Flexible Manufacturing System Production Research Articles

Modeling frame, flexible production system, search and selection algorithm, frame slot, graph diagram

Abstract. Aim. The aim of the study is to develop algorithmic support based on a frame model for information retrieval and selection of a flexible manufacturing system (FMS), as well as its technical units and control system in accordance with a process flow diagram.Method. An intelligent method based on a frame model is used in order to create a search and selection algorithm. The FRL programming language was used for programming the frame model.Results. In accordance with the published guidelines for ensuring the management and operation of archival design work, a comparative analysis of information support algorithms was carried out to in order to select flexible production systems (FPS) comprising standard elements, production modules, layout schemes and a set of information about their parameters or the location of documents. The model for creating algorithmic support was based on the modelling frame for the effective search and selection of the FMS, while its production modules and active elements were implemented in accordance with the scope of production and the purposes of each technical unit.Conclusion. To ensure the reliable functioning of the FMS automatic control system, an algorithm is proposed for searching sensors based on frame slots and achievable positioning errors of industrial robots and technological equipment. An algorithm for locating sensors and controlling the active elements of an FMS production module is presented.

Identification of performance variables which affect the FMS: a state-of-the-art review

The aim of this research is the identification of performance variables which affect the flexible manufacturing system (FMS). The study was performed to find the variables especially for performance, productivity and flexibility of FMS. Recognition of variables took place from the literature and experts opinions from the manufacturing industry. The 15 performance variables were identified and three factors, i.e., quality, productivity and flexibility, were extracted, which involves the performance of FMS. The 20 productivity variables were identified and four factors, i.e., people, quality, machine, and flexibility, were extracted, which involves the productivity of FMS. Fifteen variables were identified and four factors, i.e., production flexibility, machine flexibility, product flexibility, and volume flexibility, were extracted, which affect the flexibility of FMS. This research provides the opportunity to management for understanding the factors or variables for enhancement of performance of their manufacturing firms.

Online Scheduling of 2-Re-entrant Flexible Manufacturing Systems

Online scheduling of operations is essential to optimize productivity of flexible manufacturing systems (FMSs) where manufacturing requests arrive on the fly. An FMS processes products according to a particular flow through processing stations. This work focusses on online scheduling of re-entrant FMSs with flows using processing stations where products pass twice and with limited buffering between processing stations. This kind of FMS is modelled as a re-entrant flow shop with due dates and sequence-dependent set-up times. Such flow shops can benefit from minimization of the time penalties incurred from set-up times. On top of an existing greedy scheduling heuristic we apply a meta-heuristic that simultaneously explores several alternatives considering trade-offs between the used metrics by the scheduling heuristic. We identify invariants to efficiently remove many infeasible scheduling options so that the running time of online implementations is improved. The resulting algorithm is much faster than the state of the art and produces schedules with on average 4.6% shorter makespan.

Identification and modelling of the various factors affecting the productivity of FMS

The last few years have witnessed unparalleled changes in the world market and the manufacturing companies have to adjust to these unforeseen changes in the market quickly to survive. So more and more companies are now opting for low volume, high variety flexible manufacturing system (FMS) instead of the conventional mass production. Though FMS provides a lot of strategic and tactical benefits, yet all of these may not be possible with all installations. A manufacturing manager should know what are the specific benefits he is expecting from the FMS installation and what is the time span within which these benefits start coming in. In the present paper, an attempt has been made to accomplish the task of enlisting the factors affecting the productivity of FMS installation and further an attempt has been made to model these different factors using the interpretive structural modelling (ISM). This model is further strengthened using MICMAC analysis by defining the driving power and dependencies of these factors.

Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach

Productivity has often been cited as a key factor in a flexible manufacturing system (FMS) performance, and actions to increase it are said to improve profitability and the wage earning capacity of employees. Improving productivity is seen as a key issue for survival and success in the long term of a manufacturing system. The purpose of this paper is to make a model and analysis of the productivity variables of FMS. This study was performed by different approaches viz. interpretive structural modelling (ISM), structural equation modelling (SEM), graph theory and matrix approach (GTMA) and a cross-sectional survey within manufacturing firms in India. ISM has been used to develop a model of productivity variables, and then it has been analyzed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. CFA is carried by SEM. EFA is applied to extract the factors in FMS by the statistical package for social sciences (SPSS 20) software and confirming these factors by CFA through analysis of moment structures (AMOS 20) software. The twenty productivity variables are identified through literature and four factors extracted, which involves the productivity of FMS. The four factors are people, quality, machine and flexibility. SEM using AMOS 20 was used to perform the first order four-factor structures. GTMA is a multiple attribute decision making (MADM) methodology used to find intensity/quantification of productivity variables in an organization. The FMS productivity index has purposed to intensify the factors which affect FMS.

An Evolutionary Hybrid Algorithm for Layout Planning in Flexible Manufacturing System

The layout of a flexible manufacturing system (FMS) involves distributing different resources for achieving maximum efficiency. To provide a variety of products to customers the FMS has created a significant set of operational changes. Among this, layout planning is more important in achieving productivity of FMS. The problem of this type is combinatorial in nature and found to be Non-deterministic Polynomial (NP) complete. This paper focuses the loop layout planning of an FMS with the aim of minimizing the transportation distance and the backtracking distances of the Automated Guided Vehicles (AGV) using the hybrid Scatter Search - Particle Swarm Optimization algorithm (SS-PSO). Since the Loop layout has relatively low initial costs and contains few material handling links with higher flexibility. SS-PSO operates on unifying principles based on strategic designs compared to other metaheuristic algorithms such as GA, PSO etc. Hybrid SS-PSO approach is applied to seven and nine machines loop layout problem. It is found that the proposed algorithm provides a better solution with fast convergence rate compared with other metaheuristic algorithms.

Step-by-step cyclic processes scheduling

Automated Guided Vehicles (AGVs) fleet scheduling is one of the big problems in Flexible Manufacturing System (FMS) control. The problem is more complicated when concurrent multi-product manufacturing and resource deadlock avoidance policies are considered. The objective of the research is to provide a declarative model enabling to state a constraint satisfaction problem aimed at AGVs fleet scheduling subject to assumed itineraries of concurrently manufactured product types. In other words, assuming a given layout of FMS's material handling and production routes of simultaneously manufactured orders, the main objective is to provide the declarative framework aimed at conditions allowing one to calculate the AGVs fleet schedule in online mode. An illustrative example of the relevant algebra-like driven step-by-stem cyclic scheduling is provided.

Optimisation of FMS operations by using multiple criteria decision-making

This paper aims at presenting the topic of optimising production in flexible manufacturing systems (FMSs). Research is directed towards FMSs study to find out their behaviour and performance as well, and if possible, even before their physical realisation, and to scientifically establish the models of FMS optimisation, with application in the circular concrete shafts. Mathematical models will contain a number of parameters and optimisation criteria, objective function, performance indicators, average optimal synthesis and real time.

Integrated scheduling of flexible manufacturing system using evolutionary algorithms

Effective sequencing and scheduling of the material handling system (MHS) have an impact on the productivity of the flexible manufacturing system (FMS). The MHS cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines and MHS. This paper highlights the importance of integration between production schedule and MHS schedule in FMS. The Giffler and Thompson algorithm with different priority dispatching rules is developed to minimize the makespan in the FMS production schedule. Its output is used for MHS scheduling where the distance traveled and the number of backtrackings of the automated-guided vehicles are minimized using an evolutionary algorithms such as an ant colony optimization algorithm and particle swarm optimization (PSO) algorithm. The proposed evolutionary algorithms are validated with benchmark problems. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithms. The analysis reveals that PSO algorithm provides better solution with reasonable computational time.

A relative position-coded differential evolution for loop-based station sequencing problem

Loop layout is very common in flexible manufacturing systems. The quality of the layout scheme directly affects productivity of flexible manufacturing systems. The loop-based station sequencing problem (LSSP) that determines the arrangement of the facilities around a closed loop belongs to the category of the combinatorial optimisation problem, which is known to be NP-Hard. Therefore, the heuristic and meta-heuristic methods are generally adopted to solve the LSSP. In order to improve the existing methods, this paper takes into consideration the permutation property of the LSSP, and proposes an integer-coded differential evolution (DE) algorithm, named as relative position-coded DE. Compared with existing real-coded DEs, the proposed algorithm possesses the following favourable characteristics: (1) it does not need mapping from floating point vectors to integer sequence; (2) the coding space of the new algorithm corresponds with the solution space of LSSP; (3) it avoids the illegal chromosomes in the solving process. Experimental results showed that the proposed relative position-coded DE outperforms the existing DEs, GA, PSO, and SA, especially in the case of large-scale LSSP.

Incorporating dynamism in traditional machine loading problem: an AI-based optimisation approach

Machine breakdowns have been recognised in flexible manufacturing systems (FMS) as the most undesirable characteristic adversely affecting the overall efficiency. In order to ameliorate product quality and productivity of FMS, it is necessary to analyse, as well as to minimise the effect of breakdowns on the objective measures of various decision problems. This paper addresses the machine loading problem of FMS with a view to maximise the throughput and minimise the system unbalance and makespan. Moreover, insufficient work has been done in the domain of machine loading problem that considers effect of breakdowns. This motivation resulted in a potential model in this paper that minimises the effect of breakdowns so that profitability can be augmented. The present work employs an on-line machine monitoring scheme and an off-line machine monitoring scheme in conjunction with reloading of part types to cope with the breakdowns. The proposed model bears similarity with the dynamic environment of FMS, hence, termed as the dynamic machine loading problem. Furthermore, to examine the effectiveness of the proposed model, results for throughput, system unbalance and makespan on different dataset from previous literature has been investigated with application of intelligence techniques such as genetic algorithms (GA), simulated annealing (SA) and artificial immune systems (AIS). The results incurred under breakdowns validate the robustness of the developed model for dynamic ambient of FMS.

An enhanced MPS solution for FMS using GAs

Presents the master production scheduling (MPS) problem of a flexible manufacturing system (FMS). Earliness/tardiness production scheduling and planning (ETPSP) is one of the solutions used to integrate MRP and JIT effectively. Previous researches on ETPSP have only applied to problems of single/parallel machines with earliness and tardiness penalties on a common due date and capacity. Proposes a revised ETPSP for the purpose of developing an MPS that can fit into the FMS environment where a multiple machine capacity on multi‐processes and batch sizes is included. Outlines the application of an enhanced ETPSP method using a genetic algorithm (GA) solution to solve a multi‐product FMS production problem. Shows that the use of the improved ETPSP model can represent a real life FMS environment and that a solution can be effectively and efficiently obtained using the GA approach.

Knowledge-based workcell attribute oriented dynamic schedulers for flexible manufacturing systems

The economy of production in flexible manufacturing systems (FMS) depends mainly on how effectively the production is planned and how the resources are used. This requires efficient and dynamic factory scheduling and control procedures. This paper addresses two knowledge-based scheduling schemes (work cell attribute oriented dynamic schedulers “WCAODSs”) to control the flow of parts efficiently in real-time for FMS in which the part-mix varies continually with the planning horizon. The present work employs a hybrid optimisation approach in the generalised A1 framework. A genetic algorithm that provides an optimal combination of a set of priority dispatching rules, one for each work cell “WC” (WCwisepdr set), for each of the problem instances characterised by their WC attributes, is used for generating examples. The WC attributes reflect the information about the operating environment of each individual WC. Two inductive learning algorithms are employed to learn the examples, and scheduling rules are formulated as a knowledge base. The learning algorithms employed are: the Genetic CID3 (Continuous Interactive Dichotomister3 algorithm extended with genetic program for weight optimisation) and the Classification Decision Tree algorithm. The knowledge base obtained through the above learning schemes generates robust and effective schedules intelligently with respect to the part-mix changes in real-time, for makespan criteria. The comparison made with a GA-based scheduling methodology shows that WCAODSs provide solutions closer to the optimum.

Production planning for flexible flow systems with limited machine flexibility

A flexible manufacturing system (FMS) is highly capital-intensive and FMS users are concerned with achieving high system utilization. The production planning function for setting up an FMS prior to production should be developed in order to make the most of the potential benefits of FMSs. We consider two production planning problems of grouping and loading a flexible flow system, which is an important subset of FMSs where the routing of parts is unidirectional. We show that considering this routing restriction as well as limited machine flexibility strongly affects both the solution techniques and the quality of the solutions. Because of the complexity of the problem, we present a heuristic approach that decomposes the original problem into three interrelated subproblems. We show that the proposed approach usually finds a near-optimum solution and is superior to an approach that exists in the literature of FMS production planning. We also introduce effective heuristic methods for two new subproblems that arise because of the unidirectional flow precedence and flexibility constraints. Computational results are reported and future research issues are discussed.

AGV schedule integrated with production in flexible manufacturing systems

Flexible manufacturing systems (FMS) comprise, automated machine tools, automated material handling, and automated storage and automated retrieval systems (AS/RS) as essential components. Effective sequencing and scheduling of the material handling systems (MHS) can have a major impact on the productivity of the manufacturing system. The material handling cannot be neglected while scheduling the production tasks. It is necessary to take into account the interaction between machines, material handling systems and computer. In this context, this paper attempts to link the operation of automated guided vehicles (AGV) with the production schedule and suggests a heuristic algorithm that employs vehicle dispatching rules (vdr) for conflict resolution. The vdrs considered in this paper are: shortest operation time (SPT), longest operation time (LPT), longest travel time (LTT) and shortest travel time (STT). The performance of the vdrs in the proposed heuristic is compared with makespan criteria. The results show that the STT provides the best solutions compared to other vdrs.

Optimal random storage allocation for an AS/RS in an FMS

A flexible manufacturing system (FMS) comprises, automated machine tools, automated material handling, and an automated storage and retrieval system (AS/RS) as essential components. Optimal performance of each element of this system would improve the productivity and efficiency of the total FMS. In this context, this paper attempts to link the operation of the AS/RS with the production schedule and suggests two different classes of algorithms, namely, heuristics and genetic algorithms to improve the performance of the AS/RS operation by allocating the materials with minimum movement of the shuttle.

Production scheduling/rescheduling in flexible manufacturing

Scheduling of production in flexible manufacturing systems (FMSs) has been extensively researched over the past years and it continues to attract the interests of both academic researchers and practitioners. The generation of new and modified production schedules is becoming a necessity in today's complex manufacturing environment. Genetic algorithms are used in this paper to obtain an initial schedule. Uncertainties in the production environment and modelling limitations inevitably result in deviations from the generated schedules. This makes rescheduling or reactive scheduling essential. Four different types of uncertainties that normally cause discrepancies between the actual output and the planned output are considered in this paper. These include unforeseen machine breakdowns, increased order priority, rush orders arrival and order cancellations. In this paper, the current status of the shop is considered while rescheduling. The proposed algorithms revise only those operations that must be rescheduled and can, therefore, be used in conjunction with the existing scheduling methods to improve the efficiency of flexible manufacturing systems.

Metaplanning in FMS scheduling

Presents an object‐oriented system for scheduling in a flexible manufacturing system (FMS) with the purpose of demonstrating how the control aspects of such a system can be enhanced by using metalevel planning. Represents the objects and metaknowledge of the system using an object‐oriented approach. The resulting system gives a clear, complete and comprehensive picture of the FMS environment, as well as a modular, flexible and interactive control mechanism to generate and maintain detailed FMS production schedules.

Valid inequalities and facets for a hypergraph model of the nonlinear knapsack and the FMS part selection problems

This paper defines the dense subhypergraph problem (DSP), which provides a modelling framework for the nonlinear knapsack problem and other well-known problems arising in areas such as capital budgeting, flexible manufacturing system production planning, repair-kit selection, and compiler construction. We define several families of valid inequalities and state conditions under which these inequalities are facet-defining for DSP. We also explore the polyhedral structure of the cardinality-constrained DSP. Finally, we examine a special case of this problem that arises, for example, within the context of Lagrangian decomposition. For this case, we present a complete description of the convex hull of the feasible region.

Model of Automated Cutting Tool Flow in Flexible Manufacturing Systems

The paper describes the tool flow in flexible manufacturing systems. It defines the hardware and software model of the tool flow and the relation to the system of the cutting conditions. Such a tool management ensures the increase in productivity of flexible manufacturing systems.