Cell Formation Problem Research Articles

Cell Formation Problem with Alternative Routes and Machine Reliability: Review, Analysis, and Future Developments

The Cell Formation Problem (CFP) is a widely studied issue that aims to group machines effectively based on criteria such as productivity, lower costs, and greater efficiency. In recent years, more characteristics were summarized relating to this problem. This paper provides a bibliographic examination of methodologies addressing the CFP in cellular manufacturing, focusing on novel approaches such as alternative routes and machine reliability. The articles were obtained from Scopus and Web of Science and filtered using the PRISMA methodology. Classification based on objective functions, constraints, and methodologies facilitated informative visualizations for analysis. Findings indicate a focus on capital utilization optimization, with cost reduction via intercellular moves minimization as the primary objective. Common constraints include limits on the number of machines per cell, restricting machines to a single cell and singular production routes per part. The genetic algorithm predominates as a non-exact solution approach, while the “ε-constraint” method is commonly used. This study offers insights into contemporary trends in solving the CFP with alternative routings and machine reliability, aiding researchers and professionals in the field to improve the quality of their investigations.

A A Hybrid PSO Based Algorithm for Solving the Machine-Part Cell Formation Problem

A A Hybrid PSO Based Algorithm for Solving the Machine-Part Cell Formation Problem

Maximizing Group Efficacy for the Generalized Cell Formation Problem Using Route Rank Index and Genetic Algorithm

Cell formation plays a crucial role in the development of cellular manufacturing systems (CMS). Previous studies in this field have typically assumed that each part is associated with a single process plan. However, incorporating alternative routes offers additional flexibility in CMS design. This paper addresses the cell formation problem by considering alternative routes and presents a two-stage approach to address this problem. In the first stage, a Route Rank Index (RRI) is developed based on a correlation matrix to select the optimal alternative route for each part. Subsequently, a Genetic Algorithm (GA) is employed in the second stage to form part families and machine cells. The proposed approach's computational performance is evaluated using a set of generalized group technology datasets found in the existing literature. The results demonstrate that the proposed approach is highly effective and efficient when it comes to addressing the cell formation problem involving alternative routes. The ramifications of these findings in practice are substantial. Our suggested approach demonstrates its resilience and adaptability by achieving comparable or better grouping results across a wide variety of benchmark datasets. This shows the method can be used in a wide range of practical situations, including those involving matrices of varying sizes and shapes. The theoretical knowledge base on part-machine grouping strategies benefits from the comparison study. By comparing the results of our suggested method to those of well-known heuristics, we shed light on its benefits and drawbacks. Index Terms— Route Rank Index; Alternative Routes; Cellular Manufacturing System; Cell Formation; Genetic Algorithm.

CoCAT: a new similarity coefficient for solving the machine-part cell formation problem

CoCAT: a new similarity coefficient for solving the machine-part cell formation problem

Multi-objective machine cell formation by NSGA-II

Non-dominated Sorting Genetic Algorithm (NSGA)-II is applied for multiple objective machine part cell formation problem. Minimization of cell load variation and minimization of total moves are considered as two conflicting objectives. Pareto optimal solutions are obtained for some test problems. Results are compared with those obtained by other methods. The comparison reveals that Pareto optimal fronts obtained are better than reported and reveal the strength of NSGA-II algorithm suited for integer multiple objective cell formation problems in Cellular Manufacturing Systems.

A Binary Black Widow Optimization Algorithm for Addressing the Cell Formation Problem Involving Alternative Routes and Machine Reliability

The Cell Formation Problem (CFP) involves the clustering of machines to enhance productivity and capitalize on various benefits. This study addresses a variant of the problem where alternative routes and machine reliability are included, which we call a Generalized Cell Formation Problem with Machine Reliability (GCFP-MR). This problem is known to be NP-Hard, and finding efficient solutions is of utmost importance. Metaheuristics have been recognized as effective optimization techniques due to their adaptability and ability to generate high-quality solutions in a short time. Since BWO was originally designed for continuous optimization problems, its adaptation involves binarization. Accordingly, our proposal focuses on adapting the Black Widow Optimization (BWO) metaheuristic to tackle GCFP-MR, leading to a new approach named Binary Black Widow Optimization (B-BWO). We compare our proposal in two ways. Firstly, it is benchmarked against a previous Clonal Selection Algorithm approach. Secondly, we evaluate B-BWO with various parameter configurations. The experimental results indicate that the best configuration of parameters includes a population size (Pop) set to 100, and the number of iterations (Maxiter) defined as 75. Procreating Rate (PR) is set at 0.8, Cannibalism Rate (CR) is set at 0.4, and the Mutation Rate (PM) is also set at 0.4. Significantly, the proposed B-BWO outperforms the state-of-the-art literature’s best result, achieving a noteworthy improvement of 1.40%. This finding reveals the efficacy of B-BWO in solving GCFP-MR and its potential to produce superior solutions compared to alternative methods.

Intelligent Optimization Approach to Cell Formation and Product Scheduling for Multifactory Cellular Manufacturing Systems Considering Supply Chain and Operational Error

This study designs a joint decision model to solve cell formation and product scheduling problems together in multifactory cellular manufacturing systems. If a product is processed in a factory with a small logistics cost of its raw material, the logistics cost of the product delivered to the distributor may be large. If managers prefer the pair of equipment unit and worker with a high production rate, operational error rate may be large. Meanwhile, it influences the labor cost, raw material cost, and completion time of each product. Managers can assign the products of a type to several cells for processing. If a cell receives fewer products of a type, it can complete their processing and becomes available to process the products of other types. However, some cells receiving more products of this type may be delayed to handle the products of other types. If a type of product in a cell has a priority to be processed, its backorder cost may be none or reduced, but this schedule may delay other product types with lower priority in the cell, and give rise to their backorder cost. For solving the intertwined optimization problem, an improved bacterial foraging algorithm with a priority rule-based heuristic (IBFAP) is developed to maximize profit. Experiments are conducted to show that IBFAP outperforms the improved bacterial foraging algorithm with no heuristic, the classical bacterial foraging algorithm, genetic algorithm, and simulated annealing.

Solving a Cubic Cell Formation Problem with Quality Index Using a Hybrid Meta-Heuristic Approach

Although most previous studies on cell formation have involved the assignment of parts and machines to cells, in recent years the assignment of workers has also been considered and the studies have taken into account the human factor. An important step for the successful implementation of a Cellular Manufacturing System is to decide appropriate groups of parts, machines, and workers and then assign them to cells. Consideration of the skills of workers and machines in processing parts has enhanced cell performance. In this study, the problem of a cubic cell formation that takes into account the three-dimensional part-machine-worker matrix is addressed, and the minimization of the exceptional element and void as well as the maximization of the part quality index is aimed. The mathematical model used in this study was coded in the GAMS 24.2.1 software. A hybrid GA-SA approach was also proposed for the solution of large instances. The relative Percentage Deviation performance index was utilized to evaluate the performance of the algorithm. According to the results, the hybrid technique developed, considering technical cell performance criteria together with worker skills, shows promising results from the standpoint of the considered objective and the computational time.

Mapping and visualizing flexible manufacturing system in business and management: a systematic review and future agenda

PurposeA flexible manufacturing system (FMS) helps improve the system’s performance, thus increasing its overall competitiveness. FMS is an essential component of Industry 4.0 (I4.0), which has revolutionized the way firms manufacture their products. This study aims to investigate the diverse focus of the research being published over the years and the direction of scholarly work in applying FMSs in business and management.Design/methodology/approachA total of 1,096 bibliometric data were extracted from the Scopus database from the years 2001 to 2021. A systematic review and bibliometric analysis were performed on the data and related articles for performance measurement and scientific mapping on the FMS themes.FindingsBased on co-keyword, the study reveals four major themes in the FMS field: mathematical models and quantitative techniques, scheduling and optimization techniques, cellular manufacturing and decision-making in FMSs. Based on bibliometric coupling on 2018–2021 bibliometric data, four themes emerged for future research: scheduling problems in FMS, manufacturing cell formation problem, interplay of FMS with other latest technologies and I4.0 and FMS.Originality/valueThe originality lies in answering the following research questions: What are the most highlighting themes in FMS, and how have they evolved over the past 20 years (2001–2021)? What topics have been at the forefront of research in FMS in the past five years (2016–2021)? What are the promising avenues of research in FMS?

A novel hybrid multi-objective algorithm to solve the generalized cubic cell formation problem

The Generalized Cubic Cell Formation Problem (GCCFP) is a Multi-Objective (MO) optimization problem in manufacturing systems. It aims to find the suitable grouping of machines, products, and workers and their best assignment to manufacturing cells. During the resolution of the MO optimization problems, the decision maker should take decisions about multiple variables in order to optimize several objectives, or to find a good trade-off between the concerned objectives. Depending on the moment when the decision about the suitable configuration is taken, the resolution methods may be devised into (i) priori methods, (ii) interactive methods, and (iii) posteriori methods. In this study, a novel posteriori efficient algorithm called Robust Simulated Annealing-AUGMented ɛ-CONstraint (SA-AUGMECON-R) algorithm is introduced in order to solve the GCCFP. The proposed algorithm combines AUGMECON-R exact method and the simulated annealing meta-heuristic. To evaluate the performance of the algorithm, a comparative study was conducted. Regarding the values of the metrics that are used to evaluate the performance of MO algorithms, SA-AUGMECON-R algorithm provides excellent results comparing with AUGMECON 2, AUGMECON-R and NSGA-II.

Solving the Cubic Cell Formation Problem Using Simulated Annealing

The Cubic Cell Formation Problem (CCFP) in cellular manufacturing systems consists in decomposing a production system into a set of manufacturing cells, and assigning workers to cells besides parts and machines. The major objective is to obtain manageable cells. Manageable cells mean cells with a minimum value of inter-cell moves of parts and workers and a minimum value of heterogeneity within cells. In this paper, a solution methodology based on a modified simulated annealing heuristic with a proposed neighbourhood search procedure is proposed. The methodology allows building multiple configurations by giving to the decision-maker the ability to control some parameters. Experimental results show that the proposed algorithm gives a promising performance for all problem instances found in the literature.

A Comparative Study of Integrating Feasibility Assessment and Cell Formation Problem Using Modified Similarity Coefficient Method

A Comparative Study of Integrating Feasibility Assessment and Cell Formation Problem Using Modified Similarity Coefficient Method

A Comparative Study of Integrating Feasibility Assessment and Cell Formation Problem Using Modified Similarity Coefficient Method

A Comparative Study of Integrating Feasibility Assessment and Cell Formation Problem Using Modified Similarity Coefficient Method

A Mathematical Model for Dynamic Cell Formation Problem Based on Scheduling, Worker Allocation, and Financial Resources Constraint

A Mathematical Model for Dynamic Cell Formation Problem Based on Scheduling, Worker Allocation, and Financial Resources Constraint

An improved genetic algorithm for the machine-part cell formation problem

An improved genetic algorithm for the machine-part cell formation problem

PMP approach for solving the binary static multi-objective generalised cell formation problem

PMP approach for solving the binary static multi-objective generalised cell formation problem

A Novel Mathematical Model for Tillage Production Planning and Facility Layout Design

Cell formation and layout design are important steps in the implementation of production systems. Existing models focus mainly on the problem of cell formation and machine layout design and cell location in a dynamic environment, with the exception of a few recent studies. Also, in many existing articles, binary information has been used for cell formation and other information such as production volume, operation sequence and lower production costs have played a role in the structure of existing models. In this paper, a nonlinear programming model under possible dynamic conditions is presented. The objectives of the model include minimizing the total costs of intracellular and intercellular movements of parts, the existence of exceptional parts, intercellular layout of machines, operating costs, fixed costs of machines and the cost of the difference in demand estimated by the model from its expected value. A genetic algorithm is used to solve and the results are compared with the optimal solution.

The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession

The traditional machine-part cell formation problem simultaneously clusters machines and parts in different production cells from a zero–one incidence matrix that describes the existing interactions between the elements. This manuscript explores a novel alternative for the well-known machine-part cell formation problem in which the incidence matrix is composed of non-binary values. The model is presented as multiple-ratio fractional programming with binary variables in quadratic terms. A simple reformulation is also implemented in the manuscript to express the model as a mixed-integer linear programming optimization problem. The performance of the proposed model is shown through two types of empirical experiments. In the first group of experiments, the model is tested with a set of randomized matrices, and its performance is compared to the one obtained with a standard greedy algorithm. These experiments showed that the proposed model achieves higher fitness values in all matrices considered than the greedy algorithm. In the second type of experiment, the optimization model is evaluated with a real-world problem belonging to Human Resource Management. The results obtained were in line with previous findings described in the literature about the case study.

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.

Fuzzy Integrated Cell Formation and Production Scheduling Considering Automated Guided Vehicles and Human Factors

In today's competitive environment, it is essential to design a flexible-responsive manufacturing system with automatic material handling systems. In this article, a fuzzy mixed integer linear programming model is designed for cell formation problems including the scheduling of parts within cells in a cellular manufacturing system (CMS) where several automated guided vehicles (AGVs) are in charge of transferring the exceptional parts. Notably, using these AGVs in CMS can be challenging from the perspective of mathematical modeling due to consideration of AGVs’ collision as well as parts pickup/delivery. This article investigates the role of AGVs and human factors as indispensable components of automation systems in the cell formation and scheduling of parts under fuzzy processing time. The proposed objective function includes minimizing the makespan and intercellular movements of parts. Due to the NP-hardness of the problem, a hybrid genetic algorithm (GA/heuristic) and a whale optimization algorithm (WOA) are developed. The experimental results reveal that our proposed algorithms have a high performance compared to CPLEX and the other two well-known algorithms, i.e., particle swarm optimization and ant colony optimization, in terms of computational efficiency and accuracy. Finally, WOA stands out as the best algorithm to solve the problem.