Models for cell loading and product sequencing in labor-intensive cells

This paper focuses on cell loading issues and product sequencing in labor-intensive cells. In labor-intensive cells, there are usually more operators than number of operations and cells usually consists of simple and light-weight machines and equipment. A three-phase methodology is proposed to deal with this problem. The objectives considered are minimizing makespan, total machine requirements, and intra-cell manpower transfers. In the first phase, optimal manpower allocation to operations is determined for each product. Then, similarity among products is established based on the similarity of operator/machine levels. The second phase involves cell loading to minimize makespan and machine requirements. Two mathematical models are developed to accomplish these tasks. One mathematical model (model A) does not allow product splitting whereas model B allows product splitting. Finally, third phase treats the product sequencing problems as traveling salesman problem (TSP) where the objective is to minimize intra-cell manpower transfers. Experimentation is performed and the results are compared with those of a heuristic procedure developed earlier. The results show that model A or model B can be chosen over the heuristic procedure.     

Intra-cell manpower transfers and cell loading in labor-intensive manufacturing cells

Labor-intensive manufacturing cells consist of simple machines and equipment that require continuous operator attendance and involvement. Operators are often re-assigned to different machines when a new product is released to the cell. The main reason for this re-assignment is to maximize the output rate of the cell by balancing the flow of products through several machines with varying capacities. In this paper, first a product-sequencing problem with the objective of minimizing the total intra-cell manpower transfers is introduced. A three-phase hierarchical methodology is proposed to solve the problem optimally. Next, manpower transfer matrix values are modified considering the distances traveled among machines. In the second part of the paper, a machine-level-based similarity coefficient that uses the number of machines as a similarity measure is discussed. Later, these coefficients are used during the cell loading process to minimize makespan and also machine and space requirements. Manpower allocation decisions are made along with scheduling decisions that are critical in most labor-intensive manufacturing cells and both approaches are illustrated with an example problem.     

A six sigma based multi-objective optimization for machine grouping control in flexible cellular manufacturing systems with guide-path flexibility

Effective design of automatic material handling devices is one of the most important decisions in flexible cellular manufacturing systems. Minimization of material handling operations could lead to optimization of overall operational costs. An automated guided vehicle (AGV) is a driverless vehicle used for the transportation of materials within a production plant partitioned into cells. The tandem layout is according to dividing workstations to some non-overlapping closed zones to which a tandem automated guided vehicle (TAGV) is allocated for internal transfers. This paper illustrates a non-linear multi-objective problem for minimizing the material flow intra and inter-loops and minimization of maximum amount of inter cell flow, considering the limitation of TAGV work-loading. For reducing variability of material flow and establishing balanced zone layout, some new constraints have been added to the problem based on six sigma approach. Due to the complexity of the machine grouping control problem, a modified ant colony optimization algorithm is used for solving this model. Finally, to validate the efficacy of the proposed model, numerical illustrations have been worked out.     

An integrated model for solving cell formation and cell layout problem simultaneously considering new situations

Cellular manufacturing system (CMS) is one of the group technology (GT) usages. Among the necessary decisions for a successful CMS implementation, cell formation problem (CFP) and cell layout problem (CLP) are two most popular ones. The majority of past studies in CMS discussed on CFPs and some of those focused on CLP ones. A few researchers solve the CPF and CLP simultaneously. In this paper, we present a new integrated mathematical model considering cell formation and cell layout simultaneously. The goal of our model is to group similar parts and corresponding different machines in same cells. Machines sequence in each cell and cell positions is also specified in the system. Moreover, our proposed model considers forward and backtracking movements as well as new assumptions for distances between cells using sequence data and production volume. One appropriate adjusted measure from the literature and two new measures of performance for evaluating solutions are defined. To validate the model, two well-known critical benchmark examples are employed. Computational experiments demonstrate that our proposal is a proficient model and show the effectiveness of our implementation.     

Integrated design of workcells and unidirectional flowpath layout

In manufacturing cells layout design with a unidirectional flow system, the accurate distance between two workcells can be uncovered with both the determination of IO port locations after the layout design of the cell with its orientation and the unidirectional flowpath layout design. This paper presents the method to obtain a global solution for manufacturing workcells and unidirectional flowpath layout design (ICFLD) with consideration of IO ports of workcells. The flow distance between two workcells is calculated from output port of one workcell to input port of the other workcell through the unidirectional flowpath layout. A zero-one integer programming model is developed for the ICFLD problem. And a heuristic algorithm for the ICFLD problem is developed by decomposing the ICFLD problem into two subproblems and iteratively and alternately solving the decomposed subproblems. Computational experiments are performed and its results are analyzed.     

A heuristic algorithm for the distributed and flexible job-shop scheduling problem

The distributed manufacturing takes place in a multi-factory environment including several factories, which may be geographically distributed in different locations, or in a multi-cell environment including several independent manufacturing cells located in the same plant. Each factory/cell is capable of manufacturing a variety of product types. An important issue in dealing with the production in this decentralized manner is the scheduling of manufacturing operations of products (jobs) in the distributed manufacturing system. In this paper, we study the distributed and flexible job-shop scheduling problem (DFJSP) which involves the scheduling of jobs (products) in a distributed manufacturing environment, under the assumption that the shop floor of each factory/cell is configured as a flexible job shop. A fast heuristic algorithm based on a constructive procedure is developed to obtain good quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and promising for practical problems.     

基于扩展状态任务网的制造供应链计划

制造供应链计划是制造供应链管理的关键问题,它不仅需要分配生产任务和控制库存,还需要解决不同工厂(企业)间的运输配套问题.为统一描述具有复杂产品生产过程(包括装配型、分解型和多输入多输出型等)的生产任务、存储任务和不同模式(包括单种物料独立运输模式和多种物料组合运输模式)的运输任务,提出了扩展状态任务网(extended state task network,简称ESTN).扩展状态任务网用比例转化任务统一描述生产任务、存储任务和单种物料独立运输任务,用虚比例转化任务和组合移动任务共同描述多种物料组合运输任务.应用扩展状态任务网,meta启发式方法在求解制造供应链问题时更容易编码和操作.为求解基于ESTN的制造供应链计划模型,提出了具有多样性检测的参考解集更新策略与分散性解变异策略的路径重连算法.路径重连算法维护一个由高质量解(精英解)组成的参考解集,将一个向导精英解的属性逐步引入一个起始精英解而形成的中间解序列(路径),并用此中间解序列更新参考解集以获得进化.计算实例表明,该路径重连算法比标准遗传算法、标准Tabu搜索算法以及普通路径重连算法能够获得更好的解,证明了多样性检测对参考解集更新的关键作用以及分散性解变异策略在提高解的质量上的能力.     

A multi-objective scatter search for a dynamic cell formation problem

Cellular manufacturing system—an important application of group technology (GT)—has been recognized as an effective way to enhance the productivity in a factory. Consequently, a multi-objective dynamic cell formation problem is presented in this paper, where the total cell load variation and sum of the miscellaneous costs (machine cost, inter-cell material handling cost, and machine relocation cost) are to be minimized simultaneously. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for finding locally Pareto-optimal frontier. To demonstrate the efficiency of the proposed algorithm, MOSS is compared with two salient multi-objective genetic algorithms, i.e. SPEA-II and NSGA-II based on some comparison metrics and statistical approach. The computational results indicate the superiority of the proposed MOSS compared to these two genetic algorithms.     

A mathematical programming model for manufacturing cell formation to develop multiple configurations

This paper presents and analyses a mathematical model for the design of manufacturing cells which considers two conflicting objectives such as the heterogeneity of cells and the intercell moves. A genetic algorithm (GA) based solution methodology is developed for the model which is also solved using an optimization package. The model is suitable for getting multiple potential solutions in a structured way for the cell formation problem by making a trade-off between the two objectives, instead of reaching at a single negotiating solution. This model provides the decision maker the flexibility of choosing a suitable cell design from different alternatives by considering the practical constraints. A part assignment heuristic is also developed by which part-families can be identified and is integrated with the GA based solution procedure. A comparison of the proposed method is made with other seven methods using 36 problems from the literature. Grouping efficacy is the basis for comparison and it is found to give reasonably good results.     

Manufacturing cell formation with production data using neural networks

Batch type production strategies need adoption of cellular manufacturing (CM) in order to improve operational effectiveness by reducing manufacturing lead time and costs related to inventory and material handling. CM necessitates that parts are to be grouped into part families based on their similarities in manufacturing and design attributes. Then, machines are allocated into machine cells to produce the identified part families so that productivity and flexibility of the system can be improved. Zero-one part-machine incidence matrix (PMIM) generated from route sheet information is commonly presented as input for clustering of parts and machines. An entry of ‘1’ in PMIM indicates that the part is visiting the machine and zero otherwise. The output is generated in the form of block diagonal structure where each block represents a machine cell having more than one machines and a part family. The major limitations of this approach lies in the fact that important production factors like operation time, sequence of operations, and lot size of the parts are not accounted for. In this paper, an attempt has been made to propose a clustering methodology based on adaptive resonance theory (ART) for addressing these issues. Initially, a methodology considering only the operation sequence of the parts has been proposed. Then, the methodology is suitably modified to deal with combination of operation sequence and operation time of the parts to address generalized cell formation (CF) problem. A new performance measure is proposed to quantify the performance of the proposed methodology. The performance of the proposed algorithm is tested with benchmark problems from open literature and the results are compared with the existing methods. The results clearly indicate that the proposed methodology outperforms the existing methods in most cases.     

A directional decomposition heuristic for one-dimensional,non-equidistant machine-cell location problems

Machine-cell location (MCL) problems have been major points of interests of many researchers dealing with cell formation and material flow analysis in a manufacturing shop floor. Since the internal configuration of the production line in a cell is the outcome of both machine-group formation and machine-location problems, the locations of these cells along a material-handling track (or transporter path) are of further interests for the refinement of the results. As the processing technology improves and computational capabilities enhance, the scope of this assignment is even more captivated by potential total benefit. In this research, a one-dimensional, non-equidistant MCL problem is considered, where the input of the system starts from the output of a group formation problem. First the bi-directional movements are decomposed into backward and forward gains and these incremental gains are exploited to explore the most potential search procedure in either of the decomposed directions. The solution procedure is simple, but efficient and good with respect to both time and quality of solution. This directional decomposition heuristic generates a relatively good solution as compared to other existing solutions in MCL analysis.     

A novel intelligent particle swarm optimization algorithm for solving cell formation problem

The formation of manufacturing cells forms the backbone of designing a cellular manufacturing system. In this paper, we present a novel intelligent particle swarm optimization algorithm for the cell formation problem. The proposed solution method benefits from the advantages of particle swarm optimization algorithm (PSO) and self-organization map neural networks by combining artificial individual intelligence and swarm intelligence. Numerical examples demonstrate that the proposed intelligent particle swarm optimization algorithm significantly outperforms PSO and yields better solutions than the best solutions existed in the literature of cell formation. The application of the proposed approach is examined in a case problem where real data is utilized for cell reconfiguration of an actual company involved in agricultural manufacturing sector.

     

Capacity utilization and machine sharing in a group technology based manufacturing system

Each cell in a group technology-based manufacturing system can be assumed as being comprised of two categories of machine types: (a) Dedicated to the cell, (b) Shared by other cells. The shared machine types constitute the major problem in the implementation of a cellular manufacturing system. This paper demonstrates, using a hypothetical case study, a mathematical programming formulation that exploits the existence of shared machine types in a cellular manufacturing system. Part transfer for the parts whose output levels are restricted by available capacity or shared machine types can be found which will minimize the material handling penalties.     

Topology analysis of manufacturing service supply–demand hyper-network considering QoS properties in the cloud manufacturing system

Cloud Manufacturing (CMfg), a new manufacturing paradigm, enables an opportunity for manufacturing enterprises to respond to a wide variety of demands. The CMfg concept integrates the distributed resources and capacities in a shared economy, transfers them to the virtual manufacturing services with various quality of service (QoS) properties. These services are invoked to collaborate in producing customized demands with specific functional and non-functional properties. The operational perspective of the service-demand matching problem is considered more in the literature; however, it is a challenging problem in the CMfg ecosystem. This paper evaluates the service-demand matching problem from a structural perspective to facilitate resilience and configurable system working well in the operational interactions. Therefore, a CMfg hyper-network-based model is proposed which is enriched with QoS properties. It employs the available capacities in all small and medium enterprises (SMEs) and large companies. The service-demand matching problem is a complex issue; so, it is impossible to analyze it with the current approaches. Here, the CMfg structure is examined through topological characteristics. It helps decision-makers to provide a robust and resilient service-demand matching model. It also proposes some investment opportunities for the SME. The proposed model and its capabilities are discussed through a real case study about the COVID-19 ventilator manufacturing systems in CMfg ecosystem.     

Two-stage approach for machine-part grouping and cell layout problems

Cellular manufacturing system (CMS) which is based on the concept of group technology (GT) has been recognized as an efficient and effective way to improve the productivity in a factory. In recent years, there have been continuous research efforts to study different facet of CMS. Most of them concentrated on distinguishing the part families and machine cells either simultaneously or individually with the objective of minimizing intercellular and intracellular part movements. This is known as machine-part grouping problem (MPGP) which is a crucial process while designing CMS. Nevertheless, in reality some components may not be finished within only one cell, they have to travel to another cell(s) for further operation(s). Under this circumstance, intercellular part movement will occur. Different order/sequence of machine cells allocation may result in different total intercellular movement distance unit. It should be noted that if the production volume of each part is very large, then the total number of intercellular movement will be further larger. Therefore, the sequence of machine cells is particularly important in this aspect. With this consideration, the main aim of this work is to propose two-stage approach for solving cell formation problem as well as cell layout problem. The first stage is to identify machine cells and part families, which is the essential part of MPGP. The work in second stage is to carry out a macro-approach to study the cell formation problem with consideration of machining sequence. The impact of the sequencing for allocating the machine cells on minimizing intercellular movement distance unit will be investigated in this stage. The problem scope, which is a MPGP together with the background of cell layout problem (CLP), has been identified. Two mathematical models are formulated for MPGP and CLP respectively. The primary assumption of CLP is that it is a linear layout. The CLP is considered as a quadratic assignment problem (QAP). As MPGP and QAP are NP-hard, genetic algorithm (GA) is employed as solving algorithm. GA is a popular heuristic search technique and has proved superior performance on complex optimization problem. In addition, an industrial case study of a steel member production company has been employed to evaluate the proposed MPGP and CLP models, and the computational results are presented.     

A new branch-&-bound-enhanced genetic algorithm for the manufacturing cell formation problem

This paper deals with the manufacturing cell formation (MCF) problem, which is based on group technology principles, using a graph partitioning formulation. An attempt has been made to take into account the natural constraints of real-life production systems, such as operation sequences, minimum and maximum numbers of cells, and maximum cell sizes. Cohabitation constraints were added to the proposed model in order to deal with the necessity of grouping certain machines in the same cell for technical reasons, and non-cohabitation constraints were included to prevent placing certain machines in close vicinity.     

Optimization of a proton exchange membrane fuel cell membrane electrode assembly

A computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon’s algorithm and an adaptive finite element method in order to achieve near quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve the optimization problem of finding the optimal MEA composition for maximizing performance. To solve the optimization problem a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. By using a gradient-based method and analytical sensitivities, the framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 min, making it a viable alternative for solving large-scale fuel cell problems.     

Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm

In this paper, a comprehensive mathematical model is proposed for designing robust machine cells for dynamic part production. The proposed model incorporates machine cell configuration design problem bridged with the machines allocation problem, the dynamic production problem and the part routing problem. Multiple process plans for each part and alternatives process routes for each of those plans are considered. The design of robust cell configurations is based on the selected best part process route from user specified multiple process routes for each part type considering average product demand during the planning horizon. The dynamic part demand can be satisfied from internal production having limited capacity and/or through subcontracting part operation without affecting the machine cell configuration in successive period segments of the planning horizon. A genetic algorithm based heuristic is proposed to solve the model for minimization of the overall cost considering various manufacturing aspects such as production volume, multiple process route, machine capacity, material handling and subcontracting part operation.     

Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families on the basis of pertinent similarity measures. The bacteria foraging optimization (BFO) algorithm is a modern evolutionary computation technique derived from the social foraging behavior of Escherichia coli bacteria. Ever since Kevin M. Passino invented the BFO, one of the main challenges has been the employment of the algorithm to problem areas other than those of which the algorithm was proposed. This paper investigates the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, for this purpose matrix-based bacteria foraging optimization algorithm traced constraints handling (MBATCH) is developed. In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and a number of exceptional elements. The BFO algorithm is used to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as K-means clustering, the C-link clustering and genetic algorithm using a well-known performance measure that combined cell load variations and a number of exceptional elements. The results lie in favor of better performance of the proposed algorithm.     

Machine-vision-based identification for wafer tracking in solar cell manufacturing

Solar power is getting popular as an alternative of electricity energy. Multicrystalline solar cells dominate the current market share because of lower material and manufacturing costs. In solar cell manufacturing, a silicon ingot is sliced into thin wafers and then the wafers are further processed into solar cells. Conventional automatic identification systems such as bar codes, magnetic strips, OCR and RFID need a contacted identity on the object surface. They are not possible to implement for solar wafer tracking and data collection due to the thin, fragile silicon surface of a solar wafer.