Throughput Optimization in Constant Travel‐Time Dual Gripper Robotic Cells with Parallel Machines

Constant travel‐time robotic cells with a single gripper robot and with one or more machines at each processing stage have been studied in the literature. By contrast, cells with a dual gripper robot, although more productive, have so far received scant attention, perhaps due to their inherent complexity. We consider the problem of scheduling operations in dual gripper robotic cells that produce identical parts. The objective is to find a cyclic sequence of robot moves that minimizes the long‐run average time to produce a part or, equivalently, maximizes the throughput. We provide a structural analysis of cells with one or more machines per processing stage to obtain first a lower bound on the throughput and, subsequently, an optimal solution under conditions that are common in practice. We illustrate our analysis on two cells implemented at a semiconductor equipment manufacturer and offer managerial insights for assessing the potential productivity gains from the use of dual gripper robots.     

JOB RELEASE CONTROL USING A CYCLIC SCHEDULE*

In just‐in‐time inventory management in any manufacturing setting, the general idea has been to release jobs as late as possible (to reduce inventory costs) while still having them arrive at bottleneck machines in time to maintain the desired throughput (by not starving a bottleneck machine). When a cyclic schedule is employed, the throughput is determined by a cyclic sequence of operations known as the cyclic critical path. These operations are not, in general, all performed on a single bottleneck machine. We present an algorithm for releasing jobs that treats this cyclic critical path as the bottleneck. Although this algorithm has the somewhat complex task of not delaying any of these operations on the cyclic critical path, it is greatly simplified by being able to take advantage of the fixed sequence of the cyclic schedule. The result is that the algorithm is relatively simple to implement. Although it uses a simulation‐based analysis, this analysis can all be done and the necessary results stored in advance of its use. We test the algorithm in a job shop environment with stochastic operation times. This algorithm is shown to be effective at reducing inventory while avoiding decreases in throughput.     

A two-phase robot selection procedure

In this paper, a two-phase methodology is proposed for robot selection. In phase 1, data envelopment analysis is used as a means to determine the technically efficient robot alternatives, considering cost and technical performance parameters. Using data envelopment analysis permits us to consider the fact that the performance parameters specified by the vendors are generally unattainable in practice. In the second phase, a fuzzy robot selection algorithm is utilized to rank the technically efficient robots according to both predetermined objective criteria and additional vendor-related subjective criteria. The algorithm is based on calculating fuzzy suitability indices for the technically efficient robot alternatives, and then, ranking the fuzzy indices to select the best robot alternative. A comprehensive example is provided to illustrate the decision procedure. The algorithm proposed in here is also applicable to a broader area of decision problems, e.g. facility site selection, determination of the best CNC machine or flexible manufacturing system among a set of mutually exclusive alternatives.     

The blocking job shop with rail-bound transportation

The blocking job shop with rail-bound transportation (BJS-RT) considered here is a version of the job shop scheduling problem characterized by the absence of buffers and the use of a rail-bound transportation system. The jobs are processed on machines and are transported from one machine to the next by mobile devices (called robots) that move on a single rail. The robots cannot pass each other, must maintain a minimum distance from each other, but can also “move out of the way”. The objective of the BJS-RT is to determine for each machining operation its starting time and for each transport operation its assigned robot and starting time, as well as the trajectory of each robot, in order to minimize the makespan. Building on previous work of the authors on the flexible blocking job shop and an analysis of the feasible trajectory problem, a formulation of the BJS-RT in a disjunctive graph is derived. Based on the framework of job insertion in this graph, a local search heuristic generating consistently feasible neighbor solutions is proposed. Computational results are presented, supporting the value of the approach.     

Improving productivity of automated tissue converting lines: an empirical model and a case study

This study is focused on minor stoppages as sources of variance within automated production lines in industrial environments, and it suggests the handling of the problem through a combined phenomenon–mechanism analysis and simulation approach. The resulting seven-step methodological pattern has been applied to a real-life case study of a tissue converting line: the product type and the machine speed have been identified as causal factors for minor stoppages and the wrapper machine has been chosen to exemplify the methodology.

Results point out that the speed of the wrapping machine–which allows the daily throughput of line to be maximized–changes when products change, thus highlighting a trade off between minor stoppages and wrapper speed. However, in some other cases, minor stoppages are more detrimental than the machine speed is useful.     

Redundant configuration of automated flow lines based on “Industry 4.0”-technologies

Over the past decades, robots have been heavily used for flow lines to increase productivity and product quality and to relieve workers of repetitive and dangerous tasks. However, despite continuous improvement of robots, the occurrence of failures remains a significant challenge in the operation of automated flow lines. Due to the connection of the stations in a flow line via a material handling system, failures at one station can quickly lead to throughput losses due to blocking and starving of upstream and downstream stations, respectively. To some extent, these throughput losses can be reduced by installing buffers between the stations. However, the installation of buffers requires considerable investments and scarce factory space. Therefore, the minimization of the total number of buffers is one of the primary objectives in flow line planning. Due to the advances of manufacturing technologies that form the foundation of “Industry 4.0”, new solutions to reduce throughput losses caused by equipment failures open up. One solution is a redundant configuration, in which downstream stations automatically take over the operations of failed stations in the event of failure. The throughput loss in these situations mainly depends on the level of redundancy designed into the system. Based on existing methods for the design of automated flow lines, we present two line balancing formulations for the configuration of automated flow lines under consideration of redundancies. The first formulation aims at maximizing the lines’ level of redundancy. The second formulation aims at a balanced allocation of redundancies along the line. To evaluate the presented formulations, we compare the performance with an existing line balancing approach for automated lines. With respect to this approach, improvements of the throughput rate between 3 % and 7 % are achieved.     

Decision Models for Robot Selection: A Comparison of Ordinary Least Squares and Linear Goal Programming Methods

The profusion of robot designs, the cost of testing, and the fact that robot operational parameter maximums are often mutually exclusive are factors that create a complex selection decision for the potential user. While formal robot testing standards are now in place, formal techniques to select robots for the testing process have not been addressed. A linear goal programming model is an effective tool for the decision maker for optimizing the robot selection process in terms of requirement priorities. It is also shown that this model provides a more stable result than the ordinary least squares estimator in the presence of statistical outliers of robot parameters. The methodology is illustrated through the use of current robot specifications.     

The simulation of industrial robot systems

This paper describes the GRASP computer aided design system for modelling and evaluating industrial robot workplaces. GRASP satisfies a range of simulation needs within the context of designing, implementing and operating industrial robotic systems. The GRASP software may be used to investigate robots operating by themselves or, more likely, as part of an integrated cell. Facilities within GRASP assist workplace layout, position and velocity evaluations, clash detection and co-ordination between items. A robot library exists and is being extended, and an embryo off-line programming facility has been used under restricted conditions. GRASP has been used to help solve a wide range of practical industrial robot problems and has proved itself technically, and also as an educational tool, by showing how a proposed system would operate. The paper describes the facilities within GRASP.     

COMBINED PRODUCTION AND MAINTENANCE SCHEDULING FOR A MULTIPLE‐PRODUCT,SINGLE‐ MACHINE PRODUCTION SYSTEM

Traditionally, the problems of equipment maintenance scheduling and production scheduling in a multi‐product environment have been treated independently. In this paper, we develop a Markov decision process model that simultaneously determines maintenance and production schedules for a multiple‐product, single‐machine production system, accounting for the fact that equipment condition can affect the yield of different product types differently. The problem was motivated by an application in semiconductor manufacturing. After examining structural properties of the optimal policy, we compare the combined method to an approach often used in practice. In the nearly 6,000 test problems studied, the reward from the combined method was an average of more than 25 percent greater than the reward from the traditional method.     

Alternate Routing Strategies in Batch Manufacturing: An Evaluation

Batch manufacturing firms are experiencing significant changes because of technological developments in work center design, such as flexible manufacturing systems (FMS) and planning/control tools like computer-aided process planning (CAPP). These new developments provide production managers with some solutions to a number of complex problems. For example, numerical-controlled (NC) machine center installations are effective in providing quality parts because of tight tolerance specifications built into the equipment. However, these highly efficient centers create bottlenecks that constrain shop throughput, since production planners tend to rely too much on them. To help improve manufacturing planning, we introduce an important element to the batch production scheduling component of CAPP's mission—evaluating possible alternate routes. Production scheduling encompasses job route selection as well as machine center assignment (loading), job releasing, and setting due dates. In this paper, three routing strategies requiring different levels of shop floor information are tested and evaluated using computer simulation. Shop performance is measured by total cost and traditional measures of job flow time, lateness, and tardiness.     

Reliability analysis of man–machine systems using fuzzy cognitive mapping with genetic tuning

This article offers a method for analyzing the reliability of a man–machine system (MMS) and ranking of influencing factors based on a fuzzy cognitive map (FCM). The ranking of influencing factors is analogous to the ranking of system elements the probabilistic theory of reliability. To approximate the dependence of “influencing factors—reliability,” the relationship of variable increments is used, which ensures the sensitivity of the reliability level to variations in the levels of influencing factors. The novelty of the method lies in the fact that the expert values of the weights of the FCM graph edges (arcs) are adjusted based on the results of observations using a genetic algorithm. The algorithm's chromosomes are generated from the intervals of acceptable values of edge weights, and the selection criterion is the sum of squares of deviations of the reliability simulation results from observations. The method is illustrated by the example of a multifactor analysis of the reliability of the “driver–car–road” system. It is shown that the FCM adjustment reduces the discrepancy between the reliability forecast and observations almost in half. Possible applications of the method can be complex systems with vaguely defined structures whose reliability depends very much on interrelated factors measured expertly.     

Scheduling algorithms for computer-aided line balancing in printed circuit board assembly

Generalized flexible flow line (GFFL) is a scheduling environment comprising several machine banks which the products visit in the same order but can skip some machine banks. The type of machines in a bank can differ but they are suitable for performing the same manufacturing tasks. To change one product to another demands a set-up operation of the machine. This paper describes several scheduling algorithms for the GFFL problem. The overall structure of these algorithms is similar, consisting of machine allocation and sequencing phases. The algorithms have been integrated into an interactive production scheduling system for electronics assembly. Sample cases are used to illustrate the operation of the system in practice.     

Energy-oriented bi-objective optimization for the tempered glass scheduling

This paper investigates a real life bi-objective hybrid flow shop scheduling problem in an energy-intensive manufacturing system, in which glass is produced successively in cutting, printing and tempering stages. The problem aims to simultaneously optimize makespan and the total electricity cost under a time-of-use electricity pricing policy. The glass production has to respect the following environments: (i) the cutting and printing operations are processed in parallel machine environments; (ii) the tempering operation is processed on a batch machine; (iii) machine eligibility and setup time have to be considered in the cutting and printing stages; (iv) the whole manufacturing system is under a time-of-use electricity pricing policy. For the problem, an integer programming model is firstly proposed and shown to be strongly NP-hard. Then a model-based heuristic is adopted and a bi-objective differential evolution algorithm (BODE) is devised based on problem features. Computational experiments on randomly generated instances demonstrated that the BODE outperforms the model-based heuristic in terms of computation time and solution quality. Moreover, with mild increase on computation burden, the BODE significantly outperforms the classic NSGA II in terms of solution quality.     

Maximizing Throughput of Bucket Brigades on Discrete Work Stations

One way to coordinate workers along an assembly line that has fewer workers than work stations is to form a bucket brigade. The throughput of a bucket brigade on discrete work stations may be compromised due to blocking even if workers are sequenced from slowest to fastest. For a given work distribution on the stations we find policies that maximize the throughput of the line. When workers have very different production rates, fully cross‐training the workers and sequencing them from slowest to fastest is almost always the best policy. This policy outperforms other policies for most work distributions except for some cases in which limiting the work zones of workers produces higher throughput. In environments where the work can be adjusted across stations, we identify conditions for a line to prevent blocking.     

Extending Little’s Law to single order throughput times

It has long been established that the mean throughput time is a result from the relationship between the work in process (WIP) and output rate. In reality, however, throughput times are frequently dispersed broadly around this mean. With the aid of a model originally developed for lateness, it is now possible to describe the throughput times of individual orders. Accordingly, the throughput time can be divided into two components: WIP-dependent throughput time and sequence-dependent throughput time.     

MORM—A Petri Net Based Model for Assessing OH&S Risks in Industrial Processes: Modeling Qualitative Aspects

Because of the increase in workplace automation and the diversification of industrial processes, workplaces have become more and more complex. The classical approaches used to address workplace hazard concerns, such as checklists or sequence models, are, therefore, of limited use in such complex systems. Moreover, because of the multifaceted nature of workplaces, the use of single-oriented methods, such as AEA (man oriented), FMEA (system oriented), or HAZOP (process oriented), is not satisfactory. The use of a dynamic modeling approach in order to allow multiple-oriented analyses may constitute an alternative to overcome this limitation. The qualitative modeling aspects of the MORM (man-machine occupational risk modeling) model are discussed in this article. The model, realized on an object-oriented Petri net tool (CO-OPN), has been developed to simulate and analyze industrial processes in an OH&S perspective. The industrial process is modeled as a set of interconnected subnets (state spaces), which describe its constitutive machines. Process-related factors are introduced, in an explicit way, through machine interconnections and flow properties. While man-machine interactions are modeled as triggering events for the state spaces of the machines, the CREAM cognitive behavior model is used in order to establish the relevant triggering events. In the CO-OPN formalism, the model is expressed as a set of interconnected CO-OPN objects defined over data types expressing the measure attached to the flow of entities transiting through the machines. Constraints on the measures assigned to these entities are used to determine the state changes in each machine. Interconnecting machines implies the composition of such flow and consequently the interconnection of the measure constraints. This is reflected by the construction of constraint enrichment hierarchies, which can be used for simulation and analysis optimization in a clear mathematical framework. The use of Petri nets to perform multiple-oriented analysis opens perspectives in the field of industrial risk management. It may significantly reduce the duration of the assessment process. But, most of all, it opens perspectives in the field of risk comparisons and integrated risk management. Moreover, because of the generic nature of the model and tool used, the same concepts and patterns may be used to model a wide range of systems and application fields.     

An Optimal Model for Cell Formation Decisions

This study develops an approach to cell formation decisions for cellular manufacturing layouts in group technology settings. An optimal 0–1 integer programming model is used to provide an analysis for determining which machines and parts should be assigned to cells in cellular manufacturing layouts. This approach minimizes the cost of manufacturing exceptional parts outside the cellular system, subject to machine capacity constraints. Part–machine matrices are partitioned into disconnected cells and use far fewer 0–1 variables than earlier approaches. Formulation of the model is described with a numerical example and computer solutions to realistic problems are obtained. The characteristics of computer run times, model performance, and applications of the model are discussed.     

Agent-based interbay system control for a single-loop semiconductor manufacturing fab

This study investigates agent-based interbay control of a wafer fabrication facility with a single-loop track. Doing so will simultaneously resolve the production scheduling and vehicle dispatching problems for an interbay over-hoist-transportation system which is commonly employed in the semiconductor manufacturing industry. Two issues are focused on in this study: (i) To design appropriate agent-based control architecture for an interbay system and (ii) to develop a coordination mechanism for the autonomous agents in the interbay system, so that wafer manufacturing tasks can be accomplished efficiently. In this study, we construct a distributed interbay system architecture, in which wafer cassettes, vehicles, and manufacturing cells with stockers are modelled as independent and autonomous agents. This study develops an agent coordination mechanism that is based on a vehicle-initiated contract net protocol. Three types of bidding strategies are proposed and empirically examined via simulation. The performances of the proposed agent-based control mechanism, in terms of cassette sojourn time and throughput, are observed and our experiments have shown promising results.