Design Of Cellular Manufacturing Systems Research Articles

A mathematical programming model with present value method for optimum design of flexible cellular manufacturing systems

In this article, an integer mathematical programming model for the design problem of flexible cellular manufacturing systems is proposed. The developed mathematical programming model whose objective function is to minimize total design cost including cost of operating parts on machines, cost of using tools on machines, and cost of assigning employees to cells, also contains the present value method. Thus, it is ensured that the operational costs that occur along a certain time period are also taken into account. LINGO 19.0 optimization program software is used for the optimum solution of the integer mathematical programming model with the present value method whose objective function is to minimize the total design cost. In this article, the application of the model and the analysis related to the model are shown using a developed example problem. In addition, by ensuring the optimum design of flexible cellular manufacturing systems, the results of which alternative routes are used for processing parts, which machines are located in which cells, and which employees are assigned to which cells, are obtained. Then, a sensitivity analysis is presented to show the importance of alternative routes of parts.

Dynamic cellular manufacturing system design using genetic algorithm: a proposed model A case study in the Light Industries Company-Ashtar in Baghdad

The current research seeks to present a proposed model for the design of a dynamic cellular manufacturing system in the Light Industries Company/Ashtar in Baghdad, one of the Iraqi industrial companies. With the aim of generating an industrial environment capable of accommodating rapid changes in the market to meet the changing and diverse needs and desires of the customer at the lowest possible cost, at the right time and the required quality. To achieve the current research relied on the methods of artificial intelligence, employing them and benefiting from them in the industrial field, especially the genetic algorithm. Accordingly, the current research included a theoretical framework for the dynamic cellular manufacturing system and the genetic algorithm, in light of what was mentioned in the Arab and foreign sources related to them. The research also adopted a field framework to design this system in the researched company using the genetic algorithm, and the researchers chose the freezers and models labs as a field for study, and in light of the results of the programming language program (MATLAB). It was reached to build a proposed model that reflects the virtual reality of this system. On the basis of that, the research reached a set of conclusions, the most important of which is the genetic algorithm that contributed significantly and effectively to finding the optimal cellular path for the dynamic cellular manufacturing system and reducing both the total time and the total cost of production. In light of this a set of proposals were presented showing the importance of shifting from the existing cellular manufacturing system in the field of study to the dynamic cellular manufacturing system.

Grouping technology and a hybrid genetic algorithm‐desirability function approach for optimum design of cellular manufacturing systems

Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re-arrange machines enabling the minimisation of inter/intra- cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.

Benders decomposition approach to solve a Bi-objective cell formation in dynamic conditions

Cellular Manufacturing System (CMS) is an alternative to production systems based on process and product layout that combines the advantage of high throughput rate of flow shop system with the flexibility of job shop layout. CMS design has four steps: 1) Cell formation: grouping the parts with similar geometric design features and processing requirements in part families to use similarities to build, manufacture and assemble machines in machine lines and cells, 2) group layout: placing machines in each cell that includes intra-cell and inter-cell layout, 3) group scheduling: part family scheduling, and 4) source allocation: allocation of tools and primary resources and human force. Dynamic CMS (DCMS) can be divided into smaller periods, each of which is different from the previous one as these periods have various demands and compositions. The study presented a mixed-integer programming model for DCMS. The model considered some significant real-world conditions. In this model, the first objective function tries to minimize the costs related to the machines purchase cost, machines variable cost, the cost of movement workers, the cost of cell formation, the cost of inter-cell and intra-cell part movement, the cost of overtime, and the cost of reconfiguration.

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 inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS 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 (CMS) problem is NP-Hard, a Genetic Algorithm (GA) 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 GA and the related computational results are compared with the results obtained by the use of an optimization tool.

Similarity coefficient-based cell formation method considering operation sequence with repeated operations

Cell formation is a crucial problem in the design of cellular manufacturing systems and has a decisive effect on productivity and cost. The similarity coefficient method is the most widely used to solve this problem because of its flexibility and computational efficiency. Although various similarity coefficient methods have been developed in the literature, few of these methods consider the factor of operation sequence with repeated operations, which may cause more material intercell movements. To fill this research gap, this study presents an improved similarity coefficient method for designing machine groups in the first stage. Subsequently, a modified part assignment strategy is proposed for forming cells to achieve a higher compatibility with the improved similarity coefficient method. Finally, problems from previous studies are employed to demonstrate the performance of the proposed method. Comparison results testify that the proposed method is sensitive and efficient for solving the cell formation problem.

A mathematical model for designing a reliable cellular hybrid manufacturing-remanufacturing system considering alternative and contingency process routings

Due to the stringent awareness toward the preservation and resuscitation of natural resources and the potential economic benefits, designing sustainable manufacturing enterprises has become a critical issue in recent years. This presents different challenges in coordinating the activities inside the manufacturing systems with the entire closed-loop supply chain. In this paper, a mixed-integer mathematical model for designing a hybrid-manufacturing-remanufacturing system in a closed-loop supply chain is presented. Noteworthy, the operational planning of a cellular hybrid manufacturing-remanufacturing system is coordinated with the tactical planning of a closed-loop supply chain. To improve the flexibility and reliability in the cellular hybrid manufacturing-remanufacturing system, alternative process routings and contingency process routings are considered. The mathematical model in this paper, to the best of our knowledge, is the first integrated model in the design of hybrid cellular manufacturing systems which considers main and contingency process routings as well as reliability of the manufacturing system.

Integration of parts scheduling, MRP, production planning and generalized fixed-charge transportation planning in the design of a dynamic cellular manufacturing system

In this paper, to integrate the decisions of parts scheduling, Material Requirement Planning (MRP), Production Planning (PP) and Transportation Planning (TP) for designing a Cellular Manufacturing System (CMS) under a dynamic environment, a Mixed-Integer Nonlinear Programming (MINLP) mathematical model is formulated. The proposed mathematical model integrates extensive coverage of significant manufacturing characteristics in designing a CMS to be implemented in a three-layer supply chain. The considered features include markets demands, heterogeneous vehicles, raw materials requirements planning, parts due dates, cell size limits, machines capacity, intra/inter cell material handling time/cost, transportation time/cost, operation time, alternative processing routes in addition to the main decisions of parts scheduling, PP, TP and dynamic cell formation. Also, some novel characteristics are incorporated based on a three-layer supply chain that make the presented model remarkable respect to the literature including (1) In the first layer, planning the orders of raw materials with different lead times and usage coefficients is performed, (2) In the second layer, decisions of dynamic cell formation and parts scheduling are made, and (3) In the third layer, optimal vehicles are selected as a generalized fixed-charge TP based on transportation time and cost to satisfy multi-markets with different demand volumes. The components in the objective function to be minimized include total costs of holding the parts inventories in the markets, backorders, tardiness, transportation of the parts from the plant to the markets, purchase of raw materials, keeping raw materials in the plant warehouse, intercellular/intracellular movements and machine relocation. An illustrative numerical example is solved by the CPLEX solver to illustrate the achievements obtained by the incorporated characteristics in the integrated model. Furthermore, a sensitivity analysis is performed to assess the effects of important parameters on the model performance. Since the proposed model is NP-hard, a Simulated Annealing (SA) algorithm is improved by an elaborately-designed matrix-based chromosome representation is applied to represent all decision variables, as well as a sequential procedure generating initial solutions. Several test problems either generated randomly or taken from the literature with various sizes are solved and the results are compared with the solutions gained using CPLEX solver. The comparisons results show that the designed SA is capable of evolving optimal or near-optimal solutions with reasonable relative gaps in a computationally satisfactory manner.

Operations performance considering demand coverage scenarios for individual products and products families in supply chains

In supply chains, businesses compete to meet customer requirements by leveraging their competitive operational capabilities. In this paper, the manufacturer in its supply chain, faces uncertain market demand environment. Based on the products, operations and demand information, the manufacturer makes decisions to design its manufacturing system that saves time and effort in the production process. The methodology followed in this paper includes four phases. In Phase 1, the manufacturer benefits from the grouping of the similar products into families to save time and effort in the production and eventually the related machines are formed accordingly. In Phase 2, the decision of manufacturing system layout or design, considering a stochastic customer demand, is made including cellular manufacturing system design. In Phase 3, the expected profits generated by the system designed to meet the demand of product families are determined and optimal design is selected accordingly. In Phase 4, the problem of determining the optimal profits and quantities of the individual products in families, considering multi-demand coverage levels, is tackled using a proposed mathematical model, then results are analyzed. The results showed that similar products are grouped into four product families using p-median mathematical model and machine cells are formed accordingly. Consequently, the cellular manufacturing systems is designed for each family and the decision on the optimal number of cells is made based on the maximum expected profits generated by the system designed. Further, the problem of finding the optimal profits by individual products in families is studied considering three demand coverage probability scenarios: non-demand coverage restriction, only lower bound-demand coverage restriction and both lower and upper bounds-demand coverage restriction. Maximum profits are generated when the decision does not include any restriction on the demand; however, the product that has the lowest processing time is produced and sold where other products are not. This may leave the decision maker with either keep-the-winner perspective or not depending on the policy implemented in the competition process. Better decisions are made when more information is shared about the customer requirements.

Minimizing makespan of stochastic customer orders in cellular manufacturing systems with parallel machines

This study addresses a stochastic customer order production problem in cellular manufacturing systems with parallel machines. The objective is to minimize the long-run expected makespan of stochastic customer orders via proper cellular manufacturing system designs under a budget limit. Theoretical studies are conducted to explore the effect of demand uncertainty and production requirement on the optimal design and the objective. Explicit expressions are provided for dterministic workload case and several production requirement cases, which results in insights into basic principles for the optimal design to possess. Based on the theoretical analysis, three heuristic algorithms are further established. Experimental results demonstrate the effectiveness of the proposed algorithms under a variety of production scenarios. This paper extends the existing literature on customer orders by involving in stochastic arrivals and demands whose multi-stage processing is conducted in manufacturing cells. Besides, studies on design of cellular manufacturing system are enriched with the consideration of stochastic customer orders that require synchronized arrival and departure for products within an order.

A vibration damping optimization algorithm for the integrated problem of cell formation, cellular scheduling, and intercellular layout

The three problems of cell formation (CF), cellular scheduling, and cellular layout are closely interrelated in the design of cellular manufacturing systems (CMSs) and should, therefore, be considered in an integrated structure. This paper presents a mixed-integer programming (MIP) model to examine such concurrent design by considering many design attributes, such as the machine duplication, alternative processing routes, reentrant parts, and variable cell size. In the presented model, decisions including machine grouping, processing route selection, operation sequencing, and cell assignment into candidate locations are taken such that the total completion time is minimized. Since the problem in hand belongs to the NP-hard class, a vibration damping optimization (VDO) algorithm is proposed to solve large-sized problems. In order to verify the efficiency of the proposed algorithm comparing to the CPLEX solver of the GAMS software and two other metaheuristic algorithms, namely a genetic algorithm (GA) and an ant lion optimizer (ALO) algorithm, several sample problems with different sizes and settings are implemented. The results demonstrate that the proposed algorithm can obtain better solutions in less computational time.

A comparative study on design of static and dynamic cellular manufacturing system under deterministic production environment

In job shops and flow line manufacturing plants, the impact of Cellular Manufacturing System (CMS) with recent advancement in Group Technology (GT) and Automated Manufacturing System (AMS) has resulted in minimizing cost involved in machine maintenance and overhead, machine procurement, machine setup and intra/intercell material movement cost. Meanwhile, the industrialist addressed the problem in deciding the planning horizon to design CMS with respect to the change in customer demand pattern. The Classical CMS failed to capture the fickle in part demand and product mix. In the case of Dynamic CMS (DCMS), the fluctuation is considered by dividing the entire planning horizon into smaller periods. In this paper, a real time data of product mix and product demand were collected from an automobile components manufacturing industry located in SIPCOT Industrial Park, Sriperumbudur to visualize the potential benefits of incorporating DCMS. In this work, existing mathematical model has been used for designing DCMS and it is developed further to convert the model as static environment. Evaluation of the obtained CMS design was done by capturing the exact production parameters of the industry and it is found DCMS resulted in promising cell configuration with minimal overall objective value.

Improved similarity coefficient and clustering algorithm for cell formation in cellular manufacturing systems

Cell formation plays an important role in the design of cellular manufacturing systems. Among many methods utilized to solve the cell formation problem, the similarity coefficient method is the most widely used owing to its high flexibility and low computational requirement. However, generalized similarity coefficients ignore material flow, which is closely related to operation sequence and repeated operations. Moreover, most similarity coefficient-based clustering algorithms focus on the number of inter-cell movements but disregard distinction of the movement effort. To overcome these limitations, this study improves the generalized similarity coefficient method to form part families. In addition, a new clustering algorithm is presented to assign machines to cells with minimum intensity of material inter-cell movement, which depends on the frequency, production volume and difficulty level of inter-cell movement. Experimental results demonstrate that the proposed method has superior sensitivity and effectiveness for solving the cell formation problem.

An improved design for cellular manufacturing system associating scheduling decisions

This paper presents a model for the design of Cellular Manufacturing System (CMS) to evolve simultaneously structural design decisions of Cell Formation (CF) and operational issue decisions of optimal schedule. This integrated decision approach is important for designing a better performing cell. The model allows machine duplication and incorporates cross-flow for scheduling flexibility. The cross-flow is the term introduced to mean the inter-cell movement of parts from parent cell to identical machines in other cells though machines are available in the parent cell. This cross-flow facilitates routing flexibility and paves way for reduced schedule length thereby optimizing resources leading to minimized operational cost. A non-linear integer mathematical programming model is formulated with the objective function of minimizing operating cost which is the sum of Machine Utility Cost (MUC) and inter-cell costs. The MUC is a new cost parameter based on machine utility and it integrates CF, scheduling, and machine duplication decisions. The proposed model belongs to the class of NP-hard problems. A hybrid heuristic (HH) that has “Simulated Annealing Algorithm (SAA) embedded with Genetic Algorithm (GA)” is proposed. A comparison with the mathematical solution reveals that the proposed HH is capable of providing solutions closer to optimal in a computationally efficient manner. The model is validated by studying the effect of integrated decisions, machine duplications, and association of scheduling and cross-flow. The model validation reveals that the proposed CMS model evolves CF, scheduling, and machine duplication decisions with minimum operating cost. Thus, it can be inferred that the proposed model gives optimal integrated decisions for designing an effectively and efficiently performing cells and thus evolves improved CMS design decisions.

Design of cellular manufacturing system for power press industry to reduce total travelling time by hybrid algorithm

Part family identification and machine cell formation are the two major steps in cellular manufacturing system. Cell formation is a complex process and there are several approaches for this purpose vis., array based clustering, agglomerative clustering, mathematical programming, graph partitioning and non-traditional methods. The problem with first two approaches is that the quality of the solution is dependent of the initial part machine incidence matrix. A new algorithm for cell formation combining the techniques of array based clustering and agglomerative clustering is proposed. The above proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is the most widely used measures of quality for cellular manufacturing systems. The proposed algorithm is used to form manufacturing cells in a power press industry to increase productivity by reducing total travelling time.

A hybrid optimization algorithm with genetic and bacterial operators for the design of cellular manufacturing systems

We consider a design problem for cellular manufacturing systems. In order to improve the efficiency of the designed system, three optimization problems are considered jointly in the same optimization procedure: cell formation, workload balancing and cell layout. A hybrid metaheuristic algorithm with genetic and bacterial operators is developed to optimize the performances of the designed cellular manufacturing system. The results obtained on benchmark problems are discussed.

Design of cellular manufacturing system for power press industry to reduce total travelling time by hybrid algorithm

Part family identification and machine cell formation are the two major steps in cellular manufacturing system. Cell formation is a complex process and there are several approaches for this purpose vis., array based clustering, agglomerative clustering, mathematical programming, graph partitioning and non-traditional methods. The problem with first two approaches is that the quality of the solution is dependent of the initial part machine incidence matrix. A new algorithm for cell formation combining the techniques of array based clustering and agglomerative clustering is proposed. The above proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is the most widely used measures of quality for cellular manufacturing systems. The proposed algorithm is used to form manufacturing cells in a power press industry to increase productivity by reducing total travelling time.

Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach

This study investigates hybrid cellular manufacturing system design where products with highly similar process routes are formed as manufacturing cells, and products with dissimilar routes are produced in a process layout. A cell similarity threshold, cellularisation ratio (C-ratio), is proposed for product assignment to cells and process layout, and integrated into a mathematical modelling-based cell formation approach. The cell formation phase is carried out with the proposed mathematical model. Several case problems are tested, and selected cases are also studied with a newly developed simulation model where manufacturing system performance is also evaluated along with the proposed hybrid cellular manufacturing design alternatives. Results indicated that considering a C-ratio during cell formation can provide higher cell similarity thus better cell formation. The increase in the coverage levels resulted in higher number of cells formed. The proposed approach can be significantly beneficial for manufacturing design decisions where product mix and process complexity are high, and for a hybrid layout that consists of cellular plus process layouts could be implemented.

Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach

Hybrid cellular manufacturing system design with cellularisation ratio: an integrated mixed integer nonlinear programming and discrete event simulation approach

P-Median Approach for the Large-Size Multi-Objective Generalized Cell Formation

The p-median model is known as an effective mathematical model for small- to intermediate-size single- objective cell formation (SOCF) problem in the design of cellular manufacturing system. This paper shows that a sequential approach adopting the p-median model can be a very effective alternative for large-size multi-objective generalized cell formation (MOGCF). Our sequential approach proceeds through two-phase procedures. Phase 1 uses a comprehensive generalized part-machine incidence matrix (PMIM) incorporating realistic manufacturing factors such as alternative process plans and duplicate machines as well as processing times, operation sequences, nonconsecutive multiple visits to the same machines, and production volumes of parts. Based on a new generalized similarity coefficient, a 0-1 linear p-median model is formulated to form independent and compact machine cells. Phase 2 uses a systematic part assignment procedure based on a new classification scheme of part types with alternative process plans in pursuit of balanced workload among cells and high machine utilization. Phase 1 implemented using LINGO 16.0 reaches the global optimum within two seconds even on a large problem containing 1,600 binary variables and phase 2 completes speedy assignment of parts within two seconds with high-quality solutions.