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The purpose of this paper is to minimize the mean flow time in open shop scheduling problem (OSSP). The scheduling problems consist of n jobs and m machines, in which each job has O operations. The processing time for 50 OSSP was generated using a linear congruential random number.

Different evolutionary algorithms are used to minimize the mean flow time of OSSP. This research study used simulated annealing (SA), Discrete Firefly Algorithm and a Hybrid Firefly Algorithm with SA. These methods are referred as A1, A2 and A3, respectively.

A comparison of the results obtained from the three methods shows that the Hybrid Firefly Algorithm with SA (A3) gives the best mean flow time for 76 percent instances. Also, it has been observed that as the number of jobs increases, the chances of getting better results also increased. Among the first 25 problems (i.e. job ranging from 3 to 7), A3 gave the best results for 13 instances, i.e., for 52 percent of the first 25 instances. While for the last 25 problems (i.e. Job ranging from 8 to 12), A3 gave the best results for all 25 instances, i.e. for 100 percent of the problems.

From the literature it has been observed that no researchers have attempted to solve OOSPs using Firefly Algorithm (FA). In this research work an attempt has been made to apply the FA and its hybridization to solve OSSP. Also the research work carried out in this paper can also be applied for a real-time Industrial problem.

Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

The purpose of this paper is to determine the interaction between dynamic due date assignment methods and scheduling decision rules in a typical dynamic job shop production system in which setup times are sequence dependent. Two due date assignment methods and six scheduling rules are considered for detailed investigation. The scheduling rules include two new rules which are modifications of the existing rules. The performance of the job shop system is evaluated using various measures related to flow time and tardiness.

A discrete-event simulation model is developed to describe the operation of the job shop. The simulation results are subjected to statistical analysis based on the method of analysis of variance. Regression-based analytical models have been developed using the simulation results. Since the due date assignment methods and the scheduling rules are qualitative in nature, they are modeled using dummy variables. The validation of the regression models involves comparing the predictions of the performance measures of the system with the results obtained through simulation.

The proposed scheduling rules provide better performance for the mean tardiness measure under both the due date assignment methods. The regression models yield a good prediction of the performance of the job shop.

Other methods of due date assignment can also be considered. There is a need for further research to investigate the performance of due date assignment methods and scheduling rules for the experimental conditions that involve system disruptions, namely, breakdowns of machines.

The explicit consideration of sequence-dependent setup time (SDST) certainly enhances the performance of the system. With appropriate combination of due date assignment methods and scheduling rules, better performance of the system can be obtained under different shop floor conditions characterized by setup time and arrival rate of jobs. With reductions in mean flow time and mean tardiness, customers are benefitted in terms of timely delivery promises, thus leading to improved service level of the firm. Reductions in manufacturing lead time can generate numerous other benefits, including lower inventory levels, improved quality, lower costs, and lesser forecasting error.

Two modified scheduling rules for scheduling a dynamic job shop with SDST are proposed. The analysis of the dynamic due date assignment methods in a dynamic job shop with SDST is a significant contribution of the present study. The development of regression-based analytical models for a dynamic job shop operating in an SDST environment is a novelty of the present study.

New ways of managing change have run aground on the uncritical acceptance of a limited view of temporality, identified here as causal-time. Because it emphasizes identity and state-transitions, causal-time is inherently static and past-centered. An alternative view, called flow-time, emphasizes the dynamic of the always arriving future. The claim is made that a future-centered temporality gives access to the knowledge change agents need to cope with accelerating and ongoing change.

The purpose of this paper is to study the peristaltic flow of a Jeffrey fluid in an asymmetric channel, subjected to gravity field and rotation in the presence of a magnetic field. The channel asymmetry is produced by choosing the peristaltic wave train on the walls to have different amplitude and phase. The flow is investigated in a wave frame of reference moving with the velocity of the wave. Involved problems are analyzed through long wavelength and low Reynolds number.

The analytical expressions for the pressure gradient, pressure rise, stream function, axial velocity and shear stress have been obtained. The effects of Hartmann number, the ratio of relaxation to retardation times, time-mean flow, rotation, the phase angle and the gravity field on the pressure gradient, pressure rise, streamline, axial velocity and shear stress are very pronounced and physically interpreted through graphical illustrations. Comparison was made with the results obtained in the asymmetric and symmetric channels.

The results indicate that the effect of the Hartmann number, the ratio of relaxation to retardation times, time-mean flow, rotation, the phase angle and the gravitational field are very pronounced in the phenomena.

In the present work, the authors investigate gravity field, and rotation through an asymmetric channel in the presence of a magnetic field has been analyzed. It also deals with the effect of the magnetic field and gravity field of peristaltic transport of a Jeffrey fluid in an asymmetric rotating channel.

The purpose of this paper is the development of a CFD methodology based on LES computations to analyze the rotor–stator interaction in an axial fan stage.

A wall-modeled large eddy simulation (WMLES) has been performed for a spanwise 3D extrusion of the central section of the fan stage. Computations were performed for three different operating conditions, from nominal (Q_N) to off-design (85 per cent Q_N and 70 per cent Q_N) working points. Circumferential periodic conditions were introduced to reduce the extent of the computational domain. The post-processing procedure enabled the segregation of unsteady deterministic features and turbulent scales. The simulations were experimentally validated using wake profiles and turbulent scales obtained from hot-wire measurements.

The transport of rotor wakes and both wake–vane and wake–wake interactions in the stator flow field have been analyzed. The description of flow separation, particularly at off-design conditions, is fully benefited from the LES performance. Rotor wakes impinging on the stator vanes generate a coherent large-scale vortex shedding at reduced frequencies. Large pressure fluctuations in the stagnation region on the leading edge of the vanes have been found.

LES simulations have shown to be appropriate for the assessment of the design of an axial fan, especially for specific operating conditions for which a URANS model presents a lower performance for turbulence description.

This paper describes the development of an LES-based simulation to understand the flow mechanisms related to the rotor–stator interaction in axial fan stages.

Mechanisms to adjust planned lead times based on current work loads are desirable for time‐phased planning systems. This paper investigates the use of exponentially smoothed order flow time feedback in setting planned lead times dynamically. The system studied is a supply chain with capacity‐constrained processing stations and transit times between stations. Lot sizes are based on the minimization of flow times using queuing approximations. Both seasonal and level demand patterns with uncertainty are considered. Since both dependent and independent demands are assumed at each station, customer delivery performance depends on the distribution of inventory along the supply chain. Results show that dynamic planned lead time setting can be used effectively to control delivery performance along the supply chain. Performance is also influenced significantly by appropriate lot size selection.

In real manufacturing systems, schedules are often disrupted with uncertainty factors such as random machine breakdown, random process time, random job arrivals or job cancellations. This paper aims to investigate robust scheduling for a two-stage assembly flow shop scheduling with random machine breakdowns and considers two objectives makespan and robustness simultaneously.

Owing to its structural and algorithmic complexity, the authors proposed imperialist competitive algorithm (ICA), genetic algorithm (GA) and hybridized with simulation techniques for handling these complexities. For better efficiency of the proposed algorithms, the authors used artificial neural network (ANN) to predict the parameters of the proposed algorithms in uncertain condition. Also Taguchi method is applied for analyzing the effect of the parameters of the problem on each other and quality of solutions.

Finally, experimental study and analysis of variance (ANOVA) is done to investigate the effect of different proposed measures on the performance of the obtained results. ANOVA's results indicate the job and weight of makespan factors have a significant impact on the robustness of the proposed meta-heuristics algorithms. Also, it is obvious that the most effective parameter on the robustness for GA and ICA is job.

Robustness is calculated by the expected value of the relative difference between the deterministic and actual makespan.

Dispatching rules are usually applied dynamically to schedule jobs in flexible manufacturing systems. Despite their frequent use, one of the drawbacks that they display is that the state the manufacturing system is in dictates the level of performance of the rule. As no rule is better than all the other rules for all system states, it would be highly desirable to know which rule is the most appropriate for each given condition, and to this end this paper proposes a scheduling approach that employs inductive learning and backpropagation neural networks. Using these latter techniques, and by analysing the earlier performance of the system, “scheduling knowledge” is obtained whereby the right dispatching rule at each particular moment can be determined. A module that generates new control attributes is also designed in order to improve the “scheduling knowledge” that is obtained. Simulation results show that the proposed approach leads to significant performance improvements over existing dispatching rules.