Two-stage Assembly Flow Shop Problem Research Articles

On the two-stage assembly flow shop problem

On the two-stage assembly flow shop problem

Robust scheduling for a two-stage assembly shop with scenario-dependent processing times

Recently, finding solutions to assembly flowshop scheduling problems is a topic of extensive discussion in research communities. While existing research assumes that job processing times are constant numbers, in several practical situations, due to several external factors like machine breakdowns, working environment changes, worker performance instabilities, and tool quality variations and unavailability, job processing times may vary. In this study, therefore, we address a two-stage assembly flowshop scheduling problem with two scenario-dependent jobs processing times to minimise the maximum makepsan among both scenarios (called robust makespan) In view of the NP-hard nature, we first derive a dominance property and a lower bound to propose a branch-and-bound algorithm to find a permutation schedule with minimum makespan. Following that, we use Johnson’s rule to propose eight polynomial heuristics for finding near-optimal solutions. Furthermore, we propose four cloud theory-based simulated annealing (CSA) hyper-heuristic algorithms incorporating seven low level heuristics to solve a robust two-stage assembly flowshop problem with scenario-dependent processing times. Finally, we empirically evaluate the effectiveness of all the proposed algorithms in minimising the robust makespan.

An effective model and algorithm for two-stage assembly flow shop problems

The assembly scheduling has been recently become an active field of research. Among different assembly problems, the flow shop one is the most active one. A mathematical model and some algorithms are already developed for this problem. Yet, both available model and algorithms are unfortunately ineffective. Therefore, we have been thinking of developing an improved mathematical model as well as a new powerful algorithm for the problem. The model is in form of mixed integer linear programming model and the algorithm is a novel teaching-learning based optimisation algorithm. The proposed model and algorithm are evaluated by comparing with available model and algorithms for performance. All the experiments show the superiority of the proposed research materials.

An effective model and algorithm for two-stage assembly flow shop problems

The assembly scheduling has been recently become an active field of research. Among different assembly problems, the flow shop one is the most active one. A mathematical model and some algorithms are already developed for this problem. Yet, both available model and algorithms are unfortunately ineffective. Therefore, we have been thinking of developing an improved mathematical model as well as a new powerful algorithm for the problem. The model is in form of mixed integer linear programming model and the algorithm is a novel teaching-learning based optimisation algorithm. The proposed model and algorithm are evaluated by comparing with available model and algorithms for performance. All the experiments show the superiority of the proposed research materials.

Scheduling two-stage assembly flow shop with random machines breakdowns: integrated new self-adapted differential evolutionary and simulation approach

This paper takes random machines breakdowns and the two-stage assembly flow shop problem into consideration as a realistic assumption in industrial environments. In practical manufacturing environment, disruptions and unforeseen incidents occur, so a schedule being built based on deterministic information is not practical and may lead to poor performance. In this paper, machines in manufacturing and assembly stages are not always available due to random machines breakdowns which occur during processing of each operation. The goal is to minimize the expected the weighted sum of makespan and mean of completion time. Owning to its problem complexity and since the problem belongs to NP-hard class, use of meta-heuristic algorithms is justified to tackle the potential complexity of the problem considered, and hence, we proposed four meta-heuristics algorithms entitled: genetic algorithm, imperialist competitive algorithm, cloud theory-based simulated annealing and new self-adapted differential evolutionary (NSDE) to solve it. Machine breakdown and dynamic nature of the problem, the structural complexity increases markedly. In this regard, to overcome this form of complexity, simulation techniques are typically employed. Eventually, since the proposed problem has both types of complexities (algorithm complexity and structural complexity), simulation is integrated into the proposed meta-heuristic approaches to handle the complexities. We apply artificial neural network as a tuning tool for predicting the input parameters of each proposed meta-heuristics algorithms in uncertain condition. Also, we suggest Taguchi method as one the most important adjusting approaches for analyzing the effect of input parameters in each algorithm. The computational results show which proposed NSDE statistically is better than other proposed meta-heuristics algorithms according two important indicators: quality of solution and computational time.

Robust scheduling in two-stage assembly flow shop problem with random machine breakdowns: integrated meta-heuristic algorithms and simulation approach

PurposeIn real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously.Design/methodology/approachOwing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions.FindingsFinally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job.Originality/valueRobustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment

Abstract This paper studies the distributed two-stage assembly flowshop problem with separate setup times, which is a generalisation for the regular two-stage assembly flowshop problem in the distributed manufacturing environment. The optimization objective is to find a suitable job schedule such that the criterion of total completion time is minimized. To deal with such a problem, we propose a novel memetic algorithm (MA) based on a recently developed social spider optimization (SSO). To the best of our knowledge, it is the first effort to explore the SSO-based MA (MSSO) and to apply SSO in the field of combinational optimization. In the proposed MSSO algorithm, we first modify the original version of SSO to adapt to the distributed problems, and then integrate two improvement techniques, problem-special local search and self-adaptive restart strategy, within MA framework. In the numerical experiment, the parameters used in MSSO are calibrated and suitable parameter values are suggested based on the Taguchi method. Experimental results and comparisons with the existing algorithms validate the effectiveness and efficiency of the proposed MSSO for addressing the considered problem. In addition, the effect of problem scale parameters on MSSO and the effectiveness of the proposed improvement techniques are also investigated and demonstrated.

Modeling and Scheduling Two-stage Assembly Flow Shop Problems with Non-Identical Assembly Machines

Modeling and Scheduling Two-stage Assembly Flow Shop Problems with Non-Identical Assembly Machines

An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach

This paper investigates integrated two-stage assembly flow shop problem with preventive maintenance (PM) activities under the multi-objective optimisation approaches. Reliability models are employed to carry out the maintenance activities. This paper attempts to find the appropriate sequence of jobs on machines in order to minimise the makespan and determining when to perform the PM activities in order to minimise the system unavailability. As this problem is proven to be NP-hard two multi-objective optimisation methods that are named non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are employed to find the Pareto-optimal front. The parameters of proposed algorithms are calibrated by artificial neural network (ANN) and the performances of the algorithms on the problem of various sizes are analysed based on four metrics. The computational results reveal NRGA is statistically better than NSGA-II.

An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach

This paper investigates integrated two-stage assembly flow shop problem with preventive maintenance (PM) activities under the multi-objective optimisation approaches. Reliability models are employed to carry out the maintenance activities. This paper attempts to find the appropriate sequence of jobs on machines in order to minimise the makespan and determining when to perform the PM activities in order to minimise the system unavailability. As this problem is proven to be NP-hard two multi-objective optimisation methods that are named non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are employed to find the Pareto-optimal front. The parameters of proposed algorithms are calibrated by artificial neural network (ANN) and the performances of the algorithms on the problem of various sizes are analysed based on four metrics. The computational results reveal NRGA is statistically better than NSGA-II.

Scheduling of assembly flow shop problem and machines with random breakdowns

We investigate two-stage assembly flow shop problems (TAFSP) with considering machines breakdown and minimisation of the expected the weighted sum of makespan and mean of completion time is as objective value. This problem is NP-hard, hence we presented genetic algorithm (GA) and new self adapted differential evolutionary (NSDE) for solving random generated test problems. Artificial neural network (ANN) is applied to set parameters of two proposed algorithms also Taguchi method is used for analysing the effect of parameters of problem. The computational results reveal that NSDE is better than GA and achieve to good solutions in a shorter time.

Bi-objective optimization for integrating production and preventive maintenance scheduling in two-stage assembly flow shop problem

This paper presents a new bi-objective mixed integer programming model for the two-stage assembly flow shop scheduling problem with preventive maintenance (PM) activities, in which the reliability/availability approach is employed to model the maintenance concepts of a problem. PM activities carry out the operations on machines and tools before the breakdown takes place. Therefore, it helps to prevent failures before they happen. After developing a new bi-objective model, an Epsilon-constraint method is proposed to solve the problem. This problem has been known as Np-hard. Therefore, three multi-objective optimization methods, namely fast non-dominated sorting genetic algorithm, Multi-objective imperialist competitive algorithm, and non-dominated ranking genetic algorithm (NRGA) are employed to find the pareto-optimal front for large sized problems. The parameters of the proposed algorithms are calibrated using artificial neural network (ANN) and the performances of the proposed algorithms on the problems of various sizes are analyzed and the computational results reveal that NRGA outperform than two other proposed algorithms in quality of solutions and computational time.

Simulated imperialist competitive algorithm in two-stage assembly flow shop with machine breakdowns and preventive maintenance

This article deals with the two-stage assembly flow shop problem considering preventive maintenance activities and machine breakdowns. The objective is to minimize the makespan. This problem is proven to be NP-hard; thus, we proposed two meta-heuristic algorithms, namely, imperialist competitive algorithm and genetic algorithm, to obtain solutions of the problem. This article describes an approach incorporating simulation with imperialist competitive algorithm for the scheduling purpose having machine breakdowns and preventive maintenance activities. The results obtained are analyzed using Taguchi experimental design, and the parameters of proposed algorithms are calibrated by artificial neural network. The computational results demonstrate that imperialist competitive algorithm is statistically better than genetic algorithm in quality of solution and reaches to better solutions at the same computational time.

An efficient imperialist competitive algorithm for scheduling in the two-stage assembly flow shop problem

This paper considers a two-stage assembly flow shop problem where m parallel machines are in the first stage and an assembly machine is in the second stage. The objective is to minimise a weighted sum of makespan and mean completion time for n available jobs. As this problem is proven to be NP-hard, therefore, we employed an imperialist competitive algorithm (ICA) as solution approach. In the past literature, Torabzadeh and Zandieh (2010) showed that cloud theory-based simulated annealing algorithm (CSA) is an appropriate meta-heuristic to solve the problem. Thus, to justify the claim for ICA capability, we compare our proposed ICA with the reported CSA. A new parameters tuning tool, neural network, for ICA is also introduced. The computational results clarify that ICA performs better than CSA in quality of solutions.

Two-stage flow-shop scheduling problem with non-identical second stage assembly machines

This paper addresses the two-stage assembly flow-shop problem (TSAFP) with multiple non-identical assembly machines in second stage with the objective function of makespan minimization. This problem is a generalization of previously proposed problems in TSAFP. Mathematical mixed-integer linear programming model of this problem is defined, and for it being NP-hard, a hybrid SA heuristic is proposed. The heuristic is proved to solve the problem in reduced time with negligible error. To validate the proposed method, a real-life example is presented and solved in which the efficiency of the proposed heuristic is shown.

Two-stage assembly flow-shop scheduling problem with non-identical assembly machines considering setup times

In this paper, we address the two stage assembly flow-shop problem with multiple non-identical assembly machines in stage two to minimise weighted sum of makespan and mean completion time. Also, sequence dependent setup times are considered for the first stage. This problem is a generalisation of previously proposed two stage assembly flow-shop problems (TSAFSP). In many real world industrial and production systems, there is more than one assembly machine to assemble job components. After extending a mathematical mixed-integer linear programming model to solve the problem, we use GAMS software. The TSAFSP has been known as NP-hard. Therefore, our more general problem is NP-hard too and so for large sized problems the right way to proceed is with the use of heuristic algorithms. So in this paper a hybrid VNS heuristic, which is a combination of the variable neighbourhood search (VNS) algorithm and a novel heuristic is developed and its solutions compared with solutions obtained by GAMS. Computational experiments reveal that the hybrid VNS heuristic performs much better than GAMS with respect to the percentage errors and run times.

An integrated neural network–simulation algorithm for performance optimisation of the bi-criteria two-stage assembly flow-shop scheduling problem with stochastic activities

This paper presents an integrated computer simulation and Artificial Neural Network (ANN) algorithm for a stochastic Two-Stage Assembly Flow-Shop Scheduling Problem (TSAFSP) with setup times under a weighted sum of makespan and mean completion time (MCT) criteria, known as bi-criteria. Significantly, it should be noted that there is no mathematical model to analyse the stochastic model, therefore simulation is used to solve the problem. The simulation model enables decision makers to consider the influence of job scheduling on machines in order to examine both criteria simultaneously. Since it is not possible to evaluate all sequence combinations using the simulation model in a reasonable time, multilayered neural network meta-models have been trained and used to estimate objective function values composed of both makespan and mean completion time criteria for the stochastic TSAFSP. To the best of the authors’ knowledge, this is the first study that considers stochastic machine breakdown, processing times, setup times, makespan and mean completion time as objectives concurrently. The TSAFSP is modelled by Visual SLAM simulation software. The simulation output results are then given to the ANN as inputs to build the meta-model. This meta-model is then used to obtain the results with the optimum values. The advantage of these meta-model applications is a reduction in the number of simulation runs and consequently a reduced run time. Also, this is the first study that introduces an intelligent and flexible algorithm for handling stochastic TSAFSP.

Cloud theory-based simulated annealing approach for scheduling in the two-stage assembly flowshop

In this paper we consider the two-stage assembly flowshop problems where we have m machines in the first stage and an assembly machine in the second stage. We aim to minimize a weighted sum of makespan and mean completion time as the objective for n available jobs. The problem is NP-hard, therefore we proposed the cloud theory-based simulated annealing algorithm (CSA) to solve it. In previous literature Allahverdi and Al-Anzi [5] showed that simulated annealing (SA) is an appropriate heuristic to solve this problem, so we have compared CSA and SA in this study. The computational results reveal that CSA performs better. In addition computational time has been decreased for the CSA algorithm towards the SA.