Problem In Flexible Manufacturing System Research Articles

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.

A Maximally Permissive Deadlock Prevention Policy for FMS Based on Petri Net Siphon Control and the Theory of Regions

This paper addresses the deadlock problems in flexible manufacturing systems (FMS) by using a Petri net siphon control method and the theory of regions. The proposed policy consists of two stages. The first one, called siphons control, is to add, for every siphon that we identify, a monitor to the original net model such that it is optimally invariant controlled. In the second stage, the theory of regions is utilized to derive the net supervisors such that deadlocks can be prevented. The first-stage work significantly lowers the computational cost compared with the approach where the theory of regions is used alone. An FMS example is presented to illustrate the technique. By varying the markings of given net structures, this paper shows its computational advantages.

A dynamic heuristic-based algorithm to part input sequencing in flexible manufacturing systems for mass customization capability

In the increasingly competitive global markets, enterprises face challenges in responding to customer orders quickly, as well as producing customized products cost-effectively. This paper proposes a dynamic heuristic-based algorithm for the part input sequencing problem of flexible manufacturing systems (FMSs) in a mass customization (MC) environment. The FMS manufactures a variety of parts, and customer orders arrive dynamically with order size as small as one. Segmental set functions are established in the proposed algorithm to apply the strategy of dynamic workload balancing, and the shortest processing time (SPT) scheduling rule. Theoretical analysis is performed and the effectiveness of the algorithm in dynamic workload balancing under the complex and dynamic environment is proven. The application of the algorithm is illustrated by an example. The potential of its practical applications to the FMSs in make-to-order (MTO) supply chains is also discussed. Further research is provided.

Modified particle swarm optimization for solving machine-loading problems in flexible manufacturing systems

In flexible manufacturing systems (FMSs), the loading problem is considered as a vital pre-release decision because its operational effectiveness largely depends on a good quality solution to the loading problem. Difficulties arise in obtaining optimal solutions to such problems because of its combinatorial and NP-hard nature. In the past, numerous techniques have been suggested and found to be efficient, but they take long computational times when the problem size increases. In order to address the above issues, a meta-heuristic approach based on particle swarm optimization (PSO) has been proposed in this paper to improve the solution quality and reduce the computational effort. However, PSO has the tendency to suffer from premature convergence. Therefore, the PSO algorithm has been modified through the introduction of a mutation operator to improve efficiency of the algorithm. The proposed algorithm attempts to minimize the system unbalance while satisfying the technological constraints, such as the availability of machining time and tool slots. The proposed algorithm produces promising results in comparison to existing methods for ten benchmark instances available in the FMS literature.

The effect of strengthened linear formulations on improving the lower bounds for the part families with precedence constraints problem

The part families with precedence constraints problem (PFP) arises in industry, when flexible manufacturing systems are designed within a group technology approach. The aim of this problem is to arrange parts into families by imposing capacity constraints, concerning both the number of parts and processing times, besides precedence constraints in the building of families. Mixed binary linear programming formulations for the PFP are presented. In endeavoring to strengthen the linear relaxations for the formulations, and hence generate better lower bounds for the optimal value of PFP, some valid inequalities, based on the properties of the problem, were deduced. The lower bounds obtained significantly improved through the very weak, frequently null, bounds resulting from the original linear relaxation. Moreover, it may be concluded that these models can be a useful methodology to enforce the performance of any branch-and-bound for this very important problem in flexible manufacturing systems.

Fast clonal algorithm

The aim of this paper is to design an efficient and fast clonal algorithm for solving various numerical and combinatorial real-world optimization problems effectively and speedily, irrespective of its complexity. The idea is to accurately read the inherent drawbacks of existing immune algorithms (IAs) and propose new techniques to resolve them. The basic features of IAs dealt in this paper are: hypermutation mechanism, clonal expansion, immune memory and several other features related to initialization and selection of candidate solution present in a population set. Dealing with the above-mentioned features we have proposed a fast clonal algorithm (FCA) incorporating a parallel mutation operator comprising of Gaussian and Cauchy mutation strategy. In addition, a new concept has been proposed for initialization, selection and clonal expansion process. The concept of existing immune memory has also been modified by using the elitist mechanism. Finally, to test the efficacy of proposed algorithm in terms of search quality, computational cost, robustness and efficiency, quantitative analyses have been performed in this paper. In addition, empirical analyses have been executed to prove the superiority of proposed strategies. To demonstrate the applicability of proposed algorithm over real-world problems, Machine-loading problem of flexible manufacturing system (FMS) is worked out and matched with the results present in literature.

Modified immune algorithm for job selection and operation allocation problem in flexible manufacturing systems

The advent of automated manufacturing systems and the variability in demand pattern have forced the manufacturers to increase the flexibility and efficiency of their automated systems to stay competitive in the dynamic market. Loading decisions play an important role in determining the efficiency of manufacturing systems. Machine loading problems in flexible manufacturing systems (FMSs) are known to be NP-hard problems. Although some NP-hard problems could still be optimized for very small instances, machine loading complexity is so extensive that even small problems take excessive computational time to reach the optimal solution. To ease the tedious computations, and to get a good solution for large problems, this paper develops a special Immune Algorithm (IA) named ‘Modified immune algorithm (MIA)’. IA is a suitable method due to its self learning capability and memory acquisition. This paper improves some issues inherent in existing IAs and proposes a more effective immune algorithm with reduced memory requirements and reduced computational complexity. In order to verify the efficacy and robustness of the proposed algorithm, the paper presents comparisons to existing immune algorithms with benchmark functions and standard data sets related to the machine loading problem. In addition proposed algorithm has been tested at different noise level to examine the efficiency of algorithm on different platforms. The comparisons show consistently that the proposed algorithm outperforms the existing techniques. For all machine loading dataset proposed algorithm has shown good results as compared to the best results reported in the literature.

Using data continualization and expansion to improve small data set learning accuracy for early flexible manufacturing system (FMS) scheduling

Knowledge derived from limited data gathered in the early manufacturing stages is usually too fragile for a flexible manufacturing system (FMS). Unfortunately, production decisions have to be made quickly in a competitive environment. In a previous study, a strategy using continuous data and domain external expansion methods under a known data domain range was proposed to solve the so-called small data set learning problem in FMS. The present paper goes further in seeking a quantitative method to determine the range of domain external expansion under unknown domain bounds. The research considers the data bias phenomenon that often occurs in small data sets and provides a method for its adjustment. Beyond this, the study also compares the learning results among three types of membership functions (Bell, Trapezoid, Triangular) for data fuzzification. The results show that the proposed approach can advance the learning accuracy of a broad range of applications.

Scheduling multi-operation jobs in partially overlapping systems

This paper deals with the important and difficult scheduling problem of flexible manufacturing systems (FMS) with multi-operation parts and machines with partially overlapping capabilities. The scheduling problem of partially overlapping systems is shown to be a general form of many classical scheduling problem models and can be applied to several application domains such as distributed computing systems, machinery systems and/or robots. The quality of a schedule in this paper is measured by the minimization of part transfers and the makespan. Our approach adopts a divide-and-conquer strategy. First the problem is separated into three sub-problems based on the characteristics of the model. Then, implicit enumeration and heuristics algorithms are developed for the sub-problems. Experimental results show that the proposed solutions perform ‘well’ for a set of open shop benchmark problems and practically solve the partially overlapping scheduling problems at various sizes.

A simulated annealing algorithm based on a closed loop layout for facility layout design in flexible manufacturing systems

Facility layout design problems in flexible manufacturing systems (FMS) differ from traditional facility design problems and are more difficult to solve because there are more constraints that must be considered (i.e., cell shape, cell orientation, pick-up and drop-off point positions). The focus of this paper is on the closed loop type layout, which is based on a predetermined layout pattern. This layout pattern is commonly found in manufacturing settings since it requires a simplified material handling system configuration and since it facilitates a modular and expandable layout structure. The open-field type layout problem, where there is no predetermined layout pattern, may potentially have a more efficient configuration, since there are fewer restrictions. However, this problem is more difficult to solve and may result in configurations that are not desirable due to the lack of structure or modularity. The procedure developed in this paper improves the efficiency of the closed loop configuration by changing the rectangular shape of the loop to different sizes. In many cases, the resulting closed loop layout proves to be as efficient as the open field layout. A simulated annealing procedure (SA-CL) is used to search for the configuration that minimizes the total material handling costs. A comparison of the results with existing methods indicates that, based on solution quality and computational time, the SA-CL offers a favourable alternative for efficient layout design.

Heuristic solution approaches for combined-job sequencing and machine loading problem in flexible manufacturing systems

Job sequencing and machine loading are two vital and interrelated production planning problems in flexible manufacturing systems (FMSs). In this research, attempts have been made to address the combined job sequencing and machine loading problem using minimization of system unbalance and maximization of throughput as objective functions, while satisfying the constraints related to available machining time and tool slots. This research describes two heuristics to deal with the problems. Heuristic I uses predetermined fixed job sequencing rules as inputs for operation allocation decision on machines, whereas heuristic II uses genetic algorithm based approach for simultaneously addressing job sequences and operation machine allocation issues. Performance of these heuristics has been tested on problems representing three different FMS scenarios. Heuristic II (Genetic algorithm based) has been found more efficient and outperformed heuristic I in terms of solution quality.

Capacity and tool allocation problem in flexible manufacturing systems

This study addresses an allocation problem that arises in the semiconductor industry and flexible manufacturing systems where the tools should be loaded on computer numerical controlled (CNC) machines to process a number of operations. The time and tool magazine capacities of the CNC machines and the number of available tools of each type are limited. The objective is to maximize the total weight of operation assignments. We present a mixed integer programming formulation of the problem and show that the problem is NP-hard in the strong sense. We show that the linear programming relaxation upper bound dominates the best possible Lagrangean relaxation upper bound. We develop several lower bounding procedures and a lower bounding procedure using Lagrangean relaxation approach. Our computational results show that the upper and lower bounding procedures produce near-optimal solutions in reasonable times.

Application of genetic algorithms with dominant genes in a distributed scheduling problem in flexible manufacturing systems

Multi-factory production networks have increased in recent years. With the factories located in different geographic areas, companies can benefit from various advantages, such as closeness to their customers, and can respond faster to market changes. Products (jobs) in the network can usually be produced in more than one factory. However, each factory has its operations efficiency, capacity, and utilization level. Allocation of jobs inappropriately in a factory will produce high cost, long lead time, overloading or idling resources, etc. This makes distributed scheduling more complicated than classical production scheduling problems because it has to determine how to allocate the jobs into suitable factories, and simultaneously determine the production scheduling in each factory as well. The problem is even more complicated when alternative production routing is allowed in the factories. This paper proposed a genetic algorithm with dominant genes to deal with distributed scheduling problems, especially in a flexible manufacturing system (FMS) environment. The idea of dominant genes is to identify and record the critical genes in the chromosome and to enhance the performance of genetic search. To testify and benchmark the optimization reliability, the proposed algorithm has been compared with other approaches on several distributed scheduling problems. These comparisons demonstrate the importance of distributed scheduling and indicate the optimization reliability of the proposed algorithm.

A reallocation-based heuristic to solve a machine loading problem with material handling constraint in a flexible manufacturing system

In this research, a comprehensive heuristic solution is evolved to include all the three segments of a machine loading problem of flexible manufacturing systems. These are part type sequence determination, operation allocation on machines and reallocation of part types. The machine loading problem has been formulated keeping in view two well-known objective functions, namely minimization of system unbalance and maximization of throughput. In addition to constraints related to machine time and tool slots availability, this research considers one more constraint related to material handling, i.e. number of AGVs available in the system. The part type sequence determination has been carried out by evaluating the contribution of part type to characteristics such as batch size, total processing time, and the AGV movement. Decisions pertaining to operation allocation are taken based on the enumeration of priority index. An iterative reallocation procedure has been devised to ensure minimum positive system unbalance and maximum throughput. A test problem is simulated to represent the real shop floor environment and the same has been solved using various steps of the proposed algorithm. Extensive computational experiments have been carried out to assess the performance of the proposed heuristic and validate its relevance to solve the real shop floor problems.

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.

Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system

Some of the important planning problems that need realistic modelling and a quicker solution, especially in automated manufacturing systems, have recently assumed greater significance. In real-life industrial applications, the existing models considering deterministic situations fail as the true language adopted by foremen and technicians are fuzzy in nature. Thus, to map the situation on the shop floor to arrive at a real-time solution of this kind of tactical planning problem, it is essential to adopt fuzzy-based multi-objective goals so as to express the target desired by the management of business enterprises. This paper presents a fuzzy goal-programming approach to model the machine tool selection and operation allocation problem of flexible manufacturing systems. The model is optimized using an approach based on artificial immune systems and the results of the computational experiments are reported.

Multileveled Symbiotic Evolutionary Algorithm: Application to FMS Loading Problems

Recently, there has been an increasing effort to address integrated problems that are composed of multiple interrelated sub-problems. Many integrated problems in the real world have a multileveled structure. This paper proposes a new method of solving integrated and multileveled problems. The proposed method is named Multileveled Symbiotic Evolutionary Algorithm (MSEA). MSEA is an evolutionary algorithm that imitates the process of symbiotic evolution, including endosymbiotic evolution. It is designed to promote the balance of population diversity and population convergence. To verify its applicability, MSEA is applied to loading problems of flexible manufacturing systems with various flexibilities. Through computer experiments, the features of MSEA are shown and their effects on search capability are discussed. The proposed algorithm is also compared with existing ones in terms of solution quality. The experimental results confirm the effectiveness of our approach.

Multi-agent-based approach to solve part selection and task allocation problem in flexible manufacturing systems

Agent technology is currently being considered as an important approach for developing intelligent manufacturing systems. It offers a new way of thinking about many of the classical problems in manufacturing engineering. A multi-agent-based approach for solving the part allocation problems in flexible manufacturing systems (FMS) is presented that can easily cope with the dynamic environment. Four agents were involved in carrying out the tasks of allocating parts on different machines: communicator, machine, part and material handling device (MHD). Upon arrival in the manufacturing facility, the part informs the communicator agent about the task requirements. The communicator agent divides the task into subtasks and sends a call-for-bids message to the machine and MHD agents. Each machine responds in accordance with its process capabilities and buffer limit. This response may be for the whole task or for one or more subtasks and it contains the price and cost details for these subtasks along with the performance index and acceptance ratio of the machine. The final allocation is made based on the objective function that includes processing and transportation costs and time. An algorithm is presented that is used by the communicator agent for allocating parts to different machines. An illustrative example is given to solve the task allocation on five machines, with each machine having different performance index and acceptance ratio.

Network-based hybrid genetic algorithm for scheduling in FMS environments

Scheduling in flexible manufacturing systems (FMS) must take account of the shorter lead-time, the multiprocessing environment, the flexibility of alternative workstations with different processing times, and the dynamically changing states. The best scheduling approach, as described here, is to minimize makespan t M, total flow time t F, and total tardiness penalty p T. However, in the case of manufacturing system problems, it is difficult for those with traditional optimization techniques to cope with this. This article presents a new flow network-based hybrid genetic algorithm (hGA) approach for generating static schedules in a FMS environment. The proposed method is combined with the neighborhood search technique in a mutation operation to improve the solution of the FMS problem, and to enhance the performance of the genetic search process. We update the change in swap mutation and the local search-based mutation ration. Numerical experiments show that the proposed flow network-based hGA is both effective and efficient for FMS problems.

An evolutionary algorithm-based decision support system for managing flexible manufacturing

The high investment cost of flexible manufacturing systems (FMS) requires their management to be effective and efficient. The effectiveness in managing FMSs includes addressing machine loading, scheduling parts and dispatching vehicles and the quality of the solution. Therefore the problem is inevitably multi-criteria, and decision maker's judgement may contribute to the quality of the solution and the systems's performance. On the other hand, each of these problems of FMS is hard to optimize due to the large and discrete solution spaces (NP-hard). The FMS manager must address each of these problems hierarchically (separately) or simultaneously (aggregately) in a limited time. The efficiency of the management is related to the response time.Here we propose a decision support system that utilizes an evolutionary algorithm (EA) with a memory of “good” past experiments as the solution engine. Therefore, even in the absence of an expert decision maker the performance of the solution engine and/or the quality of the solutions are maintained.The experiences of the decision maker(s) are collected in a database (i.e., memory-base) that contains problem characteristics, the modeling parameters of the evolutionary program, and the quality of the solution. The solution engine in the decision support system utilizes the information contained in the memory-base in solving the current problem. The initial population is created based on a memory-based seeding algorithm that incorporates information extracted from the quality solutions available in the database. Therefore, the performance of the engine is designed to improve following each use gradually. The comparisons obtained over a set of randomly generated test problems indicate that EAs with the proposed memory-based seeding perform well. Consequently, the proposed DSS improves not only the effectiveness (better solution) but also the efficiency (shorter response time) of the decision maker(s).