Cellular Manufacturing Systems Research Articles

A Hybrid Ant Colony System and Tabu Search algorithm for the production planning of dynamic cellular manufacturing systems while confronting uncertain costs

Highlights:
 
 Cellular Manufacturing systems cover a wide range of industries.
 Inflation rate can impose financial harms on cellular manufacturing systems.
 The over-allocation of workers, which usually happens in dynamic systems, causes reduction of the system performance.
 The proposed algorithm in this research can successfully schedule cellular systems to reduce system costs.
 
 Goal:
 The main aim is to determine the best trade-off values between in-house manufacturing and outsourcing, and track the impact of uncertain costs on gained schedules. To be more comprehensive, the performance of human resources is restricted and the partial demands are considered uncertain.
 Design / Methodology / Approach:
 In this paper a new method for minimizing human resource costs, including operating, salary, hiring, firing, and outsourcing in a dynamic cellular manufacturing system is presented where all system costs are uncertain during manufacturing periods and can be affected by inflation rate. For this purpose, a multi-period scheduling model that is flexible enough to use in real industries has been proposed. To solve the proposed model, a hybrid Ant Colony Optimization and the Tabu Search algorithm (ACTS) are proposed and the outcomes are compared with a Branch-and-Bound based algorithm.
 Results:
 Our findings showed that the inflation rate has significant effect on multi-period system planning. Moreover, utilizing system capability by the operator, for promoting and using temporary workers, can effectively reduce system costs. It is also found that workers’ performance has significant effect on total system costs.
 Limitations of the investigation:
 This research covers the cellular manufacturing systems.
 Practical implications:
 The algorithm is applied for 17 series of dataset that are found in the literature. The proposed algorithm can be easily applied in real industries.
 Originality / Value:
 The authors confirm that the current research and its results are original and have not been published before. The proposed algorithm is useful to schedule cellular manufacturing systems and analyse various production conditions.

Cross-Trained Worker Assignment Problem in Cellular Manufacturing System Using Swarm Intelligence Metaheuristics

Cross-trained worker assignment has become increasingly important for manufacturing efficiency and flexibility in cellular manufacturing system because of the recent increase in labor cost. Researchers mainly focused on assigning skilled workers to tasks for favorable capacity or cost. However, few of them have recognized the need for skill level enhancement through cross-training to avoid excessive training, especially for workload balance across multiple cells. This study presents a new mathematical programming model aimed at minimum training and maximum workload balance with economical labor utilization, to address the worker assignment problem with a cross-training plan spanning multiple cells. The model considers the trade-off between training expenditure and workload balance to achieve a more flexible solution based on decision-maker’s preference. Considering the computational complexity of the problem, the classical swarm intelligence optimizers, i.e., particle swarm optimization (PSO) and artificial bee colony (ABC), are implemented to search the problem landscape. To improve the optimization performance, a superior tracking ABC with an augmented information sharing strategy is designed to address the problem. Ten benchmark problems are employed for numerical experiments. The results indicate the efficiency and effectiveness of the proposed models as well as the developed algorithms.

Solving a new robust green cellular manufacturing problem with environmental issues under uncertainty using Benders decomposition

ABSTRACTA cellular manufacturing system is a practical tool of group technology philosophy in a production environment. Although environmental issues have a significant impact on production processes, green considerations have not been studied in detail for such problems. This article aims to fill the gap by proposing a new mathematical model in which environmental issues, such as pollution caused by production and transportation modes, as well as waste, are considered. In addition, production planning and inventory balance among different periods are considered. Moreover, it is assumed that the processing times of products are uncertain and so a robust optimization approach is used to handle such uncertainty. The presented model is solved by the Benders decomposition algorithm. According to the outputs, the demand has the greatest effect on cell formation cost. Finally, the effect of green parameters on optimality is investigated.

An integrated cellular manufacturing system with type-2 fuzzy variables: Three tuned meta-heuristic algorithms

An integrated cellular manufacturing system with type-2 fuzzy variables: Three tuned meta-heuristic algorithms

Energy-efficient cellular manufacturing system: Eco-friendly revamping of machine shop configuration

Within the domain of sustainable manufacturing, most of the ongoing research efforts, regarding reduction in energy consumption, are focused on manufacturing processes while a little effort is being made to make other production related efforts energy-efficient. This paper presents an analytical effort for saving energy in a machine shop environment by optimizing assignment of manufacturing processes to various machines and grouping machines in various cells for minimizing parts transportation distance. A nonlinear mathematical model is developed that seeks minimization of total energy consumed in machining various quantities of multiple parts and their transportation within the machine shop. An algorithm based on simulated annealing metaheuristic is developed for solution of the mathematical model. The model is then applied for energy based revamping of a machine shop, which deals in manufacturing of various quantities of multiple jobs. It is found from its application that significant levels of production and transportation energies can be saved by assigning the manufacturing operations to the most appropriate machines and optimally grouping the machines into machine cells, respectively. The paper contributes towards energy conservation by optimizing facility layout and production planning of a manufacturing setup.

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.

Machine Reliability in a Dynamic Cellular Manufacturing System: A Comprehensive Approach to a Cell Layout Problem

Machine Reliability in a Dynamic Cellular Manufacturing System: A Comprehensive Approach to a Cell Layout Problem

A bi-level bi-objective mathematical model for cellular manufacturing system applying evolutionary algorithms

The present study aimed to design a bi-objective bi-level mathematical model for multi-dimensional cellular manufacturing system. Minimizing the total number of voids and balancing the assigned workloads to cells are regarded as two objectives of the upper level of the model. However, the lower level attempts to maximize the workers' interest to work together in a special cell. To this aim, two nested bi-level metaheuristics including particle swarm optimization (NBL-PSO) and a population-based simulated annealing algorithm (NBL-PBSA) were implemented to solve the model. In addition, the goal programming approach was utilized in the upper level procedure of these algorithms. Further, nine numerical examples were applied to verify the suggested framework and the TOPSIS method was used to find the better algorithm. Furthermore, the best weights for upper level objectives were tuned by using a weight sensitivity analysis. Based on computational results, all three objectives were different from their ideal goals when decisions about inter and intra-cell layouts, and cell formation to balance the assigned workloads by considering voids and workers' interest were simultaneously madeby considering a wide assumption-made problem closer to the real world. Finally, NBL-PBSA could perform better than NBL-PSO, which confirmed the efficiency of the proposed framework.

A Novel Robust Possibilistic Cellular Manufacturing Model Considering Worker Skill and Product Quality

Design of an appropriate cellular manufacturing system (CMS) leads to system flexibility and production efficiency by using the similarities in the manufacturing process of products. One of the main issues in these systems is to consider product quality level and worker’s skill level in the production process. This study proposes a comprehensive bi-objective possibilistic nonlinear mixed-integer programming model under uncertain environment to design a suitable CMS with aims of minimizing the total costs and total inaction of workers and machines, simultaneously. In this respect, the demand of each product with a specific quality level and linguistic parameters such as product quality level, worker’s skill level and job hardness level on machines are considered under fuzzy environment. To this end, the robust possibilistic programming approach is tailored to cope with fuzzy impute parameters. Finally, a real case study is provided to show the efficiency and applicability of the proposed model. In this respect, the proposed approach could be improved the total costs by 23.6% and the total inaction of workers and machines by 11.7% regarding the real practice. In addition, the performance of the presented model is demonstrated by comparing between the results obtained from the proposed model and actual practice.

A queuing theory- based approach for designing cellular manufacturing systems

This paper presents a new cell formation and cell layout problem considering multiple process routings and subcontracting using the principles of queuing theory. It is assumed that each machine operates as an M/M/1 queuing system and a queuing network is used to obtain in-process inventories and machine utilization. The problem is formulated as a mixed-integer nonlinear program with the objective of minimizing the total costs, including the production, subcontracting, material handling, machine idleness, and holding costs. Due to the computational complexity of the problem, a heuristic method is suggested to effectively solve the problem. A numerical example is given to clarify the proposed approach, and finally, further instances are solved to verify the performance of the solution method and to accomplish comparisons. The computational results show that the proposed heuristic is both effective and efficient.

Modeling the Trade-off between Manufacturing Cell Design and Supply Chain Design

Nowadays, we are witnessing the growth of firms that distribute the production capacity of their products to a wide geographic range to supply the demand of several markets. In this article, the relationships and interactions between cell design and supply chain design are investigated. For this purpose, a novel integrated model is presented for designing dynamic cellular manufacturing systems in supply chain design. Different components in the supply chain design, such as location of production facilities at a number of candidate sites, procurement of raw materials from suppliers, shipment of raw materials to production facilities, manufacturing of products, and distribution of products to markets are considered in dynamic environments. The costs concerning these components are minimized. Since the proposed problem is NP-hard, however, a genetic algorithm is presented for application of the model to real-sized instances. Numerical examples demonstrate that the algorithm performs successfully in searching for optimal or near-optimal solutions.

An immune system based algorithm for cell formation problem

Technological developments enable the design and manufacturing of products tailored to individual consumers. Cellular Manufacturing Systems (CMS) can be considered as to ease flexibility, to reduce setup time, throughput time, work-in-process inventories, and material handling costs. Cell formation problem (CFP) that is one of the critical CMS design problems is the assignment of parts and machines to specific cells based on their similarity. This study introduces a Clonal Selection Algorithm (CSA) with a novel encoding structure that is efficient to solve real-sized problems. Unlike the methods in literature that define the number of cells as a constant number, this algorithm is significant because it can obtain the optimum number of cell to generate best efficacy value. Proposed CSA is tested by using 67 (35 well-known and 32 less-known) test problems. CSA obtains the same 63 best-known optimal solutions, provides solutions for the 3 of the well-known test problem and a new solution for the largest test problem (50 machine 150 part) that was not possible to be solved by the mixed integer linear programming model due to the high computational complexity. Final CSA grouping results are illustrated with figures to attract attention to the singleton and residual cells.

Dynamic cellular manufacturing system considering machine failure and workload balance

Machines are a key element in the production system and their failure causes irreparable effects in terms of cost and time. In this paper, a new multi-objective mathematical model for dynamic cellular manufacturing system (DCMS) is provided with consideration of machine reliability and alternative process routes. In this dynamic model, we attempt to resolve the problem of integrated family (part/machine cell) formation as well as the operators’ assignment to the cells. The first objective minimizes the costs associated with the DCMS. The second objective optimizes the labor utilization and, finally, a minimum value of the variance of workload between different cells is obtained by the third objective function. Due to the NP-hard nature of the cellular manufacturing problem, the problem is initially validated by the GAMS software in small-sized problems, and then the model is solved by two well-known meta-heuristic methods including non-dominated sorting genetic algorithm and multi-objective particle swarm optimization in large-scaled problems. Finally, the results of the two algorithms are compared with respect to five different comparison metrics.

Concurrent cell formation and layout design based on hybrid approaches

Considering the interaction between the cell formation and cell layout problems when designing cellular manufacturing systems (CMSs), this paper investigates the integrated cell formation and layout problem (ICFLP). A comprehensive mixed-integer linear programming model is developed. This model features the simultaneous incorporation of some specific design attributes of CMSs, such as unequal machine dimensions, duplicate machines, alternative process routings, coexistence of alternative process routings, lot splitting, and production planning. Given the computational difficulty of the model, two hybrid approaches, one combining a genetic algorithm and linear programming (GALP) and the other combining simulated annealing and linear programming (SALP), are proposed to efficiently solve real-sized problems. The results of an illustrative example reveal that lower production cost and higher machine utilization rate can be generated by the concurrent cell formation and layout design, and the incorporation of relevant attributes. Comparison experiments show that the GALP and SALP outperform a previous metaheuristic and an existing heuristic for solving a simplified ICFLP model. They also excel pure GA, pure SA and CPLEX on solving the proposed model.

Framework for Part Families Associated with Setup Sequence Based Similarity in Reconfigurable Manufacturing System / Cellular Manufacturing System

Framework for Part Families Associated with Setup Sequence Based Similarity in Reconfigurable Manufacturing System / Cellular Manufacturing System

Grouping both machines and parts in cellular technology by Genetic Algorithm

Cellular manufacturing is a practical aspect of grouping technology in where similar parts and dissimilar machines are grouped into cells to exploit the cost-effectiveness of mass production and flexibility of job shop manufacturing. In this research, a Genetic Algorithm (GA) approach is developed to solve the grouping problem in cellular manufacturing systems. The grouping problem of both machines and parts is formulated as a multi-objective mathematical program; first, minimizing intra-cell and inter-cell movement costs and second, maximizing the importance of allocating the parts to the cells with regards to the location of the machines. The weight of each part for each machine is calculated by Analytical Hierarchy Process. Then, the multi-objective GA has been solved by allocating weights to objectives. The results have also been compared with the results of heuristic Jaccard Coefficient method.

OPTIMASI PROSES PERAKITAN CAR STEREO DENGAN MENGGUNAKAN SISTEM MANUFAKTUR SELULAR

Abstract: Today's global market competition has spurred the producers of manufactured products to perform a variety of efficiency and continuous improvement for increasing the productivity of workers and reducing the cost of manufacture. Its aim is for helping companies to survive in the global competition. Thus, the companies can sell their products with more competitive price. Changing the conventional working system with better one - using a cellular manufacturing system - is one of the efficiency that can be done by the manufacturing companies. However the firm which had changed its conventional manual assembling system into mobile (cellular) assembling system resulted the improvement of workers knowledge from 3 up to 6 grade previously, it reduced 2 workers with the same number of work stations, increased the efficiency of assembling lines as much as 7.18%, increased worker productivity as much as 14.28% with the same production capacity, reduced Not Right First Time / NRFT as much as 37.50%, increased the Value Added Per Person / VAPP as much as 12.70%, and raised the Floor Space Utilization / FSU as much as 3.17%.

Optimization of the material handling costs and the machine reliability in cellular manufacturing system using cuckoo search algorithm

Lean manufacturing is a systematic method to improve productivity and reduce costs for each industrial. One of the tools for lean is the cellular manufacturing that divides the production system into smaller subsystems. The objective is to reduce inventory, setup times, and material handling costs. The manufacturing cell formation problem presents the first issue faced in designing a cellular manufacturing system. In this paper, the presence of alternative process routings and machine reliability in addition to production volume, operation sequence and production time makes the problem more complex but also more realistic and appealing to real-world applications. Most authors solve this kind of problems by mathematical programming approaches that require large amounts of computational efforts. Therefore, a meta-heuristic algorithm, entitled Hybrid cuckoo search for the generalized cell formation problem, is considered in this paper. A set of benchmark problems with various scales is used to further evaluate the performance of the proposed method. Comparison of the results obtained with those obtained by LINGO and by the simulated annealing method reveals the better effectiveness and efficiency of our method.

A hierarchical cellular manufacturing system

Cellular manufacturing is one of the most common and popular methods in order to take advantage of group technology. Of particular interest, applying cellular manufacturing systems (CMSs) increases the efficiency of the applied transportation system by designing an efficient working floor. Controlling the amount of material handling, results in lower cost and energy required to transport materials/products. Hence, CMS are under consideration in the study at hand. Regarding a hybrid CMS, proper manufacturing systems such as single machines, flow shops, job-shops and open shops are applied between and within cells to harmonise the whole manufacturing procedure. A special case of hybrid CMS, referred to as hierarchical CMS is investigated in this paper. In a HCMS not only machines are placed in several interior cells, but also interior cells themselves must be located in exterior ones. Finally a mixed integer nonlinear programming mathematical model is presented and validated through numerical results.

Analytical methods for perfect partition by set theory

A recent interesting paper was published by Yin et al. (2014). They developed a structural architecture to investigate the previous literature with three types of research problems: what, why, and how. They utilised simple but rigorous set theory to define CM concepts, such as part family, machine cell, cellular manufacturing system, bottleneck machines. Then, they deduced sufficient and necessary conditions for perfectly partitioned cellular manufacturing systems. An algorithm was proposed to get perfectly partitioned manufacturing cells. The objective of this paper is to overcome the two problems in Yin et al.'s (2014) research. Firstly, we modify their algorithm to get perfectly partitioned manufacturing cells. Then we extend the analysis from machine groups to a cellular manufacturing system and compare the results with the ones of Yin et al. (2014). Based on the analysis of our new model, we propose a new algorithm to generate perfectly partitioned manufacturing cells.