The Effects of Scheduling Rules and Routing Flexibility on the Performance of a Random Flexible Manufacturing System

The increased use of flexible manufacturing systems to efficiently provide customers with diversified products has created a significant set of operational challenges for managers. Many issues concerning procedures and policies for the day-to-day operation of these systems still are unresolved. Previous studies in this area have concentrated on various problems by isolating or simplifying the systems under study. The primary objective of this study is to extend previous research by examining the effects of scheduling rules and routing flexibility on the performance of a constrained, random flexible manufacturing system (FMS). Other experimental factors considered are shop load, shop configuration, and system breakdowns. Within the bounds of this experiment, the results indicate that, in the presence of total routing flexibility, the effects of shop load, system breakdowns, and scheduling rules are significantly dampened. In particular, when total routing flexibility exists, the choice of scheduling rules is not critical. We also show that the behavior of scheduling rules in a more constrained FMS environment (i.e., where system breakdowns occur and material handling capability is limited) is consistent with the findings of previous research conducted under less constrained environments. Finally, results indicate that the shop configuration factor has little or no impact on a system's flow-time performance.     

Multiple-Objective Scheduling for the Hierarchical Control of Flexible Manufacturing Systems

This paper presents a hierarchical approach to scheduling flexible manufacturing systems (FMSs) that pursues multiple performance objectives and considers the process flexibility of incorporating alternative process plans and resources for the required operations. The scheduling problem is solved at two levels: the shop level and the manufacturing system level. The shop level controller employs a combined priority index developed in this research to rank shop production orders in meeting multiple scheduling objectives. To overcome dimensional complexity and keep a low level of work-in-process inventory, the shop controller first selects up to three production orders with the highest ranking as candidates and generates all possible release sequences for them, with or without multitasking. These sequences are conveyed to the manufacturing system controller, who then performs detailed scheduling of the machines in the FMS using a fixed priority heuristic for routing parts of multiple types while considering alternative process plans and resources for the operations. The FMS controller provides feedback to the shop controller with a set of suggested detailed schedules and projected order completion times. On receiving these results, the shop controller further evaluates each candidate schedule using a multiple-objective function and selects the best schedule for execution. This allows multiple performance objectives of an FMS to be achieved by the integrated hierarchical scheduling approach.     

Mathematical model for cyclic scheduling with work-in-process minimization

This paper is part of an original approach of mathematical modeling for solving cyclic scheduling problems. More precisely, we consider the cyclic job shop. This kind of manufacturing systems is well fitted to medium and large production demands. Many methods have been proposed to solve the cyclic scheduling problem. Among them, we chose the exact techniques, and we focus on the mathematical programming approach. We proposed, in an earlier study, a mathematical programming model for cyclic scheduling with Work-In-Process minimization. We propose here several cutting techniques to improve the practical performances of the model resolution. Some numerical experiments are used to assess the relevance of our propositions. We made a comparison between the original mathematical model and the one endowed by the proposed cuts. This comparison is based on a set of benchmarks generated for this reason. In addition, we make another comparison based on some examples from the literature.     

Near optimal manufacturing flow controller design

Flow control of flexible manufacturing systems (FMSs) addresses an important real-time scheduling requirement of modern manufacturing facilities, which are prone to failures and other controllable or stochastic discrete events affecting production capacity, such as change of setup and maintenance scheduling. Flow controllers are useful both in the coordination of interconnected flexible manufacturing cells through distributed scheduling policies and in the hierarchical decomposition of the planning and scheduling problem of complex manufacturing systems. Optimal flow-control policies are hedging-point policies characterized by a generally intractable system of stochastic partial differential equations. This article proposes a near optimal controller whose design is computationally feasible for realistic-size systems. The design exploits a decomposition of the multiple-part-type problem to many analytically tractable one-part-type problems. The decomposition is achieved by replacing the polyhedra production capacity sets with inscribed hypercubes. Stationary marginal densities of state variables are computed iteratively for successive trial controller designs until the best inscribed hypercubes and the associated optimal hedging points are determined. Computational results are presented for an illustrative example of a failureprone FMS.     

A heuristic approach to minimize expected makespan in open shops subject to stochastic processing times and failures

Many real world situations exist where job scheduling is required. This is the case of some entities, machines, or workers who have to execute certain jobs as soon as possible. Frequently what happens is that several workers or machines are not available to perform their activities during some time periods, due to different circumstances. This paper deals with these situations, and considers stochastic scheduling models to study these problems. When scheduling models are used in practice, they have to take into account that some machines may not be working. That temporal lack of machine availability is known as breakdowns, which happen randomly at any time. The times required to repair those machines are also random variables. The jobs have operations with stochastic processing times, their own release times, and there is no precedence between them. Each job is divided into operations and each operation is performed on the corresponding specialized machine. In addition, in the problems considered, the order in which the operations of each job are done is irrelevant. We develop a heuristic approach to solve these stochastic open-shop scheduling problems where random machine breakdowns can happen. The proposed approach is general and it does not depend on the distribution types of the considered random input data. It provides solutions to minimize the expected makespan. Computational experiences are also reported. The results show that the proposed approach gives a solid performance, finding suitable solutions with short CPU times.     

An empirical comparison of several clustered data approaches under confounding due to cluster effects in the analysis of complications of coronary angioplasty

In the analysis of binary response data from many types of large studies, the data are likely to have arisen from multiple centers, resulting in a within-center correlation for the response. Such correlation, or clustering, occurs when outcomes within centers tend to be more similar to each other than to outcomes in other centers. In studies where there is also variability among centers with respect to the exposure of interest, analysis of the exposure-outcome association may be confounded, even after accounting for within-center correlations. We apply several analytic methods to compare the risk of major complications associated with two strategies, staged and combined procedures, for performing percutaneous transluminal coronary angioplasty (PTCA), a mechanical means of relieving blockage of blood vessels due to atherosclerosis. Combined procedures are used in some centers as a cost-cutting strategy. We performed a number of population-averaged and cluster-specific (conditional) analyses, which (a) make no adjustments for center effects of any kind; (b) make adjustments for the effect of center on only the response; or (c) make adjustments for both the effect of center on the response and the relationship between center and exposure. The method used for this third approach decomposes the procedure type variable into within-center and among-center components, resulting in two odds ratio estimates. The naive analysis, ignoring clusters, gave a highly significant effect of procedure type (OR = 1.6). Population average models gave marginally to very nonsignificant estimates of the OR for treatment type ranging from 1.6 to 1.2 with adjustment only for the effect of centers on response. These results depended on the assumed correlation structure. Conditional (cluster-specific) models and other methods that decomposed the treatment type variable into among- and within-center components all found no within-center effect of procedure type (OR = 1.02, consistently) and a considerable among-center effect. This among-center variability in outcomes was related to the proportion of patients who receive combined procedures and was found even when conditioned on procedure type (within-center) and other patient- and center-level covariates. This example illustrates the importance of addressing the potential for center effects to confound an outcome-exposure association when average exposure varies across clusters. While conditional approaches provide estimates of the within-cluster effect, they do not provide information about among-center effects. We recommend using the decomposition approach, as it provides both types of estimates.     

Efficient Semiparametric Marginal Estimation for the Partially Linear Additive Model for Longitudinal/Clustered Data

We consider the efficient estimation of a regression parameter in a partially linear additive nonparametric regression model from repeated measures data when the covariates are multivariate. To date, while there is some literature in the scalar covariate case, the problem has not been addressed in the multivariate additive model case. Ours represents a first contribution in this direction. As part of this work, we first describe the behavior of nonparametric estimators for additive models with repeated measures when the underlying model is not additive. These results are critical when one considers variants of the basic additive model. We apply them to the partially linear additive repeated-measures model, deriving an explicit consistent estimator of the parametric component; if the errors are in addition Gaussian, the estimator is semiparametric efficient. We also apply our basic methods to a unique testing problem that arises in genetic epidemiology; in combination with a projection argument we develop an efficient and easily computed testing scheme. Simulations and an empirical example from nutritional epidemiology illustrate our methods.     

Assessment of Virucidal Ability of Chemical Disinfectants

Increasing interest has been shown in studying and testing the virucidal properties of disinfectants. At the moment there are no well-established standard methods available; quite different procedures have been employed. Therefore, there is no satisfactory way of comparing the results obtained in this field. We present some of these problems and propose standard test methods to overcome these difficulties.     

Medium term planning of biopharmaceutical manufacture using mathematical programming

Regulatory pressures and capacity constraints are forcing the biopharmaceutical industry to consider employing multiproduct manufacturing facilities running on a campaign basis. The need for such flexible and cost-effective manufacture poses a significant challenge for planning and scheduling. This paper reviews the problem of planning and scheduling of biopharmaceutical manufacture and presents a methodology for the planning of multiproduct biopharmaceutical manufacturing facilities. The problem is formulated as a mixed integer linear program (MILP) to represent the relevant decisions required within the planning process and is tested on two typical biopharmaceutical industry planning problems. The proposed formulation is compared with an industrial rule based approach, which it outperforms in terms of profitability. The results indicate that the developed formulation offers an effective representation of the planning problem and would be a useful decision tool for manufacturers in the biopharmaceutical industry particularly at times of limited manufacturing capacity.     

Scheduling unrelated parallel machines in semiconductor manufacturing by problem reduction and local search heuristics

We investigate a difficult scheduling problem in a semiconductor manufacturing process that seeks to minimize the number of tardy jobs and makespan with sequence-dependent setup time, release time, due dates and tool constraints. We propose a mixed integer programming (MIP) formulation which treats tardy jobs as soft constraints so that our objective seeks the minimum weighted sum of makespan and heavily penalized tardy jobs. Although our polynomial-sized MIP formulation can correctly model this scheduling problem, it is so difficult that even a feasible solution can not be calculated efficiently for small-scale problems. We then propose a technique to estimate the upper bound for the number of jobs processed by a machine, and use it to effectively reduce the size of the MIP formulation. In order to handle real-world large-scale scheduling problems, we propose an efficient dispatching rule that assigns a job of the earliest due date to a machine with least recipe changeover (EDDLC) and try to re-optimize the solution by local search heuristics which involves interchange, translocation and transposition between assigned jobs. Our computational experiments indicate that EDDLC and our proposed reoptimization techniques are very efficient and effective. In particular, our method usually gives solutions very close to the exact optimum for smaller scheduling problems, and calculates good solutions for scheduling up to 200 jobs on 40 machines within 10 min.     

Steady-State Analysis and Scheduling of Cyclic Job Shops with Overtaking

A cyclic shop is a production system that repeatedly produces identical sets of parts of multiple types, called minimal part sets (MPSs), in the same loading and processing sequence. A different part type may have a different machine visit sequence. We consider a version of cyclic job shop where some operations of an MPS instance are processed prior to some operations of the previous MPS instances. We call such a shop an overtaking cyclic job shop (OCJS). The overtaking degree can be specified by how many MPS instances the operations of an MPS instance can overtake. More overtaking results in more work-in-progress, but reduces the cycle time, in general. We prove that for a given processing sequence of the operations at each machine, under some conditions, an OCJS has a stable earliest starting schedule such that each operation starts as soon as its preceding operations are completed, the schedule repeats an identical timing pattern for each MPS instance, and the cycle time is kept to be minimal. To do these, we propose a specialized approach to analyzing steady states for an event graph model of an OCJS that has a cyclic structure, which can keep the MPS-based scheduling concept. Based on the steady-state results, we develop a mixed integer programming model for finding a processing sequence of the operations at each machine and the overtaking degrees, if necessary, that minimize the cycle time.     

Development and application of technology for large scale cloning of cattle

Mammalian cloning technologies originally developed as methods of testing hypotheses about the mechanisms of cell differentiation. Embryo splitting procedures demonstrated that each of the cells in the early embryo are capable of developing into a complete new individual. Nuclear transplantation technologies have shown that loss of genetic sequences or even irreversible repression of gene function are also not mechanisms of cell differentiation. Therefore, both of these methods can be used for producing genetically identical animals. Nuclear transplantation has the advantage of being able to produce unlimited numbers of identical offspring. Highly efficient procedures have been developed for nuclear transplantation in mammals and several important characteristics of donor cells have been described. Unfortunately, the efficiency of producing cloned offspring is still low and many factors affecting the development of nuclear transfer embryos to term remain to be investigated. The tremendous potential of the technology for use in agriculture and medicine, however, will ensure that these problems are addressed and solved.     

A Genetic Algorithm Approach to the Scheduling of FMSs with Multiple Routes

Usually, most of the typical job shop scheduling approaches deal with the processing sequence of parts in a fixed routing condition. In this paper, we suggest a genetic algorithm (GA) to solve the job-sequencing problem for a production shop that is characterized by flexible routing and flexible machines. This means that all parts, of all part types, can be processed through alternative routings. Also, there can be several machines for each machine type. To solve these general scheduling problems, a genetic algorithm approach is proposed and the concepts of virtual and real operations are introduced. Chromosome coding and genetic operators of GAs are defined during the problem solving. A minimum weighted tardiness objective function is used to define code fitness, which is used for selecting species and producing a new generation of codes. Finally, several experimental results are given.     

Clonal Selection Based Memetic Algorithm for Job Shop Scheduling Problems

A clonal selection based memetic algorithm is proposed for solving job shop scheduling problems in this paper. In the proposed algorithm, the clonal selection and the local search mechanism are designed to enhance exploration and exploitation. In the clonal selection mechanism, clonal selection, hypermutation and receptor edit theories are presented to construct an evolutionary searching mechanism which is used for exploration. In the local search mechanism, a simulated annealing local search algorithm based on Nowicki and Smutnicki＇s neighborhood is presented to exploit local optima. The proposed algorithm is examined using some well-known benchmark problems. Numerical results validate the effectiveness of the proposed algorithm.     

On Monitoring Outcomes of Medical Providers

An issue of substantial importance is the monitoring and improvement of health care facilities such as hospitals, nursing homes, dialysis units or surgical wards. In addressing this, there is a need for appropriate methods for monitoring health outcomes. On the one hand, statistical tools are needed to aid centers in instituting and evaluating quality improvement programs and, on the other hand, to aid overseers and payers in identifying and addressing sub-standard performance. In the latter case, the aim is to identify situations where there is evidence that the facility’s outcomes are outside of normal expectations; such facilities would be flagged and perhaps audited for potential difficulties or censured in some way. Methods in use are based on models where the center effects are taken as fixed or random. We take a systematic approach to assessing the merits of these methods when the patient outcome of interest arises from a linear model. We argue that methods based on fixed effects are more appropriate for the task of identifying extreme outcomes by providing better accuracy when the true facility effect is far from that of the average facility and avoiding confounding issues that arise in the random effects models when the patient risks are correlated with facility effects. Finally, we consider approaches to flagging that are based on the Z-statistics arising from the fixed effects model, but which account in a robust way for the intrinsic variation between facilities as contemplated in the standard random effects model. We provide an illustration in monitoring survival outcomes of dialysis facilities in the US.     

A scheduling model for multirobot assembly cells

We propose a mixed 0–1 linear programming model for repetitive scheduling of multirobot assembly and machining cells. The approach adopted is monolithic as opposed to hierarchical to avoid system suboptimization. The model permits any number of alternative ways (or modes) to perform each operation. A mode of an operation is determined by the required resources (facilities) and the duration of their use. The model incorporates facility changeover times. Robot collisions are avoided. Several objective functions are formulated to support different purposes. The scheduling problem of a multirobot assembly cell is formulated and solved by using commercially available mathematical programming software. Solutions under four different objective functions are reported. Acknowledging the complexity and considerable size of the formulation required, we prescribe and illustrate specific methods to achieve size reduction. Finally, for successful use of our model, an information processing schema is offered as a general guidance to help data management needed by the model.     

Exact methods for the robotic cell problem

This paper investigates an exact method for the Robotic Cell Problem. We present a branch-and-bound algorithm which is the first exact procedure specifically designed with regard to this complex flow shop scheduling variant. Also, we propose a new mathematical programming model as well as new lower bounds. Furthermore, we describe an effective genetic algorithm that includes, as a mutation operator, a local search procedure. We report the results of a computational study that provides evidence that medium-sized instances, with up to 176 operations, can be optimally solved. Also, we found that the new proposed lower bounds outperform lower bounds from the literature. Finally, we show, that the genetic algorithm delivers good solutions while requiring short CPU times.     

Scheduling algorithms for a two-machine flexible manufacturing system

The flexible manufacturing system (FMS) considered in this paper is composed of two CNC machines working in series—a punching machine and a bending machine connected through rollers acting as a buffer system of finite capacity. The main difference between the present problem and the standard two-machine flow shop problem with finite intermediate capacity is precisely the buffer system, which in our problem consists of two stacks of parts supported by rollers: the first stack contains the output of the punching machine, while the second stack contains the input for the bending machine. When the second stack is empty, the first stack may be moved over. Furthermore, the capacity of each stack depends on the particular part type being processed. The FMS can manufacture a wide range of parts of different types. Processing times on the two machines are usually different so that an unbalance results in their total workload. Furthermore, whenever there is a change of the part type in production, the machines must be properly reset—that is, some tools need to be changed or repositioned. A second important difference between the present problem and the usual two-machine flow shop problem is the objective. Given a list ofp part types to be produced in known quantities, the problem considered here is how to sequence or alternate the production of the required part types so as to achieve various hierarchical targets: minimize the makespan (the total time needed to complete production) and, for instance, compress the idle periods of the machine with less workload into a few long enough intervals that could be utilized for maintenance or other reasons. Although Johnson's rule is optimal in some particular cases, the problem addressed in the paper isNP-hard in general: heuristic procedures are therefore provided.     

On computer-assisted analysis of biological sequences: proline punctuation, consensus sequences, and apolipoprotein repeats

During the past several years, the use of computer programs in the analysis of protein and DNA sequences has become commonplace. In all but the simplest procedures, the ability to critically review the results obtained with computer methods requires a basic knowledge of the algorithms employed (and the assumptions upon which they are based), an awareness of the capabilities and limitations of the particular program that implements an algorithm, and some familiarity with probability and statistics. We describe a number of computer methods that have been applied to the analysis of apolipoprotein sequences. We discuss the suitability of these methods for particular problems, how the choice of initial "parameters" can affect the results, and what the results can tell us about protein or gene sequences. We also identify some outstanding problems of apolipoprotein sequence analysis where further work is needed.     

Cyclic Scheduling of Operations for a Part Type in an FMS Handled by a Single Robot: A Parametric Critical-Path Approach

We consider two problems of periodic scheduling of parts in a robotic production system functioning under a given repetitive robot's route. The objective is to determine the starting times and durations of processing operations so as to minimize the cycle length. We reduce the problems to finding parametric critical paths in networks with varying arc lengths. In contrast to previously known methods, which solve these cyclic scheduling problems in cubic time, the parametric network approach solves the problems in $O(m^2 {\text{ log }}m) $ time, m being the problem size.