Job Shop Flexible Manufacturing Systems Research Articles

Adaptive fuzzy-genetic algorithm operators for solving mobile robot scheduling problem in job-shop FMS environment

Flexible Manufacturing Systems (FMS) is known as one of the recurring themes that possess these promising characteristics with a synergistic combination of productivity-efficiency transport and flexibility through a number of machine tools alongside other material handling devices. In FMS, mobile robots are commonly deployed in material handling system for the purpose of increasing the efficiency and productivity of the manufacturing process. A reliable, efficient, and optimal scheduling is the most important in manufacturing system. The scheduling problems can become highly complex, especially in large-scale systems with numerous tasks and constraints. Thus, schedule optimization becomes crucial to enhance target performance by determining the best allocations and sequences of resources under specified constraints. Recently, Genetic Algorithm (GA) is a remarkably applicable search algorithm to solve scheduling problems to the way that near optimal could be found. While the performance of GA much depends on the selection of the main parameters, a standard GA may suffer from the issue of premature convergence due to the lack of control on its parameters especially crossover and mutation operators. As there is no specific method or way to tune these parameters, the algorithm is prone to converge on the local optimum, thereby leading to performance degradation. To overcome such flaw, this paper proposed an improved Genetic Algorithm using an adaptive Fuzzy Logic to control crossover and mutation operators (FGAOC) for the solution to the NP-hard problem of scheduling mobile robot within Job-Shop FMS environment. The proposed algorithm has been evaluated in several case studies such as small and large-scale problem, various numbers of mobile robots and the 40-test benchmark problem. The results have demonstrated that the proposed FGAOC has delivered a good performance in exploration-exploitation activities with better solution quality.

Genetic Algorithm for Solving Mobile Robot Scheduling Problem in Flexible Manufacturing Environment

The scheduling genuinely a complex process, aimed at optimizing operational activities in pursuit of one or more objectives by leveraging production data which may include previous schedules. The scheduling problem in Flexible Manufacturing System (FMS) is commonly categorized as Nondeterministic Polynomial (NP)-hard combinatorial optimization problems and it remains as an endure problem to industrial practitioners and researchers. As part of real production scheduling, once one task is finished processing on a machine, transportation equipment such as mobile robot transports the completed task to the next machine. The problem of scheduling mobile robot in FMS pertains to the task allocation process for the robots, considering the transportation costs and the time spent to complete all operations. In recent years, Genetic Algorithm (GA) has been a remarkably effective search algorithm for solving a wide range of scheduling problems in a manner that achieves near-optimal solutions. This paper presents the metaheuristic techniques, specifically genetic algorithm, to address the NP-hard scheduling problem of two identical mobile robots in Job-Shop FMS environment. The algorithm is developed with the aim of finding feasible solutions to the integrated problem by minimizing the amount of time it takes to finish all tasks, commonly referred to as makespan. The performance of GA is evaluated with some numerical experiments which is executed via Matlab software. The scheduling results shows that the developed GA able to obtained the near-optimal solution of minimal makespan and converge within a short period of time.

A Genetic-Algorithm-Based Approach for Optimizing Tool Utilization and Makespan in FMS Scheduling

This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry.

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

This article reviews the state of the art of prediction and optimization for sequence-driven scheduling in job shop flexible manufacturing systems (JS-FMSs). The objectives of the article are to (1) analyze the literature related to algorithms for sequencing and scheduling, considering domain, method, objective, sequence type, and uncertainty; and to (2) examine current challenges and future directions to promote the feasibility and usability of the relevant research. Current challenges are summarized as follows: less consideration of uncertainty factors causes a gap between the reality and the derived schedules; the use of stationary dispatching rules is limited to reflect the dynamics and flexibility; production-level scheduling is restricted to increase responsiveness owing to product-level uncertainty; and optimization is more focused, while prediction is used mostly for verification and validation, although prediction-then-optimization is the standard stream in data analytics. In future research, the degree of uncertainty should be quantified and modeled explicitly; both holistic and granular algorithms should be considered; product sequences should be incorporated; and sequence learning should be applied to implement the prediction-then-optimization stream. This would enable us to derive data-learned prediction and optimization models that output accurate and precise schedules; foresee individual product locations; and respond rapidly to dynamic and frequent changes in JS-FMSs.

More MILP models for integrated process planning and scheduling

The integration of process planning and scheduling is important for an efficient utilisation of manufacturing resources. In general, there are two types of models for this problem. Although some MILP models have been reported, most existing models belong to the first type and they cannot realise a true integration of process planning and scheduling. Especially, they are completely powerless to deal with the cases where jobs are expressed by network graphs because generating all the process plans from a network graph is difficult and inefficient. The network graph-specific models belong to the other type, and they have seldom been deliberated on. In this research, some novel MILP models for integrated process planning and scheduling in a job shop flexible manufacturing system are developed. By introducing some network graph-oriented constraints to accommodate different operation permutations, the proposed models are able to express and utilise flexibilities contained in network graphs, and hence have the power to solve network graph-based instances. The established models have been tested on typical test bed instances to verify their correctness. Computational results show that this research achieves the anticipant purpose: the proposed models are capable of solving network graph-based instances.

Evaluating the Effect of Part-mix and Routing Flexibility on FMS Performance

The flexibility in manufacturing environment is implemented to respond proactively and cost effectively towards dynamic production requirements. Previous literature has explored eight common types of flexibility in manufacturing. This paper presents a simulation study based on Part-mix and Routing Flexibility and their impact on system performance of a typical Flexible Manufacturing System (FMS). ARENA based computer simulation models have been developed to investigate the effect of flexibility on the performance under various control strategies (sequencing and dispatching rules). The performance measure used in this paper is make-span time. Unbalanced type job shop FMS consist of four CNC general purpose machining centers and one loading/ unloading station is considered for the illustration of simulation models. These demo simulation models are intended for deterministic environment, and the use of discrete event simulation approach is adopted to offer a focus on dynamic behavior of FMS operating under different manufacturing strategies and flexibility types. This simulation study, under dynamic environment, may be very useful for practitioners. Management can obtain better insight and guidelines for determining various decisions relating to process and operations improvement and investment in new facility.

Filtered-beam-search-based algorithm for dynamic rescheduling in FMS

In the practical production process of a flexible manufacturing system (FMS), unexpected disturbances such as rush orders arrival and machine breakdown may inevitably render the existing schedule infeasible. This makes dynamic rescheduling necessary to respond to the disturbances and to improve the efficiency of the disturbed FMS. Compared with the static scheduling, the dynamic rescheduling relies on more effective and robust search approaches for its critical requirement of real-time optimal response. In this paper, a filtered-beam-search (FBS) -based heuristic algorithm is proposed to solve the dynamic rescheduling problem in a large and complicated job shop FMS environment with realistic disturbances. To enhance its performance, the proposed algorithm makes improvement in the local/global evaluation functions and the generation procedure of branches. With respect to a due date-based objective (weighted quadratic tardiness), computational experiments are studied to evaluate the performance of the proposed algorithm in comparison with those of other popular methods. The results show that the proposed FBS-based algorithm performs very well for dynamic rescheduling in terms of computational efficiency and solution quality.

Modelling and simulation of an intelligent flexible manufacturing system via high-level object Petri nets (HLOPN)

A dynamic model of a flexible manufacturing system (FMS) is considered based on the information characteristics of system infrastructure. According to the physical architecture and working requirements, a software framework for FMS modelling and simulation is proposed to describe the parallel flow of different work-pieces in the shop. The model is based mainly on the concepts of both object-oriented programming and high-level Petri net methods. A material transportation protocol is designed to realize the respective processing of routine orders of work-pieces within the main resources of the FMS shop. Using a specially designed protocol, a fully automated job shop FMS can be achieved. An FMS simulation tool that can show the dynamic process of all the system resources during task execution is finally introduced.

The application of Adaptive Genetic Algorithms in FMS dynamic rescheduling

Uncertainties in the production process would inevitably result in deviations from the existing schedule in flexible manufacturing systems (FMSs). This paper tries to study the problem in an environment with realistic interruptions and a requirement of time-restricted response in rescheduling. In our paper, the rescheduling system is based on the records of a dynamic database (DDB). It is able to reform the up-to-date status of a disturbed system via summarizing the remaining resources and works in process (WIPs) precisely. By using it as the new initial state, the new schedule is configured smoothly in conjunction with the existing schedule to improve the efficiency of FMS at this critical instant. Considering both speed and economic benefit, an adaptive genetic algorithm (AGA) is proposed for finding the new sub-optimal schedule of a large and complicated job shop FMS shortly after the interruption occurred. The AGA is designed to prevent the premature convergence and refine the performance of genetic algorithms in re-scheduling. During the AGA evolution process, the probabilities of crossover and mutation are varied, depending on both the fitness value and the normalized fitness distances between solutions. With help from DDB, the FMS scheduling model and AGA, the results obtained in the test examples of rescheduling are quite satisfactory.

A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling

This paper addresses the integrated problem of process planning and scheduling in job shop flexible manufacturing systems. Due to production flexibility, it is possible to generate many feasible process plans for each job. The two functions of process planning and scheduling are tightly interwoven with each other. The optimality of scheduling depends on the result of process planning. The integration of process planning and scheduling is therefore important for an efficient utilization of manufacturing resources. In this paper, a new method using an artificial intelligent search technique, called symbiotic evolutionary algorithm, is presented to handle the two functions at the same time. For the performance improvement of the algorithm, it is important to enhance population diversity and search efficiency. We adopt the strategies of localized interactions, steady-state reproduction, and random symbiotic partner selection. Efficient genetic representations and operator schemes are also considered. While designing the schemes, we take into account the features specific to each of process planning and scheduling problems. The performance of the proposed algorithm is compared with those of a traditional hierarchical approach and an existing cooperative coevolutionary algorithm. The experimental results show that the proposed algorithm outperforms the compared algorithms. Scope and purpose The traditional job shop scheduling literature generally assumed that there is a single feasible process plan for each job. This implies that no flexibility in the process plan is considered. Today's many manufacturing systems are becoming increasingly flexible in processing operations. In such systems, most jobs may have a large number of feasible process plans. Although process planning and job shop scheduling are highly related with each other, many prior researches considered them separately or sequentially. Process planning and scheduling involve the assignment of manufacturing resources to production tasks. For an efficient use of manufacturing resources, process planning needs to be integrated with scheduling. This paper presents a new method that can efficiently solve the problem of integrating process planning with scheduling in flexible job shops. Artificial coevolution is becoming a prospective approach to solving such a problem that it consists of multiple sub-problems interrelated with each other. A coevolutionary algorithm, called symbiotic evolutionary algorithm, is employed as the underlying framework. Several strategies and genetic operators are provided to improve the performance of the proposed algorithm. Extensive computational experiments are carried out to verify the efficacy of our algorithm.

Scheduling approaches for random job shop flexible manufacturing systems

This research examines the influences that scheduling schemes and the degree of routeing flexibility have on random, job shop flexible manufacturing systems within a static environment. The first factor in the experiment includes three scheduling schemes. Two of these schemes are off-line schemes which schedule many operations prior to actual production. The first of the off-line schemes establishes an overall optimal solution, and the second off-line scheme decomposes the problem into a loading subproblem and a resulting scheduling subproblem and finds optimal solutions to both subproblems. The third scheme uses control policies and dispatching rules to establish a schedule in a real-time mode. The number of alternative machine options (i.e., the degree of routeing flexibility) is a second factor in the experiment and includes eleven levels. In addition, the effects of interaction between scheduling and routeing flexibility on performance are explored.

Simulation modeling of batch job shop type flexible manufacturing systems

In this paper, a simulation based scheduling algorithm for the performance evaluation of Batch Job Shop Flexible Manufacturing Systems (BJSFMS) has been developed. The simulation experiments have been conducted for a system with six machines processing six different type of jobs whereas the system performance has been evaluated on the basis of Maximum Makespan and Average Machine Utilization. Models for the system performance indices have been developed by the use of multiple regression analysis technique. It has been concluded that processing time of jobs and their transportation times are interdependent operating parameters and the average machine utilization becomes virtually constant when the transportation time exceeds the processing time.