Production planning for flexible manufacturing systems with multiple machine types: A practical method

We study three important production planning problems for flexible manufacturing systems (FMSs) that consist of multiple types of machines. Namely we address machine grouping, workload allocation among machine groups, and batch sizing that maximize system throughput, when part types are selected for the upcoming production period. This differs from earlier related works in that earlier works give either only qualitative characteristics of the optimum solution or provide a solution method for FMSs consisting of only one type of flexible machine. In this paper, we provide both the optimum and heuristic methods to simultaneously solve the three problems for realistic FMSs. Computational results show that the heuristic method always finds the optimum solution at a fraction of computation time and that batch sizing can significantly affect throughput. Development of the heuristic method is necessary since the optimum method can be time-consuming for a moderate size of problems.     

Scheduling of the optimal tool replacement times in a flexible manufacturing system

In Flexible Manufacturing Systems (FMSs). a cutting tool is frequently used for different operations and on different part types to minimize tool change-overs and the number of tools required, and to increase part-routing flexibility. In such situations, the tools become shared resources and work in job-dependent, changeable and nonhomogeneous conditions. It is well known that the tool failure rate depends on both age and machining conditions and that tool reliability is a function of the duration, machining conditions, and the sequence of the operations in FMS. The objective of this paper is to obtain a schedule of the optimal preventive replacement times for the cutting tools over a finite time horizon in a flexible manufacturing system. We assume that the tool will be replaced either upon failure during an operation or preventively after the completion of each operation, incurring different replacement costs. A standard stochastic dynamic programming approach is taken to obtain the optimal tool replacement times. The optimal schedule is obtained by minimizing the total expected cost over a finite time horizon for a given sequence of operations. A computational algorithm is developed and a numerical example is given to demonstrate the procedure.     

Optimal design for flexible manufacturing systems: generalized analytical methods

Advanced manufacturing systems such as Flexible Manufacturing Systems (FMSs) are capital-intensive. Designing functional yet cost-effective FMSs is a challenging task because it involves the solution of a complex series of interrelated problems. In this paper, we study a design problem for FMSs that consist of multiple types of machines. Using closed queueing network models for FMSs, this problem seeks the minimum cost design subject to meeting throughput requirements. The design decisions include the number of machine groups, the number of machines for each group, the workload allocation among machine groups, the number of pallets, the number of transporters, and the batch size. Since these design decisions are highly interdependent, we present the optimum and heuristic methods that simultaneously determine them. To our knowledge, these methods are the most general analytical methods for FMS design. Development of the heuristic methods is crucial because of the time-consuming nature of the optimum method as the number of machine types increases. Computational results show that the heuristic methods are both effective and efficient.     

Disassembly sequence generation: A Petri net based heuristic approach

The generation of a set of optimal or near-optimal disassembly sequences is an important task associated with the study of disassembly planning. This task encapsulates actions at the required disassembly depth to reach maximum net revenue while satisfying economic constraints. The Petri net modelling, combined with heuristic search procedures developed in this study, offers an efficient procedure for disassembly sequence generation. The heuristic generates and searches a partial reachability graph to arrive at an optimal or near-optimal disassembly sequence based on the firing sequence of transitions of the Petri net model. The proposed methodology reduces the search space in two areas: (1) pruning the disassembly tree (DT) and, (2) selective tracking of the reachability graph. The efficacy of the proposed methodology is demonstrated by using two examples from the literature.     

Order acceptance in food processing systems with random raw material requirements

This study considers a food production system that processes a single perishable raw material into several products having stochastic demands. In order to process an order, the amount of raw material delivery from storage needs to meet the raw material requirement of the order. However, the amount of raw material required to process an order is not exactly known beforehand as it becomes evident during processing. The problem is to determine the admission decisions for incoming orders so as to maximize the expected total revenue. It is demonstrated that the problem can be modeled as a single resource capacity control problem. The optimal policy is shown to be too complex for practical use. A heuristic approach is proposed which follows rather simple decision rules while providing good results. By means of a numerical study, the cases where it is critical to employ optimal policies are highlighted, the effectiveness of the heuristic approach is investigated, and the effects of the random resource requirements of orders are analyzed.     

HEURISTIC APPROACHES FOR LOADING PROBLEMS IN FLEXIBLE MANUFACTURING SYSTEMS

Flexible manufacturing systems (FMSs) are able to process a wide variety of operations, but the specific mix of operations that can be performed at any point in time depends upon the combination of tools loaded onto the machines. The machines have tool magazines with finite capacities. We consider the problem of assigning operations and their associated tools to machines (or groups of machines) to maximize the throughput for a specified steady-state mix of orders. Since this objective is difficult to deal with directly, we use an intermediate objective of meeting workload targets for each machine group as closely as possible. A certain form of this intermediate objective has been shown to correlate highly with the original objective.

Since it is computationally intractable to find optimal solutions for problems with more than 20 operations, fast heuristic algorithms are developed. These algorithms are adapted from multi-dimensional bin-packing algorithms. Computational results are reported.     

Joint optimization of maintenance,buffers and machines in manufacturing lines

This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.     

PRODUCTION SCHEDULING IN A FLEXIBLE MANUFACTURING SYSTEM WITH SETUPS

A model is presented for the planning and scheduling of production batches in a flexible manufacturing system environment in which setup costs and times are nonnegligible and alternate routings are possible. The formulation is an integer program with a multicommodity flow network structure. A heuristic procedure based on price-directive decompostion using column generation is used to obtain solutions. Numerical experimentation is performed to assess die quality of the heuristic versus optimal solutions, and to determine the impact of routing flexibility on total cost, inventory levels, bottlenecks, capacity utilization, throughput time, number of setups and split lots. Important cost-benefit trade-off implications are shown for the design of flexible manufacturing systems.     

A simulated annealing procedure for single row layout problems in flexible manufacturing systems

In this paper we address a layout problem in flexible manufacturing systems (FMSs). A layout type that has been extensively implemented in FMSs is the single row machine layout. In such a configuration machines are arranged along a straight track with a material handling device moving jobs from one machine to another. The single row layout problem (SRLP) deals with the optimal arrangement of the machines for the above configuration. We propose a simulated annealing (SA) heuristic for the solution of SRLP. Extensive computational results, and ways to improve the performance of the SA algorithm through parameter fine-tuning procedures, are reported.     

Multiperiod tool and production assignment in flexible manufacturing systems

Flexible manufacturing systems (FMSs) are usually composed of general purpose machines with automatic tool changing and integrated material handling. FMSs offer the advantages of high utilization levels and simultaneous production of a variety of part types with minimal changeover time. Although the trend in FMS operation appears to be towards unmanned production, there is much diversity in the policies under which FMSs can be operated. These policies are governed to some extent by the available technological resources. In this paper several policies are described. Tool loading and production assignment models for setting production plans over a short term planning horizon when using these policies are then formulated. Heuristics for the solution of these models are detailed and computational results are presented.     

Scheduling of flexible manufacturing systems based on Petri nets and hybrid heuristic search

This paper proposes and evaluates a hybrid search strategy and its application to flexible manufacturing system (FMS) scheduling in a Petri net framework. Petri nets can concisely model multiple lot sizes for each job, the strict precedence constraint, multiple kinds of resources, and concurrent activities. To cope with the complexities for FMS scheduling, this paper presents a hybrid heuristic search strategy, which combines the heuristic A* strategy with the DF strategy based on the execution of the Petri nets. The search scheme can invoke quicker termination conditions, and the quality of the search result is controllable. To demonstrate this, the scheduling results are derived and evaluated through a simple FMS with multiple lot sizes for each job. The algorithm is also applied to a set of randomly generated more complex FMSs with such characteristics as limited buffer sizes, multiple resources, and alternative routings.     

A cyclical scheduling heuristic for lot sizing with capacity constraints

This paper proposes a heuristic procedure to solve cyclical scheduling problems. Lot sizes for two or more items must be chosen simultaneously for several periods into the future, while recognizing capacity constraints at several work centres. Solving this problem is important in its own right. It also has transfer value to even more complicated situations, such as when the items have parent-component relationships. The proposed heuristic, though in the spirit of Mamie's formulation, has fewer production sequences for each item. The simplified formulation can be solved with a general linear programming code. Another advantage is its use of fixed ordering intervals, which seems to be preferred in practice. Experimental results suggest that the heuristic gives near optimal solutions and requires less computational time. Of particular interest is the heuristic's ability to satisfy capacity constraints when selecting item lot sizes.     

基于成本约束的在线多数量拍卖模型

在线拍卖是电子商务交易的一个重要组成部分。本文在在线拍卖过程中引入拍卖成本,以拍卖者的收入最大化建立了相关模型,并对在线拍卖主要的两个参数、拍卖数量和拍卖周期作了分析。     

Integrated optimisation of scheduling and ordering based on semi-finished goods delayed differentiation

The implementation of dynamic ordering policies is becoming increasingly important for the competitiveness of modern manufacturing systems. However, existing models on dynamic ordering pay little attention to production scheduling, which greatly affects the fulfilment of dynamic ordering, especially in complex manufacturing systems. Therefore, it is imperative to establish a new model which integrates both dynamic ordering and production scheduling. Accordingly, a quantitative measurement method for integration is needed. To this end, this paper proposes a semi-finished goods delayed differentiation (SFGDD) model by taking into account integration of the scheduling inventory control and dynamic ordering simultaneously. The objective of this model is to study the relationship between the shop floor inventory and the ordering control based on the semi-finished goods dynamic dispatching mechanism. In addition, the days of inventory (DOI) and a backorder penalty exponential function are developed to quantitatively measure such a relationship. To obtain the optimal results, this paper employs a heuristic genetic algorithm (HGA) with a heuristic encoding scheme to synchronise the generation and selection of inventory variables coherently. A case study on a semiconductor assembly and test manufacturing (ATM) is presented, and a significant revenue enhancement and inventory reduction are achieved accordingly.     

Computerization of tool-replacement decision making in flexible manufacturing systems: a human-systems perspective

In this article, a framework is proposed for computerization of tool-replacement decision making in flexible manufacturing systems (FMSs). Specifically, a procedure is outlined that addresses the decision of whether a tool should or should not be replaced prior to its processing of a workpart. The proposed procedure attempts to minimize the tool-related economic losses associated with these decisions while maximizing part throughput. The limitations of both the human and the computer at achieving both of these system performance objectives are discussed and form the basis to the proposed procedure. Based on a simulation study examining human capabilities at making tool-replacement decisions, various heuristics employed by humans who were successful at performing this task were identified. These heuristics as well as other considerations were incorporated into the procedure in an effort to maintain a balance between the more direct local consequences and less direct global consequences of tool-replacement decisions on the economic and throughput objectives, respectively. The article concludes with a discussion on the role of sensitivity analysis, and the use of the procedure within the context of human supervisory control of FMSs.     

Optimising lot sizing with nonlinear production rates in a multi-product multi-machine environment

In a variety of discrete manufacturing environments, it is common to experience a nonlinear production rate. In particular, our interest is in the case of an increasing production rate, where learning creates efficiencies. This leads to greater output per unit time as the process continues. However, the advantages of an increasing production rate may be offset by other factors. For examples, JIT policies typically lead to smaller lot sizes, where the value of an increasing production rate is largely lost. We develop a general model that balances the impact of various competing effects. Our research focuses on determining lot sizes that satisfy demand requirements while minimising production and holding costs. We extend our prior work by developing a multi-product, multi-machine method for modelling and solving this class of production problems. The solution method is demonstrated using the production function from the PR#2 grinding process for a production plant in Carlisle, PA. The solution heuristic provides solution times that are on average only 0.22 to 0.55% above optimum as the solution parameters are varied and the ratio of heuristic solution times to optimal solution times varies from 18.16 to 14.15%.     

Think globally act locally approach for the synthesis of a liveness-enforcing supervisor of FMSs based on Petri nets

This paper, by using Petri nets (PNs), reports a general approach, called a think globally, act locally (TGAL) method, to compute liveness-enforcing supervisors (LES) for flexible manufacturing systems (FMSs) prone to deadlocks. A place called global sink/source place (GP) is introduced provisionally help us to decide a set of monitors such that deadlock states can be removed. The TGAL method proceeds with liveness enforcement by an iterative way in which a complete state enumeration is computed at each step. The resulting LES is generally maximally permissive or suboptimal, without solving intractable integer linear programming (ILP) problems. Given a system, a sufficient condition is developed to decide whether the TGAL method can find maximally permissive, that is, optimal supervisors. Several typical FMSs popularly studied in the literature are used as the examples to demonstrate the proposed method.     

Simulation of stochastic demand data streams for network revenue management problems

For evaluating heuristic and optimal network revenue management procedures test-instances are needed. As a consequence when trying to create instances for network revenue management problems it turns out that among other things a stream of stochastic demand data is required. But, developing and implementing a generator for demand data that fits to a given network, a given set of products, and a given set of capacity constraints is far from being easy. Since to the best of our knowledge no such demand data generator is available to the public, we specify an algorithm to generate this data and we also make this algorithm available upon request. This, we hope, facilitates future research work.     

Analysis of assembly systems for interdeparture time variability and throughput

This paper studies the effect of the number of component stations (parallelism), work transfer, processing time distributions, buffers and buffer allocation schemes on throughput and interdeparture time variability of assembly systems. As an alternative to work transfer, variability transfer is introduced and its effectiveness is assessed. Previous research has indicated that the optimal throughput displays an anomaly at certain processing time distributions and, this phenomenon is now thoroughly analyzed and the underlying details are uncovered. This study also yields several new findings that convey important practical implications.     

A simple and effective heuristic for the resource constrained project scheduling problem

This paper investigates the development and application of a simple heuristic to the resource constrained project scheduling problem (RCPSP). This computer heuristic, which is based on the COMSOAL heuristic, constructs a feasible solution at each iteration and chooses the best solution of several iterations. Although COMSOAL was originally a solution approach for the assembly-line balancing problem, it can be extended to provide solutions to the resource allocation problem. The Modified COMSOAL technique presented in this paper uses priority schemes intermittently with a random selection technique. This hybrid of randomness and priority scheme allows a good solution to be found quickly while not being forced into the same solution at each iteration. Several different priority schemes are examined within this research. The COMSOAL heuristic modified with the priority schemes heuristic was tested on several established test sets and the solution values are compared with both known optimal values and the results of several other resource allocation heuristics. In the vast majority of cases, the Modified COMSOAL heuristic outperformed the other heuristics in terms of both average and maximum percentage difference from optimal. The Modified COMSOAL heuristic seems to have several advantages over other RCPSP heuristics in that it is easy to understand, easy to implement, and achieves good results.