Economic design of unit load-based FMSs employing AGVs for transport

A methodology to economically configure an automated guided vehicle based flexible manufacturing system (FMS) in terms of machine and material handling requirements is presented. The procedure uses the concept of simultaneous design to determine the number of machines required at each workstation in the FMS and the number of automated guided vehicles required to service the material handling needs of the system. The technique integrates decisions on machine requirements, vehicle requirement, and vehicle/container carrying capacity into a single cost model. Solution to the model automatically yields the best resource configuration for the shop that achieves the set production target while minimizing total manufacturing cost. A hybrid algorithm that combines numerical search, simulation, and statistical analysis is employed for solving the problem. The application of the design procedure is demonstrated with an example problem. Sensitivity analysis on machine and vehicle requirements due to changes in job attributes and unit transport quantities are also performed.     

Analysis of automated guided vehicle configurations in flexible manufacturing systems

Automated guided vehicle (AGV) systems complement the operation of flexible manufacturing systems (FMS) by providing integrated automated material handling that capitalizes on the system's flexibility. Previous research considering AGV systems for use in FMS installations has focused on complex control strategies to reduce the congestion problem often encountered in these systems. Recently, attention has been given to tandem system configurations that reduce congestion and simplify system control. The present study uses the simulation methodology to compare the performance of three AGV configurations under a variety of experimental conditions. The results indicate that system size, load/unload time, and machine failure rate factors have significant impacts on the operation of the systems considered. In general, with respect to due date performance, it is recommended to use the traditional configuration in small systems while using the tandem/loop configuration in larger systems. Furthermore, it is shown that the addition of the loop to the tandem configuration mitigates the sensitivity of the tandem configuration to the load/unload time factor as well as significantly improving its performance under high load/unload times. Thus, if tandem configuration is desired to reduce congestion and simplify system control, investments must be made to directly reduce the load/unload times or to construct a loop to avoid the load/unload time penalty.     

Dynamic cellular manufacturing system design considering alternative routing and part operation tradeoff using simulated annealing based genetic algorithm

In this paper, an integrated mathematical model of multi-period cell formation and part operation tradeoff in a dynamic cellular manufacturing system is proposed in consideration with multiple part process route. This paper puts emphasize on the production flexibility (production/subcontracting part operation) to satisfy the product demand requirement in different period segments of planning horizon considering production capacity shortage and/or sudden machine breakdown. The proposed model simultaneously generates machine cells and part families and selects the optimum process route instead of the user specifying predetermined routes. Conventional optimization method for the optimal cell formation problem requires substantial amount of time and memory space. Hence a simulated annealing based genetic algorithm is proposed to explore the solution regions efficiently and to expedite the solution search space. To evaluate the computability of the proposed algorithm, different problem scenarios are adopted from literature. The results approve the effectiveness of the proposed approach in designing the manufacturing cell and minimization of the overall cost, considering various manufacturing aspects such as production volume, multiple process route, production capacity, machine duplication, system reconfiguration, material handling and subcontracting part operation.     

Machine-tool selection and operation allocation in FMS: Solving a fuzzy goal-programming model using a genetic algorithm

The decision-making process for machine-tool selection and operation allocation in a flexible manufacturing system (FMS) usually involves multiple conflicting objectives. Thus, a fuzzy goal-programming model can be effectively applied to this decision problem. The paper addresses application of a fuzzy goal-programming concept to model the problem of machine-tool selection and operation allocation with explicit considerations given to objectives of minimizing the total cost of machining operation, material handling and set-up. The constraints pertaining to the capacity of machines, tool magazine and tool life are included in the model. A genetic algorithm (GA)-based approach is adopted to optimize this fuzzy goal-programming model. An illustrative example is provided and some results of computational experiments are reported.     

Design of an automated guided vehicle-based material handling system for a flexible manufacturing system

Automated guided vehicle (AGV)-based material handling systems (MHSs), which are widely used in several flexible manufacturing system (FMS) installations, require a number of decisions to be made. These include the number of vehicles required, the track layout, traffic pattern along the AGV tracks, and solving traffic control problems. This paper addresses the key issues involved in the design and operation of AGV-based material handling systems for an FMS. The problems arising from multi-vehicle systems are analysed, and strategies for resolving them are examined using analytical and simulation models.     

Incorporating dynamism in traditional machine loading problem: an AI-based optimisation approach

Machine breakdowns have been recognised in flexible manufacturing systems (FMS) as the most undesirable characteristic adversely affecting the overall efficiency. In order to ameliorate product quality and productivity of FMS, it is necessary to analyse, as well as to minimise the effect of breakdowns on the objective measures of various decision problems. This paper addresses the machine loading problem of FMS with a view to maximise the throughput and minimise the system unbalance and makespan. Moreover, insufficient work has been done in the domain of machine loading problem that considers effect of breakdowns. This motivation resulted in a potential model in this paper that minimises the effect of breakdowns so that profitability can be augmented. The present work employs an on-line machine monitoring scheme and an off-line machine monitoring scheme in conjunction with reloading of part types to cope with the breakdowns. The proposed model bears similarity with the dynamic environment of FMS, hence, termed as the dynamic machine loading problem. Furthermore, to examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on different dataset from previous literature has been investigated with application of intelligence techniques such as genetic algorithms (GA), simulated annealing (SA) and artificial immune systems (AIS). The results incurred under breakdowns validate the robustness of the developed model for dynamic ambient of FMS.     

Design of an FMS decision support system

Flexible manufacturing system (FMS) is an integrated system consisting of computer numerical control machine tools and automated material handling system controlled by an overall control computer system. The FMS design is broken down into strategic, tactical and operational level designs. This research is concerned with the development of a decision support system for the design of FMS (FMSDDS. Simulation is used as a primary analysis tool, making the DSS capable of solving problems at all three levels. The decision making ability is provided by a multi-attribute utility model which accounts for both quantitative and qualitative factors that affect a decision. The FMSDSS is implemented using three routines: input, analysis, and output. The input routine helps the user to select feasible FMS configurations interactively. The analysis routine consists of a generalized FMS simulation model. This routine analyses the FMS alternatives selected and provides the information required for the output analysis. The qualitative and quantitative measures of performance that have to be considered for decision making are handled by the output routine. It also combines performance measures of different units such as time and cost. The DSS is modular and hence future enhancements can be readily added.     

Part ordering through simulation-optimization in an FMS

One of the most important scheduling and control problems that must be solved concerning the efficient operation of FMS is the part ordering problem, i.e. finding the optimal sequence of parts to be released into the manufacturing system. Methods currently available for handling this problem are based on heuristic dispatching rules. In this paper an approach for solving the problem using the simulation-optimization technique is proposed. Currently available heuristic approaches can only try to get the optimum at a local level because of the complexities of the system and the dependencies of its components, whereas the proposed approach tries to get the global optimum.     

Reliability analysis of a flexible manufacturing cell

In this study, mathematical models are developed to study and compare the operations of a fully reliable and an unreliable flexible manufacturing cell (FMC), each with a flexible machine, a loading/unloading robot, and a pallet handling system. The operation times, loading/unloading times, and material handling times by the pallet are assumed to be random. The operation of the reliable cell is compared to that of an unreliable cell with respect to utilization of the cell components, including the machine, robot, and pallet handling system. The unreliable cell is assumed to operate under random (machine and robot) failures with constant failure rates for the machine and the robot. The pallet handling system is assumed to be completely reliable.     

Solving machine loading problems in a flexible manufacturing system using a genetic algorithm based heuristic approach

The machine-loading problem of a flexible manufacturing system (FMS) has been recognized as one of the most important planning problems. In this research, a Genetic Algorithm (GA) based heuristic is proposed to solve the machine loading problem of a random type FMS. The objective of the loading problems is to minimize the system unbalance and maximize the throughput, satisfying the technological constraints such as availability of machining time, and tool slots. The proposed GA-based heuristic determines the part type sequence and the operation-machine allocation that guarantee the optimal solution to the problem, rather than using fixed predetermined part sequencing rules. The efficiency of the proposed heuristic has been tested on ten sample problems and the results obtained have been compared with those of existing methods.     

A part-and-tool assignment method for the workload-balance between machines and the minimisation of tool-shortage occurrences in an FMS

In this paper, we look into the loading problem of a flexible manufacturing system (FMS) that is made up of several identical flexible manufacturing machines (CMMs). These machines are capable of different operations as long as the required tools are provided to them. The loading problem studied in this paper is a pre-release problem that deals with the pre-assignment of parts and tools before the process of an FMS begins. There are two objectives that one would like to achieve. They include minimising the number of tool-shortage occurrences and balancing the workload between machines. Since one cannot directly minimise the number of tool-shortage occurrences at the current pre-release stage, a surrogate objective of minimising the total tool-capacity shortage (TTCS) is adopted. Furthermore, because of the ‘tool movement policy’ assumption, our loading problem only involves assigning parts and tools to each machine. In this paper, we propose a part-and-tool assignment method that combines fuzzy c-means, SA (simulated annealing), and an optimal tool-assignment algorithm. The proposed part-and-tool assignment method is designed to be interactive. Because of this interactive nature, human designers can experiment with different evaluation criteria or reset the parameters of SA to look for alternative solutions. An example is given which illustrates the proposed part-and-tool assignment method. From the example, one can see that the proposed method is very efficient and effective in finding good-quality solutions.     

Control of Material Handling Transporter in Automated Manufacturing

This paper presents a real time routing method for material handling transporters in automated manufacturing shops where the performance criterion is to maximize throughput The static version of the routing problem is identified as a one machine due date problem with sequence dependent processing times and sequence dependent due dates. For real time application, a heuristic method is proposed, and its performance is evaluated and compared to other currently known rules using discrete simulation on hypothetical cell operations.     

FMS统一建模技术与方法

针对ＦＭＳ的组成结构及控制特点，提出了一种层次递阶的统一建模方法。以面向对象建模技术和方法为主线，通过消息传递驱动机制使各层递阶模型有机集成为统一的整体，并与实际系统中各个层次及其关系相对应，从而准确，方便地描术字ＦＭＳ的规划设计、调度控制、运行及仿真等各个过程。     

Cellular machine layout based on the Segmented Flow Topology

This paper introduces a facility layout design procedure for converting an existing manufacturing system with a predefined aisle structure to a cellular manufacturing system based on the 'segmented flow topology' (SFT) developed by Sinriech and Tanchoco. The proposed procedure is aimed at finding the best machine grouping, along with the locations of pick-up and delivery stations and machine layout for each cell based on an existing facility. The objective is to minimize the total material handling cost. In contrast to previous work in this area, the proposed design procedure takes into account both distance and material flow in forming machine clusters. In addition, a revised cost model for material handling system, which accounts for different aspects of capital and operating cost, is presented.     

Book review of Global Strategic Management,by P. Lasserre,Palgrave Macmillan,Basingstoke, 2003, xx + 454 pp., £24.99, softcover (ISBN 0 333 79375 7)

This paper considers the simultaneous scheduling of material handling transporters (such as automatic guided vehicles or AGVs) and manufacturing equipment (such as machines and workcentres) in the production of complex asembled product. Given the shipping schedule for the end-items, the objective of the integrated problem is to minimize the cumulative lead time of the overall production schedule (i.e. total makespan) for on-time shipment, and to reduce material handling and inventory holding costs on the shop-floor. The problem of makespan minimization is formulated as a transportation integrated scheduling problem, which is NP-hard. For industrial size problems, an effective heuritsic is developed to simultaneouly schedule manufacturing and material handling operations by exploting the critical path of an integrated operation network. The performance of the proposed heuristic is evaluated via extensive numerical studies and compared with the traditional sequential scheduling approach. The superiority of the integrated heuristic is well documented.     

An Artificial Intelligence Approach to the Scheduling of Flexible Manufacturing Systems

Scheduling in a flexible manufacturing system (FMS)must take into account the shorter lead-time, the multiprocessing environment, the flexibility of machine tools, and the dynamically changing states. The scheduling approach described in this paper employs a knowledge-based system to carry out the nonlinear planning method developed in artificial intelligence. The state-space process for plan-generation, by either forward- or backward-chaining, can handle scheduling requirements unique to the FMS environment. A prototype of this scheduling system has been implemented on a LISP machine and is applied to solve the scheduling problem in flexible manufacturing cells. This scheduling method is characterized by its knowledge-based organization, symbolic representation, state-space inferencing, and its ability for dynamic scheduling and plan revision. It provides a foundation for integrating intelligent planning, scheduling, and machine learning in FMSs.     

Structured adaptive supervisory control model and software development for a flexible manufacturing system

A three-step approach is proposed to substantially reduce the high cost of development of control software for an FMS on the shop floor. First, by introducing the concept of multiple computing processes into Automata Theory, a finite capacity machine (FCM) is presented as an extended finite machine. An FCM is used to model the control of a flexible manufacturing system, which avoids the state explosion problem. Secondly, a structured adaptive supervisory control (SASC) model meeting the desired control objectives for an FMS is derived from a composed FCM. The SASC model can be constructed systematically. Finally, generic software components for an SASC model are described, which allow the SASC model to be cost-effectively transferred into shop-floor applicable control software.     

A reallocation-based heuristic to solve a machine loading problem with material handling constraint in a flexible manufacturing system

In this research, a comprehensive heuristic solution is evolved to include all the three segments of a machine loading problem of flexible manufacturing systems. These are part type sequence determination, operation allocation on machines and reallocation of part types. The machine loading problem has been formulated keeping in view two well-known objective functions, namely minimization of system unbalance and maximization of throughput. In addition to constraints related to machine time and tool slots availability, this research considers one more constraint related to material handling, i.e. number of AGVs available in the system. The part type sequence determination has been carried out by evaluating the contribution of part type to characteristics such as batch size, total processing time, and the AGV movement. Decisions pertaining to operation allocation are taken based on the enumeration of priority index. An iterative reallocation procedure has been devised to ensure minimum positive system unbalance and maximum throughput. A test problem is simulated to represent the real shop floor environment and the same has been solved using various steps of the proposed algorithm. Extensive computational experiments have been carried out to assess the performance of the proposed heuristic and validate its relevance to solve the real shop floor problems.     

Adaptive scheduling in random flexible manufacturing systems subject to machine breakdowns

In a FMS dynamic scheduling environment, frequent rescheduling to react to disruptions such as machine breakdowns can make the behaviour of the system hard to predict, and hence reduce the effectiveness of dynamic scheduling. Another approach to handle the disruptions is to update the job ready time and completion time, and machine status on a rolling horizon basis, and consider the machine availability explicitly in generating schedules. When machine downtime has a small variation, the operation completion time is estimated by using limiting (steady-state) machine availability. However, steady-state analysis is sometimes unlikely to provide a complete picture of the system when there is a large variation in machine downtime and repair time, and frequent disruptions (e.g. tool failures) exist. Transient analysis of machine availability will be more meaningful in such a situation during a finite observation period. In this paper, an adaptive scheduling approach is proposed to make coupled decisions about part/machine scheduling and operation/tool assignments on a rolling horizon basis, while the operation completion time is estimated by a transient machine availability analysis based on a two-state continuous time Markov process. The expected tool waiting time is explicitly considered in the job machine scheduling decision. The effectiveness of the proposed method is compared with other approaches based on various dispatching heuristics such as Apparent Tardiness Cost, Cost OVER Time, and Bottleneck Dynamics, etc under different shop load and machine downtime levels.     

Determination of optimal AGV fleet size for an FMS

The required number of AGVs necessary to perform a given level of material handling task in an FMS environment is determined using analytical and simulation modelling. The analytical method involves consideration of load handling time, empty travel time, and waiting and blocking time. Load handling time is computed from given system parameters. Determination of empty vehicle travel is difficult due to the inherent randomness of an FMS. Several research studies for this purpose are discussed and a new model is proposed. It entails formulation of a mixed integer programme with an objective of minimizing empty trips. The constraints are in the form of upper and lower bounds placed on the total number of empty trips starting from or ending at a load transfer station. The phenomena of vehicle waiting and blocking are also discussed. The cumulative impact of these three time estimates are then translated into an initial estimate of AGV fleet size as predicted by individual models. The method is applied to an illustrative example. Finally, simulation methodology is used to validate the initial estimates of fleet size. The results indicate that the different models either under-estimate or over-estimate the actual number of vehicles required in the system. The proposed model, though under-estimates the minimum AGV requirement, yet provides results which are close to the simulation results. Hence, it can be used as an analytical tool prior to the simulation phase of AGVS design.