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Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs. [ABSTRACT FROM AUTHOR]

The importance of linear programming, and especially mixed-integer linear programming, has increased over time as computers have gotten more capable, algorithms have improved and more software solutions have become available. This approach has been successfully applied for solving hard problems in operations research (like traveling salesman problem, routing problems and telecommunications network design problems). In the eighteenth paper, "Exact method for the two-machine flow-shop problem with time delays" Mkadem, Moukrim and Serairi propose an exact method for solving the two-machine flow-shop problem with time delays. [Extracted from the article]

The knapsack problem arises in a variety of real world applications, including flexible manufacturing systems, railway stations, hydrological studies and others. In this paper, we propose a descent method-based heuristic for tackling a special knapsack problem: the binary quadratic knapsack with conflict graphs. The proposed method combines (i) an intensification search with a descent method for enhancing the accuracy of the solutions and (ii) a diversification strategy which is used for enlarging the search space. The method uses degrading and re-optimization strategies in order to reach a series of diversified solutions. The performance of the proposed method is evaluated on benchmark instances taken from the literature, where its achieved results are compared to those reached by both GLPK solver and the best method available in the literature. The method seems very competitive, where it is able to achieve 37 new lower bounds. [ABSTRACT FROM AUTHOR]

This paper proposes and analyzes an efficient robot task sequence for a flexible assembly system that consists of multiple 3D printers, post-processing, assembly, inspection machines, and a material handling robot. The flexible assembly system, which has been built in many large cities in Korea, has been designed to fabricate customized products for start-ups or individuals. We consider cyclic scheduling of the system in which the robot repeats a certain sequence to produce identical products. We propose a robot task sequence to minimize the cycle time. The system behavior with the proposed robot task sequence is modeled with a timed event graph (TEG), and the optimality of the sequence is proved by analyzing the circuits in the TEG. The results are extended for producing multiple types of products simultaneously. [ABSTRACT FROM AUTHOR]

The paper deals with the situation of manufacturing of customized engineering products under intense competitive environment. Such customized engineering products can be manufactured by different types of production systems such as job production, batch production, mass production, and flexible manufacturing systems. It then becomes an important decision to choose the best one among these production systems which is also dependent on multiple criteria. The three basic criteria predominantly considered in the context of manufacturing are cost, quality, and flexibility. The paper applies the efficient interpretive ranking process (IRP) as an interpretive multi-criteria ranking method to demonstrate its utility in this context. It considers the design of the decision problem by treating the cross-interaction matrix of 'Alternatives × Criteria' as a unit matrix, i.e. each alternative is linked with each criterion. In this type of decision problem, the IRP method is implemented with transitive dominance relationships. Further, it carries out the sensitivity analysis concerning different ordinal weights of various criteria. The paper also makes a methodological contribution in IRP by way of computing dominance index rather than simply considering net dominance (which would be either positive or nil or negative). The dominance index gives a better depiction of the dominance of one alternative over the others. [ABSTRACT FROM AUTHOR]

The problem of makespan minimization on a flexible flow shop with k stages and m s machines at any stage proposed by Paternina-Arboleda et al. [Ann. Oper. Res. 164:29–40, ] was recently published in Annals of Operations Research journal to solve the flexible flow-shop scheduling problems. In this work, some comments and suggestions are given to the paper. The comment is about the proposed mathematical formulation and the modification of it is suggested. [ABSTRACT FROM AUTHOR]

Petri Nets have been extensively used for modeling and simulating of the dynamics of flexible manufacturing systems. Petri Nets can capture features such as parallel machines, alternative routings, batch sizes, multiplicity of resources, to name but a few. However, Petri Nets have not been very popular for scheduling in manufacturing due to the Petri Net “state explosion” combined with the NP-hard nature of many of such problems. A promising approach for scheduling consists of generating only portions of the Petri Net state space with heuristic search methods. Thus far, most of this scheduling work with Petri Nets has been oriented to minimize makespan. The problem of minimizing total tardiness and other due date-related criteria has received little attention. In this paper, we extend the Beam A* Search algorithm presented in a previous work with capability to handle the total tardiness criterion. Computational tests were conducted on Petri Net models of both flexible job shop and flexible manufacturing systems. The results suggest that the Petri Net approach is also valid to minimize due date related criteria in flexible systems. [ABSTRACT FROM AUTHOR]

With the continuous improvement of computational performance, vehicle structural design has been addressed using computational methods, resulting in more efficient development of new vehicles. Most simulation-based optimization approaches generate deterministic optimal designs without considering variability effects in modeling, simulation, and/or manufacturing. One of the main reasons for this omission is due to the fact that the computing time of a single crash analysis for vehicle structural design still requires significant computing time using a state-of-the-art computer. This calls for the development and implementation of an efficient optimization under uncertainty method. In this paper, a new integrated stochastic optimization method, which combines the advantages of metamodeling techniques and Better Optimization of Nonlinear Uncertain Systems (BONUS), is developed for vehicle side impact design. Nonlinear metamodels are built by using a stepwise regression method to replace the expensive computational model and BONUS is employed to obtain optimal designs under uncertainty. A benchmark problem for vehicle safety design is used to demonstrate the method. The main goal of this case study is to maintain or enhance the vehicle side impact test performance while minimizing the vehicle weight under various uncertainties. [ABSTRACT FROM AUTHOR]

We consider capacity management with a long-term strategic choice, such as the number of production lines to install before demand is known, and short-term tactical decisions relating to production, inventory, and subcontracting (recourse actions made after demand is known). We present an integrated scenario-based mathematical modeling and solution framework. For a single-product environment, we examine properties of total profit as a function of demand and the long-term capacity z. We investigate two measures of risk (profit variance and mean downside risk) and their corresponding profit-risk frontiers. Computational experiments are used to illustrate parameter sensitivity results obtained from the model. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to multiobjective optimization based on the principles of probabilistic uncertainty analysis. At the core of this approach is an efficient nonlinear multiobjective optimization algorithm, Minimizing Number of Single Objective Optimization Problems (MINSOOP), to generate a true representation of the whole Pareto surface. Results show that the computational savings of this new algorithm versus the traditional constraint method increase dramatically when the number of objectives increases. A real world case study of multiobjective optimal design of a best available control technology for Nitrogen Oxides (NOx) and Sulfur Oxides (SOx) reduction illustrates the usefulness of this approach. [ABSTRACT FROM AUTHOR]

Accurate knowledge of the effect of parameter uncertainty on process design and operation is essential for optimal and feasible operation of a process plant. Existing approaches dealing with uncertainty in the design and process operations level assume the existence of a well defined model to represent process behavior and in almost all cases convexity of the involved equations. However, most of the realistic case studies cannot be described by well characterised models. Thus, a new approach is presented in this paper based on the idea of High Dimensional Model Reduction technique which utilize a reduced number of model runs to build an uncertainty propagation model that expresses process feasibility. Building on this idea a systematic iterative procedure is developed for design under uncertainty with a unique characteristic of providing parametric expression of the optimal objective with respect to uncertain parameters. The proposed approach treats the system as a black box since it does not rely on the nature of the mathematical model of the process, as is illustrated through a number of examples. [ABSTRACT FROM AUTHOR]

This article addresses the problem of finding an optimal allocation of funds among different asset classes in a robust manner when the estimates of the structure of returns are unreliable. Instead of point estimates used in classical mean-variance optimization, moments of returns are described using uncertainty sets that contain all, or most, of their possible realizations. The approach presented here takes a conservative viewpoint and identifies asset mixes that have the best worst-case behavior. Techniques for generating uncertainty sets from historical data are discussed and numerical results that illustrate the stability of robust optimal asset mixes are reported. [ABSTRACT FROM AUTHOR]

Large production variations caused by abnormal disturbances can significantly reduce the production capacity of a flexible manufacturing system (FMS). To prevent production delays, short-term capacity adjustment strategies can be used to augment the capacity of the FMS, such as working overtime, using alternative tools that are suited for faster processing, and producing parts outside of the FMS. We propose a mixed integer programming (MIP) model to obtain an optimal production plan for a multi-machine FMS. Our model evaluates both the FMS loading decision and the effective use of short-term capacity adjustment strategies to minimize the total part production cost. We develop an iterative procedure to solve the model that uses the Lagrangian relaxation method for finding lower bounds and a Lagrangian heuristic for obtaining feasible solutions. The procedure exploits certain special structures found in the Lagrangian multipliers which enable us to obtain good solutions to reasonably large test problems quickly. [ABSTRACT FROM AUTHOR]

We examine periodic pacing in a multi-stage non-synchronous production system where production stages are distributed over various locations. The processing rates and inspection failure rates at each stage are random. The optimal policy for pacing (e.g., buffering) the flow of in-process inventory between stages is pursued to minimize operational costs over a finite horizon. It is known that the optimal control is extremely complex in general. However, separability across the stages is shown under weighted penalties which results in a decentralized heuristic pacing. Numerical comparisons of heuristic and optimal solutions are reported. We obtained sufficient conditions under which the heuristic pacing is finite. As a result, an efficient algorithm is developed for the design of inter-stage buffers. [ABSTRACT FROM AUTHOR]

A cost-effective decomposition algorithm is proposed for the approximate analysis of arbitrary first-come-first-served (FCFS) open queueing network models (QNMs) with Generalised Exponential (GE) external interarrival time and service time distributions, multiple vacation periods, exhaustive service scheme and repetitive service blocking of random destination (RS-RD). The algorithm is based on a new product-form approximation suggested by the information theoretic principle of Minimum Relative Entropy (MRE), subject to marginal mean value constraints and a prior joint distribution estimate of the corresponding open QNM with infinite capacity. The censored GE/GE/1/N queue with exponential server vacations is solved via relative entropy minimisation and in conjunction with associated effective flow streams (departure, splitting, merging), plays the role of an efficient building block in the solution process. An exhaustive validation study against simulation is included to illustrate the credibility of the MRE results. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical model and a solution algorithm are developed for solving a robot acquisition and cell formation problem (RACFP). Our model considers purchasing a proper mix of robots and assigning all given workstations to purchased robots such that each robot cell satisfies its workstations' resource demands while minimizing the total system (acquisition) cost. Specifically, each robot has two capacity constraints - available work envelope and effective machine time. RACFP is formulated as a multi-type two-dimensional bin packing problem, a pure 0–1 integer program which is known to be NP-hard. In this paper, a very efficient (polynomial time bound) heuristic algorithm is developed and implemented. The algorithm consists of two major stages. The first stage employs an LP-based bounding procedure to produce a tight solution bound, whereas the second stage repetitively invokes a random search heuristic using a greedy evaluation function. The algorithm is tested by solving 450 randomly generated problems based on realistic parameter values. Computational results show that the heuristic algorithm has outperformed algorithms using general optimization techniques such as Simulated Annealing and Column Generation. All test problems are solved within an order of magnitude of 10 seconds, with a gap of less than 1% from the optimum. More importantly, over 70% of all solutions are optimal (334 out of 450). The algorithm can be easily modified for other applications such as file placement for a multi-device storage system and job scheduling for a multi-processing system. [ABSTRACT FROM AUTHOR]

The assignment of M unique machines to M locations with the objective of minimizing the total machine-to-machine material transportation cost in a flow line may be formulated as a quadratic assignment problem (QAP). Instead of having M unique machines, if an application involves one or more sets of identical machines, the location problem becomes a tertiary assignment problem (TAP). Solving a large problem of this kind is extremely difficult because of its combinatorial nature. When machine-to-machine flow is fixed, the TAP may be specialized to a QAP for which the unique machine problem is a special case. Obtaining an optimum solution to this problem when M is large is also computationally intractable. However, this problem may be solved by identifying sets of identical machines which may be partitioned into individual, ‘unique’ machines. Properties of a special type of matrix called the amoebic matrix are used in the partitioned problems to provide approximate solutions, which are relabeled to prescribe a solution to the original problem. Results are demonstrated along with suggestions for further research. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop an efficient heuristic for the process selection and part cell assignment problem. The study assumes a production environment where each part has several process plans, each manifested by a required set of tools. These tools can be assigned to different machines based on a tool-machine compatibility matrix. An additional assumption is that all relevant data such as periodic demand, processing time, processing cost, tool magazine capacity, tool changing time, tool life and tool cost are fixed and known. A mixed integer linear program which takes all relevant data into account is developed to minimize the production cost. The suggested solution approach to solve this model makes use of Genetic Algorithms: a class of heuristic search and optimization techniques that imitate the natural selection and evolutionary process. First, the encoding of the solutions into integer strings is presented, as well as the genetic operators used by the algorithm. Next, the efficiency and robustness of the solution procedure is demonstrated through several different examples. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a new methodology of analysis and evaluation of Flexible Manufacturing System (FMS) design configurations. It examines the use of an integrated, systemic, global and user oriented approach for the complex problem of selecting among several configuration alternatives the most suitable for a specific case. The methodology is implemented using the Decision Support System framework, offering an objective tool to help managers and engineers during the design stage of the development cycle of these manufacturing systems. A case study illustrates the effectiveness of the developed methodology. [ABSTRACT FROM AUTHOR]

A general framework to describe operating policies in manufacturing cells is presented. A policy can be characterized by assigning appropriate values to a set of descriptive parameters. System configuration is described by one set of parameters and operating policies by another. Examples are presented to illustrate the choice of parameter values. The framework forms the basis for a general-purpose discrete-event simulator. This simulator is used to study various operating philosophies under a wide variety of operating environments. [ABSTRACT FROM AUTHOR]

This paper defines the dense subhypergraph problem (DSP), which provides a modelling framework for the nonlinear knapsack problem and other well-known problems arising in areas such as capital budgeting, flexible manufacturing system production planning, repair-kit selection, and compiler construction. We define several families of valid inequalities and state conditions under which these inequalities are facet-defining for DSP. We also explore the polyhedral structure of the cardinality-constrained DSP. Finally, we examine a special case of this problem that arises, for example, within the context of Lagrangian decomposition. For this case, we present a complete description of the convex hull of the feasible region. [ABSTRACT FROM AUTHOR]

Two algorithms are developed to allocate M buffers to N service stations connected arbitrarily in a feed-forward manner. All service times and the interarrival times are assumed to be exponentially distributed. Both methodologies are easy-to-use tools to explore alternative buffer storage configurations and parameter settings during the initial design stages of production systems, communications networks, and flexible manufacturing systems. [ABSTRACT FROM AUTHOR]

In this paper we compare the exponential symmetric shortest queue system with two related systems: the shortest queue system with Threshold Jockeying and the shortest queue system with Threshold Blocking. The latter two systems are easier to analyse and are shown to give tight lower and upper bounds respectively for the mean waiting time in the shortest queue system. The approach also gives bounds for the distribution of the total number of jobs in the system. [ABSTRACT FROM AUTHOR]

This paper deals with a general class of piecewise deterministic control systems that encompasses FMS flow control models. One uses the Markov renewal decision process formalism to characterize optimal policies via a discrete event dynamic programming approach. A family of control problems with a random stopping time is associated with these optimality conditions. These problems can be reformulated as infinite horizon deterministic control problems. It is then shown how the so-called turnpike property should hold for these deterministic control problems under classical convexity assumptions. These turnpikes have the same generic properties as the attractors obtained via a problem specific approach in FMS flow control models and production planning and are called hedging points in this literature. [ABSTRACT FROM AUTHOR]

This paper considers a flexible manufacturing system (FMS) comprised of programmable machine tools capable of performing multiple operations and which are interconnected by computer-controlled automated material handling equipment. The specific problem addressed is similar to a job-shop problem in which at least a certain number of each of several types of jobs needs to be performed on a given set of machines. The factor which contributes to the flexibility in the system is that each job can be performed in a variety of ways, with each possible manner of performing it called an ‘alternate routing combination’ (ARC). An ARC for a job specifies the number of operations and the associated machine sequence. The problem is to select a set of jobs to be performed along with their associated ARCs, and to schedule their operations on the machines so as to minimize the makespan. This problem is mathematically modelled, and exact and heuristic algorithms are presented together with computational results. [ABSTRACT FROM AUTHOR]

The plant layout problem is usually formulated as a quadratic assignment problem (QAP). In this paper, we incorporate the throughput related aspects of the automated manufacturing system into the layout problem. We modify the QAP formulation by explicitly considering the throughput requirements of automated manufacturing systems. Our formulation also incorporates the cost of keeping a certain number of pallet-fixtures in the system. The resulting modified quadratice assignment problem (MQAP) is presented. An optimal solution method for MQAP is developed and its computational performance is evaluated. [ABSTRACT FROM AUTHOR]

As advancing technology makes Flexible Manufacturing Systems (FMSs) a viable option for an increasing number of firms and products, determining their economic value has become increasingly important. Despite considerable research in this area, the effect of the ability or inability to hold inperperiod inventories on the value of FMSs has received relatively little attention. This paper proposes a model of the interaction between investment in product-flexible manufacturing technology and the use of seasonal inventories. Analytic results as well as numerical examples are presented for the case of periodic market conditions that are uncertain at the lime the investment decision must be made. The analysis leads to some surprising results, including the observation that interperiod inventories and flexible capacity can be complements as well as substitutes. [ABSTRACT FROM AUTHOR]

This paper is the second part of a work devoted to timed marked graphs, their underlying algebra, and their applications to model the dynamic behavior of production systems in which a set of activities is performed repetitively in fixed sequences. Theoretical aspects arc considered in the first part. In addition, an algorithm to properly characterize and capture the periodic behavior of such systems is described. The relevance of this class of models and the associated solution algorithm to the performance evaluation of certain types of flexible manufacturing systems is emphasized here. Examples of various modeling capabilities are provided. The analysis of timed marked graphs is an extension of PERT/CPM-like techniques to asynchronous and repetitive processes, whose graph-theoretic models contain cycles of activities. [ABSTRACT FROM AUTHOR]

A frequently encountered design issue for a flexible manufacturing system (FMS) is to find the lowest cost configuration, i.e. the number of resources of each type (machines, pallets, …), which achieves a given production rate. In this paper, an efficient method to determine this optimal configuration is presented. The FMS is modelled as a closed queueing network. The proposed procedure first derives a heuristic solution and then the optimal solution. The computational complexity for finding the optimal solution is very reasonable even for large systems, except in some extreme cases. Moreover, the heuristic solution can always be determined and is very close (and often equal) to the optimal solution. A comparison with the previous method of Vinod and Solberg shows that our method performs very well. [ABSTRACT FROM AUTHOR]

This paper describes an FMS scheduling method that treats an FMS as a group of problem-solving agents cooperating to perform manufacturing jobs. The main thrusts of such a method include the ability to handle the dynamically changing production conditions, its taking into account the communication method, the improved reliability, and the use of distributed control. The paper emphasizes research issues associated with various aspects of the cooperative problem-solving method, including: (1) dynamic task assignments, (2) the coordination mechanism, and (3) knowledge-based scheduling as problem solving. A simulation study which compares the performance of the cooperative problem solving approach with that of the more traditional scheduling approaches is also reported. [ABSTRACT FROM AUTHOR]

Scheduling methods are proposed for a class of flexible manufacturing systems consisting of machine centers served by a cyclic conveyor. The minimization of finish time is considered as the optimization criterion. An optimal algorithm is proposed for the minimum conveyor speed (S = 1) for a particular system in the class under consideration. Scheduling algorithms based on a job matching principle, that are developed for the general systems, yield good results particularly for low conveyor speeds. [ABSTRACT FROM AUTHOR]

We present in this work a hierarchical approach for generating alternatives for production planning in a generic floor shop problem within the environment of Flexible Manufacturing Systems (hereafter, FMS). Briefly, the problem can be staled as follows: Given the resources of a FMS and the characteristics of the parts to be produced along a planning horizon, obtain the loading ordering of the parts in the FMS, the execution ordering of the operations and the processing route of each part (i.e.. the working stations where each operation is to be executed), such that the production and transport costs are minimized and the modules workload is levelized. The problem is decomposed into three subproblems which are arranged in a hierarchy; a variety of models is presented, as well as the input/output relations that allow to integrate them; we also propose some algorithmic ideas to exploit the special structure of the problem. Computational experience is reported. [ABSTRACT FROM AUTHOR]

As flexible manufacturing technology has become available across a broad range of applications, an increasingly large number of firms have confronted decisions about the adoption of flexible automation versus transfer lines versus some combination of these technologies. While the work of previous authors has provided some guidance to such decision making, the modular character of flexible capacity and the indivisible character of a transfer line have not been the basis, previously, for the development of formal decision rules. This paper makes use of a mixed-integer mathematical programming model to generate formal decision rules which, in turn, become an instrument for analyzing several fundamental hypotheses about the introduction of flexible and/or dedicated automation. The firm is assumed to have the objective of maximizing its present value and must account for the interaction of its markets with technological opportunities, present and anticipated. [ABSTRACT FROM AUTHOR]

This paper presents the application of the stochastic quasigradient method (SQG) of Ermoliev and Gaivaronski to the performance optimization of asynchronous flexible assembly systems (AFAS). These systems are subject to blocking and starvation effects that make complete analytic performance modeling difficult. A hybrid algorithm is presented in this paper which uses a queueing network model to set the number of pallets in the system and then an SQG algorithm is used to set the buffer spacings to obtain optimal system throughput. Different forms of the SQG algorithm are examined and the specification of optimal buffer sizes and pallet numbers for a variety of assembly systems with ten stations are discussed. The combined Network-SQG method appears to perform well in obtaining a near optimal solution in this discrete optimization example, even though the SQG method was primarily designed for application to differentiable performance functionals. While a number of both theoretical and practical problems remain to be resolved, a heuristic version of the SQG method appears to be a reasonable technique for analyzing optimization problems for certain complex manufacturing systems. [ABSTRACT FROM AUTHOR]

An optimal routing policy is obtained for Flexible Manufacturing Systems (FMSs) with limited buffers at the work stations. This policy is used to effectively drive a robotic material handling system. The routing decisions are made by a supervising computer on a real-time basis in order to avoid any work station running out of inputs and to control the blocking of the material handling system. Using our model, general material handling times can be assumed. The optimal policy and several key performance measures are computed, following the problem formulation as a continuous-time, semi-Markovian decision process. Fast convergence and computational stability are ensured by the ergodic solution algorithm augmented to solve the functional equations of the renewal process. The solution algorithm was implemented, tested on an extensive range of problems regarding the structure and the performance of the optimal policy. Complex environments involving diverse processing times, as well as very limited buffer storage, were examined. The interaction between the allocation of buffer spaces to work stations, the structural properties of the optimal monotone (threshold-type) policy and the system performance are also investigated. [ABSTRACT FROM AUTHOR]

Most scheduling papers consider flexible machining and assembly systems as being independent. In this paper, a heuristic two-level scheduling algorithm for a system consisting of a machining and an assembly subsystem is developed. It is shown that the upper level problem is equivalent to the two machine flow shop problem. The algorithm at the lower level schedules jobs according to the established product and part priorities. Related issues, such as batching, due dates, process planning and alternative routes, are discussed. The algorithm and associated concepts are illustrated on a number of numerical examples. [ABSTRACT FROM AUTHOR]

Most of the current academic flexible manufacturing system (FMS) scheduling research has focused on the derivation of algorithms or knowledge-based techniques for efficient FMS real-time control. Here, the limitations of this view are outlined with respect to effective control of actual real-time FMS operation. A more realistic paradigm for real-time FMS control is presented, based on explicit engineering of human and automated control functions and system interfaces. To illustrate design principles within the conceptual model, an example of algorithmic and operator function models for a specific real-time FMS control problem are developed. [ABSTRACT FROM AUTHOR]

Stecke [21] has developed mathematical programming approaches for determining, from a set of part type requirements, the production ratios (part types to be produced next, and their proportions) which maximize overall machine utilizations by balancing machine workloads in a flexible manufacturing system (FMS). These mathematical programming (MP) approaches are aggregate in the sense that they do not take into account such things as contention for transportation resources, travel time for work-in-process, contention for machines, finite buffer space, and dispatching rules. In the current study, the sensitivity of machine utilizations to these aggregations is investigated through simulation modeling. For the situation examined, it is found that achieved machine utilizations are a strong function of some of the factors ignored in the MP methodology, ranging from 9.1% to 22.9% less than those theoretically attainable under the mathematical programming assumptions. The 9.1% degradation results from modeling with nonzero work-in-process travel times (i.e. 2 minutes per transfer) and using only central work-in-process buffers. Resource levels (e.g. the number of automated guided vehicles; the amount of work-in-process; the number of slack buffers) needed to limit the degradation to 9.1% correspond to FMS operating conditions which are feasible in practice. [ABSTRACT FROM AUTHOR]

This paper describes an application of expert systems to the area of technological planning for robot-based flexible assembly systems. The work described in this paper is part of the research being done in the Republic of Ireland as part of the European Strategic Programme for Research and Development in Information Technology (ESPRIT), Project No. 623. The discussion focuses on the role of technological planning, an explanation of the generative approach to planning, the use of robot-based flexible assembly systems, and the need for a planning procedure within these systems. Following this, a description of the logic of the assembly planning system being developed at the University College Galway (U.C.G.) is given, followed by a review of the knowledge which is encoded in the expert system. The final section details some developments, which have been carried out on the system as a result of two case studies. [ABSTRACT FROM AUTHOR]

Consider a manufacturing system consisting of multiple cells; each cell processes a certain type of part family. For such systems, we study the problem of optimally allocating a joint setup among these cells. The production process of each cell is modelled by a birth process. Certain monotonicity properties of these birth process are established and used to show that the efficient discrete marginal allocation approach of Fox [1] can be applied here. [ABSTRACT FROM AUTHOR]

Every leading institution and manufacturing company in the world is dealing with some aspects of flexible automation. This is because they have found that designing and making goods and products on order rather than for stock is the key issue in cutting manufacturing costs and lead time, and eventually staying in business in the future. This paper discusses the way in which this above outlined goal can be implemented in the assembly business, as well as giving design rules on how to design flexible assembly and inspection cells and systems. The rules are justified by solid model simulation and the author's industrial and laboratory experience. [ABSTRACT FROM AUTHOR]

An information-theoretic approach is applied for measuring the flexibility in flexible manufacturing systems (FMSs). The general relation between flexibility and entropy is discussed. The entropy for a Markovian process is obtained and then applied to closed queueing network models of FMSs to discuss loading flexibility which arises from the power to regulate the frequency of the visit of a part to different work stations. The concept of operations entropy as a measure of operations flexibility, which arises from the power to choose the work station and the corresponding operations, is introduced. The operations entropy has been decomposed into entropies within and between operations and entropies within and between groups of operations. This measure has been used to determine the next operation to be performed on a part by using the principle of least reduction of flexibility. [ABSTRACT FROM AUTHOR]

Both analytic and simulation models were used to analyze the capabilities and requirements of an automated circuit card manufacturing system. Analytic models were used to determine the sensitivity of the measures of effectiveness (MOEs) to various design parameters. This analysis gave approximate results and bounded the range of input parameters for the simulation model. A detailed simulation model was required for use during both the design and production phases of the project. This simulation model incorporated only those variables to which the MOEs are most sensitive, and provided additional features to observe system behavior. The benefits and appropriate uses for each class of models are discussed. [ABSTRACT FROM AUTHOR]

This paper considers the problem of the design of an FMS system. An integrated design procedure is outlined, which involves three stages — planning, design and implementation. Within each stage the design decisions to be made are discussed, together with the techniques and skills required to complete each design stage. At each stage, reference is made to the views of other designers and researchers. Finally, the procedure is illustrated through a case study of a miniature FMS designed and installed at the Cranfield Institute of Technology. [ABSTRACT FROM AUTHOR]

Deciding whether FMS technology is viable for a given application, and if so, what machines should comprise the FMS and what parts should be produced on it, can be a difficult task. Manual methods suffice only for situations where a small number of FMS-type machines are to be considered and less than a few dozen candidate parts are to be chosen from. When both machines and parts are to be selected from a larger number of candidates, manual methods become cumbersome and time consuming, and computer-based decision aids become a necessity. This paper gives an overview of the required decision process, and then focuses on those stages for which computer-based decision aids can be used effectively. Particular decision aids are described, and case studies are cited to illustrate their motivation and use. [ABSTRACT FROM AUTHOR]

A GPSS/H model is presented for a hypothetical flexible manufacturing system. The FMS consists of six machines composed of three machine types, manufactures three types of parts, and uses automatic guided vehicles (AGVs) to transport in-process parts between appropriate machines and wait spaces in the system. Three logical modules have been designed for the model, with copies of these modules then being appropriately distributed and interfaced throughout the model and tailored to achieve overall representation of the specific FMS. The same technique can be used by others to build analogous or extended GPSS/H models for other specific FMSs in which AGVs are used as transporters. Simulations can then be performed with such models to research FMS design and control alternatives. [ABSTRACT FROM AUTHOR]

In this paper we present an approach for modelling and analyzing flexible manufacturing systems (FMSs) using Petri nets. In this approach, we first build a Petri net model (PNM) of the given FMS in a bottom-up fashion and then analyze important qualitative aspects of FMS behaviour such as existence/absence of deadlocks and buffer overflows. The basis for our approach is a theorem we state and prove for computing the invariants of the union of a finite number of Petri nets when the invariants of the individual nets are known. We illustrate our approach using two typical manufacturing systems: an automated transfer line and a simple FMS. [ABSTRACT FROM AUTHOR]

A real-time hierarchical routing control scheme for a large class of material handling systems is presented. The higher level (coordinator) performs resource allocation tasks and supplies parameter values to the lower (local control) level. The lower level operates in an autonomous (without continuous supervision) and distributed fashion. If state information is made available to the coordinator, the routing strategy can furthermore be adaptively adjusted. [ABSTRACT FROM AUTHOR]

This paper introduces an analysis and optimization technique for discrete event dynamic systems, such as flexible manufacturing systems (FMSs), and other discrete part production processes. It can also be used for enhancement of the simulation results of, or the monitoring of the operations of such systems in real time. Extensive references are given where readers may pursue futher details. [ABSTRACT FROM AUTHOR]

The recent perturbation analysis approach to discrete event systems is applied to flexible manufacturing systems (FMS). While analytic (queueing) models are useful in preliminary design of such systems, they are not accurate enough at the detailed design/operation stage. Thus, experimentation on detailed simulations or on the actual system has been the way to optimize system performance. Perturbation analysis allows us to derive the sensitivity of system performance, with respect to several design/operating parameters, by observing a single experiment (and without having to actually alter the parameters — often a costly operation). Thus, observation of one experiment can give accurate directions for the improvement of several parameter values. Here we give a simulation example illustrating how perturbation analysis could be used on-line on an FMS to improve its performance, including reducing its operating cost. Experimental results are also presented validating the estimates obtained from this technique. [ABSTRACT FROM AUTHOR]