Problem In Manufacturing System Research Articles

Uncertain association rule mining algorithm for the cell formation problem in cellular manufacturing systems

Although data mining has enjoyed popularity in recent years with advances in both academia and industry, the application of data mining to cellular manufacturing, one of the most powerful management innovations in job-shop or batch-type production, is still under-utilized. Based on association rule mining, Chen initially developed a cell formation approach. One problem of such a cell formation algorithm is that various real-life production factors were ignored. In this paper we propose a new cell formation algorithm by way of uncertain association rule mining. The proposed algorithm incorporates several key production factors, such as operation sequence, production volume, batch size, alternative process routings, cell size, the number of cells, and the path coefficient of material flow. The efficacy and efficiency of the proposed algorithm were tested using several numerical problems.

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

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

Beam search algorithm for capacity allocation problem in flexible manufacturing systems

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

Graph theory-based approach to optimize single-product flow-line configurations of RMS

Generating economical and distinctive configurations for a given demand period is an important optimization problem for reconfigurable manufacturing systems (RMS) at both initial design and reconfiguration stages. This paper presents a graph model to optimize capital cost of singe-product flow-line (SPFL) configurations of RMS. The parameters of the SPFL configuration include number of workstations, number and type of machines and assigned operations for each workstation. The full topological sorting and graph augmentation procedures are developed to derive a combined machine graph from the operation precedence graph of a specific product. Then a two-phase optimization approach is proposed. In the first phase, the optimal and K-1 suboptimal configurations are generated by solving a constrained K-shortest paths problem on the machine graph. In the second phase, a dissimilarity index is introduced to measure the dissimilarity between every pair of found configurations. Subsequently p distinctive ones out of K configurations are obtained using the algorithms for p-dispersion problem. Case studies illustrate the effectiveness of our approach and show some superiority of our approach compared to existing approaches. Computational experience shows that the approach is fit for small-to-medium size problems of configuration generation. The approach is also applied to optimizing SPFL in mechanical production.

The tool transporter movements problem in flexible manufacturing systems

We consider a job sequencing and tool transporter movements problem on a single flexible machine with limited tool magazine capacity. A tool transporter having limited capacity is used in transporting the tools between the machine and tool crib area. Our aim is to minimize the number of the tool transporter movements. We present several lower and upper bounds, propose a Branch-and-Bound algorithm and a Beam Search procedure, and report results from a computational experiment. We find that optimal solutions can be quickly obtained for medium-sized instances with 25 jobs and 25 tools. For large-sized problem instances, Beam Search provides high quality solutions very quickly. Finally, we address the problem of minimizing the total flow time.

Fuzzy rule generation for adaptive scheduling in a dynamic manufacturing environment

This paper proposes a fuzzy rule-based system for an adaptive scheduling, which dynamically selects and applies the most suitable strategy according to the current state of the scheduling environment. The adaptive scheduling problem is generally considered as a classification task since the performance of the adaptive scheduling system depends on the effectiveness of the mapping knowledge between system states and the best rules for the states. A rule base for this mapping is built and evolved by the proposed fuzzy dynamic learning classifier based on the training data cumulated by a simulation method. Distributed fuzzy sets approach, which uses multiple fuzzy numbers simultaneously, is adopted to recognize the system states. The developed fuzzy rules may readily be interpreted, adopted and, when necessary, modified by human experts. An application of the proposed method to a job-dispatching problem in a hypothetical flexible manufacturing system (FMS) shows that the method can develop more effective and robust rules than the traditional job-dispatching rules and a neural network approach.

A novel approach to determine cell formation, intracellular machine layout and cell layout in the CMS problem based on TOPSIS method

This paper deals with the cellular manufacturing system (CMS) that is based on group technology (GT) concepts. CMS is defined as identifying the similar parts that are processed on the same machines and then grouping them as a cell. The most proposed models for solving CMS problems are focused on cell formation and intracellular machine layout problem while cell layout is considered in few papers. In this paper we apply the multiple attribute decision making (MADM) concept and propose a two-stage method that leads to determine cell formation, intracellular machine layout and cell layout as three basic steps in the design of CMS. In this method, an initial solution is obtained from technique for order preference by similarity to the ideal solution (TOPSIS) and then this solution is improved. The results of the proposed method are compared with well-known approaches that are introduced in literature. These comparisons show that the proposed method offers good solutions for the CMS problem. The computational results are also reported. Scope and purpose In the previous array-based clustering methods, arrays are defined by binary numbers that are indicated as the set of machines that process each part. The main problem of these methods is that grouping parts and machines are made regardless of production volume, operational sequences, production cost, inventory and other production system's limitations. In this paper we consider the previous common problems of array-based clustering methods and apply the logical idea of TOPSIS method for solving the cellular manufacturing system problem in which arrays in the part-machine incidence matrix are defined by operational sequences. The TOPSIS is a multiple attribute decision making (MADM) technique in which the alternatives are ranked by their distances between positive and negative ideal solution.

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.

Solid Deformable Multibody Dynamic Problems in Precision Manufacturing Systems

The recursive projection schemes used in most existing recursive methods for solid deformable structure dynamic problems in precision manufacturing systems lead to dense coefficient matrices in the acceleration equations and consequently there is a strong dynamic coupling between the joint and elastic coordinates. When the number of elastic degrees of freedom in engineering materials increases, the size of the coefficient matrix in the acceleration equations becomes large and consequently the use of these recursive methods for solving the joint and elastic accelerations becomes less efficient. This paper discusses the problems associated with the recursive projection schemes used in the existing recursive methods, and it is shown that decoupling the joint and elastic accelerations using the nonlinear recursive method requires the factorization of nonlinear matrices whose dimensions are independent of the number of elastic degrees of freedom of the multibody system. An amalgamated formulation that can be used to decouple the elastic and joint accelerations for different multibody manufacturing systems is then proposed. The use of the nonlinear recursive method developed in this paper is demonstrated using the open-loop and closedloop chains in precision manufacturing systems.

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.

An optimization problem of manufacturing systems with stochastic machine breakdown and rework process

AbstractThis paper is concerned with optimization of production run time that takes stochastic breakdown and the reworking of defective items into consideration. In a real‐life manufacturing process, production of imperfect quality items as well as random breakdowns of production equipment is inevitable. All defective items produced are assumed to be repairable through a rework process right after the regular production stops in each cycle. This research starts with derivations of the cost functions for production systems with breakdown (no‐resumption policy is considered) and without breakdown taking place, respectively. Then cost functions of both cases are integrated. Theorems on conditional convexity of the overall cost function and bounds for optimal production run time are proposed and proved. This study concludes that although the optimal run time cannot be expressed in a closed form, it falls within the range of bounds. Hence, it can be pinpointed by the use of the bisection method based on the intermediate value theorem. A numerical example is provided to demonstrate its practical usages. Copyright © 2007 John Wiley & Sons, Ltd.

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.

A distributed transient inter-production scheduling for flexible manufacturing systems

The study in this paper is related with the cyclic scheduling problem for Flexible Manufacturing Systems (FMS). We present a Multi-Agent Model for scheduling the transient states between successive cyclic productions which is called transient inter-productions. This new state allows going from a cyclic state to the following one. This model is composed of cooperating agents aiming to minimize the global makespan while reducing temporal complexity. In fact, in the cyclic context, the determination of the cyclic productions to respect the initial demand is allowed by the planning phase. These cyclic productions have to be sequenced each one in relation to the others and this is done by our model which also optimize the transient inter-production states.

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.

A parallel multiple Markov chain simulated annealing for multi-period manufacturing cell formation problems

Simulated annealing (SA) is a general purpose optimization technique capable of finding optimal or near optimal solutions in various applications. The major disadvantage of this technique is its slow convergence making it not suitable for solving many complex optimization problems. This limitation may be alleviated by parallel computing using a multiprocessor computer or a cluster of workstations. In this paper, we present an integer programming model for solving a multi-period cell formation problem in cellular manufacturing system. In order to solve the mathematical model efficiently, we developed a multiple Markov chain simulated annealing algorithm which allows multiple search directions to be traced simultaneously. Our computational results on a single processor machine showed that multiple Markov chain SA is much more efficient than a conventional single Markov chain SA. The parallel implementation of the multiple Markov chain SA further improves its computational efficiency in terms of solution quality and execution time.

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

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

An integrated tool planning and production planning problem for flexible manufacturing systems using genetic algorithm

This paper presents a novel concept of integrating tool planning and production planning problems in flexible manufacturing system. A mathematical objective function has been formulated that minimises the broad objectives of total costs comprising various parameters such as tool room capacity, tool budget, tool regrinding and procurement lead times, ordering, setup, holding and back-ordering costs of tools and parts. This paper explores the integrating notion by utilising the genetic-based search methodology. An illustrative example is taken into account for demonstrating the robustness of the proposed model. Genetic Algorithm (GA) has been implemented in the example under consideration and its performance is compared with Tabu Search (TS) and Simulated Annealing (SA). The extensive computations over the problems of varying complexities and dimensions prove the superiority of genetic algorithm. It has been observed that GA outperforms the standard algorithms (TS and SA) in the context of the underlying problem.