Simulation-based SERL,System Formation And Its Performance Comparison With Line System

With the acceleration of globalization and the continuous improvement of people’s living standards,the manufacturing industry is not only facing fierce market competition at home and abroad,but also facing the challenge of market demand fluctuation.With the injection of foreign high-tech products,customers begin to accept and even prefer foreign products.The continuous improvement of residents’ living standards has brought about great changes in residents’ consumption concepts.Standardized and personalized products are difficult to meet the diversified and personalized needs of consumers.With the continuous development of science and technology,the upgrading speed of electronic technology products is accelerated,which greatly shortens the life cycle of products and makes the products on the market diversified.Therefore,the market demand environment of manufacturing industry gradually shows the following characteristics:multiple product varieties,small batch,short product life cycle,short product delivery time and high product quality requirements.With the increasingly fierce market competition,manufacturing enterprises will face a very severe living environment.It is urgent to strengthen their core competitiveness in production efficiency,production cost and production stability.The traditional assembly line is difficult to adapt to the complex and changeable market demand environment.Enterprises using large-scale production also begin to expose many problems.Manufacturing enterprises urgently need to change and innovate the production mode.The proposal of Japan’s Seru production mode has greatly improved the production environment of Japanese manufacturing enterprises.Seru is a renovation of traditional assembly line,which has the characteristics of high efficiency and flexibility,and has strong adaptability to the market demand environment of multi-varieties and small batches.But at present,the related research only stays in the certain production scenario,lacks the research on the production scenario with strong randomness,and the strong randomness conforms to the development trend of the times.Therefore,this paper studies the problem of line-cell conversion in stochastic production scenarios and uses simulation technology overcomes the shortcomings of precise mathematical model which is difficult to describe randomness.This paper solves the problem of Seru formation in stochastic production scenario,and verifies and explains the advantages and application scenarios of Seru by comparing the performance of assembly line in the same production scenario and analyzing the influencing factors and provides guidance and basis for Chinese local enterprises to implement this production mode.As an important part of NSFC’s key international cooperation project(7141001024),this paper studies the problem of line-cell conversion in stochastic production scenarios,and studies its conversion methods and applicable scenarios.The main work of this paper includes:1)Construct the index system to describe and evaluate the production system in stochastic production scenario.The performance indicators of traditional research mainly focus on static and one-time indicators,such as the completion time and the total labor time of demand-determined orders.In this paper,from the perspective of stochastic production scenario,it is extended to three evaluation indexes:the expectation of unit product production time(TTPTR),the variation coefficient of unit product production time(TTPTRV),and the expectation of unit product labor cost(TLCR).It enriches and develops the theory of the Seru production system formation from different perspectives,further expands the application scope of the theory and method of unit assembly system management,and lays the theoretical foundation for the follow-up research.2)Optimizing the formation of the Seru production system in stochastic scenario.This paper will use the hybrid heuristic algorithm of NSGA-II and neighborhood search to optimize the formation of the Seru production system.By embedding the neighborhood search algorithm into the NSGA-II algorithm as an operator to update the offspring of NSGA-II algorithm during iterative updating,it not only improves the local optimization ability of the NSGA-II algorithm,but also overcomes the defect that the neighborhood search algorithm depends on the initial solution structure.In addition,the hybrid heuristic algorithm also sets the neighborhood search probability parameters,which can balance the efficiency and time of solution.Generally,the higher probability of neighborhood search,the higher solving efficiency,and the longer relative solving time.By optimizing the Seru production system formation,the feasible Pareto frontier solution set under multi-objective can be obtained,and the formation strategy of the Seru production system can be selected according to users’ preferences for different objectives.3)Design and build simulation model and simulation management system.In order to solve the problem of randomness,this paper uses simulation technology to simulate the real scene,and uses Arena simulation software to establish line-cell simulation model to calculate the performance indicators of the optimized Seru production system and assembly line.At the same time,this paper also designs and develops a simulation management system for the management and use of simulation models.Its main functions include adding and loading production scenarios,setting up order resource parameters,optimizing Seru production system,simulation operation management,overview of historical results,design experiments and comparison of experimental results.The system is easy to operate and efficient to help enterprises solve the line-cell conversion problem.4)A comparative analysis of the performance of Seru production system and assembly line in stochastic production scenarios.In different production scenarios,the performance of assembly line is not necessarily lower than that of Seru production system.Therefore,this paper studies and analyzes the influence of production factors,such as the number of stations,product types and lot sizes,on P_TTPTR(relative improvement degree of production time per unit product)and P_TLCR(relative improvement degree of labor cost per unit product)after line-cell conversion under different production scenarios,such as certain production scenarios(CERT),lot size fluctuation scenarios(LOT-VAR),product type fluctuation scenarios(TYPE-VAR)and common fluctuation stochastic production scenarios(CO-VAR).At the same time,the results in system stability between deterministic production scenarios and stochastic production scenarios are analyzed,and the following conclusions are drawn:(1)The TLCR performance always improved.However,the TTPTR performance of the Seru is lower than that of the assembly line when TLCR is minimized.(2)The number of stations has a positive effect on the performance improvement of TTPTR and TLCR by line-cell conversion.However,when the number of stations reaches the upper limit of the worker’s effective operation process,there may be turning points.(3)Product type has a positive effect on TTPTR performance improvement by line-cell conversion,but has no effect on TLCR.(4)Lot size has a negative effect on TTPTR and TLCR performance improvement by line-cell conversion.(5)Demand-determined production scenarios have no volatility,and the final solution is generally relatively good.However,stochastic production scenarios have volatility,its solution will have some deviation.(6)In stochastic production scenarios,with the increase of product types,the randomness increases.FCFS scheduling will make the Seru production lose the advantage of flexibility,while demand-determined production scenarios generally do not have this problem.