An efficient heuristic for scheduling batches of parts in a flexible flow system

Flexible manufacturing systems (FMSs) are a class of automated systems that can be used to improve productivity in batch manufacturing. Four stages of decision making have been defined for an FMS—the design, planning, scheduling, and control stages. This research focuses on the planning stage, and specifically in the area of scheduling batches of parts through the system.The literature to date on the FMS planning stage has mostly focused on the machine grouping, tool loading, and parttype selection problems. Our research carries the literature a step further by addressing the problem of scheduling batches of parts. Due to the use of serial-access material-handling systems in many FMSs, the batch-scheduling problem is modeled for a flexible flow system (FFS). This model explicitly accounts for setup times between batches that are dependent on their processing sequence.A heuristic procedure is developed for this batch-scheduling problem—the Maximum Savings (MS) heuristic. The MS heuristic is based upon the savings in time associated with a particular sequence and selecting the one with the maximum savings. It uses a two-phase method, with the savings being calculated in phase I, while a branch-and-bound procedure is employed to seek the best heuristic solution in phase II. Extensive computational results are provided for a wide variety of problems. The results show that the MS heuristic provides good-quality solutions.     

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.     

The Application and Evaluation of Banker''s Algorithm for Deadlock-Free Buffer Space Allocation in Flexible Manufacturing Systems

Deadlock-free operation is essential for operating highly automated manufacturing systems. The seminal deadlock avoidance procedure, Banker's algorithm, was developed for computer operating systems, an environment where very little information regarding the future resource requirements of executing processes is known. Manufacturing researchers have tended to dismiss Banker's algorithm as too conservative in the manufacturing environment where future resource requirements are well defined by part routes. In this work, we investigate this issue by developing variants of Banker's algorithm applicable to buffer space allocation in flexible manufacturing. We show that these algorithms are not overly conservative and that, indeed, Banker's approach can provide very good operational flexibility when properly applied to the manufacturing environment.     

Recursive newton-euler formulation for flexible dynamic manufacturing analysis of open-loop robotic systems

The main objective of this paper is to develop a recursive formulation for the flexible dynamic manufacturing analysis of open-loop robotic systems. The nonlinear generalized Newton-Euler equations are used for flexible bodies that undergo large translational and rotational displacements. These equations are formulated in terms of a set of time invariant scalars, vectors and matrices that depend on the spatial coordinates as well as the assumed displacement fields. These time invariant quantities represent the dynamic manufacturing couplings between the rigid body motion and elastic deformation. This formulation applies recursive procedures with the generalized Newton-Euler equations for flexible bodies to obtain a large, loosely coupled system equation describing motion in flexible manufacturing systems. The techniques used to solve the system equations can be implemented in any computer system. The algorithms presented in this investigation are illustrated using cylindrical joints for open-loop robotic systems, which can be easily extended to revolute, slider and rigid joints. The recursive Newton-Euler formulation developed in this paper is demonstrated with a robotic system using cylindrical mechanical joints.     

Management of product variety in cellular manufacturing systems

In today’s markets, non-uniform, customized products complicate the manufacturing processes significantly. In this paper, we propose a cellular manufacturing system design model to manage product variety by integrating with the technology selection decision. The proposed model determines the product families and machine groups while deciding the technology of each cell individually. Hedging against changing market dynamics leads us to the use of flexible machining systems and dedicated manufacturing systems at the same facility. In order to integrate the market characteristics in our model, we proposed a new cost function. Further, we modified a well known similarity measure in order to handle the operational capability of the available technology. In the paper, our hybrid technology approach is presented via a multi-objective mathematical model. A filtered-beam based local search heuristic is proposed to solve the problem efficiently. We compare the proposed approach with a dedicated technology model and showed that the improvement with the proposed hybrid technology approach is greater than 100% in unstable markets requiring high product varieties, regardless of the volumes of the products.     

Performance Comparison of Some Classes of Flexible Flow Shops and Job Shops

Flexible manufacturing systems (FMSs) for two-stage production may possess a variety of operating flexibilities in the form of tooling capabilities for the machines and alternative routings for each operation. In this paper, we compare the throughput performance of several flexible flow shop and job shop designs. We consider two-stage assembly flow shops with m parallel machines in stage 1 and a single assembly facility in stage 2. Every upstream operation can be processed by any one of the machines in stage 1 prior to the assembly stage. We also study a similar design where every stage 1 operation is processed by a predetermined machine. For both designs, we present heuristic algorithms with good worst-case error bounds and show that the average performance of these algorithms is near optimal. The algorithms presented are used to compare the performance of the two designs with each other and other related flexible flow shop designs. It is shown, both analytically and experimentally, that the mode of flexibility possessed by a design has implications on the throughput performance of the production system.     

Recursive Newton–Euler formulation for flexible dynamic manufacturing analysis of open-loop robotic systems

The main objective of this paper is to develop a recursive formulation for the flexible dynamic manufacturing analysis of open-loop robotic systems. The nonlinear generalized Newton–Euler equations are used for flexible bodies that undergo large translational and rotational displacements. These equations are formulated in terms of a set of time invariant scalars, vectors and matrices that depend on the spatial coordinates as well as the assumed displacement fields. These time invariant quantities represent the dynamic manufacturing couplings between the rigid body motion and elastic deformation. This formulation applies recursive procedures with the generalized Newton–Euler equations for flexible bodies to obtain a large, loosely coupled system equation describing motion in flexible manufacturing systems. The techniques used to solve the system equations can be implemented in any computer system. The algorithms presented in this investigation are illustrated using cylindrical joints for open-loop robotic systems, which can be easily extended to revolute, slider and rigid joints. The recursive Newton–Euler formulation developed in this paper is demonstrated with a robotic system using cylindrical mechanical joints.     

A heuristic algorithm to batching and loading problems in a flexible manufacturing system

Part type selection and machine loading are two major problems in the production planning of flexible manufacturing systems (FMS). The two problems are viewed as selecting subsets from the jobs of part types in a planning horizon and allocating jobs of the subsets among machines. In this paper, in order to develop a practical and efficient approach to solving FMS production planning problems, a heuristic algorithm is suggested that develops heuristic rules with the objective of minimisation of the number of tool changes and minimisation of the imbalance in per machine. To compare the proposed algorithm, a series of computational experiments is done on randomly generated test problems and the results show that the developed algorithm is very simple and efficient.     

Dissimilarity Maximization Method for Real-time Routing of Parts in Random Flexible Manufacturing Systems

This paper presents a dissimilarity maximization method (DMM) for real-time routing selection and compares it via simulation with typical priority rules commonly used in scheduling and control of flexible manufacturing systems (FMSs). DMM aims to reduce the congestion in the system by selecting a routing for each part among its alternative routings such that the overall dissimilarity among the selected routings is maximized. In order to evaluate the performance of DMM, a random FMS, where the product mix is not known prior to production and off-line scheduling is not possible, is selected for the simulation study. A software environment that consists of a computer simulation model, which mimics a physical system, a C++ module, and a linear program solver is used to implement the DMM concept. In addition to DMM, the simulation study uses two priority rules for routing (i.e., machine) selection and seven priority rules for selecting parts awaiting service at machine buffers. The results show (1) DMM outperforms the other two routing selection rules on production rate regardless of the part selection rule used, and (2) its performance is highly dependent on the part selection rules it is combined with.     

Simultaneous scheduling of machines and vehicles in an FMS environment with alternative routing

Scheduling of flexible manufacturing systems is a well-known NP-hard problem which is very complex, due to additional considerations like material handling, alternative routing, and alternative machines. Improvement in the performance of a flexible manufacturing system can be expected by efficient utilization of its resources, by proper integration and synchronization of their scheduling. Differential evolution is a powerful tool which proved itself as a better alternative for solving optimization problems like scheduling. In this paper, the authors addressed simultaneous scheduling of both machines and material handling system with alternative machines for the makespan minimization objective. The authors proposed a machine selection heuristic and a vehicle assignment heuristic which are incorporated in the differential evolution approach to assign the tasks, to appropriate machine and vehicle, and to minimize cycle time.     

Application of an efficient modified particle swarm optimization algorithm for process planning

In the modern manufacturing system, many flexible manufacturing system and NC machines are introduced to improve the production efficiency. Therefore, most parts have a large number of flexible process plans. However, a part can use only one process plan in the manufacturing process. So, the process planning problem has become a crucial problem in the manufacturing environment. It is a combinatorial optimization problem to conduct operations selection and operations sequencing simultaneously with various constraints deriving from the practical workshop environment as well as the parts to be processed. It is a NP-hard problem. In order to solve this problem effectively, this paper proposes a novel modified particle swarm optimization (PSO) algorithm to optimize the process planning problem. To improve the performance of the approach, efficient encoding, updating, and random search methods have been developed. To verify the feasibility and effectiveness of the proposed approach, seven cases have been conducted. The proposed algorithm has also been compared with the genetic algorithm and simulated annealing algorithm. The results show that the proposed modified PSO algorithm can generate satisfactory solutions and outperform other algorithms.     

Production Capacity of Flexible Manufacturing Systems with Fixed Production Ratios

Determining the production capacity of flexible manufacturing systems is a very important issue in the design of such systems. We propose an approach for determining the production capacity (i.e., the maximum production rate) of a flexible manufacturing system with several part types, dedicated pallets, and fixed production ratios among the different part types. We show that the problem reduces to the determination of a single parameter for which we propose an iterative procedure. Simulation or approximate analytical techniques can be used as the building block performance evaluation technique in the iterative procedure.     

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.     

Design of cellular manufacturing systems using objective functional clustering algorithms

Cellular manufacturing (CM) has emerged as an alternative to conventional batch-type manufacturing owing to the former's capability of reducing set-up times, in-process inventories and throughput times. It provides the basis for implementation of just-in-time (JIT) and flexible manufacturing systems (FMS). The machine-part group formation is an important issue in the design of CMSs. This paper presents objective functional clustering algorithms for cell formation problems in the design of cellular manufacturing systems. A deterministic objective functional algorithm (hard clustering) and a fuzzy objective functional algorithm (fuzzy clustering) are used to form the part families and machine cells simultaneously. A collection of data sets from open literature is used to test these algorithms. A software package has been developed to verify the implementation.     

An Evaluative Study of Operation Grouping Policies in an FMS

The increased use of flexible manufacturing systems to provide customers with diversified products efficiently has created a significant set of operational challenges for managers. This technology poses a number of decision problems that need to be solved by researchers and practitioners. In the literature, there have been a number of attempts to solve design and operational problems. Special attention has been given to machine loading problems, which involve the assignment of job operations and allocation of tools and resources to optimize specific measures of productivity. Most existing studies focus on modeling the problem and developing heuristics in order to optimize certain performance metrics rather than on understanding the problem and the interaction between the different factors in the system. The objective of this paper is to study the machine loading problem. More specifically, we compare operation aggregation and disaggregation policies in a random flexible manufacturing system (FMS) and analyze its interaction with other factors such as routing flexibility, sequencing flexibility, machine load, buffer capacity, and alternative processing-time ratio. For this purpose, a simulation study is conducted and the results are analyzed by statistical methods. The analysis of results highlights the important factors and their levels that could yield near-optimal system performance.     

A heuristic algorithm for the loading problem in flexible manufacturing systems

The paper considers the loading problem in flexible manufacturing systems (FMSs). This problem involves the assignment to the machine tools of all operations and associated cutting tools required for part types that have been selected to be produced simultaneously. The loading problem is first formulated as a linear mixed 0–1 program with the objective to minimize the greatest workload assigned to each machine. A heuristic procedure is presented in which an assignment of operations to machine tools is obtained by solving a parameterized generalized assignment problem with an objective function that approximates the use of tool slots required by the operations assigned to the machines. The algorithm is coded in FORTRAN and tested on an IBM-compatible personal computer. Computational results are presented for different test problems to demonstrate the efficiency and effectiveness of the suggested procedure.     

Evaluating the design of flexible manufacturing systems

Evaluating the design of flexible manufacturing systems is complex. Developing a measure of performance useful for evaluating alternate designs continues to be interesting. Here, total productivity of the system is proposed as an appropriate measure. Specification of parameters based upon strategic considerations for this measure are discussed. Finally, the usefulness of the measure is demonstrated through an example.     

Inventory issues in incremental FMS implementation

In recent years, numerous studies have demonstrated convincingly that impressive benefits can be obtained by the adoption of flexible manufacturing systems (FMSs). To obtain the benefits of an FMS requires the development of a completely integrated system. However, FMS implementations are frequently done incrementally through the introduction of subsystems such as flexible machining centers into an existing conventional system. The purpose of this research is to investigate some of the operational issues associated with the introduction of a CNC (computer numerically controlled) machine tool into a conventional system. The primary objective of the present study is to explore the relative effects on inventory holding cost of installing a single CNC at different locations within three different system configurations. Additionally, the study examines the sensitivity of these impacts to changes in (1) System utilization; (2) the ratios of setup times to run times in the conventional work centers; and (3) the rates of increase in holding costs for parts as they move through the system. Results indicated that, in general, introduction of a CNC into an otherwise conventional system reduces inventory holding cost for the system as a whole. However, the degree of this reduction varies depending on the position of the CNC in the system. In some cases the reduction in inventory holding cost is substantial, while in other cases it is relatively small.     

Batch sizes and lead-time performance in flexible manufacturing systems

Production lead-time performance in flexible manufacturing systems is influenced by several factors which include: machine groupings, demand rates, machine processing rates, product batching, material handling system capacity, and so on. Hence, control of lead-time performance can be affected through the manipulation of one or more of these variables. In this article, we investigate the potential of batch sizing as a control variable for lead-time performance through the use of a queueing network model. We establish a functional relationship between the two variables, and incorporate the relationship in an optimization model to determine the optimal batch size(s) which minimizes the sum of annual work-in-process inventory and final inventory costs. The nonlinear batch sizing problem which results is solved by discrete optimization via marginal analysis. Results show that batch sizing can be a cheap and effective variable for controlling flexible manufacturing system throughput.     

Logical Cell Formation in FMS, Using Flexibility-Based Criteria

Flexible manufacturing systems often are organized into a cellular architecture for ease of operation. The formation of these cells sometimes has been treated as an extension of the conventional cell-formation problem. This paper argues that, owing to the existence of flexible routing and transfer capabilities, the cell-formation problem in FMSs should be treated as quite distinct from that in conventional manufacturing systems and shows that a flexibility-based procedure is apt for overcoming the deficiencies of earlier forays into this area. Manufacturing cell flexibility is defined as a composite of three flexibility measures: producibility,processivity , and transferability. The problem of cell formation is modeled as flexibility maximization, and a procedure is developed for the simultaneous formation of machine cells and part families, while heuristically maximizing within-cell flexibility.