Layout Planning Technology of Cellular Manufacturing System Based on Process Interconnection Analysis

With sustaining change of production mode,layout planning is no longer a thing built once for all.Cellular layout(CL) is becoming a hotspot in the research field of manufacturing system layout.Traditional researches on layout planning are mainly concentrating on aspects of layout arithmetic,style and evaluation,etc.Relatively seldom efforts are paid to CL and its specific problems as cell formation(CF),equipment sharing and CL analysis.Through problem analyzing of layout in cellular manufacturing system(CMS),research approach of cell formation,interactive layout and layout analysis threaded with process interconnection relationship(PIR) is proposed.Typical key technologies in CL like CF technology based on similarity analysis of part processes,interactive visual layout technology,layout evaluation technology founded on PIR analysis and algorithm of cell equipment sharing are put forward.Against the background of one enterprise which encounters problems of low utility of key equipments and disperse material logistic,an example of four-cell layout is given.The CL adjustment and analysis results show that equipment with high level of sharing degree should be disposed around the boundary of its main cell,and be near to other sharing cells as possible; process route should be centralized by all means,so equipment adjustment is to be implemented along direction that route intersection can be decreased; giving consideration to the existence of discrete cell,logistic route and its density should be centralized to cells formed.The proposed research can help improve equipment utility and material logistic efficiency of CL,and can be popularized to other application availably.

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

This article presents a comprehensive mathematical model for the design and analysis of Dynamic Cellular Manufacturing Systems （DCMS）. The proposed DCMS model considers several manufacturing attributes such as multi period production planning, dynamic system reconfiguration, duplicate machines, machine capacity, the available time for workers, worker assignments, and machine procurement. The objective is to minimize total costs; consisting of holding cost, outsourcing cost, inter-cell material handling cost, maintenance and overhead cost, machine relocation cost. While a study of published articles in the area of Cellular Manufacturing Systems （CMS） shows that workforce management issues have not sufficiently been addressed in the literature, the model presented also incorporates CMS workforce management issues such as salaries, hiring and firing costs of workers in addition to the manufacturing attributes. In-depth discussions on the results for two numerical examples are presented to illustrate applications of the proposed model. The model developed aims to raise the envelope by expanding and improving several CMS models previously presented in the literature.

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

This paper presents a new,bi-criteria mixed_integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system.The objective of this model is to minimize the makespan and intercell movements simultaneously,while considering sequence-dependent cell setup times.In the cellular manufacturing systems design and planning,three main steps must be considered,namely cell formation(i.e,piece families and machine grouping),inter and intra-cell layouts,and scheduling issue.Due to the fact that the cellular manufacturing systems problem is NP-Hard,a genetic algorithm as an efficient meta-heuristic method is proposed to solve such a hard problem.Finally,a number of test problems are solved to show the efficiency of the proposed genetic algorithm and the related computational results are compared with the results obtained by the use of an optimization tool.

A review of intelligent optimization for group scheduling problems in cellular manufacturing

Given that group technology can reduce the changeover time of equipment,broaden the productivity,and enhance the flexibility of manufacturing,especially cellular manufacturing,group scheduling problems(GSPs)have elicited considerable attention in the academic and industry practical literature.There are two issues to be solved in GSPs:One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each group.Although a number of studies on GSPs have been published,few integrated reviews have been conducted so far on considered problems with different constraints and their optimization methods.To this end,this study hopes to shorten the gap by reviewing the development of research and analyzing these problems.All literature is classified according to the number of objective functions,number of machines,and optimization algorithms.The classical mathematical models of single-machine,permutation,and distributed flowshop GSPs based on adjacent and position-based modeling methods,respectively,are also formulated.Last but not least,outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

Current status,enablers&barriers of implementing cellular manufacturing system in Indian industries

The cellular manufacturing （CM） has been proved as a well-known manufacturing strategy that helps to improve manufacturing efficiency and productivity by utilizing the philosophy of group technology. Large num- ber of papers has been published in the area of design issues of CM system. Unfortunately, the issues related to acceptability of CM in Indian industries are typically not examined rigorously as technical issues. This paper pre- sents the results of a survey carded out to find the status, enabler and barrier of implementing CM system in Indian industries.

EVOLUTIONARY ALGORITHMS WITH PREFERENCE FOR MANUFACTURING CELLS FORMATION

Due to the combinatorial nature of cell formation problem and the characteristics of multi-objective and multi-constrain, a novel method of evolutionary algorithm with preference is proposed. The analytic hierarchy process （AHP） is adopted to determine scientifically the weights of the sub-objective functions. The satisfaction of constraints is considered as a new objective, the ratio of the population which doesn＇t satisfy all constraints is assigned as the weight of new objective. In addition, the self-adaptation of weights is applied in order to converge more easily towards the feasible domain. Therefore, both features multi-criteria and constrains are dealt with simultaneously. Finally, an example is selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.

Production and characterization of high porosity porous Fe?Cr?C alloys by the space holder leaching technique

Spherical carbamide particles were employed to produce porous Fe–Cr–C alloy with high porosity and large aperture via the space-holder leaching technique. A series of porous samples were prepared by regulating the processing parameters, which included the carbamide content and the compaction pressure. The pore characteristics and compression properties of the produced samples were investigated. The samples were characterized by scanning electron microscopy, image analysis, and compression tests. The results showed that the macro-porosity and the mean pore size were in the ranges 40.4%–82.4% and 0.6–1.5 mm, respectively. The compressive strength varied between 25.38 MPa and 127.9 MPa, and was observed to decrease with increasing total porosity.

Improved PSO for integrating dynamic cell formation and layout problems

To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of product demands and the lim it of machine capacity,the existing layout needed to be rearranged to a high degree.Secondly,a mathematical model was established for the objective function of minimizing the total costs.Thirdly,a novel dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm based on the communication learning strategy(CLS)was developed.Toavoid falling into local optimum and slow convergence,each swarm shared their optimal locations before regrouping.Finally,simulation experiments were conducted under different conditions.Numerical results indicate that the proposed algorithm has better stability and it converges faster than other existing algorithms.

COST AND SERVICE-LEVEL-BASED MODEL FOR A SERU PRODUCTION SYSTEM FORMATION PROBLEM WITH UNCERTAIN DEMAND

With increasing demand diversification and short product lifecycles, industries now encounter challenges of demand uncertainty. The Japanese seru production system has received increased attention owing to its high efficiency and flexibility. In this paper, the problem of seru production system formation under uncertain demand is researched. A multi-objective optimization model for a seru production system formation problem is developed to minimize the cost and maximize the service level of the system. The purpose of this paper is to formulate a robust production system that can respond efficiently to the stochastic demand. Sample average approximation （SAA） is used to approximate the expected objective of the stochastic programming. The non-dominated sorting genetic algorithm II （NSGA-II） is improved to solve the multi-objective optimization model. Numerical experiments are conducted to test the tradeoffbetween cost and service level, and how the performance of the seru production system varies with the number of product types, mean and deviation of product volume, and skill-level-based cost.

Solving the rotating seru production problem with dynamic multi-objective evolutionary algorithms

Today's volatile market conditions in electronic industries have lead to a new production system,seru(which is the Japanese pronunciation for cell),and has been widely implemented in hundreds of Japanese and other Asia companies.In particular,the rotating seru has been widely implemented,where workers are fully cross-trained with the same skill level but may be different on the proficiency of performing tasks.The rotating seru production problem,which determines the rotating sequence of workers as well as the assembling sequence of jobs,is difficult to solve due to conflicting objectives and dynamic release of customer demands.To solve this problem,we propose a dynamic multiobjective NSGA-II based memetic algorithm.Moreover,to preserve desirable population diversity and improve the searching efficiency,we propose different problem-specific evolutionary strategies.Finally,we test the performance of our proposed memetic algorithm with other state-of-the-art multi-objective evolutionary algorithms and demonstrate the effectiveness of our proposed algorithm.