Flow Shop Benchmark Research Articles

Green manufacturing electrical network scheduling using Slime Mould and Interior search algorithms

The strategic reduction of power consumption in electrical machines, particularly within manufacturing networks, is critical for achieving energy efficiency and sustainability. Bio-inspired algorithms have demonstrated remarkable potential for optimization, with their adaptability to specific problem domains being a key factor in their success. This study employs discrete optimization algorithms tailored for scheduling in an electrical manufacturing network modeled as a hybrid flow shop, where energy efficiency is paramount. Experimental evaluations were conducted on a customized hybrid flow shop benchmark comprising approximately 45 cases and 900 instances, utilizing a random distribution of hourly energy consumption and a specific tariff structure for monthly usage. The findings demonstrate the efficacy of the proposed discretized metaheuristics in significantly reducing power consumption costs while maintaining a minimized total makespan. This work highlights the potential of bio-inspired algorithms for green hybrid flow shop scheduling within electrical networks, contributing to enhanced operational efficiency, sustainability, and energy conservation in modern manufacturing environments.

Bounding Strategies for obtaining a lower bound for N-job and M-machine flowshop scheduling problem with objective of minimizing the total flowtime of jobs

In this paper, bounding strategies for determining a lower bound on the completion time of a job sequenced in each position in the permutation sequence on each machine in permutation flowshop scheduling problem with minimisation of total flowtime of jobs as objective are discussed. Basically, the bounding strategies are machine-based bounding strategies used for determining the lower bound on total flowtime of jobs for all the small-sized and large-sized benchmark flowshop scheduling problem instances proposed by Vallada et al. (2015). The lower bound matrix can be pruned as tightening constraints into the mixed integer linear programming (MILP) model with objective of minimisation of total flowtime of jobs. Since the flowshop scheduling problem with total flowtime objective is difficult, two kinds of linear programming (LP) relaxation methods are used for determining an LP-based lower bound on total flowtime of jobs for some benchmark problem instances proposed by Vallada et al. (2015).

A two-phase simulated annealing algorithm to minimise the completion time variance of jobs in flowshops

In this paper, simulated annealing (SA) algorithm is employed to solve the flowshop scheduling problem with the objective of minimising the completion time variance (CTV) of jobs. Four variants of the two-phase SA algorithm (SA-I to SA-IV) are proposed to solve flowshop scheduling problem with the objective of minimising the CTV of jobs without considering the right shifting of completion time of jobs on the last machine. The proposed SA algorithms have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed SA variants are compared with the best CTV of jobs reported in the literature, and the proposed SA-III is found to perform well in minimising the chosen performance measure (CTV) particularly in medium and large size problems.

A Hybrid Algorithm for Multi-Objective Optimization of Minimizing Makespan and Total Flow Time in Permutation Flow Shop Scheduling Problems

This paper proposes, a multi-objective optimization of minimizing makespan and total flow time of jobs for permutation flow shop scheduling is considered. Bi-objective issues are comprehended by doling out uniform weight to every objective function in view of its preference or determining every competent solutions. In flow shop scheduling environment, many meta-heuristic algorithms have been used to find optimal or near-optimal solutions due to the computational cost of determining exact solutions. This work provides a hybridization of genetic algorithm and simulated annealing algorithm (HGASA) based multi-objective optimization algorithm for flow shop scheduling. HGASA could be a simple and proficient algorithm that is utilized to determine for every single and multi-objective problem in flow shop scheduling shop environment. This algorithm can works simply for realistic manufacturing system applications. The proposed hybrid algorithm searches the optimal solution for objectives by considering the makespan and total flow time. The performance of the proposed HGASA was tested on standard flow shop benchmark problems to calculate its performance. The test results show that the HGASA algorithm performed better in terms of searching quality and efficiency than other meta-heuristic algorithms.

On the Robust and Stable Flowshop Scheduling Under Stochastic and Dynamic Disruptions

In this paper, we consider a permutation flowshop scheduling problem with the total flow time as the schedule performance measure. A proactive–reactive approach is designed to simultaneously deal with stochastic disruptions (e.g., machine breakdowns) and dynamic events (e.g., newly arriving jobs and delay in job availability). In the proactive stage, the stochastic machine breakdown is hedged against the construction of a robust and stable baseline schedule. This schedule is either optimized by incorporating uncertainty into two surrogate measures or obtained by simulation. Robustness is measured by the expected schedule performance, while stability is measured by the aggregation of dissatisfactions of manager, shopfloor operator, and customers using the prospect theory. In the reactive stage, we assume that the stochastic and dynamic disruptions concurrently occur. Unlike the simple right-shifting method, a more effective rescheduling approach is proposed to balance the realized schedule performance with stability. A common issue in these two stages is the conflict between objectives. Thus, we propose a hybridization strategy that successfully enhances the classic Non-dominated Sorting Genetic Algorithm (NSGA-II and the hybridized algorithm outperforms NSGA-II, multiobjective evolutionary algorithm based on decomposition, and multiobjective memetic algorithms designed for deterministic scheduling problems. Finally, extensive computational studies on the Taillard flowshop benchmark instances are conducted to illustrate the effectiveness of the proposed proactive–reactive approach and the algorithm hybridization strategy.

A Bat Algorithm with Generalized Walk for the Two-Stage Hybrid Flow Shop Problem

In the last years, a set of bio-inspired metaheuristics has proved their efficiencies in combinational and continues optimization areas. This paper intends to hybrid a discrete version of Bat Algorithm (BA) with Generalized Evolutionary Walk Algorithm (GEWA) to solve the mono-processors two stages Hybrid Flow Shop scheduling. The authors compare the modified bat algorithm with the original one, with Particle Swarm Optimization (PSO) and with others results taken from literature. Computational results on a standard two-stage hybrid flow shop benchmark of 70 cases, and about 1700 instances, indicate that the proposed algorithm finds the best makespan (Cmax) in a good processing time comparing to the original bat algorithm and other algorithms.

Scheduling Blocking Flow Shops Using Meta-RaPS

Abstract A single machine that includes loading/unloading areas for each job processed, loading and unloading could be performed while the machine is running causing minimized jobs completion time with lowest machine idle time. This design requires special kind of scheduling technique to ensure the accomplishment of those objectives if jobs’ processing, loading and unloading times are varying. The machine is modelled as a flow shop with blocking constraint. This research focuses on finding a solution to schedule this special case of flow shop of more than two machines with objective of minimizing jobs maximum completion time (makespan). The proposed solution in this research includes using a newly developed meta-heuristic known as Meta-heuristic for Randomized Priority Search (Meta-RaPS). Meta-RaPS construction phase is applied with the use of NEH flow shop scheduling algorithm and would provide very good schedules. The suggested technique is evaluated in comparison to top performing current meta-heuristics and construction heuristics on the famous benchmark flow shop data set of Taillard20. The results would suggest that applying Meta-RaPS for this flow shop problem is a great choice for constructing solutions and would provide high quality solutions with opportunity of more improvement in further research.

Effect of problem size on the performance of constructive algorithms for flow shop scheduling problems with sequence dependent setup times

This paper focus on the scheduling problem of a flow shop operating in a sequence dependent setup time (SDST) environment. Two constructive algorithms of contrasting characteristics are analysed for the performance with respect to change in problem size; the first algorithm is processing time-based and the second algorithm is setup time-based. The problem size is characterised by the variables namely, number of jobs and number of machines. An extensive performance analysis of the two constructive algorithms has been carried out using 960 SDST flow shop benchmark problem instances. The graphical analysis of the results reveals the correlation between the relative performance of the algorithms and problem size. The study shows that the performance of the setup time-based algorithm increases with increase in number of jobs and decreases with increase in number of machines. The coefficient of variation analysis is used to investigate the performance variation of the algorithm with change in number of machines. The analysis reveals that as the number machines increases, the coefficient of variation of the summed setup time matrix decreases which causes the change in performance of the setup time-based algorithm.

A heuristic algorithm to minimise the total flowtime of jobs in permutation flowshops

A heuristic algorithm with six implementations (HA-I to HA-VI) is presented in this paper, and it is used to solve the permutation flowshop scheduling problem to minimise the total flowtime of jobs. These implementations have been evaluated by using the 90 benchmark flowshop scheduling problem instances, and the solutions yielded are compared with the solutions reported in the literature. The comparison shows that the implementations all together have produced better solutions for 28 problem instances and same solutions for 29 problem instances, out of 90 problem instances considered with respect to the solutions by Iterated Local Search algorithm proposed by Dong et al. (2009). They have also produced better solutions for 50 problem instances and the same solutions for 19 problem instances, out of 90 problem instances considered with respect to the solutions by PSOvns algorithm proposed by Tasgetiren et al. (2007), as reported in Dong et al. (2009).

Nonpermutation flow line scheduling by ant colony optimization

AbstractA flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Computation experiments show that the proposed algorithm is incumbent to the state-of-the-art ant colony optimization for flowshop with higher job to machine ratios, using the makespan as the optimization criterion.

Impact of varying levels of setup time on the performance of algorithms for scheduling a flow shop with sequence dependent setup time

This paper presents a comparative study of two algorithms for the flow shop scheduling problem with sequence dependent setup time. The objective is to investigate the effect of setup time on makespan criterion under varying proportions of setup time. The two constructive algorithms used in the study involve a completely different approach in constructing the sequence. The first algorithm is processing time-based and the second algorithm setup time-based. Taillard benchmark flow shop problems are used for the development of benchmark problems for flow shop scheduling with sequence dependent setup time. Eight different levels of setup time matrices are developed for each problem size. The two algorithms are tested for the 960 problem instances developed. The results are analysed graphically and statistically for the varying levels of setup time. The analysis reveals that the setup time-based algorithm improves its performance when the setup time increases as a percentage of processing time. The results of statistical analysis validate the variability in performance of the two algorithms considered at different levels of setup time.

A modified ant-colony optimisation algorithm to minimise the completion time variance of jobs in flowshops

In this work, the flowshop scheduling problem is considered with the objective of minimising the completion-time variance (CTV) of jobs, and an Ant Colony Optimisation (ACO) algorithm is presented. Two implementations of the Modified Ant Colony Optimisation algorithm (MACO-I and MACO-II) are proposed to solve the permutation flowshop scheduling problem. The proposed ant-colony-algorithm implementations have been tested on 90 benchmark flowshop scheduling problems. The solutions yielded by the proposed MACO implementations are compared with various algorithms and with the best CTV of jobs reported in the literature. The proposed MACO implementations are found to perform very well in minimising the chosen performance measure.

Effect of Varying Levels of Setup Time on Makespan Criteria: A Comparative Study of Two Algorithms on SDST Flow Shop

Scheduling problems with setup time consideration are the realistic versions of classical scheduling problems. This paper analyses the effect of setup time level variation on the performance of two constructive algorithms for flow shop scheduling with sequence dependent setup time. The two constructive algorithms considered in the study differ in their approach in constructing the sequence; the first algorithm is processing time based while the second algorithm is setup time and processing time based. The setup time based algorithm known as fictitious job setup ranking algorithm is developed as a part of the present study. Taillard benchmark flow shop scheduling problems are used for development of benchmark problems for flow shop scheduling with sequence dependent setup time. The two algorithms are tested for the set of benchmark problem instances developed. The results are analyzed graphically and statistically for the varying levels of setup time. The study reveals that the setup time based algorithm improves its performance when the setup time increases as percentage of processing time. This paper proposes a framework namely setup dominance matrix, which serves as an aid to identify the algorithm that is suitable for a specific size flow shop scheduling problem at a particular level of setup time.

Effective heuristics for the no-wait flow shop scheduling problem with total flow time minimization

The no-wait flow shop scheduling problem with total flow time criterion has important applications in industrial systems. Heuristics that explore specific characteristics of the problem are essential to find good solutions in limited computational time for many practical applications. This paper first presents two constructive heuristics, namely improved standard deviation heuristic (ISDH) and improved Bertolissi heuristic (IBH), by combining the standard deviation heuristic (Gao et al., Int J Adv Manf Technol 56:683–692, 2011) and Bertolliso heuristic (Bertolissi, J Mater Process Technol 107:459–465, 2000) with the procedure of the constructive heuristic of Laha (Int J Adv Manf Technol 41:97–109, 2009). Then, four composite heuristics, i.e., ISDH with local search, IBH with local search, ISDH with iteration, and IBH with iteration, are separately proposed using the insertion-based local search method and iteration operator to improve the solutions of the ISDH and IBH. Extensive computational experiments are carried out based on a set of well-known flow shop benchmark instances that are considered as no-wait flow shop instances. Computational results and comparisons show that the proposed composite heuristics perform significantly better than the existing ones, and the proposed composite heuristics further improve the presented constructive heuristics for the no-wait flow shop scheduling problem with total flow time criterion.

Flow Shop Scheduling Problem with Transportation Times, Two-Robots and Inputs/Outputs with Limited Capacity

In this paper we study a generalized flow-shop problem with two identical transport robots between machines. Besides transportation times for the jobs, empty moving times for the robots are taken into account. The objective is to minimize the Makespan. An ant colony algorithm with appropriate probability transfer structures is proposed. Computational results are presented for test data arising from flow-shop benchmark instances enlarged by transportation and empty moving times.

Local search methods for the flowshop scheduling problem with flowtime minimization

Flowshop scheduling is a very active research area. This problem still attracts a considerable amount of interest despite the sheer amount of available results. Total flowtime minimization of a flowshop has been actively studied and many effective algorithms have been proposed in the last few years. New best solutions have been found for common benchmarks at a rapid pace. However, these improvements many times come at the cost of sophisticated algorithms. Complex methods hinder potential applications and are difficult to extend to small problem variations. Replicability of results is also a challenge. In this paper, we examine simple and easy to implement methods that at the same time result in state-of-the-art performance. The first two proposed methods are based on the well known Iterated Local Search (ILS) and Iterated Greedy (IG) frameworks, which have been applied with great success to other flowshop problems. Additionally, we present extensions of these methods that work over populations, something that we refer to as population-based ILS (pILS) and population-based IG (pIGA), respectively. We calibrate the presented algorithms by means of the Design of Experiments (DOE) approach. Extensive comparative evaluations are carried out against the most recent techniques for the considered problem in the literature. The results of a comprehensive computational and statistical analysis show that the presented algorithms are very effective. Furthermore, we show that, despite their simplicity, the presented methods are able to improve 12 out of 120 best known solutions of Taillard’s flowshop benchmark with total flowtime criterion.

An improved artificial bee colony algorithm for the blocking flowshop scheduling problem

This paper presents an improved artificial bee colony (IABC) algorithm for solving the blocking flowshop problem with the objective of minimizing makespan. The proposed IABC algorithm utilizes discrete job permutations to represent solutions and applies insert and swap operators to generate new solutions for the employed and onlooker bees. The differential evolution algorithm is employed to obtain solutions for the scout bees. An initialization scheme based on the problem-specific heuristics is presented to generate an initial population with a certain level of quality and diversity. A local search based on the insert neighborhood is embedded to improve the algorithm's local exploitation ability. The IABC is compared with the existing hybrid discrete differential evolution and discrete artificial bee colony algorithms based on the well-known flowshop benchmark of Taillard. The computational results and comparison demonstrate the superiority of the proposed IABC algorithm for the blocking flowshop scheduling problems with makespan criterion.

HYBRID FUZZY LOGIC-BASED PARTICLE SWARM OPTIMIZATION FOR FLOW SHOP SCHEDULING PROBLEM

This paper, proposes a hybrid fuzzy logic-based particle swarm optimization (PSO) with cross-mutated operation method for the minimization of makespan in permutation flow shop scheduling problem. This problem is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed hybrid PSO, fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the inertia weight becomes adaptive. The cross-mutated operation effectively forces the solution to escape the local optimum. To make PSO suitable for solving flow shop scheduling problem, a sequence-order system based on the roulette wheel mechanism is proposed to convert the continuous position values of particles to job permutations. Meanwhile, a new local search technique namely swap-based local search for scheduling problem is designed and incorporated into the hybrid PSO. Finally, a suite of flow shop benchmark functions are employed to evaluate the performance of the proposed PSO for flow shop scheduling problems. Experimental results show empirically that the proposed method outperforms the existing hybrid PSO methods significantly.

Clustered enhanced differential evolution for the blocking flow shop scheduling problem

A novel clustered population paradigm is presented in this paper which is based on Chaos principles of edges and attractors. Convergence in evolutionary algorithms is viewed as a manifestation through cyclic dynamics and thus a new population is developed which is clustered and separated through new segregation bias rules. This population is embedded on the Enhanced Differential Evolution and the flow shop scheduling problem with blocking is solved. The two flow shop benchmark problems of Rec/Car/Hel and Taillard are solved with this new approach and the results favorably compared with published results in literature. A total of 49 new upper bounds for the Taillard problems was obtained during experimentation.

Joint scheduling of jobs and Preventive Maintenance operations in the flowshop sequencing problem: a resolution with sequential and integrated strategies

This paper presents a comparative study on joint production and Preventive Maintenance (PM) scheduling according to the most interesting joint scheduling strategies: sequential and integrated ones regarding Flow Shop Problems (FSPs). For each strategy, we present three heuristics: Genetic Algorithms (GAs), Taboo Search (TS) and NEH heuristic. The proposed heuristics have been applied to non-standard test problems, which represent joint production and maintenance benchmark flowshop scheduling problems taken from Benbouzid et al. (2003). A comparison of the solutions yielded by the heuristics developed in this paper with the heuristic solutions given by Taillard (1993) is undertaken with respect to the minimisation of performance loss after maintenance insertion. The comparison shows that the proposed integrated GAs are clearly superior to all the analysed algorithms.