Showing papers on "Flow shop scheduling published in 2012"
TL;DR: A globally optimal scheduling Scheme and a locally optimal scheduling scheme for EV charging and discharging which is not only scalable to a large EV population but also resilient to the dynamic EV arrivals are proposed.
Abstract: The vehicle electrification will have a significant impact on the power grid due to the increase in electricity consumption. It is important to perform intelligent scheduling for charging and discharging of electric vehicles (EVs). However, there are two major challenges in the scheduling problem. First, it is challenging to find the globally optimal scheduling solution which can minimize the total cost. Second, it is difficult to find a distributed scheduling scheme which can handle a large population and the random arrivals of the EVs. In this paper, we propose a globally optimal scheduling scheme and a locally optimal scheduling scheme for EV charging and discharging. We first formulate a global scheduling optimization problem, in which the charging powers are optimized to minimize the total cost of all EVs which perform charging and discharging during the day. The globally optimal solution provides the globally minimal total cost. However, the globally optimal scheduling scheme is impractical since it requires the information on the future base loads and the arrival times and the charging periods of the EVs that will arrive in the future time of the day. To develop a practical scheduling scheme, we then formulate a local scheduling optimization problem, which aims to minimize the total cost of the EVs in the current ongoing EV set in the local group. The locally optimal scheduling scheme is not only scalable to a large EV population but also resilient to the dynamic EV arrivals. Through simulations, we demonstrate that the locally optimal scheduling scheme can achieve a close performance compared to the globally optimal scheduling scheme.
651 citations
TL;DR: A hybrid genetic algorithm based on the fitness value and the concentration value and its convergence was proved and the results demonstrated the effectiveness of the proposed algorithm.
163 citations
11 Apr 2012
TL;DR: HyFlex as discussed by the authors is a software framework for the development of cross-domain search methodologies, which features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific.
Abstract: This paper presents HyFlex, a software framework for the development of cross-domain search methodologies. The framework features a common software interface for dealing with different combinatorial optimisation problems and provides the algorithm components that are problem specific. In this way, the algorithm designer does not require a detailed knowledge of the problem domains and thus can concentrate his/her efforts on designing adaptive general-purpose optimisation algorithms. Six hard combinatorial problems are fully implemented: maximum satisfiability, one dimensional bin packing, permutation flow shop, personnel scheduling, traveling salesman and vehicle routing. Each domain contains a varied set of instances, including real-world industrial data and an extensive set of state-of-the-art problem specific heuristics and search operators. HyFlex represents a valuable new benchmark of heuristic search generality, with which adaptive cross-domain algorithms are being easily developed and reliably compared.This article serves both as a tutorial and a as survey of the research achievements and publications so far using HyFlex.
147 citations
TL;DR: Computational results show that the proposed model and method are efficient for solving both assignment and scheduling problems in various kinds of systems.
145 citations
TL;DR: A novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation to solve an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and no-idling production cases.
Abstract: Lot-streaming flow shops have important applications in different industries including textile, plastic, chemical, semiconductor and many others. This paper considers an n-job m-machine lot-streaming flow shop scheduling problem with sequence-dependent setup times under both the idling and no-idling production cases. The objective is to minimize the maximum completion time or makespan. To solve this important practical problem, a novel estimation of distribution algorithm (EDA) is proposed with a job permutation based representation. In the proposed EDA, an efficient initialization scheme based on the NEH heuristic is presented to construct an initial population with a certain level of quality and diversity. An estimation of a probabilistic model is constructed to direct the algorithm search towards good solutions by taking into account both job permutation and similar blocks of jobs. A simple but effective local search is added to enhance the intensification capability. A diversity controlling mechanism is applied to maintain the diversity of the population. In addition, a speed-up method is presented to reduce the computational effort needed for the local search technique and the NEH-based heuristics. A comparative evaluation is carried out with the best performing algorithms from the literature. The results show that the proposed EDA is very effective in comparison after comprehensive computational and statistical analyses.
142 citations
TL;DR: The scheduling problem in hybrid clouds is introduced, presenting the main characteristics to be considered when scheduling workflows, as well as a brief survey of some of the scheduling algorithms used in these systems.
Abstract: Schedulers for cloud computing determine on which processing resource jobs of a workflow should be allocated. In hybrid clouds, jobs can be allocated on either a private cloud or a public cloud on a pay per use basis. The capacity of the communication channels connecting these two types of resources impacts the makespan and the cost of workflow execution. This article introduces the scheduling problem in hybrid clouds presenting the main characteristics to be considered when scheduling workflows, as well as a brief survey of some of the scheduling algorithms used in these systems. To assess the influence of communication channels on job allocation, we compare and evaluate the impact of the available bandwidth on the performance of some of the scheduling algorithms.
142 citations
TL;DR: Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed HSFLA for solving the multi-objective flexible job shop scheduling problem.
132 citations
TL;DR: This paper proposes an effective scheduling method based on Best-so-far Artificial Bee Colony (Best- so-far ABC) for solving the JSSP and demonstrates that the proposed method is able to produce higher quality solutions than the current state-of theart heuristic-based algorithms.
118 citations
TL;DR: The robustness on the relative ranking of the performance quality is checked for the various priority rules when applied on larger problem instances, on the extension of multiple machines possibilities per job as well as on the introduction of sequence-dependent setup times.
Abstract: In this paper, a comparison and validation of various priority rules for the job shop scheduling problem under different objective functions is made. In a first computational experiment, 30 priority rules from the literature are used to schedule job shop problems under two flow time-related and three tardiness-related objectives. Based on this comparative study, the priority rules are extended to 13 combined scheduling rules in order to improve the performance of the currently best-known rules from the literature. Moreover, the best-performing priority rules on each of these five objective functions are combined into hybrid priority rules in order to be able to optimise various objectives at the same time. In a second part of the computational experiment, the robustness on the relative ranking of the performance quality is checked for the various priority rules when applied on larger problem instances, on the extension of multiple machines possibilities per job as well as on the introduction of sequence-dependent setup times. Moreover, the influence of dynamic arrivals of jobs has also been investigated to check the robustness on the relative ranking of the performance quality between static and dynamic job arrivals. The results of the computational experiments are presented and critical remarks and future research avenues are suggested.
115 citations
01 Aug 2012
TL;DR: An effective co-evolutionary genetic algorithm (CGA) is developed for the minimization of fuzzy makespan and Computational results show that CGA outperforms those algorithms compared.
Abstract: Fuzzy flexible job shop scheduling problem (FfJSP) is the combination of fuzzy scheduling and flexible scheduling in job shop environment, which is seldom investigated for its high complexity. We developed an effective co-evolutionary genetic algorithm (CGA) for the minimization of fuzzy makespan. In CGA, the chromosome of a novel representation consists of ordered operation list and machine assignment string, a new crossover operator and a modified tournament selection are proposed, and the population of job sequencing and the population of machine assignment independently evolve and cooperate for converging to the best solutions of the problem. CGA is finally applied and compared with other algorithms. Computational results show that CGA outperforms those algorithms compared.
111 citations
TL;DR: Simulation results demonstrate that MSM and OSM respectively using GGA and GA outperform current methods used in practice.
Abstract: Based on Genetic Algorithm (GA) and Grouping Genetic Algorithm (GGA), this research develops a scheduling algorithm for job shop scheduling problem with parallel machines and reentrant process. This algorithm consists of two major modules: machine selection module (MSM) and operation scheduling module (OSM). MSM helps an operation to select one of the parallel machines to process it. OSM is then used to arrange the sequences of all operations assigned to each machine. A real weapon production factory is used as a case study to evaluate the performance of the proposed algorithm. Due to the high penalty of late delivery in military orders and high cost of equipment investment, total tardiness, total machine idle time and makespan are important performance measures used in this study. Based on the design of experiments, the parameters setting for GA and GGA are identified. Simulation results demonstrate that MSM and OSM respectively using GGA and GA outperform current methods used in practice.
03 Dec 2012
TL;DR: A new Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from where the one that better suits the user requirements can be manually selected.
Abstract: Nowadays, scientists and companies are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds that have to be simultaneously optimized. Multi-objective scheduling of scientific workflows in distributed systems is therefore receiving increasing research attention. Most existing approaches typically aggregate all objectives in a single function, defined a-priori without any knowledge about the problem being solved, which negatively impacts the quality of the solutions. In contrast, Pareto-based approaches having as outcome a set of several (nearly-) optimal solutions that represent a tradeoff among the different objectives, have been scarcely studied. In this paper, we propose a new Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from where the one that better suits the user requirements can be manually selected. We demonstrate the potential of MOHEFT for a bi-objective scheduling problem that optimizes makespan and economic cost in a Cloud-based computing scenario. We compare MOHEFT with two state-of-the-art approaches using different synthetic and real-world workflows: the classical HEFT algorithm used in single-objective scheduling and the SPEA2∗ genetic algorithm used for multi-objective optimisation problems.
TL;DR: In this article, two simple constructive heuristics, namely weighted profile fitting (wPF) and PW, are proposed by combining the PF, wPF, and PW with the enumeration procedure of the Nawaz-Enscore-Ham (NEH) heuristic.
Abstract: The blocking flowshop scheduling problem with makespan criterion has important applications in a variety of industrial systems. Heuristics that explore specific characteristics of the problem are essential for many practical systems to find good solutions with limited computational effort. This paper first presents two simple constructive heuristics, namely weighted profile fitting (wPF) and PW, based on the profile fitting (PF) approach of McCormick et al. [Sequencing in an assembly line with blocking to minimize cycle time. Operations Research 1989;37:925–36] and the characteristics of the problem. Then, three improved constructive heuristics, called PF-NEH, wPF-NEH, and PW-NEH, are proposed by combining the PF, wPF, and PW with the enumeration procedure of the Nawaz–Enscore–Ham (NEH) heuristic [A heuristic algorithm for the m-machine, n-job flow shop sequencing problem. OMEGA-International Journal of Management Science 1983;11:91–5] in an effective way. Thirdly, three composite heuristics i.e., PF-NEHLS, wPF-NEHLS, and PW-NEHLS, are developed by using the insertion-based local search method to improve the solutions generated by the constructive heuristics. Computational simulations and comparisons are carried out based on the well-known flowshop benchmarks of Taillard [Benchmarks for basic scheduling problems. European Journal of Operation Research 1993;64:278–85] that are considered as blocking flowshop instances. The results show that the presented constructive heuristics perform significantly better than the existing ones, and the proposed composite heuristics further improve the presented constructive heuristics by a considerable margin. In addition, 17 new best-known solutions for Taillard benchmarks with large scale are found by the presented heuristics.
01 Jan 2012
TL;DR: Experimental results show effectiveness and efficiency of the genetic algorithm-based task scheduling model in comparison with existing task scheduling models, which are the round-robin task Scheduling model, the load index-basedtask scheduling model, and the ABC based task schedulingmodel.
Abstract: Task scheduling is an important and challenging issue of Cloud computing. Existing solutions to task scheduling problems are unsuitable for Cloud computing because they only focus on a specific purpose like the minimization of execution time or workload and do not use characteristics of Cloud computing for task scheduling. A task scheduler in Cloud computing has to satisfy cloud users with the agreed QoS and improve profits of cloud providers. In order to solve task scheduling problems in Cloud computing, this paper proposes a task scheduling model based on the genetic algorithm. In the proposed model, the task scheduler calls the GA scheduling function every task scheduling cycle. This function creates a set of task schedules and evaluates the quality of each task schedule with user satisfaction and virtual machine availability. The function iterates genetic operations to make an optimal task schedule. Experimental results show effectiveness and efficiency of the genetic algorithm-based task scheduling model in comparison with existing task scheduling models, which are the round-robin task scheduling model, the load index-based task scheduling model, and the ABC based task scheduling model.
01 Jun 2012
TL;DR: A new approach hybridizing PSO with bottleneck heuristic to fully exploit the bottleneck stage, and with simulated annealing to help escape from local optima to solve the HFS problem.
Abstract: Hybrid flow shops (HFS) are common manufacturing environments in many industries, such as the glass, steel, paper and textile industries. In this paper, we present a particle swarm optimization (PSO) algorithm for the HFS scheduling problem with minimum makespan objective. The main contribution of this paper is to develop a new approach hybridizing PSO with bottleneck heuristic to fully exploit the bottleneck stage, and with simulated annealing to help escape from local optima. The proposed PSO algorithm is tested on the benchmark problems provided by Carlier and Neron. Experimental results show that the proposed algorithm outperforms all the compared algorithms in solving the HFS problem.
TL;DR: A heuristic solution approach based on Benders’ decomposition is developed and compared to exact methods and to previously proposed approaches, and it is proved that the finite scenario sample average approximation problem is NP-complete.
Abstract: This article develops algorithms for a single-resource stochastic appointment sequencing and scheduling problem with waiting time, idle time, and overtime costs. This is a basic stochastic scheduling problem that has been studied in various forms by several previous authors. Applications for this problem cited previously include scheduling of surgeries in an operating room, scheduling of appointments in a clinic, scheduling ships in a port, and scheduling exams in an examination facility. In this article, the problem is formulated as a stochastic integer program using a sample average approximation. A heuristic solution approach based on Benders’ decomposition is developed and compared to exact methods and to previously proposed approaches. Extensive computational testing shows that the proposed methods produce good results compared with previous approaches. In addition, it is proved that the finite scenario sample average approximation problem is NP-complete.
TL;DR: Comparisons of WLC order release methods against the best-performing purely periodic and continuous release rules across a range of flow directions demonstrate that LUMS COR and the continuous WLC release methods consistently outperform purely periodic release and Constant WIP.
Abstract: Protecting throughput from variance is the key to achieving lean. Workload control (WLC) accomplishes this in complex make-to-order job shops by controlling lead times, capacity, and work-in-process (WIP). However, the concept has been dismissed by many authors who believe its order release mechanism reduces the effectiveness of shop floor dispatching and increases work center idleness, thereby also increasing job tardiness results. We show that these problems have been overcome. A WLC order release method known as “LUMS OR” (Lancaster University Management School order release) combines continuous with periodic release, allowing the release of work to be triggered between periodic releases if a work center is starving. This paper refines the method based on the literature (creating “LUMS COR” [Lancaster University Management School corrected order release]) before comparing its performance against the best-performing purely periodic and continuous release rules across a range of flow directions, from the pure job shop to the general flow shop. Results demonstrate that LUMS COR and the continuous WLC release methods consistently outperform purely periodic release and Constant WIP. LUMS COR is considered the best solution in practice due to its excellent performance and ease of implementation. Findings have significant implications for research and practice: throughput times and job tardiness results can be improved simultaneously and order release and dispatching rules can complement each other. Thus, WLC represents an effective means of implementing lean principles in a make-to-order context.
TL;DR: An efficient scheduling method to deal with diverse complex cluster tool scheduling problems by using timed Petri nets (TPN), and a new mixed integer programming (MIP) model that can efficiently determine the optimal cyclic schedules is proposed.
Abstract: Cluster tools are automated production cells which are largely used for semiconductor manufacturing. They consist of several processing modules (PMs) and a transportation robot. Since cluster tools have limited buffers and diverse scheduling requirements such as complex wafer flow patterns, parallel PMs, wafer residency time constraints, and dual-arm robot, and so on, their scheduling problems are difficult. Due to the diversity of scheduling problems, dealing with those problems one by one may be impractical. Computational complexity is another difficulty. In this paper, we propose an efficient scheduling method to deal with diverse complex cluster tool scheduling problems by using timed Petri nets (TPN). We propose TPN models of cluster tools with various scheduling requirements. Then, based on the TPN models and their state equations, we develop a new mixed integer programming (MIP) model that can efficiently determine the optimal cyclic schedules. We show that many kinds of scheduling requirements such as parallel, reentrant and multiple material flows, a dual-armed robot, and time constrained PMs can be dealt with by the MIP model. Through experiments, we also show that the MIP model can efficiently solve most practical cluster tool scheduling problems.
TL;DR: This paper presents a hybrid discrete differential evolution (HDDE) algorithm for the no-idle permutation flow shop scheduling problem with makespan criterion that is superior to the existing state-of-the-art algorithms by a significant margin.
TL;DR: The hybrid model proposed here surpasses most similar systems in solving many more traditional benchmark problems and real-life problems and achieves by the combined impact of several small but important features such as powerful chromosome representation, effective genetic operators, restricted neighbourhood strategies and efficient search strategies along with innovative initial solutions.
Abstract: In recent decades many attempts have been made at the solution of Job Shop Scheduling Problem using a varied range of tools and techniques such as Branch and Bound at one end of the spectrum and Heuristics at the other end However, the literature reviews suggest that none of these techniques are sufficient on their own to solve this stubborn NP-hard problem Hence, it is postulated that a suitable solution method will have to exploit the key features of several strategies We present here one such solution method incorporating Genetic Algorithm and Tabu Search The rationale behind using such a hybrid method as in the case of other systems which use GA and TS is to combine the diversified global search and intensified local search capabilities of GA and TS respectively The hybrid model proposed here surpasses most similar systems in solving many more traditional benchmark problems and real-life problems This, the system achieves by the combined impact of several small but important features such as powerful chromosome representation, effective genetic operators, restricted neighbourhood strategies and efficient search strategies along with innovative initial solutions These features combined with the hybrid strategy employed enabled the system to solve several benchmark problems optimally, which has been discussed elsewhere in Meeran and Morshed (8th Asia Pacific industrial engineering and management science conference, Kaohsiung, Taiwan, 2007) In this paper we bring out the system's practical usage aspect and demonstrate that the system is equally capable of solving real life Job Shop problems
TL;DR: This work designs a hierarchical reliability-driven scheduling architecture that includes both a local scheduler and a global scheduler that performs much better than the existing scheduling algorithms in terms of system reliability, schedule length, and speedup.
03 Aug 2012
TL;DR: The three scheduling techniques Min-Min, Max-Min and Genetic Algorithm have been discussed and performance metrics of Min- Min andMax-Min have been shown and the performance of the standard Genetic Al algorithm and the proposed Improved Genetic Algorithms have been checked against the sample data.
Abstract: Cloud computing is a new technology and it is becoming popular day by day because of its great features. In this technology almost everything like hardware, software and platform are provided as a service. These services are charged from users on the pay-per-use bases. A cloud provider in cloud computing provides services on the basis of clients' requests. An important issue in cloud computing is the scheduling of users' requests means how to allocate resources to these requests, so that the requested tasks can be completed in a minimum time according to the user defined time. A good scheduling technique also helps in efficient utilization of the resources. Many scheduling algorithms have been researched like Min-Min, Max-Min, X-Sufferage, Genetic Algorithm, Particle Swarm Optimization etc. In this paper the three scheduling techniques Min-Min, Max-Min and Genetic Algorithm have been discussed and performance metrics of Min-Min and Max-Min have been shown. The performance of the standard Genetic Algorithm and the proposed Improved Genetic Algorithm have been checked against the sample data. A new scheduling idea is also proposed in which Min-Min and Max-Min can be combined in Genetic Algorithm.
TL;DR: In this paper, a mathematical model for the integrated process planning and scheduling (IPPS) is established, and an improved genetic algorithm (IGA) is proposed for the problem.
Abstract: Process planning and scheduling are two of the most important functions involved in manufacturing process and they are actually interrelated; integration of the two is essential to improve the flexibility of scheduling and achieve a global improvement for the performance of a manufacturing system. In order to facilitate the optimization of process planning and scheduling simultaneously, a mathematical model for the integrated process planning and scheduling (IPPS) is established, and an improved genetic algorithm (IGA) is proposed for the problem. For the performance improvement of the algorithm, new initial selection method for process plans, new genetic representations for the scheduling plan combined with process plans and genetic operator method are developed. To verify the feasibility and performance of the proposed approach, experimental studies are conducted and comparisons are made between this approach and others with the makespan and mean flow time performance measures. The results show that the proposed approach on IPPS has achieved significant improvement in minimizing makespan and obtained good results for the mean flow time performance measure with high efficiency.
TL;DR: In this paper, an imperialist competitive algorithm (ICA) is proposed to address the problem of process planning and scheduling with an objective of makespan minimisation, and an extended operation-based representation scheme is presented to include information on various flexibilities of process plans with respect to job shop scheduling.
Abstract: Effective performance of modern manufacturing systems requires integrating process planning and scheduling more tightly, which is consistently challenged by the intrinsic interrelation and intractability of these two problems. Traditionally, these two problems are treated sequentially or separately. Integration of process planning and scheduling (IPPS) provides a valuable approach to improve system performance. However, IPPS is more complex than job shop scheduling or process planning. IPPS is strongly NP-hard in that, compared to an NP-hard job shop scheduling problem with a determined process plan, the process plan for each job in IPPS is also to be optimised. So, an imperialist competitive algorithm (ICA) is proposed to address the IPPS problem with an objective of makespan minimisation. An extended operation-based representation scheme is presented to include information on various flexibilities of process planning with respect to determined job shop scheduling. The main steps of the proposed ICA, inc...
TL;DR: This paper addresses the serial batch scheduling problem embedded in a job shop environment to minimize makespan with a tabu search algorithm which consists of various neighborhood functions, multiple tabu lists and a sophisticated diversification structure.
22 Jul 2012
TL;DR: In this paper, the authors formulated the problem of scheduling for large-scale charging of electric vehicles as a deadline scheduling problem and proposed a utility function that combines both amount of charge and tightness of the deadline.
Abstract: The problem of scheduling for large scale charging of Electric Vehicles (EVs) is considered As part of the future EV infrastructure, a Large Scale Charging (LSC) facility is capable of charging hundreds of electric vehicles simultaneously As an intelligent load in the future smart grid, LSC requires properly designed pricing and scheduling algorithms that take into account the electricity consumed, the arrival-departure characteristics, and overall charging capacity The scheduling of LSC is formulated as a deadline scheduling problem Utility functions that combine both amount of charge and tightness of the deadline are proposed Under arbitrary (and deterministic) arrival, departure, and charging characteristics, a scheduling policy referred to as deadline scheduling with admission control is proposed The proposed algorithm achieves the highest competitive ratio (against the best offline scheduling) for the utility function linear in charging level among all online scheduling algorithms It also offers significant gain over benchmark scheduling algorithms such as the Earliest Deadline First (EDF) scheduling and the First Come First Serve (FCFS) scheduling in terms of average performance for general utility functions when tested with randomly generated charging requests
TL;DR: This paper considers an approach to solve the task-resource scheduling problem optimally based on constructing a logical model for the problem based on the satisfiability problem (SAT) and allows for particle swarm optimization algorithms for scheduling workflows.
Abstract: Cloud computing is a new and rapidly emerging computing paradigm where applications, data and IT services are provided over the Internet. The task-resource management is the key role in cloud computing systems. Task-resource scheduling problems are premier which relate to the efficiency of the whole cloud computing facilities. Task-resource scheduling problem is NP-complete. In this paper, we consider an approach to solve this problem optimally. This approach is based on constructing a logical model for the problem. Using this model, we can apply algorithms for the satisfiability problem (SAT) to solve the task-resource scheduling problem. Also, this model allows us to create a testbed for particle swarm optimization algorithms for scheduling workflows.
TL;DR: A novel decomposition-based approach (DBA) is presented, which combines both the shortest processing time (SPT) and the genetic algorithm (GA) to minimizing the makespan of a flexible flow shop (FFS) with stochastic processing times.
01 Jan 2012
TL;DR: A general scheduling model that considers the effects of position-dependent learning and time-dependent deterioration simultaneously simultaneously and shows that they are polynomially solvable and optimal under certain conditions is introduced.
Abstract: Job deterioration and learning co-exist in many realistic scheduling situations. This paper introduces a general scheduling model that considers the effects of position-dependent learning and time-dependent deterioration simultaneously. In the proposed model, the actual processing time of a job depends not only on the total processing time of the jobs already processed but also on its scheduled position. This paper focuses on the single-machine scheduling problems with the objectives of minimizing the makespan, total completion time, total weighted completion time, discounted total weighted completion time, and maximum lateness based on the proposed model, respectively. It shows that they are polynomially solvable and optimal under certain conditions. Additionally, it presents some approximation algorithms based on the optimal schedules for the corresponding single-machine scheduling problems and analyzes their worst case error bound.
TL;DR: An improved hybrid Cuckoo Search (IHCS) algorithm is developed and the proposed algorithm has been implemented for some benchmark problems in the literature and the results are compared with some other metaheuristics algorithms.
Abstract: Permutation flow shop scheduling problems with makespan minimisation are considered in this paper. Flow shop scheduling is one important type of scheduling problems for the past several decades. Flow shop scheduling problems are non-deterministic polynomial time hard (NP-hard) problems. Hence the exact methods can not be used to solve these problems. Many heuristics and metaheuristics were addressed in the literature to solve the flow shop scheduling problems. Cuckoo Search is a recently developed metaheuristics algorithm. The efficiency of the algorithm may decrease as the parameters of the Cuckoo Search are constant. Hence an improved hybrid Cuckoo Search (IHCS) algorithm is developed in the present work to solve the permutation flow shop scheduling problems. The proposed algorithm has been implemented for some benchmark problems in the literature and the results are compared with some other metaheuristics algorithms.