Analusis and Modeling of Flexible Manufacturing System

Analysis and modeling of flexible manufacturing system (FMS) consists of scheduling of the system and optimization of FMS objectives. Flexible manufacturing system (FMS) scheduling problems become extremely complex when it comes to accommodate frequent variations in the part designs of incoming jobs. This research focuses on scheduling of variety of incoming jobs into the system efficiently and maximizing system utilization and throughput of system where machines are equipped with different tools and tool magazines but multiple machines can be assigned to single operation. Jobs have been scheduled according to shortest processing time (SPT) rule. Shortest processing time (SPT) scheduling rule is simple, fast, and generally a superior rule in terms of minimizing completion time through the system, minimizing the average number of jobs in the system, usually lower in-process inventories (less shop congestion) and downstream idle time (higher resource utilization). Simulation is better than experiment with the real world system because the system as yet does not exist and experimentation with the system is expensive, too time consuming, too dangerous. In this research, Taguchi philosophy and genetic algorithm have been used for optimization. Genetic algorithm (GA) approach is one of the most efficient algorithms that aim at converging and giving optimal solution in a shorter time. Therefore, in this work, a suitable fitness function is designed to generate optimum values of factors affecting FMS objectives (maximization of system utilization and maximization of throughput of system by Genetic Algorithm (GA) approach

Machine Loading in Flexible Manufacturing system using Artificial Immune algorithm

In this project thesis the FMS loading problem is discussed with the objective to minimize the system unbalance and throughput by the use of Artificial Immune system. Manufacturing technology focuses primarily on flexibility and productivity. With the product variety and product life being the characterizing standards it is important that the flexibility of the job shop is maintained as its efficiency is increases. The complexity of a basic Machine loading problem in FMS is very high due to the different flexibility criteria as Part selection, Operation allocation and the various constraints involved. This dissertation proposes a soft computing technique with constraints on tool capacity and workload of the machine. The aim of using this algorithm is to reach an optimal solution and to ease the tedious computations in large problems involving loading which are NP hard problems. Immune algorithm is a very suitable method due to its self learning and memory acquisition abilities. First some sample machine loading problems are collected from the literature and the optimal system unbalance of the machine is calculated using LINGO optimization software. This project improves some issues inherent in existing techniques and proposes an effective Immune algorithm with reduced memory requirements and reduced computational complexity. The proposed Algorithm is tested on 3 problems adopted from literatures and the results reveal substantial improvement in solution quality over the existing basic mathematical approaches

Heuristic scheduling algorithms for dedicated and flexible manufacturing systems

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The investigation of the effect of scheduling rules on FMS performance

The application of Flexible Manufacturing Systems (FMSs) has an effect in competitiveness, not only of individual companies but of those countries whose manufactured exports play a significant part in their economy (Hartley, 1984). However, the increasing use of FM Ss to effectively provide customers with diversified products has created a significant set of operational challenges for managers (Mahmoodi et al., 1999). In more recent years therefore, there has been a concentration of effort on FMS scheduling without which the benefits of an FMS cannot be realized. The objective of the reported research is to investigate and extend the contribution which can be made to the FMS scheduling problem through the implementation of computer-based experiments that consider real-time situations. [Continues.

A Genetic-Algorithm-Based Approach for Optimizing Tool Utilization and Makespan in FMS Scheduling

This paper proposes a genetic algorithm approach to solve the identical parallel machines problem with tooling constraints in job shop flexible manufacturing systems (JS-FMSs) with the consideration of tool wear. The approach takes into account the residual useful life of tools and allocates a set of jobs with specific processing times and tooling requirements on identical parallel machines. Two metrics are introduced to evaluate the scheduling decisions and optimize the scheduling process, with the competitive goal of maximizing tool utilization and minimizing production makespan. The proposed approach searches for a set of optimal solutions on the Pareto front that offers the best possible balance between these two objectives, achieving optimal local performance in terms of both makespan and tool utilization. The approach is implemented with a customized genetic algorithm and validated on a real case study from a company operating in the aerospace sector, which confirms its effectiveness in increasing tool utilization and reducing the makespan. The results show that the proposed approach has significant practical implications for the manufacturing industry, particularly in the production of high-value materials such as those in the aerospace sector that require costly tools. This paper contributes to the operational research community by providing advanced scheduling algorithms that can optimize both the makespan and the tool utilization concurrently, improving production efficiency and maintaining competitiveness in the manufacturing industry

Dynamic Scheduling of Flexible Manufacturing Systems

To date, group scheduling research has primarily focused on examining the performance of different group heuristics under various experimental conditions. However, the dynamic selection of group heuristics has not received sufficient attention from researchers. The objective of this paper is to demonstrate a mechanism for the dynamic selection of group heuristics from several candidate alternatives by exploiting real time information from the Flexible Manufacturing System (FMS). In this regard, two tools, viz., Analytic Hierarchy Process (AHP) and Simple Multi-Attribute Rating Technique Exploiting Ranks (SMARTER), are used to develop models for part type and family selection. The experimental results indicate that the performance of the proposed models are better than the common group scheduling heuristics under varied experimental conditions.Singapore-MIT Alliance (SMA

Simultaneous Workload Allocation and Capacity Dimensioning for Distributed Production Control

abstract: Capacity dimensioning in production systems is an important task within strategic and tactical production planning which impacts system cost and performance. Traditionally capacity demand at each worksystem is determined from standard operating processes and estimated production flow rates, accounting for a desired level of utilization or required throughput times. However, for distributed production control systems, the flows across multiple possible production paths are not known a priori. In this contribution, we use methods from algorithmic game-theory and traffic-modeling to predict the flows, and hence capacity demand across worksystems, based on the available production paths and desired output rates, assuming non-cooperative agents with global information. We propose an iterative algorithm that converges simultaneously to a feasible capacity distribution and a flow distribution over multiple paths that satisfies Wardrop's first principle. We demonstrate our method on models of real-world production networks

The optimality of balancing workloads in certain types of flexible manufacturing systems

Symmetric mathematical programming is used to analyze the optimality of balancing workloads to maximize the expected production in a single-server closed queuing network model of a flexible manufacturing system (FMS). In particular, using generalized concavity we prove that, even though the production function is not concave, balancing workloads maximizes the expected production in certain types of m-machine FMS's with n parts in the system. Our results are compared and contrasted with previous models of production systems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25713/1/0000270.pd

Development of Job Scheduling and Machine Loading System in FMS

Manufacturing industries are rapidly changing from production of scale to production of scope characterized by short product life cycles and increased product varieties. This implies a need to improve the efficiency of job shops while still maintaining their flexibility. These objectives are achieved by Flexible manufacturing systems (FMS). The basic aim of FMS is to bring together the productivity of flow lines and the flexibility of job shops, this duality of objectives makes the management of FMS complex. In this research, the loading problem in FMS, which is viewed as selecting a subset of jobs from the job pool and allocating them among available machines, is considered. The research investigates the number of machine loading approaches, which aim to meet the delivery dates of production orders, and at the same time reduce the manufacturing cost