No-wait Flow Shop Scheduling Problem Research Articles

Discrete Particle Swarm Optimization Algorithm for Lot-Streaming No-Wait Flow Shop Scheduling Problem

This paper proposed discrete particle swarm optimization(DPSO) algorithm to solve lot-streaming no-wait flow shop scheduling problem(LNFSP) with the objective of the maximum completion time. The natural encoding scheme based on job permutation and newly-designed methods were adopted to produce new individuals . After the DPSO-based exploration, a efficient fast local search based on swap neighborhood structure is used to enhance the exploitation capability. Simulation results show the effectiveness of the proposed algorithms.

Multi-objective no-wait flowshop scheduling problems: models and algorithms

This article studies multi-objective no-wait flowshop scheduling problems to minimise both makespan and total tardiness. This problem is commonly denoted by . This article mathematically formulates the problem using two effective multi-objective mixed integer linear programming models. The multi-objective mathematical models are then solved by a multiple criteria decision making approach, called Fuzzy programming method. Moreover, the article proposes a novel multi-objective iterated local search algorithm incorporating three types of local search engine: greedy, moderate and curtailed fashions. The algorithm is carefully evaluated for its performance against two available algorithms by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others.

A HYBRID HARMONY SEARCH ALGORITHM FOR THE NO-WAIT FLOW-SHOP SCHEDULING PROBLEMS

This paper presents a hybrid harmony search (HHS) algorithm for solving no-wait flow shop scheduling problems with total flowtime criterion. First, an initial harmony memory (HM) is formed by taking advantage of the NEH heuristic. Second, the harmony memory is divided into several small groups and each group executes its evolution process independently. At the same time, groups share information reciprocally by dynamic re-grouping mechanism. Third, to stress the balance between the global exploration and local exploration, a variable neighborhood search algorithm is developed and embedded in the HHS algorithm. In addition, a speed-up method is applied to reduce the running time requirement. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed HHS algorithm is superior to the recently published hybrid DE-based (HDE) algorithm and hybrid particle swarm optimization (HPSO) algorithm in terms of effectiveness and efficiency.

Optimization Techniques for No-Wait Manufacturing Scheduling: A Review

This paper deals with a general continuous or no-wait manufacturing scheduling problem. Due to its applications in advanced manufacturing systems, no-wait scheduling has gained much attention in both practical and academic fields. Due to its NP-hard nature, most of the contributions focus on development of approximation based optimization methods or heuristics for the problem. Several heuristic procedures have been developed to solve this problem. This paper presents a survey of various methodologies developed to solve no-wait flow shop scheduling problem with the objective of minimizing single performance measure

Multi-objective no-wait hybrid flowshop scheduling problem with transportation times

This paper studies multi-objective no-wait hybrid flowshop scheduling problems to minimise both makespan and total tardiness. Furthermore, we consider transportation times between machines. This transportation time can be either job-dependent or job-independent. We consider that all transportations are job-independent; and transportations between two machines have to be done by one transporter. This paper presents a new multi-objective electromagnetism algorithm (MOEA). Electromagnetism algorithm is known as a flexible and effective population-based algorithm utilising an attraction/repulsion mechanism to move the particles towards optimality. The algorithm is carefully evaluated for its performance against two available algorithms by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others.

No-wait scheduling of a two-machine flow-shop to minimise the makespan under non-availability constraints and different release dates

In this paper, we consider the two-machine no-wait flow-shop scheduling problem, when every machine is subject to one non-availability constraint and jobs have different release dates. The non-availability intervals of the machines overlap and they are known in advance. We aim to find a non-resumable schedule that minimises the makespan. We propose several lower bounds and upper bounds. These bounding procedures are used in a branch-and-bound algorithm. Computational experiments are carried out on a large set of instances and the obtained results show the effectiveness of our method.

An adaptive learning approach for no-wait flowshop scheduling problems to minimize makespan

No-wait flowshop scheduling problem (NW-FSSP) with the objective to minimize the makespan is an important sequencing problem in the production plans and applications of no-wait flowshops can be found in several industries. In a NW-FSSP, jobs are not allowed to wait between two successive machines. The NW-FSSPs are addressed to minimize makespan and the NW-FSSP is known as a NP- Hard problem. In this study, Agarwal et al.'s1 adaptive learning approach (ALA) is improvement for NW-FSSPs. Improvements in adaptive learning approach is similar to neural-network training. The improvement adaptive learning approach (IALA) is applied to all of the 192 problems. The proposed IALA method for NW-FSSP is compared with Aldowaisan and Allahverdi's2 results by using Genetic heuristic. The results of computational experiments on randomly generated NW-FSSPs are show that the proposed adaptive learning approach performs quite well.

Discrete harmony search algorithm for the no-wait flow shop scheduling problem with total flow time criterion

In this paper, a discrete harmony search algorithm (DHS) is developed for solving the no-wait flow shop scheduling problem with the objective to minimize total flowtime. Firstly, a harmony is represented as a discrete job permutation, and a new heuristic based on the well-known NEH method is proposed to initialize the harmony memory. Secondly, a novel pitch adjustment rule is employed in the improvisation to produce a new harmony. Thirdly, a local search is presented and embedded to enhance the algorithm's local exploitation ability. Extensive computational experiments are carried out based on a set of well-known benchmark instances. Computational results and comparison show the effectiveness of the presented DHS algorithm in solving the no-wait flow shop scheduling with total flowtime criterion.

Fitness landscape analysis for the no-wait flow-shop scheduling problem

The fitness landscape of the no-wait (continuous) flow-shop scheduling problem is investigated by examining the ruggedness of the landscape and the correlation between the quality of a solution and...

An algorithm for large scale no-wait flow shop scheduling problem

An algorithm for large scale no-wait flow shop scheduling problem

An adaptive learning approach for no-wait flowshop scheduling problems to minimize make-span

An adaptive learning approach for no-wait flowshop scheduling problems to minimize make-span

A hybrid genetic algorithm for solving no-wait flowshop scheduling problems

A hybrid genetic algorithm is proposed in this paper to minimize the makespan and the total flowtime in the no-wait flowshop scheduling problem, which is known to be NP-hard for more than two machines. The Variable Neighborhood Search is used as an improvement procedure in the last step of the genetic algorithm. First, comparisons are provided with respect to several techniques that are representative of the previous works in the area. Then, we compare the results given by three proposed algorithms. For the makespan criterion as well as for the total flowtime, the computational results show that our algorithms are able to provide competitive results and new best upper bounds.

Discrete particle swarm optimisation combined with no-wait algorithm in stages for scheduling mill roller annealing process

In this paper, a mill roll annealing operation scheduling problem is investigated against the background of a cast steel plant of Machinery & Mill Roll Co., Ltd in mainland China. During the annealing processes, a roller needs to be processed in multistage process with different types of furnaces and there is no waiting between the stages. Based on the analysis of the feature of the mill roll annealing process, this problem can be formulated as a no-wait hybrid flow shop scheduling problem, of which the ‘job’ is a batch of rough roll, and the ‘machine’ is a heating furnace. As each batch will be of different sizes, the jobs will have different sizes and processing times. According to the no-wait characteristic between two sequential operations of a job, the no-wait algorithm in stages is designed to obtain the initial solution. Combined with the no-wait algorithm in stages, a discrete particle swarm optimisation algorithm has been developed to solve the integer programming model. In the simulation experiment with real data, the applicability and the effectiveness of algorithms are demonstrated by the comparisons and the analysis of the experiment results, and the equipment reformation strategies of the actual reference value are given as well which is beneficial for the policy-maker to arrange production reasonably. Furthermore, some large-scale instances about the no-wait hybrid flow shop scheduling problem are studied effectively.

A note on two-machine no-wait flow shop scheduling with deteriorating jobs and machine availability constraints

This paper is concerned with two-machine no-wait flow shop scheduling problems in which the actual processing time of each job is a proportional function of its starting time and each machine may have non-availability intervals. The objective is to minimize the makespan. We assume that the non-availability intervals are imposed on only one machine. Moreover, the number of non-availability intervals, the start time and end time of each interval are known in advance. We show that the problem with a single non-availability interval is NP-hard in the ordinary sense and the problem with an arbitrary number of non-availability intervals is NP-hard in the strong sense.

An Effective Immune Based Approach for the No-Wait Flow Shop Scheduling Problems with Multiple Machines

For scheduling problems, no-wait constraint is an important requirement for many industries. As known, the no-wait scheduling problem is NP-hard and has several practical applications. This paper applies an immune algorithm to solve the multiple-machine no-wait flow shop scheduling problem with minimizing the makespan. Twenty-three benchmark problems on the OR-Library are solved by the immune algorithm. Limited numerical results show that the immune algorithm performs better than the other typical approaches in the literature for most of instances.

A DE-based approach to no-wait flow-shop scheduling

This paper proposes an effective hybrid differential evolution (HDE) for the no-wait flow-shop scheduling problem (FSSP) with the makespan criterion, which is a typical NP-hard combinational optimization problem. Firstly, a largest-order-value (LOV) rule is presented to transform individuals in DE from real vectors to job permutations so that the DE can be applied for solving FSSPs. Secondly, the DE-based parallel evolution mechanism and framework is applied to perform effective exploration, and a simple but efficient local search developed according to the landscape of FSSP is applied to emphasize problem-dependent local exploitation. Thirdly, a speed-up evaluation method and a fast Insert-based neighborhood examining method are developed based on the properties of the no-wait FSSPs. Due to the hybridization of DE-based evolutionary search and problem-dependent local search as well as the utilization of the speed-up evaluation and fast neighborhood examining, the no-wait FSSPs can be solved efficiently and effectively. Simulations and comparisons based on well-known benchmarks demonstrate the efficiency, effectiveness, and robustness of the proposed HDE.

A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems

This paper presents a novel discrete differential evolution (DDE) algorithm for solving the no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, the individuals in the DDE algorithm are represented as discrete job permutations, and new mutation and crossover operators are developed based on this representation. Second, an elaborate one-to-one selection operator is designed by taking into account the domination status of a trial individual with its counterpart target individual as well as an archive set of the non-dominated solutions found so far. Third, a simple but effective local search algorithm is developed to incorporate into the DDE algorithm to stress the balance between global exploration and local exploitation. In addition, to improve the efficiency of the scheduling algorithm, several speed-up methods are devised to evaluate a job permutation and its whole insert neighborhood as well as to decide the domination status of a solution with the archive set. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed DDE algorithm is superior to a recently published hybrid differential evolution (HDE) algorithm [Qian B, Wang L, Huang DX, Wang WL, Wang X. An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers. Computers & Operations Research 2009;36(1):209–33] and the well-known multi-objective genetic local search algorithm (IMMOGLS2) [Ishibuchi H, Yoshida I, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation 2003;7(2):204–23] in terms of searching quality, diversity level, robustness and efficiency. Moreover, the effectiveness of incorporating the local search into the DDE algorithm is also investigated.

Multi-objective no-wait flow-shop scheduling with a memetic algorithm based on differential evolution

In this paper, a memetic algorithm (MA) based on differential evolution (DE), namely MADE, is proposed for the multi-objective no-wait flow-shop scheduling problems (MNFSSPs). Firstly, a largest-order-value rule is presented to convert individuals in DE from real vectors to job permutations so that the DE can be applied for solving flow-shop scheduling problems (FSSPs). Secondly, the DE-based parallel evolution mechanism is applied to perform effective exploration, and several local searchers developed according to the landscape of multi-objective FSSPs are applied to emphasize local exploitation. Thirdly, a speed-up computing method is developed based on the property of the no-wait FSSPs. In addition, the concept of Pareto dominance is used to handle the updating of solutions in sense of multi-objective optimization. Due to the well balance between DE-based global search and problem-dependent local search as well as the utilization of the speed-up evaluation, the MNFSSPs can be solved effectively and efficiently. Simulation results and comparisons demonstrate the effectiveness and efficiency of the proposed MADE.

A multi-objective scatter search for a bi-criteria no-wait flow shop scheduling problem

The flow shop problem as a typical manufacturing challenge has gained wide attention in academic fields. This article considers a bi-criteria no-wait flow shop scheduling problem (FSSP) in which weighted mean completion time and weighted mean tardiness are to be minimized simultaneously. Since a FSSP has been proved to be NP-hard in a strong sense, a new multi-objective scatter search (MOSS) is designed for finding the locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed algorithm, various test problems are solved and the reliability of the proposed algorithm, based on some comparison metrics, is compared with a distinguished multi-objective genetic algorithm (GA), i.e. SPEA-II. The computational results show that the proposed MOSS performs better than the above GA, especially for the large-sized problems.

A novel multi-objective particle swarm optimization algorithm for no-wait flow shop scheduling problems

The current paper presents a novel multi-objective particle swarm optimization (MOPSO) algorithm for solving no-wait flow shop scheduling problems with makespan and maximum tardiness criteria. First, in the algorithm, particles are represented as job permutations and updated directly in the discrete domain. Second, the concept of Pareto dominance is applied to compare different solutions of multi-objective optimization, and a set is employed to hold and to update the obtained non-dominated solutions, where a randomly selected non-dominated solution is assigned as the global best particle to maintain the diversity of the searching direction and to speed up the convergence process to the Pareto front. Third, a new multi-objective heuristic, named the PWQ (Pan—Wang—Qian) heuristic, is proposed to produce a population of initial particles with relatively good performances. Fourth, a simple but effective multi-objective local search algorithm is developed to embed in the MOPSO algorithm for stressing the balance between global exploration and local exploitation. In addition, two speed-up methods are devised to evaluate a job permutation and its insert neighbourhood respectively. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed algorithm is superior to a recently published multi-objective hybrid differential evolution (MHDE) algorithm in terms of searching quality, diversity level, robustness, and efficiency.