Problem In Manufacturing System Research Articles

Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency

The dynamic rescheduling problem in flexible manufacturing systems (FMS) has historically emphasized the schedule efficiency. However, energy consumption is a basic need for different purposes in manufacturing systems around the world. This paper proposes an innovative approach to study the dynamic scheduling problem in FMS, taking the objectives of minimum or maximum energy consumption into account. A new goal programming mathematical model, which considers the energy consumption and the schedule efficiency simultaneously, is presented for solving this problem. A rescheduling method based on the genetic algorithm is introduced to address the dynamic rescheduling problem in FMS. A period policy is selected to deal with the dynamic feature of the problem. Numerical experiments have been designed to test and evaluate the performance of the proposed model. The experimental results show that the minimum energy consumption model can save the energy consumption and enhance the schedule efficiency.

Fuzzy weighted association rule based solution approaches to facility layout problem in cellular manufacturing system

This study aims to propose new solution approaches based on fuzzy weighted association rules for facility layout problem in a cellular manufacturing system. The facility layout problem under concern has two important aspects, namely; fuzzy product route and fuzzy machine weight. Number of machine types as well as the number of machines in each type is determined in order to define a fuzzy product route. A fuzzy set is then assigned for each machine type to match them with appropriate products. Alternative routes for each product and machine weights are defined by fuzzy functions. Market demand is also used to assign weights to the machines. Three solution approaches, namely fuzzy weighted association rule mining(FWARM), Gyenesei’s weighted quantitative association rules (WQAR) and normalised weighted association rule mining(NWARM) based approaches, are proposed in this study. To the authors’ best knowledge, this is the first attempt which considers fuzzy weighted association rule-based data mining approaches to the facility layout problem. An illustrative example is presented to demonstrate the usefulness of the proposed approaches.

A hybrid genetic algorithm for solving machine layout problem with consideration of industrial constraints

This paper focuses on loop layout problem in flexible manufacturing systems using one load and unload machine. The objective of this problem is to determine the ordering of machines around a loop, to minimize the total cost of transporting parts within each manufacturing cell. The novelty of this study lies on the reformulation of the problem while taking into account new variables generally neglected by recent researches like proximity constraints and machine dimensions. Hence, we aim to place these machines on a grid that represents the surface of the cell, in order to construct a loop layout while respecting proximity constraints. And as objective, we try to minimize the total cost of transporting parts within each manufacturing cell. This new formulation led us to propose a two-stage approach to solve this problem. The first step consists in positioning the machines on a grid while respecting the proximity constraints and machines dimensions. The second step aims to optimize the path betweense machines already positioned in order to minimize number of the loops travelled by parts. In this paper, we are interested in the second step. To solve this problem, we use genetic algorithms. This choice is motivated by the well-known of the efficiency of genetic algorithms to solve quadratic assignment problems. Hence, we proposed three hybrid genetic algorithms. The effectiveness of our approaches is demonstrated through numerical examples.

Hybrid principal component analysis technique to machine-part grouping problem in cellular manufacturing system

This article portrays a hybrid principal component analysis (PCA)-based technique to construct production cells in cellular manufacturing system (CMS). The key problem in CMS is to recognise the machine cells and corresponding part families and subsequently the formation of production cells. A novel approach is considered in this study to systematise a hybrid multivariate clustering technique based on covariance analysis to form the machine cells in CMS. The intended technique is demonstrated in three segments. Firstly, a similarity matrix is developed by exploiting the covariance analysis procedure. In the second stage, the PCA is utilised to identify the potential clusters in CMS with the assistance of eigenvalue and eigenvector computation. In the last stage, an adjustment heuristic is adopted to improve the solution quality and consequently the clustering efficiency. This article states that, the addition of the adjustment heuristic approach into a traditional multivariate PCA-based clustering technique not only enhances the solution quality significantly, but also downgrades the inconsistency of the solutions achieved. The hybrid technique is tested on 24 test datasets available in published articles and it is shown to outperform other published methodologies by enhancing the solution quality on the test problems.

Computationally Improved Optimal Control Methodology for Linear Programming Problems of Flexible Manufacturing Systems

Deadlock prevention policies are used to solve the deadlock problems of FMSs. It is well known that the theory of regions is the efficient method for obtaining optimal (i.e., maximally permissive) controllers. All legal and live maximal behaviors of Petri net models can be preserved by using marking/transition-separation instances (MTSIs) or event-state-separation-problem (ESSP) methods. However, they encountered great difficulties in solving all sets of inequalities that is an extremely time consuming problem. Moreover, the number of linear programming problems (LPPs) of legal markings is also exponential with net size when a plant net grows exponentially. This paper proposes a novel methodology to reduce the number of MTSIs/ESSPs and LPPs. In this paper, we used the well-known reduction approach Murata (1989) to simply the construct of system such that the problem of LPPs can then be reduced. Additionally, critical ones of crucial marking/transition-separation instances (COCMTSI) are developed and used in our deadlock prevention policy that allows designers to employ few MTSIs to deal with deadlocks. Experimental results indicate that the computational cost can be reduced. To our knowledge, this deadlock prevention policy is the most efficient policy to obtain maximal permissive behavior of Petri net models than past approaches.

Cascade Quality Prediction Method Using Multiple PCA+ID3 for Multi-Stage Manufacturing System

Quality prediction model, as the key to realize the real-time online quality monitoring process, has been developed using various data mining techniques. However, most of quality prediction models are developed in single-stage manufacturing system, where the relationship between manufacturing operation and quality variables is straightforward. Previous studies show that single-stage quality system cannot solve quality problem in multi-stage manufacturing system due to the complex variable relationships. This study is intended to propose a data mining method to develop quality prediction model which is able to deal with the complex variable relationships in multi-stage manufacturing system. This method, named Cascade Quality Prediction Method (CQPM), is developed by considering the complex variables relationships in multi-stage manufacturing system. CQPM employs the combination of multiple Principal Component Analysis and Iterative Dichotomiser 3 algorithm. A case study in semiconductor manufacturing shows that the prediction model that has been developed using CQPM is performed better in predicting both positive and negative classes compared to others.

An optimal production planning and maintenance policy for a multiple-product and single machine under failure rate dependency

This paper addresses the production and maintenance problem of multiple-product manufacturing system satisfying several random demands corresponding to every product. The goal of this study is to establish an economical production planning followed by an optimal maintenance strategy, taking into account the influence of the multiple-product production rate on the materiel degradation. Analytical models are developed in order to minimize sequentially the production/holding costs and the total maintenance cost. A numerical example is presented to prove the developed approach.

An improved ant-colony algorithm for the grouping of machine-cells and part-families in cellular manufacturing systems

In the present study, we address the machine-cell design and part-family formation problem in cellular manufacturing systems. The objective of the study is to group machines into machine-cells and parts into part-families such that the grouping efficacy is maximised. We propose an improved ant-colony optimisation (IACO) algorithm to obtain machine-cells and part-families. The performance of the algorithm is tested by using benchmark datasets available in the literature. The grouping efficacy obtained by the proposed algorithm is compared with the grouping efficacies obtained by the existing approaches present in the literature. The comparative analysis shows that the proposed IACO performs very well in maximising the grouping efficacy.

Order-oriented cooperative sequencing optimisation in multi-mix-model assembly lines

Order-oriented products assembly sequence among different assembly lines becomes a critical problem for mass customisation manufacturing systems. It significantly affects system productivity, delivery time, and manufacturing cost. In this paper, we propose a new approach to extend the traditional products sequencing from mixed model assembly line (MMAL) to multi-mixed model assembly lines (MMMALs) to obtain the optimal assembly sequence with the objectives of minimising consumption waviness of each material in the lines, assembly line setup cost, and lead-time. A multi-objective optimisation algorithm based on variable neighbourhood search methods (VNS) is developed. We perform an industrial case study in order to demonstrate the practicality and effectiveness of the proposed approach.

Research on a Main-Branch Hybrid Flow Shop Scheduling Problem in Production Manufacturing System

A main-branch hybrid Flow shop scheduling problem in production manufacturing system is studied. Under the premise of JIT, targeting of smallest cost, a Flow-Shop production line scheduling model is built in cycle time of buffer. Two stages Quantum Genetic Algorithm (QGA) is proposed. By the results of numerical example, the effective and advantageous of QGA was shown.

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

This paper deals with a problem of reconfigurable manufacturing systems (RMSs) design based on products specifications and reconfigurable machines capabilities. A reconfigurable manufacturing environment includes machines, tools, system layout, etc. Moreover, the machine can be reconfigured to meet the changing needs in terms of capacity and functionality, which means that the same machine can be modified in order to perform different tasks depending on the offered axes of motion in each configuration and the availability of tools. This problem is related to the selection of candidate reconfigurable machines among an available set, which will be then used to carry out a certain product based on the product characteristics. The selection of the machines considers two main objectives respectively the minimization of the total cost (production cost, reconfiguration cost, tool changing cost and tool using cost) and the total completion time. An adapted version of the non- dominated sorting genetic algorithm (NSGA-II) is proposed to solve the problem. To demonstrate the effectiveness of the proposed approach on RMS design problem, a numerical example is presented and the obtained results are discussed with suggested future research.

The control of myopic behavior in semi-heterarchical production systems: A holonic framework

Heterarchical control architectures are essentially founded on cooperation and full local autonomy, resulting in high reactivity, no master/slave relationships and local information retention. Consequently, these architectures experience myopic decision-making, bringing entities towards local optimality rather than the system’s overall performance. Although this issue has been identified as an important problem within heterarchical control architectures, it has not been formally studied. The aim of this paper is to identify the dimensions of myopic behavior and propose mechanisms to control this behavior. This study focuses on myopic behavior found in manufacturing control. For this particular study, we propose a holonic framework and a holonic organization that integrates specific mechanisms to control the temporal and social myopia. Our proposal was validated using simulations designed for solving the allocation problem in flexible manufacturing systems. These simulations were conducted to show the improvement by integrating the new mechanisms. These simulation results indicate that the myopic control mechanisms achieve better performance than the reactive strategies, because not only they introduce a planning horizon, but also because they balance local and global objectives, seeking a consensus.

Complexity of Buffer Capacity Allocation Problems for Production Lines with Unreliable Machines

Buffer capacity allocation problems for flow-line manufacturing systems with unreliable machines are studied. These problems arise in a wide range of manufacturing systems and concern determining buffer capacities with respect to a given optimality criterion which can depend on the average production rate of the line, buffer cost, inventory cost, etc. Here, this problem is proven to be NP-hard for a tandem production line and oracle representation of the revenue and cost functions, and NP-hard for a series-parallel line and stepwise revenue function.

A hybrid algorithm optimization approach for machine loading problem in flexible manufacturing system

The production planning problem of flexible manufacturing system (FMS) concerns with decisions that have to be made before an FMS begins to produce parts according to a given production plan during an upcoming planning horizon. The main aspect of production planning deals with machine loading problem in which selection of a subset of jobs to be manufactured and assignment of their operations to the relevant machines are made. Such problems are not only combinatorial optimization problems, but also happen to be non-deterministic polynomial-time-hard, making it difficult to obtain satisfactory solutions using traditional optimization techniques. In this paper, an attempt has been made to address the machine loading problem with objectives of minimization of system unbalance and maximization of throughput simultaneously while satisfying the system constraints related to available machining time and tool slot designing and using a meta-hybrid heuristic technique based on genetic algorithm and particle swarm optimization. The results reported in this paper demonstrate the model efficiency and examine the performance of the system with respect to measures such as throughput and system utilization.

New mathematical model for problem of dynamic cell formation based on number and average length of intra and intercellular movements

This article presents a new nonlinear integer programming model for dynamic cell formation problem in cell manufacturing system. Difference between this model and other models developed thus far lies in implementation of the idea of more material flow in shorter distance in formation of cells. The presented model is NP-hard. A new strategy known as simulated annealing embedded in branch and cut was developed to solve the problem. In comparison with standard branch and cut, results of solving different problems showed speed and efficiency of simulated annealing embedded in branch and cut.

Characterizing and Modelling uncertainties in Production systems

Numerous decisions must be taken within the framework of conception, optimization and management of production system or supply chain. With this aim, managers need to model their systems. Unfortunately, data can be imprecise at the design stage or can evolve during the exploitation of the system. However, data can have a deep impact on the system performance. Thus, decision-makers have to take into account these uncertainties and use the appropriate tools and methodology to model them. In this paper we propose a conceptual framework for imperfect data characterizing and modelling. The goal of this framework is to provide a decision tool to help users to choose the most suitable modelling approach for their manufacturing system problems.

Quality and production control in multiple-product unreliable manufacturing system

This paper seeks to address the joint production and quality specification control problem in multiple-product unreliable manufacturing systems. In this context, the decision maker should make the best compromise between production and quality policy to adopt so as to maximize the long term average per unit time profit. This compromise is required given that the policies governing the production and quality decision making can be conflicting. In fact, tight process specifications will generally lead to products with good quality and higher market values, but at the same time associated with higher rate of non-conforming parts rejection leading to higher non quality costs and lower plant productivity. Moreover, one should consider the unreliability of the manufacturing system. To hedge against future capacity shortages caused by machine failures, the decision maker should adopt an adequate production policy in order to meet customer demand and minimize the incurred total cost. The paper tackle the problem in a dynamic stochastic context with a combined approach, based on optimal control theory, simulation modeling and response surface methodology. The obtained results are promising and show that the profit under the considered joint policy is improved up to 7% compared with dissociated production and quality strategies.

Solving the serial batching problem in job shop manufacturing systems

This paper addresses the serial batch scheduling problem embedded in a job shop environment to minimize makespan. Sequence dependent family setup times and a job availability assumption are also taken into account. In consideration of batching decisions, we propose a tabu search algorithm which consists of various neighborhood functions, multiple tabu lists and a sophisticated diversification structure. Computational experiments show that our algorithm outperforms a well-known tabu search approach which is developed for solving the traditional job shop problem. These results also confirm the benefits of batching.

Computationally Improved Optimal Deadlock Control Policy for Flexible Manufacturing Systems

Deadlock prevention, deadlock detection, and deadlock avoidance strategies are used to solve the deadlock problems of flexible manufacturing systems. The theory of regions is recognized as the unique method for obtaining maximally permissive (i.e., optimal) controllers in the existing literature. All legal and live maximal behavior of a Petri net model can be preserved by using a marking/transition-separation instance (MTSI). However, obtaining them is an extremely time consuming problem. This work proposes crucial MTSIs that allow designers to employ much fewer MTSIs to deal with deadlocks. The advantage of the proposed policy is that an optimal deadlock controller can be obtained with drastically reduced computation. Experimental results, by varying the markings of given net structures, indicate that it is the most efficient policy to obtain such controllers.

A Dynamic Rescheduling Model with Multi-Agent System and Its Solution Method

Dynamic rescheduling problem is an important issue in modern manufacturing system with the feature of combinatorial computation complexity. A dynamic rescheduling model, which is based on Multi-Agent System (MAS), was proposed. The communication and negotiation mechanism between agents were addressed to support the autonomic decision for each individual agent and the multi-agent system. Furthermore, the simulation results in dynamic scheduling accompanying with its perturbation show that the proposed model and the algorithm are effective to the dynamic scheduling problem in manufacturing system.