Total Flow Time Research Articles

Solving permutation flowshop scheduling problems with a discrete differential evolution algorithm

In this paper a new discrete Differential Evolution algorithm for the Permutation Flowshop Scheduling Problem with the total flowtime and makespan criteria is proposed. The core of the algorithm is the distance-based differential mutation operator defined by means of a new randomized bubble sort algorithm. This mutation scheme allows the Differential Evolution to directly navigate the permutations search space. Experiments were held on a well known benchmarks suite and they show that the proposal reaches very good performances compared to other state-of-the-art algorithms. The results are particularly satisfactory on the total flowtime criterion where also new upper bounds that improve on the state-of-the-art have been found.

An Integer Batch Scheduling Model for a Single Machine with Simultaneous Learning and Deterioration Effects to Minimize Total Actual Flow Time

In the manufacturing industry, several identical parts can be processed in batches, and setup time is needed between two consecutive batches. Since the processing times of batches are not always fixed during a scheduling period due to learning and deterioration effects, this research deals with batch scheduling problems with simultaneous learning and deterioration effects. The objective is to minimize total actual flow time, defined as a time interval between the arrival of all parts at the shop and their common due date. The decision variables are the number of batches, integer batch sizes, and the sequence of the resulting batches. This research proposes a heuristic algorithm based on the Lagrange Relaxation. The effectiveness of the proposed algorithm is determined by comparing the resulting solutions of the algorithm to the respective optimal solution obtained from the enumeration method. Numerical experience results show that the average of difference among the solutions is 0.05%.

Review and classification of constructive heuristics mechanisms for no-wait flow shop problem

Permutation flow shop scheduling has been studied since the 1950s. Due to practical applications and because of its complex nature, several constrains have been studied, for example the no-wait flow shop problem. Classified as non-deterministic polynomial-time hard (NP-hard), several constructive heuristics have been proposed to reach the best trade-off between quality of the solution and computational time. This paper aims to present a review and classification of constructive heuristics proposed for no-wait flow shop problem with additional constraints (setup times, machine unavailability, release dates, and due dates) and several criterion objectives (makespan, total flow time, due date consideration, and multi-criterion objectives). As a result of the literature review, constructive heuristics were classified according to its composition (initial sequence generation, constructive phase, and improvement phase) and category (simple or composite). Several different mechanisms from the considered phases could be identified. These mechanisms can be combined to develop new constructive heuristics and can be applied for other scheduling problems. Besides, final considerations highlight directions for future possible researches, mainly the development of new strategies, for each phase considered.

Robust scheduling of parallel machines considering total flow time

An effective approach to structuring uncertainty and making decisions under uncertain conditions is the robustness approach. The robust scheduling approach tries to create a schedule that minimizes the effect of disturbances caused by uncertainty during the operation on the objective function of initial scheduling. The present research was an attempt to study the problem of maximizing the probability that the total flow time does not exceed a predetermined limit in identical parallel machines while the processing time of each job is stochastic. In order to find an optimal solution to this problem, several theorems were proposed. The proposed theorems considerably reduced the search space and resulted in a branch and bound method to the problem with a specific branching scheme. Moreover, development of theorems to determine the dominance set along with introduction of dominance rules, an upper bound, and a lower bound helped find optimal solutions to the problems of 45 jobs and 5 machines. In addition, it was found that the method proposed in this paper for several machines is more effective than the methods developed for a single machine.

An Application of New Heuristic Approach on Small Scale Industry for Flow Shop Environment

In this paper, permutation flow-shop scheduling problem is solved with the objectives of minimizing total flow time. A new constructive heuristic is applied to solve the problem by using Rank Index heuristic for flow time. It is called as RIH-F approach. The proposed approach is applied on the taillard's bench mark flow-shop scheduling problem. Performance of the heuristic is measured by comparing the computational result with other heuristic approach result. All heuristic approach relative percent deviation are calculated with the best known value reported in the literature. From the computational result, the proposed approach is an alternative for the permutation flow-shop scheduling problem considering total flow time. Then the new heuristic approach is applied to demonstrate on small scale industry for flow shop environment.

Multi-Objective Memetic Search Algorithm for Multi-Objective Permutation Flow Shop Scheduling Problem

The multiobjective permutation flowshop scheduling problem (MOPFSSP) is one of the most popular machine scheduling problems with extensive engineering relevance of manufacturing systems. There have been many attempts at solving MOPFSSP using heuristic and meta-heuristic methods, such as evolutionary algorithm. In this paper, a novel multiobjective memetic search algorithm (MMSA), is proposed to solve the MOPFSSP with makespan and total flowtime. First, a problem-specific Nawaz–Enscore–Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a global search embedded with a perturbation operation is used to improve the solution of the entire population. Then, a single insert-based local search is used to improve each individual and then a further local search strategy is used to find the better solution for the non-improved individual in the single insert-based local search. The performance of our proposed algorithm is validated and compared with the four state-of-the-art algorithms on a number of benchmark problem. The experimental results show that the proposed MMSA provides better solutions than several state-of-the-art algorithms.

Multilayer nitride coating performance optimized by an artificial neural network approach

One of the most important problems occurred in many industries is due to friction and wear process. Over the years, minimizing friction and controlling wear is one of the difficult tasks for the researchers. Both properties can be minimized by the application of adequate coating technology. Many coating deposition technologies have been employed to limit friction and wear but only few succeeded, those are directly affected by the nature of the material under investigation and process parameters. A suitable coating strategy varying from single layer to multilayer should be applied to the materials whose superficial properties such as low friction, improved wear resistance, and adhesion are the prime interest. Multilayer coatings possess high hardness, ductility and fracture strength compared to single layer coatings. The advantageous properties of these multilayers can be preciously tailored according to specific application. For this purpose Physical Vapour Deposition (PVD) coatings have been developed considerably due to increasing industrial demands. In the present research, friction and wear study of multilayer PVD-nitride coating deposited on tool steel by unbalanced reactive magnetron sputtering technique have been discussed. Later on an Artificial Neural Network approach was used to predict the tribological properties of multilayer nitride films. Bias voltage, total gas flow rate, lap, time, velocity and load were considered as controllable factors. The regression and performance curve analysis is used to assess the optimized outcome of deposited film properties such as friction and wear. The analyzed results shows that experimented and predicted values are in a good agreement.

A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

In this paper, we consider m-machine flow shop scheduling problems (FSSPs). The objective is to schedule the available n jobs to minimise the total flow time. The flow shop scheduling problems have been proved to be strongly non-deterministic polynomial-time hard (NP-hard). Hence, exact methods cannot be used to solve even small size problems. Researchers addressed many heuristics and meta-heuristics to solve the problems. Firefly algorithm (FA) is one of the recently developed meta-heuristic algorithms. We propose a hybrid discrete firefly algorithm (HDFA) to solve the FSSPs to minimise the total flow time. To validate the performances of the HDFA, computational experiments are conducted on a number of randomly generated test problems with different parameters and the results proved the effectiveness of the proposed algorithm.

An Enhanced Discrete Artificial Bee Colony Algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers

This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms.

A hybrid discrete firefly algorithm to solve flow shop scheduling problems to minimise total flow time

In this paper, we consider m-machine flow shop scheduling problems (FSSPs). The objective is to schedule the available n jobs to minimise the total flow time. The flow shop scheduling problems have been proved to be strongly non-deterministic polynomial-time hard (NP-hard). Hence, exact methods cannot be used to solve even small size problems. Researchers addressed many heuristics and meta-heuristics to solve the problems. Firefly algorithm (FA) is one of the recently developed meta-heuristic algorithms. We propose a hybrid discrete firefly algorithm (HDFA) to solve the FSSPs to minimise the total flow time. To validate the performances of the HDFA, computational experiments are conducted on a number of randomly generated test problems with different parameters and the results proved the effectiveness of the proposed algorithm.

A NSGA-II and NSGA-III comparison for solving an open shop scheduling problem with resource constraints

This paper presents an open shop scheduling problem based on a real mechanical workshop made of m machines that process n jobs. It deals with different resource constraints related to the tools allocation and the multi-skills staff assignment. Resource skills and their availability are required to execute one process task. In this work, we expose a multi-objective problem where the idea is to minimize three objectives simultaneously. The first one considers the minimization of the total flow time of jobs in the production system, then the workload balancing concerning both, humans and machines is addressed. We propose and compare two multi-objective methods: NSGA-II and NSGA-III. Computational experiments are designed according to the literature, we expose the small and large sized instances to present the general performance of these algorithms using the hyper-volume metric to compare them.

Scheduling controllable processing time jobs with position-dependent workloads

In various real life applications job processing times are controllable through the allocation of a limited resource. To date research has been conducted under the assumption that the relationship between the amount of resource allocated to a job and its processing time is independent of the number of tasks processed previously. However, there exist many manufacturing and service systems where workers and machines acquire, develop and refine skills through the repetition of identical or similar operations. In this paper we consider a scheduling model where job processing times are a convex function of the amount of resource they are allocated. In addition, we assume that the parameters of this function are position-dependent, i.e., vary with the job׳s position in the sequence. This assumption reflects general processes of learning or aging, or a combination of both. We first focus on a single machine setting and the makespan and total flowtime criteria. We show that the combined problem of finding an optimal job sequence and an optimal resource allocation can be solved in O(n3) time. We show that our algorithm can be used to address a bicriteria objective comprising of a linear combination of makespan and the total flowtime criteria on a single machine. We then extend the results to a parallel machine setting for the total flowtime criteria.

Open shop scheduling problem with a multi-skills resource constraint: a genetic algorithm and an ant colony optimisation approach

The continuous evolution of manufacturing environments leads to a more efficient production process that controls an increasing number of parameters. Production resources usually represent an important constraint in a manufacturing activity, specially talking about the management of human resources and their skills. In order to study the impact of this subject, this paper considers an open shop scheduling problem based on a mechanical production workshop to minimise the total flow time including a multi-skill resource constraint. Then, we count with a number of workers that have a versatility to carry out different tasks, and according to their assignment a schedule is generated. In that way, we have formulated the problem as a linear as and a non-linear mathematical model which applies the classic scheduling constraints, adding some different resources constraints related to personnel staff competences and their availability to execute one task. In addition, we introduce a genetic algorithm and an ant colony optimisation (ACO) method to solve large size problems. Finally, the best method (ACO) has been used to solve a real industrial case that is presented at the end.

NEW HEURISTICS FOR THE NO-WAIT FLOWSHOP WITH SEQUENCE-DEPENDENT SETUP TIMES PROBLEM

In this paper, we address the problem of scheduling jobs in a no-wait flowshop with sequence-dependent setup times with the objective of minimizing the makespan and the total flowtime. As this problem is well-known for being NP-hard, we present two new constructive heuristics in order to obtain good approximate solutions for the problem in a short CPU time, named GAPH and QUARTS. GAPH is based on a structural property for minimizing makespan and QUARTS breaks the problem in quartets in order to minimize the total flowtime. Experimental results demonstrate the superiority of the proposed approachs over three of the best-know methods in the literature: BAH and BIH, from Bianco, Dell´Olmo and Giordani (1999) and TRIPS, by Brown, McGarvey and Ventura (2004).

Application of Box Behnken Design to Optimize the Parameters to Synthesis Graphene by CVD Process

This paper discusses the use of Box Behnken design (BBD) approach to plan the experiments for turning the yield of CVD, thickness and layer number of graphene sheets with an overall objective of optimizing the process to provide higher graphene production volume, fewer layers and thinness structure of graphene. BBD is having the maximum efficiency for an experiment involving four factors such as total gas flow, gas ratio (H2/CH4), temperature, and reaction time in three levels. The proposed BBD requires 25 runs of experiment for data acquisition and modeling the response surface. Three regression models were developed and their adequacies were verified to predict the output values at nearly all conditions. Further, the models were validated by performing experiments, taking three sets of random input values. The output parameters measured through experiments (actual) are in good consistency with the predicted values using the models. This work resulted in identifying the optimized set of turning parameters for CVD process to achieve high yield value and good structure of graphene. In the best condition, yield of process is 6.1%.

Multiobjective Local Search Algorithm-Based Decomposition for Multiobjective Permutation Flow Shop Scheduling Problem

This paper focuses on the multiobjective solutions of the flow shop scheduling problem with and without sequence dependent setup times. In our case, the two objectives are the minimization of makespan and total flowtime. These problems are solved with a novel multiobjective local search framework-based decomposition, called multiobjective local search based decomposition (MOLSD), which decomposes a multiobjective problem into a number of single objective optimization subproblems using aggregation method and optimizes them simultaneously. First, a problem-specific Nawaz–Enscore–Hoam heuristic is used to initialize the population to enhance the quality of the initial solution. Second, a Pareto local search embedded with a heavy perturbation operator is applied to search the promising neighbors of each nondominated solution found so far. Then, when solving each subproblem, a single insert-based local search, a multiple local search strategy, and a doubling perturbation mechanism are designed to exploit the new individual. Finally, a restarted method is used to avoid the algorithm trapping into the local optima, which has a significant effect on the performance of the MOLSD. Comprehensive experiments have been conducted by two standard multiobjective metrics: 1) hyper-volume indicator; and 2) set coverage. The experimental results show that the proposed MOLSD provides better solutions that several state of the art algorithms.

A model for performance of sulfite oxidation of ammonia-based flue gas desulfurization system

A mathematical model was developed to simulate the oxidation of total sulfite in the holding tank of ammonia-based flue gas desulfurization (FGD) system. The model could provide predictions of the oxidation ratio of total sulfite at different operating condition such as pH, concentration of total sulfur, air flow and residence time. The calculation results of an ammonia-based FGD system for 2 × 150 MW boilers by this model were compared to that of corresponding measured values. The comparison results show that the simulated oxidation ratio of total sulfite agrees well with the measured data. The difference between calculated values and measured data is below 4.2%. This model appears to be beneficial for optimizing design and operation of total sulfite oxidation of an ammonia-based FGD system.

A constructive algorithm and a simulated annealing approach for solving flowshop problems with missing operations

In many practical cases of flowshop environments and especially in flowline manufacturing cells, some or all jobs may not require processing on all machines. Hence, this paper focuses on the flowshop scheduling problem with missing operations. A modification of the constructive NPS-set heuristic is proposed, which generates non-permutation schedules effectively. Furthermore, a two-phase simulated annealing (SA) method is presented that specifically considers missing operations in its procedure. The modified NPS-set heuristic and the two-phase SA are tested and statistically evaluated by an extensive computational study for total flow time criteria. The results show that the modified NPS-set heuristic as well as the specific consideration of missing operations can enhance the algorithms’ performance significantly.

ADRC or adaptive controller – A simulation study on artificial blood pump

Active disturbance rejection control (ADRC) has gained popularity because it requires little knowledge about the system to be controlled, has the inherent disturbance rejection ability, and is easy to tune and implement in practical systems. In this paper, the authors compared the performance of an ADRC and an adaptive controller for an artificial blood pump for end-stage congestive heart failure patients using only the feedback signal of pump differential pressure. The purpose of the control system was to provide sufficient perfusion when the patients’ circulation system goes through different pathological and activity variations. Because the mean arterial pressure is equal to the total peripheral flow times the total peripheral resistance, this goal was converted to an expression of making the mean aortic pressure track a reference signal. The simulation results demonstrated that the performance of the ADRC is comparable to that of the adaptive controller with the saving of modeling and computational effort and fewer design parameters: total peripheral flow and mean aortic pressure with ADRC fall within the normal physiological ranges in activity variation (rest to exercise) and in pathological variation (left ventricular strength variation), similar to those values of adaptive controller.

PENJADWALAN FLOWSHOP DENGAN MIXED INTEGER PROGRAMMING UNTUK MEMINIMASI TOTAL FLOWTIME DAN TOTAL TARDINESS

Abstract Adi Bungsu (AB) Cigarette Company produces Clove Cigarette in four stages. Each stage is processed by one machine. Thus, the production line is described as flowshop. AB receive order from customer over country. Every month, AB should schedule the order from customer. Thus, AB should prioritize which order to be processed firstly. At this time, AB schedules their order by using First Come First Serve algorithm which prioritize the first order as the first processed job. Previously, this algorithm results in tardiness and high work-in-process. Therefore, this study aims to reduce the tardiness and work-in-process level. In this study, the scheduling problem is solved by using mixed integer programming. To minimize tardiness and work-in-process simultaneously, the objective include minimizing total tardiness and total flowtime. Meanwhile, this study also consider setup time separated from processing time so that setup can be done early while waiting job to be processed in previous stage. The results of this study show that mixed integer programming with two-criteria can be used to schedule job ordered by customer. In case of bicriteria scheduling problem with separated setup time, mixed integer programming generate better schedule than FCFS algorithm.