Scheduling Problem In Flexible Manufacturing System Research Articles

A Particle Swarm Optimization –Based Heuristic for Scheduling in FMS Review

This paper focus on the problem of simultaneous scheduling of machine and automated guided vehicle (AGV) in a flexible manufacturing system (FMS) so as to minimize the makespan The FMS scheduling problem has been tackled by various traditional optimization techniques. While these methods can give an optimal solution to small-scale problems, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple bjectives,i.e.,minimising the idle time of the machine andminimising the total penalty cost for not meeting the deadline concurrently. Two optimization algorithms ( genetic algorithm and particle swarm algorithm) are compared and conclusions are presented

Modified Dynamic Programming Algorithm for Optimization of Total Energy Consumption in Flexible Manufacturing Systems

Based on the Petri net (PN) models of the flexible manufacturing systems (FMSs), this paper focuses on solving the scheduling problem of minimizing the total energy consumption of FMSs. In the view of different energy consumption rates of resources under different working statuses, two energy consumption functions are considered. The dynamic programming (DP) models of the scheduling problems based on PNs are established, where a reachable marking of a PN model, start processing time vector, route vector, and the transition sequence leading to the reachable marking from the initial one, is regarded as a state, and the Bellman equation is based on transition firing. For small-size scheduling problems, their optimal solutions can be obtained by the presented DP algorithm. However, it is difficult to solve larger-scale ones since the number of explored states increases exponentially with the problem size, which makes DP computationally infeasible. To obtain an optimal or suboptimal schedule in an acceptable time, modified DP (MDP) algorithm is proposed, in which fewer states are explored. In MDP, two ways are introduced through which only the most promising states are explored. One is keeping only one transition sequence for each marking through an evaluation function. Another is selecting the most promising states in each stage for further exploration through a heuristic function. To guarantee that the generated states are safe, a deadlock controller is applied in the recursion procedure of MDP. Experimental results on manufacturing systems and comparisons with existing works are provided to show the effectiveness of MDP. Note to Practitioners —This paper is motivated by the need of optimizing the energy consumption of manufacturing systems, considering the increasing energy cost and environmental concerns. Existing studies on energy optimization rarely focus on flexible manufacturing systems (FMSs) which exhibit a high degree of resource sharing and route flexibility and can be highly adaptable to various production plans and goals. Scheduling becomes more challenging when facing deadlock-prone FMSs. This paper provides a method to solve this complex scheduling problem efficiently. In this paper, dynamic programming (DP) is formulated for it, and the optimal energy consumption schedules are found. Considering the computational burden of implementing DP in the scheduling of large-size FMSs, a novel scheduling method named modified DP (MDP) is proposed. Experimental tests on an FMS and a stamping system suggest that MDP can successfully find feasible solutions. Besides, it can be applied to other FMS scheduling problems and industrial cases, once their processing time of operations and energy consumption of resources per unit time are known.

Role of batch size in scheduling optimization of flexible manufacturing system using genetic algorithm

Flexible manufacturing system (FMS) readily addresses the dynamic needs of the customers in terms of variety and quality. At present, there is a need to produce a wide range of quality products in limited time span. On-time delivery of customers’ orders is critical in make-to-order (MTO) manufacturing systems. The completion time of the orders depends on several factors including arrival rate, variability, and batch size, to name a few. Among those, batch size is a significant construct for effective scheduling of an FMS, as it directly affects completion time. On the other hand, constant batch size makes MTO less responsive to customers’ demands. In this paper, an FMS scheduling problem with n jobs and m machines is studied to minimize lateness in meeting due dates, with focus on the impact of batch size. The effect of batch size on completion time of the orders is investigated under following strategies: (1) constant batch size, (2) minimum part set, and (3) optimal batch size. A mathematical model is developed to optimize batch size considering completion time, lateness penalties and setup times. Scheduling of an FMS is not only a combinatorial optimization problem but also NP-hard problem. Suitable solutions of such problems through exact methods are difficult. Hence, a meta-heuristic Genetic algorithm is used to optimize scheduling of the FMS.

Non-Reshuffle-Based Approach for Rescheduling of Flexible Manufacturing System

Scheduling and rescheduling play vital roles in ensuring the effectiveness of the production control in flexible manufacturing system (FMS). The failure of these systems may interrupt the efficiency of the production activities and thus may lessen the profit to be gained by the company. The FMS scheduling problem is considered as dynamic as new orders may get in every day. The new orders need to be immediately desegregated with the existing production schedule by preserving the efficiency and stability of the existing schedule. This research applies the non-reshuffle-based genetic match-up algorithms which admit new orders by manipulating available machine idle times to address rescheduling problem in a FMS that practises the pull strategy. The idea of the match-up approach is to update only a part of the initial schedule and genetic algorithms used to optimise the solution within the rescheduling horizon in such a way in order to preserve the efficiency and stability of the shop floor. The proposed methodology has been tested using different rescheduling parameters. The experiments show that the rescheduling method improves efficiency and stability of the new schedule.

Deadlock-Free Scheduling Method for Flexible Manufacturing Systems Based on Timed Colored Petri Nets and Anytime Heuristic Search

This paper addresses the deadlock (DL)-free scheduling problem of flexible manufacturing systems (FMS) characterized by resource sharing, limited buffer capacity, routing flexibility, and the availability of material handling systems. The FMS scheduling problem is formulated using timed colored Petri net (TCPN) modeling where each operation has a certain number of preconditions, an estimated duration, and a set of postconditions. Based on the reachability analysis of TCPN modeling, we propose a new anytime heuristic search algorithm which finds optimal or near-optimal DL-free schedules with respect to makespan as the performance criterion. The methodology tackles the time-constrained problem of very demanding systems (high diversity production and make-to-order) in which computational time is a critical factor to produce optimal schedules that are DL-free. In such a rapidly changing environment and under tight customer due-dates, producing optimal schedules becomes intractable given the time limitations and the NP-hard nature of scheduling problems. The proposed anytime search algorithm combines breadth-first iterative deepening A* with suboptimal breadth-first heuristic search and backtracking. It guarantees that the search produces the best solution obtained so far within the allotted computation time and provides better solutions when given more time. The effectiveness of the approach is evaluated on a comprehensive benchmark set of DL-prone FMS examples and the computational results show the superiority of the proposed approach over the previous works.

Genetic algorithm parameter optimisation using Taguchi method for a flexible manufacturing system scheduling problem

The flexible manufacturing system (FMS) scheduling problem is one of the most difficult NP-hard combinatorial optimisation problems. The exact solution of an FMS scheduling problem cannot be found within a reasonable amount of time, even for small size problems. Therefore, a metaheuristic algorithm is required to solve such a problem. The objective of this study is to develop a genetic algorithm (GA) approach to minimise makespan of the scheduling problem. A Taguchi orthogonal array is proposed instead of a full factorial experimental design for determining the parameters of the GA. The effects of the GA parameters on the minimum makespan values are determined and an analysis of variance is performed to investigate significance factors on the results.

Anytime heuristic search for scheduling flexible manufacturing systems: a timed colored Petri net approach

Given the fluctuations in demand, diversity in products, production flexibility requirements, and tight customer due dates, obtaining optimal production schedules is considered a complex research problem. This can drastically affect the survival of some manufacturing companies in today’s fiercely competitive global market. In a very demanding decision-making environment, scheduling problems are dealt with in a short-term horizon, in which computation time is a critical factor. Producing optimal solutions is practically impossible given the time limitations and the nondeterministic polynomial (NP)-hard nature of scheduling problems. This paper presents an anytime-heuristic search approach based on a simulation-optimization framework that combines evaluation methods (simulation) and search methods (optimization) through the reachability analysis (or state space) of timed colored Petri net models to schedule flexible manufacturing systems (FMS). The anytime search algorithm is capable of finding a first suboptimal solution very quickly and continuously improves the solution quality over time. If given enough computation time, the algorithm eventually converges to an optimal solution. The proposed approach is aimed at obtaining optimal or near-optimal solutions to FMS scheduling problems in relatively short computation times with the objective of minimizing the makespan. Its effectiveness is highlighted with excellent results that outperform previous methods on benchmark examples with flexible material handling systems, machine, and routing configurations. The approach can also serve as a decision support tool to assist production schedulers that require rapid and almost real-time responses to time-critical production scheduling on the shop floor.

Genetic algorithms for match-up rescheduling of the flexible manufacturing systems

► Rescheduling methods based on genetic algorithms to address arrivals of new orders. ► Genetic algorithms for match-up rescheduling with non-reshuffle strategy. ► Genetic algorithms for match-up rescheduling with reshuffle strategy. ► Non-reshuffle strategy manipulates the idle timeslots to assign new orders. ► Reshuffle strategy re-sequences the operations to accommodate new orders. Scheduling plays a vital role in ensuring the effectiveness of the production control of a flexible manufacturing system (FMS). The scheduling problem in FMS is considered to be dynamic in its nature as new orders may arrive every day. The new orders need to be integrated with the existing production schedule immediately without disturbing the performance and the stability of existing schedule. Most FMS scheduling methods reported in the literature address the static FMS scheduling problems. In this paper, rescheduling methods based on genetic algorithms are described to address arrivals of new orders. This study proposes genetic algorithms for match-up rescheduling with non-reshuffle and reshuffle strategies which accommodate new orders by manipulating the available idle times on machines and by resequencing operations, respectively. The basic idea of the match-up approach is to modify only a part of the initial schedule and to develop genetic algorithms (GAs) to generate a solution within the rescheduling horizon in such a way that both the stability and performance of the shop floor are kept. The proposed non-reshuffle and reshuffle strategies have been evaluated and the results have been compared with the total-rescheduling method.

Development of a simulation-based optimisation for controlling operation allocation and material handling equipment selection in FMS

Flexible manufacturing system (FMS) is described as a set of computerised numerical controlled machines, input–output buffers interconnected by automated material handling devices. This paper develops a bi-objective operation allocation and material handling equipment selection problem in FMS with the aim of minimising the machine operation, material handling and machine setup costs and maximising the machine utilisation. The proposed model is solved by a modified chaotic ant swarm simulation based optimisation (CAS2O) while applying pre-selection and discrete recombination operators is surveyed a capable method to simulate different experiments of FMS problems. A test problem is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method to solve the FMS scheduling problem.

LEARNING-BASED SCHEDULING OF FLEXIBLE MANUFACTURING SYSTEMS USING SUPPORT VECTOR MACHINES

Dispatching rules are usually applied to dynamically schedule jobs in flexible manufacturing systems (FMSs). Despite their frequent use a significant drawback is that the performance level of the rule is dictated by the current state of the manufacturing system. Because no rule is better than any other for every system state, it would be highly desirable to know which rule is the most appropriate for each given condition. To achieve this goal we propose a scheduling approach using support vector machines (SVMs). By using this technique and by analyzing the earlier performance of the system, “scheduling knowledge” is obtained whereby the right dispatching rule at each particular moment can be determined. Simulation results show that the proposed approach leads to significant performance improvements over existing dispatching rules. In the same way it is also confirmed that SVMs perform better than other traditional machine learning algorithms as the inductive learning when applied to FMS scheduling problem, due to their better generalization capability.

An MILP for scheduling problems in an FMS with one vehicle

This paper concerns the mathematical formulation and optimal solutions for the Flexible Manufacturing Systems Scheduling Problem (FMSSP) with one vehicle. This linear formulation differs from the previously published ones as it takes into account the maximum number of jobs allowed in the system, limited input/output buffer capacities, empty vehicle trips and no-move-ahead trips simultaneously. Our objective is to propose optimal solutions for small and medium-sized instances and to examine a number of commonly used assumptions and heuristics. Computational experiments are carried out on instances adapted from Bilge and Ulusoy [Bilge, Ü., Ulusoy, G., 1995. A time window approach to simultaneous scheduling of machines and material handling system in an FMS. Operations Research 43, 1058–1070] and the following heuristics are evaluated: FIFO (First In First Out) rules for input/output buffer management; and FIFO, SPT (Shortest Processing Time), STT (Shortest Travel Time) and MOQS (Maximum Outgoing Queue Size) rules concerning the vehicle. The consequences of classical assumptions are also studied: ignoring empty trips, ignoring no-move-ahead constraints, and ignoring vehicle-disjunction constraints. The numerical experiments provide a set of optimal solutions and allow to evaluate the performances of heuristic search schemes.

Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach

Abstract In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.

A Constraint Programming Approach to FMS Scheduling. Consideration of Storage and Transportation Resources

Flexible manufacturing systems (FMS) require efficient scheduling strategies to achieve their principal advantage of being able to combine high flexibility with high productivity. This paper presents a novel constraint programming (CP) formulation addressing the FMS scheduling problem for systems that work under the operational policy of part movement. The formulation addresses some critical features found in these problems and proposes a search strategy to speed/up the solution process. This work also presents an ad-hoc procedure, based on different initialisation points, that aims at making problems tractable when their size is big enough to make difficult the attainment of good quality solutions in a reasonable CPU time.

Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm

The increased use of flexible manufacturing systems (FMS) to efficiently provide customers with diversified products has created a significant set of operational challenges. Although extensive research has been conducted on design and operational problems of automated manufacturing systems, many problems remain unsolved. In particular, the scheduling task, the control problem during the operation, is of importance owing to the dynamic nature of the FMS such as flexible parts, tools and automated guided vehicle (AGV) routings. The FMS scheduling problem has been tackled by various traditional optimisation techniques. While these methods can give an optimal solution to small-scale problems, they are often inefficient when applied to larger-scale problems. In this work, different scheduling mechanisms are designed to generate optimum scheduling; these include non-traditional approaches such as genetic algorithm (GA), simulated annealing (SA) algorithm, memetic algorithm (MA) and particle swarm algorithm (PSA) by considering multiple objectives, i.e., minimising the idle time of the machine and minimising the total penalty cost for not meeting the deadline concurrently. The memetic algorithm presented here is essentially a genetic algorithm with an element of simulated annealing. The results of the different optimisation algorithms (memetic algorithm, genetic algorithm, simulated annealing, and particle swarm algorithm) are compared and conclusions are presented .

A global MILP model for FMS scheduling

Flexible manufacturing systems (FMS) require intelligent scheduling strategies to achieve their principal benefit — combining high flexibility with high productivity. A mixed-integer linear programming model (MILP) is presented here for FMS scheduling. The model takes a global view of the problem and specifically takes into account constraints on storage and transportation. Both of these constrained resources are critical for practical FMS scheduling problems and are difficult to model. The MILP model is explained and justified and its complexity is discussed. Two heuristic procedures are developed, based on an analysis of the global MILP model. Computational results are presented comparing the performance of the different solution strategies. The development of iterative global heuristics based on mathematical programming formulations is advocated for a wide class of FMS scheduling problems.

A fuzzy approach to operation selection

Abstract Production management and scheduling problems inflexible manufacturing systems (FMSs) are complicated, compared with those problems in job shops and transfer lines. Due to the flexibility of FMSs, alternative operations are common. This paper presents a fuzzy approach to operation and routing selection via simulation. The rule developed here is compared with some other well-known conventional rules like WINQ (work in queue) and SNQ (shortest number of jobs in queue).

Sequential Modelling of the Planning and Scheduling Problems of Flexible Manufacturing Systems

Research on the planning and scheduling issues of flexible manufacturing systems (FMS) has not been sparse. Most, if not all, studies, however, have focused on either developing the planning model or examining the performance of different scheduling rules. To date, the FMS planning and scheduling problems have not been studied together, though they are highly interrelated. This paper takes a first step to simultaneously address the planning and scheduling problems of flexible manufacturing systems. The problems are solved as a hierarchical process. We first integrate and formulate batching, loading, and routeing, three of the most important FMS planning problems, as a 0-1 mixed integer program. According to the optimal decisions provided by the integrated planning model, we then develop an off-line scheduling scheme that is capable of generating detail parts sequencing in the sequence independent environment (i.e. the operations are not constrained by a process sequence). Finally, we suggest several extensions and future research directions.

The classification of FMS scheduling problems

SUMMARY This paper proposes a classification scheme for scheduling problems in flexible manufacturing systems (FMSs) based on an analysis and discussion of scheduling decisions in an FMS. The classification scheme attempts for the first time to identify and describe all the major factors which affect the modelling of, and the solution to, FMS scheduling problems. It provides a systematic framework for the description and the analysis of FMS scheduling problems and for the development, evaluation and comparison of FMS scheduling approaches. Examples are given to demonstrate the usefulness of the proposed scheme.

Artificial neural networks for flexible manufacturing systems scheduling

The scheduling function plays an important role in flexible manufacturing systems. However, flexible manufacturing system scheduling is tremendously complex due to combinatorial explosion, technological constraints and goals to be achieved. Heuristics involving dispatching rules have been widely utilized to obtain good solutions. Nevertheless, it is difficult to identify any single dispatching rule as the best in all scheduling problems. Hence, a pattern-matching structure which can identify the “best” dispatching rule for a given problem will increase the probability of the generation of good schedules. In this paper, the utilization of artificial neural networks as pattern-matching devices for pattern-directed scheduling is reviewed. Several flexible manufacturing system scheduling problems and on-going research will be explained. Conclusions about the feasibility of an adaptive scheduler based on an integrated artificial intelligence framework and further research issues will be presented.

A constructive heuristic algorithm for concurrently selecting and sequencing jobs in an FMS environment

This paper deals with the concurrent solution of the loading and scheduling problems in a flexible manufacturing system ( FMS) environment. It is assumed that the FMS environment has production planned periodically and each job in the system has a number of operations to be processed on flexible machines. A heuristic approach using a constructive scheduling method is developed to solve the FMS loading and scheduling problems concurrently. The computational results are compared to an existing procedure that considers a hierarchical approach with a similar problem environment. The comparison study shows a significant improvement over the existing hierarchical procedure. This experiment indicates that a concurrent solution approach can solve the FMS loading and scheduling problems very effectively.