Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

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

Performance evaluation of flexible manufacturing systems under uncertain and dynamic situations

The present era demands the efficient modelling of any manufacturing system to enable it to cope with unforeseen situations on the shop floor. One of the complex issues affecting the performance of manufacturing systems is the scheduling of part types. In this paper, the authors have attempted to overcome the impact of uncertainties such as machine breakdowns, deadlocks, etc., by inserting slack that can absorb these disruptions without affecting the other scheduled activities. The impact of the flexibilities in this scenario is also investigated. The objective functions have been formulated in such a manner that a better trade-off between the uncertainties and flexibilities can be established. Consideration of automated guided vehicles (AGVs) in this scenario helps in the loading or unloading of part types in a better manner. In the recent past, a comprehensive literature survey revealed the supremacy of random search algorithms in evaluating the performance of these types of dynamic manufacturing system. The authors have used a metaheuristic known as the quick convergence simulated annealing (QCSA) algorithm, and employed it to resolve the dynamic manufacturing scenario. The metaheuristic encompasses a Cauchy distribution function as a probability function that helps in escaping the local minima in a better manner. Various machine breakdown scenarios are generated. A ‘heuristic gap’ is measured, and it indicates the effectiveness of the performance of the proposed methodology with the varying problem complexities. Statistical validation is also carried out, which helps in authenticating the effectiveness of the proposed approach. The efficacy of the proposed approach is also compared with deterministic priority rules

Non-Reshuffle-Based Approach for Rescheduling of Flexible Manufacturing System

Scheduling and rescheduling play vital roles in ensuring the effectiveness of the production control in flexible manufacturing system (FMS).  The failure of these systems may interrupt the efficiency of the production activities and thus may lessen the profit to be gained by the company. The FMS scheduling problem is considered as dynamic as new orders may get in every day. The new orders need to be immediately desegregated with the existing production schedule by preserving the efficiency and stability of the existing schedule. This research applies the non-reshuffle-based genetic match-up algorithms which admit new orders by manipulating available machine idle times to address rescheduling problem in a FMS that practises the pull strategy. The idea of the match-up approach is to update only a part of the initial schedule and genetic algorithms used to optimise the solution within the rescheduling horizon in such a way in order to preserve the efficiency and stability of the shop floor. The proposed methodology has been tested using different rescheduling parameters. The experiments show that the rescheduling method improves efficiency and stability of the new schedule

A genetic algorithm approach for the scheduling in a robotic-centric flexible manufacturing system

Scheduling assumes a crucial importance in manufacturing systems, optimizing the allocation of operations to the right resources at the most appropriate time. Particularly in the Flexible Manufacturing System (FMS) topology, where the combination of possibilities for this association exponential increases, the scheduling task is even more critical. This paper presents a heuristic scheduling method based on genetic algorithm for a robotic-centric FMS. Real experiments show the effectiveness of the proposed algorithm, ensuring a reliable and optimized scheduling process.This work was funded by the ERDF European Regional Development Fund through the COMPETE Programme (operational programme for competitive-ness) and by National Funds through the FCT within projects SAICTPAC/0034/2015,POCI-01-0145-FEDER-016418, POCI-01-0145-FEDER-007043 and UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio

A Predictive-reactive Approach for JSP with Uncertain Processing Times

The paper is supported by the Asia-Link project funded by the European Commission (CN/ASIA-LINK/024 (109093)), the National Natural Science Foundation of China (50705076, 50705077), and the National Hi-Tech R&D Program of China (2007AA04Z187)JSP with discretely controllable processing times (JSP-DCPT) that are perturbed in a turbulent environment is formulated, based on which, a time-cost tradeoff based predictive-reactive scheduling approach is proposed for solving the problem. In the predictive scheduling process, on the basis of a proposed three-step decomposition approach for solving JSP-DCPT, a solution initialization algorithm is presented by incorporating a hybrid algorithm of tabu search and simulated annealing and a fast elitist non-dominated sorting genetic algorithm; in the reactive scheduling process, Pareto-optimal schedules are generated, among which every schedule that is not dominated by any initial schedule can be selected as the responding schedule so as to maintain optimality of the objective that is to minimize both the makespan and the cost. Experimental simulations demonstrate the effectiveness of the proposed approach

The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

Scheduling of Flexible Manufacturing Systems using Intelligent heuristic search algorithm (IHSA*)

The complete scheduling of FMS includes two independent processes: sequencing of jobs and scheduling those prioritized jobs. In a flow shop or a Progressive type FMS, scheduling problem involves sequencing of ‘n’ jobs on ‘m’ machines with minimum makespan. Intelligent heuristic search algorithm (IHSA*) is used in this paper, which ensure to find an optimal solution for flow-shop problem involving arbitrary number of machines and jobs provided the job sequence is same on each machine. The initial version of IHSA* is based on the A* algorithm. The final version of IHSA* is the modification of the initial IHSA*. There are three modifications: first modification concerned with the selection of an admissible heuristic function, second modification concerned with the procedure which determine heuristic estimate as the search progresses and the third modification concerned with the searching of multiple optimal solution, if they exist. Both version of the IHSA* are presented in this paper with an example which illustrates the use of both