While deterministic scheduling models have been well studied, the use of these models is not well documented in manufacturing environments. Previous research has indicated that deterministic scheduling approaches quickly lose their advantage compared to dispatching rules when processing time uncertainty is present. This research presents the case of a Printed Wiring Board Manufacturer's drilling operation, which is a group of unrelated parallel machines. The manufacturer wishes to minimise makespan, number of late jobs, total overtime, average machine finishing time and machine utilisation when stochastic uncertainty is present. While deterministic scheduling has been shown to be a good solution approach when processing time variability is low, this research attempts to extend the boundaries in which scheduling is useful by investigating job and machine hedges as well as periodic and event driven rescheduling policies. The success of the approach is evaluated using a simulation model to evaluate the performance over a number of sequential schedules under various distributional assumptions.

How to deal with the collaboration between task decomposition and task scheduling is. the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the sub tasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.

In Supply chain (SC) environments, schedules inevitably experience various unexpected disruptions. In these cases, rescheduling is essential to minimise the negative impact on the performance of the system. In this study, a hybrid rescheduling technique is developed for solving coordinated manufacturing tasks scheduling problems with due date changes and machine breakdowns. According to the dynamic features of this problem, a strategy combined with event and periodic driven methods is proposed to improve the stability and robustness of manufacturing performance in a coordinated SC. Moreover, an application case is introduced to test and evaluate the effect of different initialisations in two types of disruption scenarios. The experimental results show that the proposed rescheduling technique has good effectiveness and efficiency in the coordinated manufacturing environment.

This research considers the generation of random processing times for parallel machine scheduling problems. We present several processing time generation schemes that consider different levels and combinations of machine correlation and job correlation. Also, metrics to evaluate the amounts of machine relatedness and job uniformity for the randomly generated processing times of a given problem instance are presented. The proposed schemes generate desirable problem instances that can be used to test different solution approaches (such as heuristics, dynamic programming, and branch-and-bound). Computational results indicate that the schemes provide problem instances with many desirable properties.

This research proposes two heuristics and a Genetic Algorithm (GA) to find non-dominated solutions to multiple-objective unrelated parallel machine scheduling problems. Three criteria are of interest, namely: makespan, total weighted completion time, and total weighted tardiness. Each heuristic seeks to simultaneously minimize a pair of these criteria; the GA seeks to simultaneously minimize all three. The computational results show that the proposed heuristics are computationally efficient and provide solutions of reasonable quality. The proposed GA outperforms other algorithms in terms of the number of non-dominated solutions and the quality of its solutions. Published by Elsevier B.V.

Because of its benefits - from lowered inventory costs to greater flexibility in adapting to shifting market forces - the push-pull strategy is being widely used in today's competitive supply-chain designs. The push-pull strategy also brings potential supply-chain risks related to order fulfilment capability and robustness against external variability. More specifically, the use of this strategy often results in an inability to minimise the impact of lead-time variability. We present a new, hybrid push-pull strategy that incorporates additional stock points after the push-pull boundary as the pulling points in a serial supply chain, which can mitigate the risks and improve the robustness of the push-pull strategy without sacrificing its benefits in inventory cost reduction. For the evaluation and comparison of different supply-chain strategies, a nonlinear, mixed-integer programming model with a cost-minimisation objective function is developed and implemented in the numerical experimentation, with simulated annealing as the search algorithm. Results from the experiments demonstrate the potential improvement by our proposed strategy in terms of the robustness and cost-effectiveness against external variability. The results also verify the risks and limitations of the conventional push-pull strategy and provide some managerial implications regarding the use of push-pull supply chains.

This research compares the performance of various heuristics and one metaheuristic for unrelated parallel machine scheduling problems. The objective functions to be minimized are makespan, total weighted completion time, and total weighted tardiness. We use the least significant difference (LSD) test to identify robust heuristics that perform significantly better than others for a variety of parallel machine environments with these three performance measures. Computational results show that the proposed metaheuristic outperforms other existing heuristics for each of the three objectives when run with a parameter setting appropriate for the objective.

The successful implementation of composite dispatching rules depends on the values of their scaling parameters. A unified four-phase method to determine robust scaling parameters for composite dispatching rules is proposed, with the goal of achieving reasonably good scheduling performance with the least computational effort in implementation. In phase 1, factor ranges that characterize the problem instances in each tool group (one or more machines operating in parallel) are calculated. In phase 2, a face-centered cube design is used to decide the placement of design points in the factor region. The third phase involves using mixture experiments to find good scaling parameter values at each design point. In the last phase, the central point of the area in which all of the good scaling parameters lie is identified with the robust scaling parameter. The proposed method is applied to determine the robust scaling parameter for the Apparent Tardiness Cost with Setups (ATCS) rule to solve the Pm vertical bar sjk vertical bar Sigma wj Tj scheduling problem in a case study. The results of this case study show that the proposed method is more efficient and effective than existing methods in the literature. It requires many fewer experiments and achieves more than a 30% improvement in the average scheduling performance (i.e., total weighted tardiness) and more than a 60% improvement in the standard deviation of the scheduling performance.

Chapter 1. Introduction to Quality. 1.1 The Meaning of Quality and Quality Improvement. 1.2 A Brief History of Quality Control and Improvement. 1.3 Statistical Methods for Quality Control and Improvement. 1.4 Quality and Productivity. 1.5 Quality Costs. 1.6 Legal Aspects of quality. 1.7 Implementing Quality Improvement. Chapter 2. Management Aspects of Quality. 2.1 Introduction. 2.2 Quality Philosophy and Management Strategies. 2.3 The DMAIC Process. Chapter 3. Tools and Techniques for Quality Control and Improvement. 3.1 Introduction. 3.2 Chance and Assignable Causes of Quality Variation. 3.3 The Control Chart. 3.4 The Rest of the Magnificent Seven. 3.5 Implementing SPC in a Quality Improvement Program. 3.6 An Application of SPC. 3.7 Applications of Quality Process and Quality Improvement Tools in Transactional and Service Businesses. Chapter 4. Statistical Inference about Product and Process Quality. 4.1 Describing Variation. 4.2 Probability Distributions. 4.3 The Normal Distribution. 4.4 Statistical Inference. 4.5 Statistical Inference for a Single Sample. 4.6 Statistical Inference for Two Chapter 5. Control Charts for Variables. 5.1 Introduction. 5.2 and R x Charts. 5.3 and S Charts. 5.4 Shewart Control Chart for Individual Measurements. 5.5 Summary of Procedures for , R, S, and Individuals Charts. 5.6 Example Applications of , R, S, and Individuals Charts. 5.7 Cumulative Sum Control Charts. 5.8 Exponentially Weighted Moving Average Control Charts. 5.9 Process Capability Analysis Using Control Charts. Chapter 6. Control Charts for Attributes. 6.1 Introduction. 6.2 The Control Chart for Fraction Nonconforming. 6.3 Control Charts for Nonconformities (Defects). 6.4 Choice between Attributes and Variables Control Charts. 6.5 Guidelines for Implementing Control Charts. Chapter 7. Lot-by-Lot Acceptance Sampling Procedures. 7.1 The Acceptance Sampling Problem. 7.2 Single-Sampling Plans for Attributes. 7.3 Double, Multiple, and Sequential Sampling. 7.4 Military Standard 105E (ANSI/ASQC Z1.4, ISO 2859). 7.5 The Dodge Romig Sampling Plans. 7.6 Military Standard 414 (ANSI/ASQ Z1.9). 7.7 Chain Sampling. 7.8 Continuous Sampling. 7.9 Skip-Lot Sampling Plans. Chapter 8. Process Design and Improvement with Designed Experiments. 8.1 What Is Experimental Design? 8.2 Examples of Designed Experiments in Process and Product Improvement. 8.3 Guidelines for Designing Experiments. 8.4 The Analysis of Variance. 8.5 Factorial Experiments. 8.6 The 2k Factorial Design. 8.7 Fractional Replication of the 2k Design. 8.8 Response Surface Methods. 8.9 Robust Product and Process Design. Chapter 9. Reliability. 9.1 Basic Concepts of Reliability. 9.2 Life Distributions. 9.3 Instantaneous Failure Rate. 9.4 Life Cycle Reliability. 9.5 Determining System Reliability from Component Reliabilities. 9.6 Life Testing and Reliability Estimation. 9.7 Availability and Maintainability. 9.8 Failure Mode and Effects Analysis. References. Glossary. Appendix. A. I Summary of Common Probability Distribution Cities Used in Quality Control and Improvement. A. II Cumulative Standard Normal Distribution. A. III Percentage Points of the Distribution. A. IV Percentage Points of the t Distribution. A. V Percentage Points of the F Distribution. A. VI Factors for Constructing Variables Control Charts. Answers to Selected Exercises. Index.

This research proposes a two-phase heuristic and an improvement procedure for scheduling unrelated parallel machines problems to minimize makespan.

We consider bicriteria scheduling on identical parallel machines in a nontraditional context: jobs belong to two disjoint sets, and each set has a different criterion to be minimized. The jobs are all available at time zero and have to be scheduled (non-preemptively) on m parallel machines. The goal is to generate the set of all non-dominated solutions, so the decision maker can evaluate the tradeoffs and choose the schedule to be implemented. We consider the case where, for one of the two sets, the criterion to be minimized is makespan while for the other the total completion time needs to be minimized. Given that the problem is NP-hard, we propose an iterative SPT–LPT–SPT heuristic and a bicriteria genetic algorithm for the problem. Both approaches are designed to exploit the problem structure and generate a set of non-dominated solutions. In the genetic algorithm we use a special encoding scheme and also a unique strategy – based on the properties of a non-dominated solution – to ensure that all parts of the non-dominated front are explored. The heuristic and the genetic algorithm are compared with a time-indexed integer programming formulation for small and large instances. Results indicate that the both the heuristic and the genetic algorithm provide high solution quality and are computationally efficient. The heuristics proposed also have the potential to be generalized for the problem of interfering job sets involving other bicriteria pairs.

We consider bicriteria scheduling on identical parallel machines in a nontraditional context: jobs belong to two disjoint sets, and each set has a different criterion to be minimized. The jobs are all available at time zero and have to be scheduled (non-preemptively) on m parallel machines. The goal is to generate the set of all non-dominated solutions, so the decision maker can evaluate the tradeoffs and choose the schedule to be implemented. We consider the case where, for one of the two sets, the criterion to be minimized is makespan while for the other the total completion time needs to be minimized. Given that the problem is NP-hard, we propose an iterative SPT–LPT–SPT heuristic and a bicriteria genetic algorithm for the problem. Both approaches are designed to exploit the problem structure and generate a set of nondominated solutions. In the genetic algorithm we use a special encoding scheme and also a unique strategy – based on the properties of a non-dominated solution – to ensure that all parts of the non-dominated front are explored. The heuristic and the genetic algorithm are compared with a time-indexed integer programming formulation for small and large instances. Results indicate that the both the heuristic and the genetic algorithm provide high solution quality and are computationally efficient. The heuristics proposed also have the potential to be generalized for the problem of interfering job sets involving other bicriteria pairs. 2008 Elsevier B.V. All rights reserved.

In this research we are interested in scheduling jobs with ready times on identical parallel machines with sequence dependent setups. Our objective is to minimize the total weighted tardiness. As this problem is NP-Hard, we develop a heuristic to solve this problem in reasonable time. Our approach is an extension of the apparent tardiness cost with setups (ATCS) approach by [Lee, Y. H., Pinedo, M. (1997). Scheduling jobs on parallel machines with sequence dependent setup times. European Journal of Operational Research, 100, 464–474.] to allow non-ready jobs to be scheduled – meaning we allow a machine to remain idle for a high priority job arriving at a later time. To determine the scaling parameters for our composite dispatching rule (called ATCSR), we first develop a ‘grid approach’ that considers multiple values for the scaling parameters, generates multiple schedules, and chooses the best schedule for the solution. This experimentation was then used to develop regression equations to predict the values of the scaling parameters that would yield the highest quality solution. The grid and regression versions of ATCSR provide better performance than grid and empirically based formula versions of ATCS, BATCS, and X-RM which are the prominent algorithms in the literature.

are n jobs that have to be assigned and sequenced on m unrelated parallel machines. Each job has a weight that represents the priority of the corresponding customer order, a given due date, and a release date. An Automated Guided Vehicle is used to transport at maximum jobs into a storage space in front of the machines in a given period of time. We consider T consecutive peri- ods of time, and are interested in minimizing the total weighted tardiness (TWT) of the jobs across the T periods. To solve the problem, we present a mixed integer program (MIP) and a heuristic decomposition methodology. These methodologies are tested using stochastically generated test instances and compared. Results indicate that the decomposition approach performs comparably to the MIP while having reasonable solution times. max Load

This paper is motivated by the problem of meeting due dates in a flowshop production environment with jobs with different weights and uncertain processing times. Enforcement of a permutation schedule to varying degrees for dynamic flowshops is investigated with the goal of minimizing total weighted tardiness (TWT). The approaches studied are categorized as follows: (1) pure permutation scheduling (2) shift-based scheduling (3) pure dispatching (which leads to non-permutation sequences). A simulation-based experimental study was carried out to study the performance of the above methods with respect to minimizing TWT when new jobs arrive to the flowshop at every shift change. Results indicate significant gains in performance are possible when the permutation requirement is relaxed and shift-based scheduling is allowed. Shift-based scheduling yields competitive results with respect to the pure dispatching approach, even though dispatching has the advantage of a full relaxation of the permutation requirement.

In this paper, we study a planning and scheduling problem for unrelated parallel machines. There are n jobs that have to be assigned and sequenced on m unrelated parallel machines. Each job has a weight that represents the priority of the corresponding customer order, a given due date, and a release date. An Automated Guided Vehicle is used to transport at maximum jobs into a storage space in front of the machines in a given period of time. We consider T consecutive periods of time, and are interested in minimizing the total weighted tardiness (TWT) of the jobs across the T periods. To solve the problem, we present a mixed integer program (MIP) and a heuristic decomposition methodology. These methodologies are tested using stochastically generated test instances and compared. Results indicate that the decomposition approach performs comparably to the MIP while having reasonable solution times. max

In this research, we model a semiconductor wafer fabrication process as a complex job shop, and adapt a Modified Shifting Bottleneck Heuristic (MSBH) to facilitate the multi-criteria optimization of makespan, cycle time, and total weighted tardiness using a desirability function. The desirability function is implemented at two different levels of the MSBH: the subproblem solution procedure level (SSP level) and the machine criticality measure level (MCM level). In addition, we suggest two different methods of choosing the critical toolgroup at the MCM level: (1) the Local MCM approach, which chooses the critical toolgroup based on local desirability values from the SSP level and (2) the Global MCM approach, which chooses the critical toolgroup based on its impact on the desirability of the entire disjunctive graph. Results demonstrate the desirability-based approaches’ ability to simultaneously minimize all three objectives.

We examine a complex, multi-objective semiconductor manufacturing scheduling problem involving two batch processing steps linked by a timer constraint. This constraint requires that any job completing the first processing step must be started on the succeeding second machine within some allowable time window; otherwise, the job must repeat its processing on the first step. We present a random keys implementation of NSGA-II (nondominated sorting genetic algorithm) for our problem of interest and investigate the efficacy of different batching policies in terms of the number of approximate efficient solutions that are produced by NSGA-II over a wide range of experimental problem instances. Experimental results suggest a full batch policy can produce superior solutions as compared to greedy batching policies under the experimental conditions examined