6 P1, P13, P25, P26, P31, P39 M7, M10
6 1 P17, P18, P22 M1, M2, M8 2 P19 M16, M17 3 P1, P2, P3, P4, P5, P6, P7, P16 M3, M4, M5, M6, M7
7 1 P7, P18 M1, M7, M16, M20 2 P2, P10, P13, P14 M2, M10, M11, M13, M17, M19 3 P5, P15, P20 M3, M8 4 P3, P8, P11, P12, P17 M4, M14, M15, M18 5 P16 M5 6 P1, P4, P6, P9 M6, M9
8 1 P1, P9, P16, P17, P33 M1, M13, M21, M22 2 P10, P13, P14, P22, P35, P36 M2, M5, M11, M19 3 P2, P11, P12, P15, P23, P24, P31, P34 M3, M20 4 P8, P19, P21, P28, P37, P38, P39 M4, M16
9 1 P1, P18 M1, M4, M11 2 P2, P3, P9, P17 M2, M5, M9, M13 3 P4, P6, P8, P10, P11 M3, M10
10 1 P16, P34, P50 M1 VII. CONCLUSION
The objective of the study is to employ the assignment allocation algorithm to different cell formation problems. This
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Figure 10 shows the results between the hybridg algorithm compared to other algorithms with various mathematical functions as presented by privous sessions. It is cleard to see that the hybride algorithm performed better than other techniques. Surrprisingly, the Ackley function performed closely to the hybride algorithm. Figure 11 - 14 shows that PSO reached the optimal result very quickly because this algorithm works as a local search which makes a narrow space for the search of a solution, rather than other algorithms which work as a global search
The remaining results used to obtain the graph in the next section can be obtained by, iteratively substituting the parameters shown in table 3 below for the various architectures and various population sizes. The system parameters given in [16] is shown in table 3.
In this report will be analysed the current production line of a manufacturing company that produces three different products. Because the system is consuming too much resources and it’s not very efficiently, ways to improve all this factors is needed. By identifying the bottleneck an alternative solution can be proposed for this problem.
In reality, many creators have upheld the view that AI can make a significant contribution to enhancing control and manufacturing systems. The rest of the paper is composed as takes after: in the following area, we introduce a case for supporting the view that AI can prompt enhancing manufacturing systems. In area three we layout the AI methods that are considered in this paper together with the parts of a rearranged shrewd assembling framework. Segment 4 will take a gander at the distinctive AI techniques used for the different components of of an intelligent manufacturing
The job shop scheduling problem is the combinatorial problem in which there can be many objective functions. In this paper we have selected the makespan to be the objective function. The main objective of any scheduling is to create a schedule which specifies when each task is to begin and what resources it will use that satisfies all the constraints while taking as little overall time as possible. The scheduling in which there is a specific order of a job to be processed by perticular machines is called as job-shop scheduling problem. This kind of scheduling and sequecing of operations deals with the allocations of resources optimally over time so as
Mass production and customization used to be considered opposites and were considered to be opposite ends of a spectrum (Beaty, 1996). Mass customization allows that company to provide products meeting unique specifications while at the same time achieving a low manufacturing cost by using mass production technologies to provide one-of-a-kind products (Daft, 2013; Welborn, 2007; Pine & Gilmore, 1997). This idea has been around for over a decade, but just recently has technology caught up with the idea and allowed it to become an efficient and profitable process (Sherman, 2013; Weintraub, 2013).
Flexible manufacturing systems are a form of customizable technology that allow companies to gain a competitive advantage through adjusting their output in terms of what and how much is produced. "'System' is the key word. Philosophically, FMS incorporates a system view of manufacturing. The buzz word for today's manufacturer is 'agility.' An agile manufacturer is one who is the fastest to the market, operates with the lowest total cost and has the greatest ability to 'delight' its customers. FMS is simply one way that manufacturers are able to achieve this agility" (Flexible manufacturing systems, n.d, The University of Kentucky).
In the present scenario of manufacturing, agile manufacturing calls for flexibility in the global market which involves rapid changes. Flexible manufacturing technology such as agent manufacturing plays a vital role to achieve agility in the system. (Yeung, W. 2012) in job shop floor the project scheduling technique provides the dynamic and unique way of dispatching the jobs if there is any variabilities in the manufacturing system, resources, production disturbances and irregular arrival of products. The main emphasis is designing the network schedules which will be applicable to be implemented in a competitive operating condition. These software scheduling tools will be able to represent the manufacturing tasks, sub tasks, work system and people involved. (Wang, 2015) using simulation tools, the performance of a (simulated) multi-agent manufacturing system under particular operating conditions can be analyzed in terms of. Simulation models are developed based on current system to eliminate bottlenecks, to prevent under or over-utilization of resources, and to optimize system performance.
The traditional design of manufacturing control systems does not allow for rapidly expanding options in materials, processes, interfaces to product models that have a number of variants. Holons allow the implementation of production variants and the rapid reconfiguration of the machines and robots. Proposed MES architecture supports the capability of production systems. Demand chain management will allow for a significant reduction in waste and will increase the profitability of production systems. Flexible production planning will effect in reducing the setup and changeover time and costs. The strategic target of proposed MES solution is "one piece flow production" that means the feasibility of short series production (up to one element) by using the production lines designed formerly for mass manufacturing.
overall it 's very obvious that both flexibility and reconfigurability are the best way to deal with production system changes and both are very closely related to human beings who involves in the manufacturing system which ever cannot be achieved by using technology alone. [15]
population based or metaheuristic algorithm. It used the successful characteristicsof bees in different section such as are employed, onlooker and scout bees. The number of employed bees or the onlooker bees is equal to the number of solutions inthe swarm. The employed, onlooker bees used for exploitation process for a given problem towards best solution space given in equation (3). While scout bees use forexploration process through the following strategy as given in equation (4). 〖 V〗_ij=x_ij+θ_ij (x_ij-x_kj ) (3)Where vij is a new solution in the neighbourhood of xij for the employed bees, k isa solution in the neighbourhood of i, Φ is a random number in the range [-1, 1]. 〖 x 〗_ij^rand=〖x
The cluster boundary algorithm is divided in units (1) searching for the extreme genes in cluster (2) calculate the middle point of the extreme genes (3) calculate the mid-points between each extreme point-pairs (4) determine the radius of a ball with interior point in the cluster (p4).
A common process of EC algorithms is as follows. Initially, the population are randomly scattered the search space. Along with the evolution process, the population
The first type of problem discussed was the global location allocation problem. These problems are often seen in large business and can be solved by combining AHP and GP. However, in this smaller example the decision maker is choosing where to send his sales associates. According to the AHP, the goal must first be set, then the criteria, then the alternative locations, as seen in Figure 2.
Job – shop is a system that process n number of tasks on m number of machines. In this type of environment, products are made to order and in a low volume. Usually, these orders are differ in term of processing requirements, materials needed, processing time, processing sequence and setup times. Genetic algorithms are inspired by Darwin 's theory about evolution. Solution to a problem solved by genetic algorithms is evolved. Algorithm is started with a set of solutions (represented by chromosomes) called population.