Beam search algorithm for capacity allocation problem in flexible manufacturing systems

This study considers the operation assignment and tool allocation problem in flexible manufacturing systems. A set of operations together with their required tools are selected so as to maximize the total weight. The machines have limited time and tool magazine capacities and the tools are available in limited quantities. We develop a beam search algorithm and obtain near optimal solutions for large size problems very quickly.     

Deadlock-free scheduling for flexible manufacturing systems using Petri nets and heuristic search

Deadlock-free control and scheduling are two different problems for flexible manufacturing systems (FMSs). They are significant for improving the behaviors of the systems. Based on the Petri net models of FMSs, this paper embeds deadlock control policies into heuristic search algorithm, and proposes a deadlock-free scheduling algorithm to minimize makespan for FMSs. Scheduling is performed as heuristic search in the reachability graph of the Petri net. The searching process is guided by a heuristic function based on firing count vectors of state equation for the Petri net. By using the one-step look-ahead method in the optimal deadlock control policy, the safety of a state is checked. Experimental results are provided to show effectiveness of the proposed heuristic search approach in deadlock-free scheduling for FMSs.     

Search strategy for scheduling flexible manufacturing systems simultaneously using admissible heuristic functions and nonadmissible heuristic functions

To scheduling flexible manufacturing system (FMS) efficiently, we propose and evaluate an improved search strategy and its application to FMS scheduling in the P-timed Petri net framework. On the execution of Petri net, the proposed method can simultaneously use admissible heuristic functions and nonadmissible heuristic functions for A^* algorithm. We also prove that the resulting combinational heuristic function is still admissible and more informed than any of its constituents. The experimental results of an example FMS and several sets of random generated problems show that the proposed search method performs better as we expected.     

Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS

Due to the global competition in manufacturing environment, firms are forced to consider increasing the quality and responsiveness to customization, while decreasing costs. The evolution of flexible manufacturing systems (FMSs) offers great potential for increasing flexibility and changing the basis of competition by ensuring both cost effective and customized manufacturing at the same time. Some of the important planning problems that need realistic modelling and quicker solution especially in automated manufacturing systems have assumed greater significance in the recent past. The language used by the industrial workers is fuzzy in nature, which results in failure of the models considering deterministic situations. The situation in the real life shop floor demands to adopt fuzzy-based multi-objective goals to express the target set by the management. This paper presents a fuzzy goal programming approach to model the machine tool selection and operation allocation problem of FMS. An ant colony optimization (ACO)-based approach is applied to optimize the model and the results of the computational experiments are reported.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Bounding approaches for operation assignment and capacity allocation problem in flexible manufacturing systems

This study considers an operation assignment and capacity allocation problem that arises in flexible manufacturing systems. Automated machines are assumed to have scarce time and tool magazine capacities and the tools are available in limited quantities. The aim is to select a subset of operations with maximum total weight. The weight of an operation may represent its profit, processing load, relative priority. Several upper bounding procedures have been taken into account. The results of computational tests have revealed that the proposed upper bounding procedures produce satisfactory solutions in reasonable CPU times. We suggest using some of the bounds when the quality of the solutions is more important than the speed of achieving them and some others when the speed is more important than the quality.     

不同尺寸工件批调度问题的自适应蚁群退火算法*

研究不同尺寸工件单机批调度问题,将蚁群算法与模拟退火算法相结合,引入自适应状态转移概率,提出了一种自适应蚁群退火算法AACSA(adaptive ant colony simulated annealing)。该算法利用模拟退火算法实现了一种新的混合信息素更新策略,此外根据停滞次数,动态改变状态转移概率,有效地避免算法陷入停滞以及局部最优,提高算法的性能。仿真实验结果表明,AACSA与蚁群优化算法BACO、模拟退火算法SA、启发式规则BFLPT相比,算法求解的性能更好。     

A constraint programming approach to tool allocation and production scheduling in flexible manufacturing systems

This paper presents a constraint programming (CP) methodology to deal with the scheduling of flexible manufacturing systems (FMSs). The proposed approach, which consists of both a model and a search strategy, handles several features found in industrial environments, such as limitations on number of tools in the system, lifetime of tools, as well as tool magazine capacity of machines. In addition, it tackles the problem in a integrated way by considering tool planning and allocation, machine assignment, part routing, and task timing decisions altogether in the approach. The formulation, which is able to take into account a variety of objective functions, has been successfully applied to the solution of test problems of various sizes and degrees of difficulty.     

Robust scheduling optimization for flexible manufacturing systems with replenishment under uncertain machine failure disruptions

This paper studies the scheduling problem for the flexible manufacturing systems (FMSs) under uncertain machine failure disruptions, where machine allocations and job schedules need to be determined to achieve a set of production due-date requirements as well as possible. A robust scheduling optimization model is proposed based on the concept of threshold scenario, bounded by which the due-dates are guaranteed to be achieved. It is shown that the associated stochastic scheduling problem can be equivalently solved by computing the solution of a mixed-integer linear program (MILP). Computational results show that our proposed model performs well in achieving the planned due-dates under uncertainty when compared to various standard approaches. The practical applicability of our approach is verified using a real stamping industry application, in which the stamping parts are various types of voice coil motor yokes used in commercial hard disk drive actuators. Apart from FMSs, the proposed approach can also be applied to various other industries including project scheduling, airline scheduling, transportation scheduling.     

A novel 0-1 linear integer programming model for dynamic machine-tool selection and operation allocation in a flexible manufacturing system

This paper considers a problem of dynamic machine-tool selection and operation allocation with part and tool movement policies in a flexible manufacturing system (FMS) environment. For this purpose, a novel 0-1 linear integer programming model is presented in such a way that each part and each tool can move during the production phase. It is assumed that there are a given set of tools and machines that can produce different kinds of orders (or part types). The objective of this model is to determine a machine-tool combination for each operation of the part type by minimizing some production costs, such as machining costs, setup costs, material handling costs and tool movement costs. In addition, due to the NP-hard nature of the problem, a new heuristic method based on five simple procedures (FSP) is proposed for solving the given problem, whose performance is tested on a number of randomly generated problems. The related results are compared with results obtained by a branch-and-bound method. It has been found that the proposed heuristic method gives good results in terms of objective function values and CPU times.     

Hybrid heuristic search approach for deadlock-free scheduling of flexible manufacturing systems using Petri nets

Based on the Petri net models of flexible manufacturing systems (FMSs), this paper focuses on deadlock-free scheduling problem with the objective of minimizing the makespan. Two hybrid heuristic search algorithms for solving such scheduling problems of FMSs are proposed. To avoid deadlocks, the deadlock control policy is embedded into heuristic search strategies. The proposed algorithms combine the heuristic best-first strategy with the controlled backtracking strategy based on the execution of the Petri nets. The scheduling problem is transformed into a heuristic search problem in the reachability graph of the Petri net, and a schedule is a transition sequence from the initial marking to the final marking in the reachability graph. By using the one-step look-ahead method in the deadlock control policy, the safety of a state in the reachability graph is checked, and hence, deadlock is avoided. Experimental results are provided and indicate the effectiveness of the proposed hybrid heuristic search algorithms in solving deadlock-free scheduling problems of FMSs. Especially, the comparison against previous work shows that both new algorithms are promising in terms of solution quality and computing times.     

A synergistic approach to manufacturing systems control using machine learning and simulation

This paper describes a synergistic approach that is applicable to a wide variety of system control problems. The approach utilizes a machine learning technique, goal-directed conceptual aggregation (GDCA), to facilitate dynamic decision-making. The application domain employed is Flexible Manufacturing System (FMS) scheduling and control. Simulation is used for the dual purpose of providing a realistic depiction of FMSs, and serves as an engine for demonstrating the viability of a synergistic system involving incremental learning. The paper briefly describes prior approaches to FMS scheduling and control, and machine learning. It outlines the GDCA approach, provides a generalized architecture for dynamic control problems, and describes the implementation of the system as applied to FMS scheduling and control. The paper concludes with a discussion of the general applicability of this approach.     

Multi-period part selection and loading problems in flexible manufacturing systems

We consider multi-period part selection and loading problems in flexible manufacturing systems with the objective of minimizing subcontracting costs. The part selection problem is to select sets of part types and to determine their quantities to be produced during the upcoming planning horizon while satisfying due dates of all orders for the parts, and the loading problem involves allocation of operations and required tools to machines. Production demands should be satisfied for periods through subcontracting if production demands cannot be satisfied by the system due to machine capacity or tool magazine capacity constraints. For the part selection and loading problems, we develop three iterative algorithms, called the forward algorithm, the backward algorithm and the capacity approximation algorithm, that solve the part selection and loading problems iteratively for each period. To compare the three algorithms, a series of computational experiments is done on randomly generated test problems.     

A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system

Manufacturing industries are rapidly changing from economies of scale to economies of scope, characterized by short product life cycles and increased product varieties. This implies a need to improve the efficiency of job shops while still maintaining their flexibility. These objectives are achieved by Flexible manufacturing systems (FMS). The basic aim of FMS is to bring together the productivity of flow lines and the flexibility of job shops. This duality of objectives makes the management of an FMS complex. In this article, the loading problem in random type FMS, which is viewed as selecting a subset of jobs from the job pool and allocating them among available machines, is considered. A heuristic based on multi-stage programming approach is proposed to solve this problem. The objective considered is to minimize the system unbalance while satisfying the technological constraints such as availability of machining time and tool slots. The performance of the proposed heuristic is tested on 10 sample problems available in FMS literature and compared with existing solution methods. It has been found that the proposed heuristic gives good results.     

Heuristic search for scheduling flexible manufacturing systems using lower bound reachability matrix

For scheduling flexible manufacturing systems efficiently, we propose new heuristic functions for A^* algorithm that is based on the T-timed Petri net. In minimizing makespan, the proposed heuristic functions are usually more efficient than the previous functions in the required number of states and computation time. We prove that these heuristic functions are all admissible and one of them is more informed than that using resource cost reachability matrix. We also propose improved versions of these heuristic functions that find a first near-optimal solution faster. In addition, we modify the heuristic function of Yu, Reyes, Cang, and Lloyd (2003b) and propose an admissible version in all states. The experimental results using a random problem generator show that the proposed heuristic functions perform better as we expected.     

An efficient approach to determine cell formation,cell layout and intracellular machine sequence in cellular manufacturing systems

Cellular manufacturing systems (CMS) are used to improve production flexibility and efficiency. They involve the identification of part families and machine cells so that intercellular movement is minimized and the utilization of the machines within a cell is maximized. Previous research has focused mainly on cell formation problems and their variants; however, only few articles have focused on more practical and complicated problems that simultaneously consider the three critical issues in the CMS-design process, i.e., cell formation, cell layout, and intracellular machine sequence. In this study, a two-stage mathematical programming model is formulated to integrate the three critical issues with the consideration of alternative process routings, operation sequences, and production volume. Next, because of the combinatorial nature of the above model, an efficient tabu search algorithm based on a generalized similarity coefficient is proposed. Computational results from test problems show that our proposed model and solution approach are both effective and efficient. When compared to the mathematical programming approach, which takes more than 112 h (LINGO) and 1139 s (CPLEX) to solve a set of ten test instances, the proposed algorithm can produce optimal solutions for the same set of test instances in less than 12 s.     

Robust design of flexible manufacturing systems using, colored Petri net and genetic algorithm

A method is presented for the robust design of flexible manufacturing systems (FMS) that undergo the forecasted product plan variations. The resource allocation and the operation schedule of a FMS are modeled as a colored Petri net and an associated transition firing sequence. The robust design of the colored Petri net model is formulated as a multi-objective optimization problem that simultaneously minimizes the production costs under multiple production plans (batch sizes for all jobs), and the reconfiguration cost due to production plan changes. A genetic algorithm, coupled with the shortest imminent operation time (SIO) dispatching rule, is used to simultaneously find the near-optimal resource allocation and the event-driven schedule of a colored Petri net. The resulting Petri net is then compared with the Petri nets optimized for a particular production plan in order to address the effectiveness of the robustness optimization. The simulation results suggest that the proposed robustness optimization scheme should be considered when the products are moderately different in their job specifications so that optimizing for a particular production plan creates inevitably bottlenecks in product flow and/or deadlock under other production plans.     

Design and planning problems in flexible manufacturing systems: a critical review

This review paper describes the state-of-the-art research on flexible manufacturing systems (FMS) design and planning issues. The emphasis is on presenting research results coming out of the current FMS literature that help the FMS manager in setting up a highly efficient manufacturing system. In addition to that, it discusses relevant research contributions after 1986, that were not part of any of the previous survey papers on operations research models for FMSs. Also, applications of combinatorial optimization approaches to FMS planning problems are adequately exposed in the paper.     

Modeling machine loading problem of FMSs and its solution methodology using a hybrid tabu search and simulated annealing-based heuristic approach

In this article, a machine loading problem of a flexible manufacturing system (FMS) is discussed having the bicriterion objectives of minimizing system unbalance and maximizing throughput in the presence of technological constraints such as available machining time and tool slots. A generic 0–1 integer programming formulation with the objective functions and constraints described above has been proposed. A hybrid algorithm based on tabu search and simulated annealing (SA) is employed to solve the problem. The main advantage of this approach is that a short-term memory provided by the tabu list can be used to avoid revisiting the solution while preserving the stochastic nature of the SA method. The proposed methodology has been tested on ten standard problems and the results obtained are compared with those from some of the existing heuristics.     

Simulation modeling and analysis of tool sharing and part scheduling decisions in single-stage multimachine flexible manufacturing systems

This paper focuses on a simulation-based study of tool sharing problem in single-stage multimachine Flexible Manufacturing Systems. Three different scenarios are considered for investigation. A simulation model has been developed for each of these scenarios. A number of scheduling rules are incorporated in the simulation models for the decisions such as tool request selection and part launching in the context of tool sharing environment. The performance measures evaluated are mean tardiness, conditional mean tardiness and mean flow time. Based on the analysis of the simulation results, the best possible scheduling rule combinations for part launching and tool request selection have been identified for the three scenarios.