Simulation analysis of a multi-product flexible assembly line

This thesis involves the use of simulation models to evaluate and optimize existing manufacturing assembly lines of electrical components. The goal of the simulation is primarily to mimic the existing production scenario in order to identify problematic areas such as bottleneck operations, conveyor speeds limiting production and factors inhibiting the performance of the resources. This simulation project uses a combination of the AweSim ® software and logic programming using MS Visual Basic ® . Through coding, the logic of the flow of the parts is demonstrated in the course of the steps such as the intermittent conveyors with single and double part flow. There are line selection rules in the models that follow restrictions which will affect the makespan, mean flow time and the utilization of the resources. Using different scenarios conclusions and recommendations are made on modifications to the existing production in order to improve makespan and mean flow time.

Pengembangan Model Optimasi Artificial Neural Network pada Penjadwalan Produksi Snack Tortilla

Production on May 2019, PT X produced 29,159 kg of Tortillas to be marketed domestically and abroad. The large amount of production shows the high consumer interest in Tortilla, which makes PT X produce large quantities in 1 month. Production of 29,159 kg was completed in 3 weeks with 3 shifts in 7 working days in the first and third week, and 6 working days in the second week. Inaccurate production planning makes Tortilla production exceed warehouse capacity, indicating that the production process is still running even though the number in June is as much as 17,346 kg and 26,835 kg in July resulting in overproduction of 6% in May 2019 and 15% in June 2019 so that there was an increase in July 2019 to 50%. The implementations of the Artificial Neural Network (ANN) method based on Particle Swarm Optimization (PSO) using the Steepest Ascent Hill Climbing Algorithm (SAHC) areoptimize the final mean flow time by 51%, reduction in makespan by 0.5 in MayJune and 0.1 in July, and a reduction in lateness by 13% after reprocessing results in an optimization that can overcome the problem of overproduction.

Multi Objective Flexible Jobshop Scheduling Problem using Heuristic Algorithm

Flexible workshop problem (FJSP) is an extension of the classic job shop problem (JSP) that allows one operation that can be performed from a collection of alternative machines on a single machine. It is closer to the actual condition in manufacturing. Due to the additional conditions to assess the allocation of system operations, FJSP is more It's also a typical problem in combinatorial optimization. But the difference is that all the workers in the shop floor may or may not be handled in all the computers. In just one machine or two machines, a job can be processed or a separate task in all machines may have to go through the processing in order to be finished. Each computer has different work sequences. So it's an internet str complex. The classical workshop scheduling problem varies from the problem of the flow shop and the work flow is not unidirectional. It is more complex than JSP, combining all JSP's problems and complexities. All workers have the same operations series. In this field, in the objective of minimizing "make period time" and mean flow time, the problem is considered with bi-criteria.. nitially manual calculation is done with the question of literature and then with the method of Gantt chart for collecting industrial data.

The mean flow time in open shop scheduling

Purpose The purpose of this paper is to minimize the mean flow time in open shop scheduling problem (OSSP). The scheduling problems consist of n jobs and m machines, in which each job has O operations. The processing time for 50 OSSP was generated using a linear congruential random number. Design/methodology/approach Different evolutionary algorithms are used to minimize the mean flow time of OSSP. This research study used simulated annealing (SA), Discrete Firefly Algorithm and a Hybrid Firefly Algorithm with SA. These methods are referred as A1, A2 and A3, respectively. Findings A comparison of the results obtained from the three methods shows that the Hybrid Firefly Algorithm with SA (A3) gives the best mean flow time for 76 percent instances. Also, it has been observed that as the number of jobs increases, the chances of getting better results also increased. Among the first 25 problems (i.e. job ranging from 3 to 7), A3 gave the best results for 13 instances, i.e., for 52 percent of the first 25 instances. While for the last 25 problems (i.e. Job ranging from 8 to 12), A3 gave the best results for all 25 instances, i.e. for 100 percent of the problems. Originality/value From the literature it has been observed that no researchers have attempted to solve OOSPs using Firefly Algorithm (FA). In this research work an attempt has been made to apply the FA and its hybridization to solve OSSP. Also the research work carried out in this paper can also be applied for a real-time Industrial problem.

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

This paper considers the multiobjective scheduling of flexible manufacturing systems (FMSs). Due to high degrees of route flexibility and resource sharing, deadlocks often exhibit in FMSs. Manufacturing tasks cannot be finished if any deadlock appears. For solving such problem, this work develops a deadlock-free multiobjective evolutionary algorithm based on decomposition (DMOEA/D). It intends to minimize three objective functions, i.e., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. A type of a discrete differential evolution (DDE) algorithm is also developed for solving each subproblem. The mutation operator of the proposed DDE is based on the hamming distance of two randomly selected solutions, while the crossover operator is based on Generalization of Order Crossover. Experimental results demonstrate that the proposed DMOEA/D can significantly outperform a Pareto domination-based algorithm DNSGA-II for both 2-objective and 3-objective problems on the studied FMSs.

Performance optimization of a flexible manufacturing system using simulation: the Taguchi method versus OptQuest

The Taguchi method is widely used in the field of manufacturing systems performance simulation and improvement. On the other hand, Arena/OptQuest is one of the most efficient contemporary simulation/optimization software tools. The objective of this paper is to evaluate and compare these two tools applied to a flexible manufacturing system performance optimization context, based on simulation. The principal purpose of this comparison is to determine their performances based on the quality of the obtained results and the gain in the simulation effort. The results of the comparison, applied to a flexible manufacturing system mean flow time optimization, show that the Arena/OptQuest optimization platform outperforms the Taguchi optimization method. Indeed, the Arena/OptQuest permits one, through the lowest experimental effort, to reliably minimize the mean flow time of the studied flexible manufacturing system more than the Taguchi method.

A Genetic Algorithm for Hybrid Job-Shop Scheduling Problems with Minimizing the Makespan or Mean Flow Time

We address a generalization of the classical job-shop problem which is called a hybrid job-shop problem. The criteria under consideration are the minimization of the makespan and mean flow time. In the hybrid job-shop, machines of type [Formula: see text] are available for processing the specific subset [Formula: see text] of the given operations. Each set [Formula: see text] may be partitioned into subsets for their processing on the machines of type [Formula: see text]. Solving the hybrid job-shop problem implies the solution of two subproblems: an assignment of all operations from the set [Formula: see text] to the machines of type [Formula: see text] and finding optimal sequences of the operations for their processing on each machine. In this paper, a genetic algorithm is developed to solve these two subproblems simultaneously. For solving the subproblems, a special chromosome is used in the genetic algorithm based on a mixed graph model. We compare our genetic algorithms with a branch-and-bound algorithm and three other recent heuristic algorithms from the literature. Computational results for benchmark instances with 10 jobs and up to 50 machines show that the proposed genetic algorithm is rather efficient for both criteria. Compared with the other heuristics, the new algorithm gives most often an optimal solution and the average percentage deviation from the optimal function value is about 4%.

Pengembangan Algoritma Non Delay Pada Kasus Penjadwalan Non-Permutation Hybrid Flowshop Untuk Minimasi Mean Flowtime

Production scheduling is one of the key success factors in the production process. Scheduling approach with Non-Permutation flow shop is a generalization of the traditional scheduling problems Permutation flow shop for the manufacturing industry to allow changing the job on different machines with the flexibility of combinations. This research tries to develop a heuristic approach that is non-delay algorithm by comparing Shortest Processing Time (SPT) and Largest Remaining Time (LRT) in the case of non-permutation flow shop to produce minimum mean flow time ratio. The result of simulation shows that the SPT algorithm gives less mean flow time value compared to LRT algorithm which means that SPT algorithm is better than LRT in case of non-permutation hybrid flow shop.

Reduction in Lead Time of Automated Gas Fired Kit Fitting Box Using QC Tools and Strategies.

Recently in a competitive manufacturing sector, lead time is the most important factor in order to compete in the market. This leads every manufacturer to decrease production time and increase quality of the product. Due to improper work cycle delay of a product occurs. The major changes required are operation sequence, flow of material in the workshop, work cycle, etc. In this paper, the authors have delved into the causes of excessive lead time and suggest practical inexpensive strategies for reducing it. Recommendations are based on detailed study of manufacturing facility and processes for a time period of 6 months in the industry. After describing the relationship between lead time, material flow time and variance and reviewing potential methods for reducing lead time by reducing mean flow time of material and operation time of an automated gas fired kit fitting box (hereafter termed as “box”). The aim of this paper is also to demonstrate the use of QC tools and Kaizen in the industry as to tool for improvement in manufacturing sector especially in small to medium scale industries.