Problem In Flexible Manufacturing System Research Articles

An estimation of distribution algorithm for scheduling problem of flexible manufacturing systems using Petri nets

• This is the first report on applying estimation of distribution algorithm (EDA) to the studied problem. • A kind of PN-based deadlock controllers for FMSs is imbedded to exclude infeasible individuals. • An effective voting procedure is adopted to construct the probabilistic model of EDA. • The longest common subsequence is also embedded in the model for mining excellent genes. • A new modified variable neighborhood search is developed as an efficiency enhancement of EDA. Based on the place-timed Petri net models of flexible manufacturing systems (FMSs), this paper proposes a novel effective estimation of distribution algorithm (EDA) for solving the scheduling problem of FMSs. A candidate solution is represented as an individual with two sections: the first contains the route information while the second is a permutation with repetition for parts. The feasibility of individuals is checked and guaranteed by a highly permissiveness deadlock controller. A feasible individual is interpreted into a deadlock-free schedule while the infeasible ones are amended. The probabilistic model in EDA is constructed via a voting procedure. An offspring individual is then produced based on the model from a seed individual, and the set of seed individuals is extracted by a roulette method from the current population. The longest common subsequence is also embedded into the probabilistic model for mining good genes. A modified variable neighborhood search is applied on offspring individuals to obtain better solutions in their neighbors and hence to improve EDA’s performance. Computational results show that our proposed algorithm outperforms all the existing ones on benchmark examples for the studied problem. It is of important practice significance for the manufacturing of time-critical and multi-type products.

Non-dominated sorting biogeography-based optimization for bi-objective reentrant flexible manufacturing system scheduling

Scheduling in flexible manufacturing systems (FMS) is described as an NP-Hard problem. Its complexity has increased significantly in line with the development of FMS over the past years. This paper presents a non-dominated sorting biogeography-based optimization (NSBBO) for scheduling problem of FMS having multi loading-unloading and shortcuts infused in the reentrant characteristics. This model is formulated to identify the near optimal trade-off solutions capable of addressing the bi-objectives of minimization of makespan and total earliness. The goal is to simultaneously determine the best machine assignment and job sequencing to satisfy both objectives. We propose the development of NSBBO by substituting the standard linear function of emigration-immigration rate with three approaches based on sinusoidal, quadratic and trapezoidal models. A selection of test problems was examined to analyze the effectiveness, efficiency and diversity levels of the proposed approaches as compared to standard NSBBO and NSGA-II. The results have shown that the NSBBO-trapezoidal model performed favorably and is comparable to current existing models. We conclude that the developed NSBBO and its variants are suitable alternative methods to achieve the bi-objective satisfaction of reentrant FMS scheduling problem.

A suboptimal deadlock control policy for designing non-blocking supervisors in flexible manufacturing systems

This work aims to resolve deadlock problems in flexible manufacturing systems. A novel deadlock control policy in the frame of Petri nets formalism is presented for a class of generalized Petri nets, namely G-systems, which can usually model assembly and disassembly operations of multiple resource acquisition. Based on the concept of elementary siphons, all strict minimal siphons (SMS) in an uncontrolled net system are first divided into elementary siphons and dependent ones. After that, a set of linear inequality constraints expressed by state vectors can be formalized for elementary siphons. After being modified by utilizing the conjunctive/disjunctive resources upstream neighborhood policy, a set of generalized mutual exclusion constraints expressed by marking vectors can be obtained. Then, the additional monitors are only designed for elementary siphons, whose control depth variables can be obtained by solving a linear programming problem. As a result, the controllability of dependent ones can be ensured by properly supervising its elementary siphons. Finally, all strict minimal siphons are max′-controlled and no insufficiently marked siphon is generated, which indicates that the sequential resources are allocated reasonably to guarantee the absence of deadlock states. The proposed method can usually lead to a near-optimal non-blocking supervisor with simple structure. A G-system example prone to deadlocks is used to illustrate the applicability and the effectiveness of the proposed method.

Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

Solving machine loading problem of flexible manufacturing systems using a modified discrete firefly algorithm

An algorithmic framework for tool switching problems with multiple objectives

The tool switching problem is a classical and extensively studied problem in flexible manufacturing systems. The standard example is a CNC machine with a limited number of tool slots to which tools for drilling and milling have to be assigned, with the goal of minimizing the number of necessary tool switches and/or the number of machine stops over time. In this work we present a branch-and-bound based algorithmic framework for a very general and versatile formulation of this problem (involving arbitrary setup and processing times) that allows addressing both of these objectives simultaneously (or only of them), and that improves over several known approaches from the literature. We demonstrate the usefulness of our algorithm by rigorous theoretical analysis and by experiments with both large real-world and random instances.

Scheduling Problems of Flexible Manufacturing Systems: Review, Classification and Opportunities

This paper will relate initially to the scheduling characteristics of flexible manufacturing systems, and more specifically, the scheduling problems in flowshop and hybrid flowshop type systems representing interesting structures for the modeling of several problems resulting from the industrial world. Subsequently, we will focus our attention on the principal methods for solving scheduling problems, while presenting in the following the main published works for the aforementioned systems. Lastly, a comparative analysis will be carried out to highlight the fundamental ideas leading to the adoption of an effective approach capable of producing an optimal solution in a reasonable calculation time.

A hybrid discrete differential evolution algorithm for deadlock-free scheduling with setup times of flexible manufacturing systems

This paper proposes an effective hybrid discrete differential evolution (DDE) algorithm for solving a scheduling problem of flexible manufacturing systems (FMSs), where sequence-dependent setup times are considered. The objective is to find a deadlock-free schedule that minimizes the makespan. Based on the timed Petri net models of FMSs, a possible solution of the scheduling problem is represented as an individual that is a permutation with repetition of jobs. For the existence of deadlocks, most of the individuals cannot be directly decoded into feasible (live) schedules. Therefore, a deadlock controller is applied in the decoding scheme, and infeasible individuals are amended into feasible ones. Moreover, in order to overcome the premature convergence of DDE algorithm and improve solution quality, a variable neighbourhood search algorithm, which performs a systematic change of neighbourhood in solution searching, is adopted. Then a hybrid scheduling algorithm that combines a DDE with a variable neighbourhood search is presented. Computational results and comparison based on a variety of instances show the feasibility and superiority of the proposed algorithm.

On multistage approach for flexible routing in flexible manufacturing systems

Optimizing flexible routing in flexible manufacturing systems is one of the aspects that increase the efficiency of flexible manufacturing systems especially in dynamic environment systems. This article presents a multistage approach to solve flexible routing problem in flexible manufacturing systems. Multistage approach includes three stages; the first stage is a production simulation system to find the fitness of the flexible manufacturing systems corresponding to different products’ routes’ groups. The second stage proposes an artificial neural network approach to predict the products’ routes’ group in flexible manufacturing systems. The last stage combines genetic algorithms and artificial neural network to optimize proper routes for all product types in flexible manufacturing systems. Multistage approach proposed in this study aims to reduce the computational time required to obtain and optimize the flexible routes in flexible manufacturing systems. The results of this study show that the artificial neural network can be used efficiently to predict the flexible routes in flexible manufacturing systems and it reduces the computational time for routes’ optimization required with production simulation system. This characteristic improves the flexibility of flexible manufacturing systems since it can be adapted frequently against any change in production ratios.

Energy-aware integration of process planning and scheduling of advanced machining workshop

Increasing energy consumption of manufacturing industry demands novel approaches to achieve energy conservation and emission reduction. Most of the previous research efforts in this area focused more on analyzing manufacturing energy consumption of a process or that of a machine tool with less concern on the system level of advanced machining workshop, especially a flexible manufacturing system. In this article, a new energy-saving approach of flexible manufacturing system is put forward based on energy evaluation model for integration of process planning and scheduling problem in flexible manufacturing system (flexible manufacturing system-integration of process planning and scheduling). First, complying with feature precedence and other technological requirements, flexible manufacturing system -integration of process planning and scheduling is mapped as an asymmetric traveling salesman problem of which operations are provinces and candidate operations are cities belonging to different provinces. To evaluate the performance of each solution of the asymmetric traveling salesman problem, energy consumption evaluation criteria for flexible manufacturing system-integration of process planning and scheduling are established and three energy efficiency indicators are also provided to perform further analysis on manufacturing energy consumption, that is, part energy efficiency, machine tool energy efficiency and feasible solution energy efficiency. Then, a mutation-combined ant colony optimization algorithm is proposed to solve the flexible manufacturing system-integration of process planning and scheduling which combined roulette and mutation selection methods to pick out the next candidate operation. The pheromone trails associated with edges are released by the so-far-best ant or the iteration-best ant probabilistically to both keep the search directed and avoid converging to the local best. Finally, a case study of flexible manufacturing system in advanced machining workshop is employed to demonstrate the feasibility and applicability of this approach in three different scenarios and compared with the “process planning then scheduling” approach; energy consumption obtained by the proposed method drops 10.7%.

Design and optimisation of single row layout problem in flexible manufacturing systems using metaheuristic algorithms

Design and optimisation of single row layout problem in flexible manufacturing systems using metaheuristic algorithms

Performance Evaluation of Proposed Artificial Immune System and Improved Sheep Flock Heredity Algorithms for Single Row Layout Problem in Flexible Manufacturing System

One of the important concerns affecting the operating efficiency, system flexibility and system capacity of Flexible Manufacturing System is laid out the facilities. Designing the FLPs deals with arranging the machines in an order, it reduces the total flow between the machined in FMS. The most common layout utilized in FMS is a SRFL-[Single Row Facility Layout]. Several numbers of optimization algorithms are used, for a set of facilities to be arranged in such a manner to obtain an optimum distance between two facilities from SRFL problem. The key aim of this paper is, to optimize the material handling cost for SRFL system. In this paper, an AIS-[Artificial Immune System], Improved Sheep Flock Heredity Algorithm-[ISFHA] is proposed for SRFL problem, where simulated AIS results are compared with the simulated ISFHA results. For evaluating the performance, the benchmark problems are carried out and the simulation output shows the efficacy of the AIS and ISFHA.

Sheep flock heredity algorithm to solve the loop layout problem in flexible manufacturing system

The flexible manufacturing systems (FMS) layout problem is dealt with the arrangement of facilities in a given location. In FMS layouts, the loop layout is more desirable than the other because of their relative lower initial investment costs. The objective is to determine an assignment of machines that yields a minimum materials handling cost without congestion. In this paper, sheep flock heredity algorithm (SFHA) is proposed for solving the loop layout problem (LLP), and validated through benchmark problems. In SFHA, the pairwise mutation process is removed by single point mutation process which guarantees the feasibility of the solutions in the local search domain. Chromosomal crossover is removed by robust-replace heuristic to develop the global search. The computational results showed that the SFHA algorithm is producing good quality solutions for large sized problems compared with other algorithms.

Design and optimisation of single row layout problem in flexible manufacturing systems using metaheuristic algorithms

In the manufacturing process, single row layout is preferred for configurations in flexible manufacturing systems (FMS) because of their material handling systems for transporting materials economically. The objective is to minimise the total distance of the travel by the material handling device. In this paper, particle swarm optimisation (PSO) technique, genetic algorithm (GA), artificial immune system (AIS) algorithm are proposed for solving the single row unequal area layout problem which is validated through benchmark problems. The computational results showed that the AIS algorithm is producing good quality solutions for large sized problems when compared to other algorithms in a number of test problems. Comparative results show that the AIS algorithm produced better averaged objective function values (OFVs) than AIS and other algorithms for large sized problems.

An Improved Version of Discrete Particle Swarm Optimization for Flexible Job Shop Scheduling Problem with Fuzzy Processing Time

The fuzzy processing time occasionally exists in job shop scheduling problem of flexible manufacturing system. To deal with fuzzy processing time, fuzzy flexible job shop model was established in several papers and has attracted numerous researchers’ attention recently. In our research, an improved version of discrete particle swarm optimization (IDPSO) is designed to solve flexible job shop scheduling problem with fuzzy processing time (FJSPF). In IDPSO, heuristic initial methods based on triangular fuzzy number are developed, and a combination of six initial methods is applied to initialize machine assignment and random method is used to initialize operation sequence. Then, some simple and effective discrete operators are employed to update particle’s position and generate new particles. In order to guide the particles effectively, we extend global best position to a set with several global best positions. Finally, experiments are designed to investigate the impact of four parameters in IDPSO by Taguchi method, and IDPSO is tested on five instances and compared with some state-of-the-art algorithms. The experimental results show that the proposed algorithm can obtain better solutions for FJSPF and is more competitive than the compared algorithms.

A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems

This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm to solve the deadlock-free scheduling problem of flexible manufacturing systems (FMSs) that are characterized with lot sizes, resource capacities, and routing flexibility. Based on the timed Petri net model of FMS, a random-key based solution representation is designed to encode the routing and sequencing information of a schedule into one particle. For the existence of deadlocks, most of the particles cannot be directly decoded to a feasible schedule. Therefore, a deadlock controller is applied in the decoding scheme to amend deadlock-prone schedules into feasible ones. Moreover, two improvement strategies, the particle normalization and the simulated annealing based local search, are designed and incorporated into particle swarm optimization algorithm to enhance the searching ability. The proposed HPSO is tested on a set of FMS examples, showing its superiority over existing algorithms in terms of both solution quality and robustness.

Reentrant FMS scheduling in loop layout with consideration of multi loading-unloading stations and shortcuts

The scheduling problem in flexible manufacturing systems (FMS) environment with loop layout configuration has been shown to be a NP-hard problem. Moreover, the improvement and modification of the loop layout add to the difficulties in the production planning stage. The introduction of multi loading-unloading points and turntable shortcut resulted on more possible routes, thus increasing the complexity. This research addressed the reentrant FMS scheduling problem where jobs are allowed to reenter the system and revisit particular machines. The problem is to determine the optimal sequence of the jobs as well as the routing options. A modified genetic algorithm (GA) was proposed to generate the feasible solutions. The crowding distance-based substitution was incorporated to maintain the diversity of the population. A set of test was applied to compare the performance of the proposed approach with other methods. Further computational experiments were conducted to assess the significance of multi loading-unloading and shortcuts in reducing the makespan, mean flow time, and tardiness. The results highlighted that the proposed model was robust and effective in the scheduling problem for both small and large size problems.

Optimization of Linear Layout Problem in Flexible Manufacturing System Using Evolutionary Algorithms - Case Study

Linear layout is the commonly major preferred arrangement of the flexible manufacturing systems (FMS). The proposed enhanced sheep flock heredity algorithm to solving the unequal area linear layout problem through the real case study problem. The proposed model is to minimize the transportation cost with non-overlapping. Computational results show that proposed sheep flock heredity algorithm (SFHA) can obtain better than particle swarm optimization (PSO) and existing method.

An Improved Particle Swarm Optimization Approach for Solving Machine Loading Problem in Flexible Manufacturing System

Machine loading problem in flexible manufacturing system is considered as a vital pre-release decision. Loading problem is concerned with assignment of necessary operations of the selected jobs to various machines in an optimal manner to minimize system unbalance under technological constraints of limited tool slots and operation time. Such a problem is combinatorial in nature and found to be NP-hard; thus, finding the exact solutions is computationally intractable and becomes impractical as the problem size increases. To alleviate above limitations, a meta-heuristic approach based on particle swarm optimization (PSO) has been proposed in this paper to solve the machine loading problem. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast toward global optimum and hence reduce computational effort further. Twenty benchmark problems available in open literature have been solved using the proposed heuristic. Comparison between the results obtained by the proposed heuristic and the existing methods show that the results obtained are encouraging at significantly less computational effort.

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.

Segment set-based part input sequencing in flexible manufacturing systems

In competitive and uncertain business environments, manufacturing firms face various challenges requiring quick response, customisation and cost effectiveness to sustain competitive advantage. This paper proposes a segment set-based approach for the part input sequencing problem of flexible manufacturing systems (FMSs). The segment set theorems are built for the development of the segment set-based approach for the problem. A numerical study is done using simulation, and validated through statistical analyses. Our approach results in the significant performance improvement of the FMSs. Managerial implications are discussed. Further research is also provided.