Flowshop Sequencing Problem Research Articles

A Constructive Heuristic for Automated Parallel Tests Assembly

Parallel tests contain different items but have the same measurement properties. They are administered at the same or different time slots and their measurement results must be comparable. The problem of automated parallel tests assembly is studied for a long time, and many (mostly improvement) heuristic solutions are proposed and elaborated in literature. Such approaches frequently suffer from algorithms of unpredictable execution time, forcing the methods to terminate execution when some time limit or solution quality is reached. This paper proposes an efficient method of polynomial complexity, as a complete solution to the automated parallel tests assembly problem. The method uses the idea of Nawaz, Enscore, and Ham constructive heuristic algorithm to reduce the number of examined permutations, originally exploited for solving the permutation flow-shop sequencing problem. We compared the experimental results of the proposed method with two methods based on improvement heuristics that solve the same problem formulation, simulated annealing and variable neighborhood search. The main advantages of the proposed method are predictable execution time and implementation simplicity. Achieved quality of assembled tests, combined with predictable test assembly execution time, may be of particular interest in cases when computational resources for test assembly and administering are overloaded.

Scheduling in hospital administration, OPD, surgeries and emergencies

This paper attempts to provide the solution in Hospital Administration, to get various jobs completed well in time, with limited Resources, viz., Medical Equipments (MRI, Cat Scanning, Ultrasound, X – Ray Machines), Medical Laboratories, Manpower, Paramedical Staff, Nurses, Doctors, Surgeons, by using Optimization Criteria, for Flowshop Sequencing Problems, with Optimizer, as Waiting Time. The problem is solved with the objective of obtaining a schedule of the given jobs which minimizes a certain performance measure, as Total Flow time & Waiting Time.

A New Slope Index for Solving NxM Flow Shop Sequencing Problems with Minimum Makespan

A New Slope Index for Solving NxM Flow Shop Sequencing Problems with Minimum Makespan

Adaptation and parameters studies of CS algorithm for flow shop scheduling problem

Scheduling concerns the allocation of limited resources overtime to perform tasks to fulfill certain criterion and optimize one or several objective functions. One of the most popular models in scheduling theory is that of the flow-shop scheduling. During the last 40 years, the permutation flow-shop sequencing problem with the objective of makespan minimization has held the attraction of many researchers. This problem characterized as Fm/prmu/Cmax in the notation of Graham, involves the determination of the order of processing of n jobs on m machines. In addition, there was evidence that m-machine permutation flow-shop scheduling problem (PFSP) is strongly NP-hard for m ≥3. Due to this NP-hardness, many heuristic approaches have been proposed, this work falls within the framework of the scientific research, whose purpose is to study Cuckoo search algorithm. Also, the objective of this study is to adapt the cuckoo algorithm to a generalized permutation flow-shop problem for minimizing the total completion time, so the problem is denoted as follow: Fm | | Cmax. Simulation results are judged by the total completion time and algorithm run time for each instance processed.

Flow shop scheduling problem with position-dependent processing times

This study addresses a flow shop sequencing problem with position-dependent processing times. The aim of this work is to minimize the maximum tardiness and analyze the effects of the deterioration factor. This work is different from existing works in the literature, in that processing times are not per se deteriorating; instead, the resources deteriorate. This model allows the analysis of worker fatigue in a hand-intensive manufacturing system, such as in the luxury or textile industries. A case study of the problem is presented. In addition, a mathematical model, one sequencing heuristic, two improvement heuristics, and two metaheuristics are proposed, i.e., iterated local search and a greedy randomized adaptive search procedure. Computational experiments are carried out using random-generated instances. Results show the efficiency and efficacy of the proposed metaheuristics, and provide insights into the impact of the deterioration factor in flow shop scheduling problems.

Joint Scheduling of Jobs and Variable Maintenance Activities in the Flowshop Sequencing Problems: Review, Classification and Opportunities

Much of the scheduling theory assumes that machines are always available to process jobs at any time during the scheduling horizon. However, machines may be unavailable for various reasons in realistic practices, such as unexpected failures or variable maintenance activities. This article discusses in depth the works published in the literature of joint scheduling of jobs and variable maintenance activities in the flowshop sequencing problems. Our literature review focuses first on the basic concepts of scheduling problems, and more specifically, the scheduling strategies of production and maintenance that have been identified in the literature. Subsequently, we focus our attention on the principal methods for solving scheduling problems, while presenting in the following the main published works for the aforementioned systems. Lastly, a comparative analysis is carried out to highlight the fundamental ideas leading to the adoption of an effective approach capable of producing an optimal solution in a reasonable calculation time.

Game theory-based integration of scheduling with flexible and periodic maintenance planning in the permutation flowshop sequencing problem

We introduce a novel game theory approach to the problem of integrating periodic and flexible preventive maintenance and production scheduling for permutation flowshops. Our main contribution is to propose game modeling that allows decision maker to have compromise solutions meeting at best production and maintenance criteria. To achieve this, two new games formulations for the problem are proposed, and a stable solution for the players is obtained through the concept of equilibrium. The first game follows a constructive approach while the second one follows an improvement one. Moreover, to cope with the concept of game types, we proposed two player’s behaviors: rational and non-rational. Extensive experiments are carried out to validate our proposed approaches and developed methods. Besides, the results of the proposed methods are compared against the state of the art, highlighting our contributions and showing improvements.

Research on group search optimizers for a reconfigurable flow shop sequencing problem

A reconfigurable manufacturing system is usually designed for quick re-adjusting of production capacity in response to market changes. In this paper, we study a flow shop sequencing problem (FSSP) ...

A Discrete Inter-Species Cuckoo Search for flowshop scheduling problems

This paper presents a discrete version of the Inter-Species Cuckoo Search (ISCS) algorithm and illustrates its use for solving two significant types of the flow-shop scheduling problems. These are Hybrid Flow-shop Scheduling (HFS) and Permutation Flow-shop Sequencing Problems (PFSP). Hybrid flowshop scheduling problems are a generalization of flowshops having parallel machines in some stages and these problems are known to be NP-hard. A heuristic rule called the Smallest Position Value (SPV) is used to enable the continuous inter-species cuckoo search to be applied to most types of sequencing problems. Makespan and mean flow time are the objective functions considered and computational experiments are carried out to compare the proposed Discrete Inter-Species Cuckoo Search (DISCS) with other state-of-the-art meta-heuristic algorithms. Experimental results confirm the superiority of DISCS with respect to many other existing metaheuristic search algorithms.

Migrating Birds Optimization for Flow Shop Sequencing Problem

FSSP is a typical NP-Hard problem which is desired to be minimum makespan. This study consid- ers Migrating Birds Optimization (MBO) which is metaheuristic approach for the solution of Flow Shop Sequencing Problem (FSSP). As the basic MBO algorithm is designed for discrete problems. The performance of basic MBO algorithm is tested via some FSSP data sets exist in literature. Obtained results are compared with optimal results of related data sets.

A multi-agent approach for scheduling jobs and maintenance operations in the flowshop sequencing problem

Many works refer to the scheduling problem of both preventive maintenance and production activities. Few works concern the dynamic scheduling problem of these two activities. This aspect is mainly concerned by corrective maintenance activities (equipment failure) and by condition-based maintenance (degradation). Decision-making tools are very helpful to schedule these activities. In this regard, the proposed multi-agent system provides a cooperation medium for scheduling independent jobs and maintenance operations in the flowshop sequencing problem based on the sequential and integrated strategies. The objective is then to optimise an objective function which takes into account both maintenance and production criterion. It also provides a framework in order to react to the disturbances occurring in the workshop. Moreover, it introduces a dialogue between two communities of agents (production and maintenance) leading to a high level of cooperation. A comparison of the solutions yielded by the strategies 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 main point is to show how the proposed multi-agent system is used to generate a joint production and maintenance schedule that provides a better compromise between the satisfactions of respective objectives of these two functions.

A note on ‘A new dynamic programming formulation of (n × m) flowshop sequencing problems with due dates’

A dynamic programming formulation was proposed in the paper entitled ‘A new dynamic programming formulation of (n × m) flowshop sequencing problems with due dates’ [Sonmez, A.I. and Baykasoglu, A., 1998, International Journal of Production Research, 36 (8), 2269–2283] to deal with a flow shop problem considering sequence-dependent setup times to minimise the total weighted tardiness. Since the original dynamic programming formulation is confusing and of the six examples given in the original paper four are partially or totally wrong, both the dynamic programming formulation and the examples need to be rectified. In this note, the confusing interpretations of the original formulation are analysed, and a more accurate dynamic programming formulation is proposed. Based on the new dynamic programming formulation, all four incorrect examples are recalculated.

Effective heuristics for the blocking flowshop scheduling problem with makespan minimization

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-NEH LS, wPF-NEH LS, and PW-NEH LS, 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.

A chaotic harmony search algorithm for the flow shop scheduling problem with limited buffers

In this paper, a chaotic harmony search (CHS) algorithm is proposed to minimize makespan for the permutation flow shop scheduling problem with limited buffers. First of all, to make the harmony search algorithm suitable for solving the problem under consideration, a rank-of-value rule is applied to convert continuous harmony vectors to discrete job permutations. Secondly, an efficient initialization scheme based on the Nawaz–Enscore–Ham heuristic [M. Nawaz, E.E.J. Enscore, I. Ham, A heuristic algorithm for the m-machine, n-job flow shop sequencing problem, OMEGA-International Journal of Management Science 11 (1983) 91–95] and its variants is presented to construct an initial harmony memory with a certain level of quality and diversity. Thirdly, a new improvisation scheme is developed to well inherit good structures from the best harmony vector in the last generation. In addition, a chaotic local search algorithm with probabilistic jumping scheme is presented and embedded in the proposed CHS algorithm to enhance the local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It is shown that the proposed CHS algorithm generates better results not only than the two recently developed harmony search algorithms but also than the existing hybrid genetic algorithm and hybrid particle swarm optimization in terms of solution quality and robustness.

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.

Discrete particle swarm optimisation algorithms for minimising the completion-time variance of jobs in flowshops

In this paper, the problem of scheduling in the permutation flowshop scheduling problem is considered with the objective of minimising the completion-time variance of jobs (CTV). Two particle swarm optimisation algorithms (PSOAs) are proposed and analysed. The first PSOA is inspired from the solution construction procedures that are used in ant colony optimisation algorithms. The second algorithm is a newly developed one. The proposed algorithms are applied to a set of benchmark flowshop scheduling problems, and performances of the algorithms are evaluated by comparing the obtained results with the results published in the literature. The performance analysis demonstrates the effectiveness of the proposed algorithms in solving the permutation flowshop sequencing problem with the CTV objective.

An ant colony optimisation approach considering jointly scheduling and preventive maintenance in the flowshop sequencing problem

This paper presents INTACO, a hybrid Ant Colony Optimisation (ACO) algorithm coupled with a local search applied to the joint production and preventive maintenance scheduling problem in the flowshop sequencing problem. INTACO uses pheromone trail information to perform modifications on complete joint production and preventive maintenance solutions unlike more traditional ant systems that use pheromone trail information to construct complete solutions. Several new interesting features are proposed and evaluated. In particular, the use of a common representation of preventive maintenance and production data to optimise a common objective function which takes into account both preventive maintenance and production criteria with a new pheromone evaluation rule. Moreover, to enhance the performances of the proposed ACO algorithm, new local search procedures for ants are proposed. INTACO is tested on a set of non-standard test problems. We compare the results obtained to those of a genetic algorithm developed in previous works. The results and experiments carried out indicate that the proposed ant-colony algorithm provide very effective solutions for this problem.

An empirical analysis of the optimality rate of flow shop heuristics

Abstract If a certain optimization problem is NP-hard or even harder, one could expect that the chances of solving it optimally should rather decrease with an increase of the problem size. We reveal, however, that the opposite occurs for a strongly NP-hard problem, which requires sequencing n jobs through an m machine flow shop so as to minimize the makespan. In particular, we empirically examine optimality rates (the probability of being optimal) of the famous NEH heuristic of Nawaz et al. [Nawaz, M., Enscore, Jr., E., Ham, I., 1983. A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, The International Journal of Management Science 11, 91–95] and two improved versions of NEH. By using millions of simulation trials and a new effective lower bound on the shortest makespan, we observe relatively high optimality rates of the three heuristics for small values of m. Rather surprisingly, for larger values of n, the heuristics become more frequently optimal as n increases. Neither theoretical nor empirical studies of optimality rates of flow shop heuristics have been conducted so far, and – to the best of our knowledge – no similar studies are known in the field of operations research.

Solving Flow Shop Sequencing Problem for Deteriorating Jobs by Using Electro Magnetic Algorithm

Solving Flow Shop Sequencing Problem for Deteriorating Jobs by Using Electro Magnetic Algorithm

An iterated local search algorithm for the permutation flowshop problem with total flowtime criterion

An ILS algorithm is proposed to solve the permutation flowshop sequencing problem with total flowtime criterion. The effects of different initial permutations and different perturbation strengths are studied. Comparisons are carried out with three constructive heuristics, three ant-colony algorithms and a particle swarm optimization algorithm. Experiments on benchmarks and a set of random instances show that the proposed algorithm is more effective. The presented ILS improves the best known permutations by a significant margin.