimum makespan criterion, we introduce a three-level representation and a novel initialization method. To enhance the search ability of the proposed algorithms, we design three local search methods and two restart procedures according to characteristics of the problem. Then, by incorporating the problem specific knowledge with the social spider optimization algorithm (SSO), we propose three SSO variants: the SSO with hybrid local search strategies (HSSO), the HSSO with restart procedures (HSSOR), and the HSSOR with self-adaptive selection probability (HSSORP). Finally, 810 extended instances based on the famous instances are used to test the proposed algorithms. In most cases, HSSOR performs the best, with an average comparison metric value of 0.158% across three termination conditions, while the average comparison metric value for the best comparison method is 2.446%, which is 15.481 times that of HSSOR. Numerical results demonstrate that the proposed algorithms can solve the problem efficiently.

Mathematical models for multi-stage hybrid assembly flow-shop scheduling with preventive maintenance and release times

Effective job scheduling is crucial in production to reduce costs, improve customer satisfaction, enhance efficiency, and boost competitiveness, leading to transformative impacts in production environments. This article presents a pioneering approach that addresses assembly and processing at multiple stages in the scheduling process. Specifically, we introduce the first formulation of mixed integer linear programs (MILP) for a Multi-stage Hybrid Assembly Flow-shop with identical parallel machines. The models incorporate considerations for job release times and service tasks to implement preventive maintenance and effectively captures the intricacies of a Multistage Assembly flow-shop. Existing literature in Assembly flow-shop scheduling primarily focuses on the two-stage assembly flow-shop scheduling problem (TSAFSP) and its extensions, such as the three-stage assembly flow-shop scheduling problem, the Distributed two-stage assembly flow-shop scheduling problem (DTSAFSP), the Distributed assembly permutation flow-shop Scheduling Problem (DAPFSP), and the multi-stage assembly flow shop (MSAF). Our work deals with a challenging NP-hard problem. Inspired by a real-life production site dedicated to manufacturing heavy machinery, we validate our proposed mathematical models by solving various instances of the problem using exact methods. The experimental results confirm the effectiveness and robustness of our approach in tackling multi-stage hybrid assembly flow shop scheduling challenges.

An effective hyper heuristic-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem

In this paper, an effective Hyper Heuristic-based Memetic Algorithm (HHMA) is proposed to solve the Distributed Assembly Permutation Flow-shop Scheduling Problem (DAPFSP) with the objective of minimizing the maximum completion time. A novel searching-stage-based solution representation scheme is presented for both improving the search efficiency and maintaining potential solutions. In the global search stage, Estimation of Distribution Algorithm (EDA) is employed as the high level strategy of EDA-based Hyper Heuristic (EDAHH) to find promising product sequences for further exploitation. Based on the newly found knowledge of critical-products, several efficient Low-Level Heuristics (LLHs) are well designed to construct the LLH set so that the powerful exploration ability of the EDAHH can be guaranteed. A simulated-annealing-like type of acceptance criterion is also embedded into each LLH to avoid premature convergence. Then a Critical-Products-based Referenced Local Search (CP-RLS) method is proposed to improve the quality of superior sub-population by operating on the sub-job-sequences derived from the critical products. The benefit of the presented CP-RLS lies in the excellent exploitation ability with substantially reduced computational cost. Finally, performance evaluation and comparison are both carried out on a benchmark set and the results demonstrate the superiority of HHMA over the state-of-the-art algorithms for the DAPFSP.

An effective memetic algorithm for the distributed flowshop scheduling problem with an assemble machine

The distributed flowshop has been a hot topic in research in recent years. This paper considers a distributed permutation flowshop scheduling problem with an assemble machine, so-called the distributed assembly permutation flowshop scheduling problem (DAPFSP), with total tardiness criterion. We propose an effective memetic algorithm (EMA). Firstly, a constructive heuristic combining the well-known earliest due date rule and largest processing time rule is presented for producing a seed sequence. On this basis, an effective initialisation method is used to generate an initial population with a high level of quality and diversity. The EMA uses a new structure of a small iteration nested within a large iteration. Moreover, an improved crossover and mutation expand the solution space in a good direction. According to different operation situations and objects, we design four targeted and flexible local search methods. We evaluate the strategies of the EMA, compare and analyse it with seven efficient algorithms based on the 810 benchmark instances. The experimental results confirm the effectiveness and efficiency of the proposed EMA.

Distributed assembly permutation flow-shop scheduling problem with non-identical factories and considering budget constraints

PurposeThis paper aims to address a distributed assembly permutation flow-shop scheduling problem (DAPFSP) considering budget constraints and factory eligibility. The first stage of the considered production system is composed of several non-identical factories with different technology levels and so the factories' performance is different in terms of processing time and cost. The second stage is an assembly stage wherein there are some parallel work stations to assemble the ready parts into the products. The objective function is to minimize the maximum completion time of products (makespan).Design/methodology/approachFirst, the problem is formulated as mixed-integer linear programing (MIP) model. In view of the nondeterministic polynomial (NP)-hard nature, three approximate algorithms are adopted based on variable neighborhood search (VNS) and the Johnsons' rule to solve the problem on the practical scales. The proposed algorithms are applied to solve some test instances in different sizes.FindingsComparison result to mathematical model validates the performance accuracy and efficiency of three proposed methods. In addition, the result demonstrated that the proposed two-level self-adaptive variable neighborhood search (TLSAVNS) algorithm outperforms the other two proposed methods. Moreover, the proposed model highlighted the effects of budget constraints and factory eligibility on the makespan. Supplementary analysis was presented by adjusting different amounts of the budget for controlling the makespan and total expected costs. The proposed solution approach can provide proper alternatives for managers to make a trade-off in different various situations.Originality/valueThe problem of distributed assembly permutation flow-shop scheduling is traditionally studied considering identical factories. However, processing factories as an important element in the supply chain use different technology levels in the real world. The current paper is the first study that investigates that problem under non-identical factories condition. In addition, the impact of different technology levels is investigated in terms of operational costs, quality levels and processing times.

An improved iterated greedy algorithm for the distributed assembly permutation flowshop scheduling problem

This paper considers a distributed assembly permutation flowshop scheduling problem (DAPFSP) with total flowtime (TF) criterion, which is of great significance to both industry and research community. We propose an improved iterative greedy algorithm based on the groupthink (gIGA) for solving the problem. Firstly, based on the solution representation, we present an effective initialization procedure by combining the well-known NEH heuristic and Palmer method. Secondly, in order to improve the efficiency of the algorithm, both the destruction reconstruction process and the local search process are elaborately designed for the products and jobs separately. Next, our algorithm adaptively extracts jobs in the destruction stage with regard to the size of instances. In addition, we employ a novel selection method that is based on the objective values and the ages of the individuals in the population. Through a total of 810 benchmark instances, the proposed algorithm is compared with seven state-of-the-art algorithms in the literature. The experimental results show that the proposed algorithm performs significantly better than the other algorithms in comparison by three analytical methods for solving the DAPFSP with TF criterion.

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 enhanced genetic algorithm for the distributed assembly permutation flowshop scheduling problem

The distributed assembly permutation flowshop scheduling problem (DAPFSP) is a new generalisation of the distributed permutation flowshop scheduling problem (DPFSP) and the assembly flowshop scheduling problem (AFSP), aiming to minimise makespan. This production mode is more complicated and competitive in the real production process and includes two phases: production and assembly. Firstly, the production is conducted in several identical factories, and the production in each factory can be considered to a permutation flowshop scheduling problem (PFSP) with multi-machines. Then, the jobs produced in the first stage are assembled into final products. An enhanced population-based meta-heuristic – genetic algorithm (GA) is proposed for this problem. A greedy mating pool is designed to select promising parents in the selection operation, and an effective crossover strategy is designed based on the local search for speeding up convergence. To enhance the exploitation capability, several different local search strategies are incorporated into the algorithm, which are based on two neighbourhood structures. The exhaustive experiment and statistical analysis show that the proposed algorithms outperform the existing algorithms.

An enhanced genetic algorithm for the distributed assembly permutation flowshop scheduling problem

The distributed assembly permutation flowshop scheduling problem (DAPFSP) is a new generalisation of the distributed permutation flowshop scheduling problem (DPFSP) and the assembly flowshop scheduling problem (AFSP), aiming to minimise makespan. This production mode is more complicated and competitive in the real production process and includes two phases: production and assembly. Firstly, the production is conducted in several identical factories, and the production in each factory can be considered to a permutation flowshop scheduling problem (PFSP) with multi-machines. Then, the jobs produced in the first stage are assembled into final products. An enhanced population-based meta-heuristic – genetic algorithm (GA) is proposed for this problem. A greedy mating pool is designed to select promising parents in the selection operation, and an effective crossover strategy is designed based on the local search for speeding up convergence. To enhance the exploitation capability, several different local search strategies are incorporated into the algorithm, which are based on two neighbourhood structures. The exhaustive experiment and statistical analysis show that the proposed algorithms outperform the existing algorithms.

Scheduling the distributed assembly flowshop problem to minimize the makespan

In this paper, we studied the distributed assembly permutation flowshop scheduling problem (DAPFSP) with the makespan minimization as objective. We proposed iterated greedy-based approach that is labelled the bounded-search iterated greedy algorithm BSIG. In addition, four local search methods are designed to improve solution quality. The computational results show the effectiveness of BSIG in solving the DAPFSP specially with small instances where BSIG outperforms most of the existing algorithms.

Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion

Abstract Distributed assembly permutation flowshop scheduling problem (DAPFSP) has important applications in modern assembly systems. In this paper, we present three variants of the discrete invasive weed optimization (DIWO) for the DAPFSP with total flowtime criterion. For solving such a problem, we present a two-level representation that consists of a product permutation and a number of job sequences. We introduce neighbourhood operators for both the product permutation and job sequences. We design effective local search procedures respectively for product-permutation-based neighbourhood and job-sequence-based neighbourhood. By combining the problem-specific knowledge and the idea of invasive weed optimization, we present three DIWO-based algorithms: a two-level discrete invasive weed optimization (TDIWO), a discrete invasive weed optimization with hybrid search operators (HDIWO), and a HDIWO with selection probability. The algorithms explore the two neighbourhoods in quite a different way. We calibrate the presented DIWO algorithms by means of the design of experimental method, and carry out a comprehensive computational campaign based on the 810 benchmark instances in the literature. The numerical experiments show that the presented DIWO algorithms perform significantly better than the other competing algorithms in the literature. Among the proposed algorithms, HDIWO is the best one.

A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem

Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is recognized as an important class of problems in modern supply chains and manufacturing systems. In this paper, a backtracking search hyper-heuristic (BS-HH) algorithm is proposed to solve the DAPFSP. In the BS-HH scheme, ten simple and effective heuristic rules are designed to construct a set of low-level heuristics (LLHs), and the backtracking search algorithm is employed as the high-level strategy to manipulate the LLHs to operate on the solution space. Additionally, an efficient solution encoding and decoding scheme is proposed to generate a feasible schedule. The effectiveness of the BS-HH is evaluated on two typical benchmark sets and the computational results indicate the superiority of the proposed BS-HH scheme over the state-of-the-art algorithms.

An effective hybrid biogeography-based optimization algorithm for the distributed assembly permutation flow-shop scheduling problem

Distributed assembly permutation flow-shop scheduling problem (DAPFSP) is widely exists in modern supply chains and manufacturing systems. In this paper, an effective hybrid biogeography-based optimization (HBBO) algorithm that integrates several novel heuristics is proposed to solve the DAPFSP with the objective of minimizing the makespan. Firstly, the path relinking heuristic is employed in the migration phase as product local search strategy to optimize the assembly sequence. Secondly, an insertion-based heuristic is used in the mutation phase to determine the job permutation for each product. Then, a novel local search method is designed based on the problem characteristics and embedded in the HBBO scheme to further improve the most promising individual. Finally, computational simulations on 900 small-sized instances and 810 large-sized instances are conducted to demonstrate the effectiveness of the proposed algorithm, and the new best known solutions for 162 instances are found.

An Estimation of Distribution Algorithm-Based Memetic Algorithm for the Distributed Assembly Permutation Flow-Shop Scheduling Problem

In this paper, an estimation of distribution algorithm (EDA)-based memetic algorithm (MA) is proposed for solving the distributed assembly permutation flow-shop scheduling problem (DAPFSP) with the objective to minimize the maximum completion time. A novel bi-vector-based method is proposed to represent a solution for the DAPFSP. In the searching phase of the EDA-based MA (EDAMA), the EDA-based exploration and the local-search-based exploitation are incorporated within the MA framework. For the EDA-based exploration phase, a probability model is built to describe the probability distribution of superior solutions. Besides, a novel selective-enhancing sampling mechanism is proposed for generating new solutions by sampling the probability model. For the local-search-based exploitation phase, the critical path of the DAPFSP is analyzed to avoid invalid searching operators. Based on the analysis, a critical-path-based local search strategy is proposed to further improve the potential solutions obtained in the EDA-based searching phase. Moreover, the effect of parameter setting is investigated based on the Taguchi method of design-of-experiment. Suitable parameter values are suggested for instances with different scales. Finally, numerical simulations based on 1710 benchmark instances are carried out. The experimental results and comparisons with existing algorithms show the effectiveness of the EDAMA in solving the DAPFSP. In addition, the best-known solutions of 181 instances are updated by the EDAMA.

The Distributed Assembly Permutation Flowshop Scheduling Problem

Nowadays, improving the management of complex supply chains is a key to become competitive in the twenty-first century global market. Supply chains are composed of multi-plant facilities that must be coordinated and synchronised to cut waste and lead times. This paper proposes a Distributed Assembly Permutation Flowshop Scheduling Problem (DAPFSP) with two stages to model and study complex supply chains. This problem is a generalisation of the Distributed Permutation Flowshop Scheduling Problem (DPFSP). The first stage of the DAPFSP is composed of f identical production factories. Each one is a flowshop that produces jobs to be assembled into final products in a second assembly stage. The objective is to minimise the makespan. We present first a Mixed Integer Linear Programming model (MILP). Three constructive algorithms are proposed. Finally, a Variable Neighbourhood Descent (VND) algorithm has been designed and tested by a comprehensive ANOVA statistical analysis. The results show that the VND algorithm offers good performance to solve this scheduling problem.