Dynamic Cellular Manufacturing System Research Articles

Overview of Manufacturing Systems—Evolution from Group Technology to Dynamic Cellular Manufacturing System

Overview of Manufacturing Systems—Evolution from Group Technology to Dynamic Cellular Manufacturing System

Dynamic virtual cellular reconfiguration for capacity planning of market-oriented production systems

<p style='text-indent:20px;'>Market-oriented production systems generally have the characteristics of multi-product and small-batch production. Dynamic virtual cellular manufacturing systems create virtual manufacturing cells periodically in a planning horizon to respond to changing demands flexibly and quickly, and thus are suitable for production planning problems of market-oriented production systems. In the current research, we propose a dynamic virtual cell reconfiguration framework under a dynamic environment with unstable demands and multiple planning cycles. In this framework, we formulate a two-phase dynamic virtual cell formation (DVCF) model. In the first phase, the proposed model aims to maximize processing similarity and balance the workload in the system. In the second phase, we consider the objective of reconfiguration stability based on the first phase model. To address the proposed model, we design a hybrid metaheuristic named Lévy-NSGA-Ⅱ, and perform various computational experiments to examine the performance of the proposed algorithm. Results of experiments indicate that the proposed Lévy-NSGA-Ⅱ based algorithm outperforms multi-objective cuckoo search (MOCS) and NSGA-Ⅱ in solution quality and optimal solution size.</p>

Integrated Dynamic Cellular Manufacturing Systems and Hierarchical Production Planning with Worker Assignment and Stochastic Demand

This study deals with the interaction of dynamic cellular manufacturing (DCM) and hierarchical production planning (HPP) problems with stochastic demands for the first time. Each of these alone does not consider the system factors such as stochastic demands and dynamic cellular formation separately. Accordingly, to fill this gap, this paper presents an integrated optimized model incorporating the most comprehensive design of DCM systems and HPP problems with stochastic demands. This model helps administrators get the optimal size and number of cells to decrease costs. Also, the model applies the principles of HPP to reduce the complexity of the integrated model. Since demands are uncertain, they need to be accurately predicted. Therefore, this study aims to combine the most precise decision variables with the most realistic conditions. A case study from an agriculture mechanization and industrial development company shows that an integrated model can provide managers with a feasible solution to meet demand, reconfigure cells in each period, provide new machinery to increase the required production capacity, and adjust production capacity to help them cope with demand fluctuations. A sensitivity analysis was performed and the results show that increase in forecast error and inter-cell move cost cause less significant changes in total cost but the total cost is sensitive to intra-cell move cost, available time capacities and cell quantity. It is also shown that the total cost was very sensitive to available regular time and available over time and the system should try to increase the time capacity.

A Multiobjective Optimization Model for a Dynamic and Sustainable Cellular Manufacturing System under Uncertainty.

For many years, cellular manufacturing has been implemented by owners of manufacturing units. Furthermore, the increasing importance of sustainable development has led manufacturers and managers to consider the concepts of sustainable manufacturing. Sustainable manufacturing includes three components: economic, environmental, and social responsibility. Many research and studies have been conducted in the field of cellular manufacturing, and also in most studies, only the economic component or at most two components of sustainable manufacturing have been taken into account. With increasing concerns about global warming, environmental issues have become particularly important in the production of products and goods. On the other hand, customer satisfaction as one of the aspects of social responsibility is of significant importance. In this research, we put the sustainable manufacturing system in the dynamic cellular manufacturing system under uncertainty (fuzzy parameters). A multiobjective sustainable mathematical model with objective functions of minimizing costs minimizing CO2 emissions and minimizing product shortages (customer satisfaction) was proposed. In order to confirm the validity and accuracy of the proposed model, a small example was solved in GAMS software with CPLEX solver and epsilon constraint method, and its basic variables were investigated. Then, due to the high complexity of the proposed cellular manufacturing model, two meta-heuristic algorithms NSGA-II and MOGWO were used to solve larger problems in MATLAB software. To compare the performance of the two proposed algorithms, ten problems with different dimensions were designed and then the two algorithms were compared with each other based on several performance evaluation indicators. Also, in order to investigate the significance of the difference between the two algorithms based on each index, a statistical analysis was carried out by Minitab software. Taguchi method was also used to adjust the parameters of both algorithms. Based on the analytic results and statistical analysis, the MOGWO algorithm performed better than NSGA-II algorithm and the exact solution method, GAMS software.

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.

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.

Robust optimisation to design a dynamic cellular manufacturing system integrating group layout and workers' assignment

Robust optimisation to design a dynamic cellular manufacturing system integrating group layout and workers' assignment

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.

A mathematical model to design dynamic cellular manufacturing systems in multiple plants with production planning and location–allocation decisions

In this research, a mixed-integer nonlinear programming model is formulated to design cellular manufacturing systems (CMSs) in multiple plants in a dynamic condition. The proposed mathematical model integrates significant manufacturing characteristics in designing CMSs along with the main strategies of production planning (PP) and location–allocation (LA) problems. In addition, there are some novel characteristics that make the designed model remarkable regarding the literature including: (1) multi-plant location; (2) allocation of plants capacity to fulfill multiple markets demand; and (3) the integration of dynamic cell formation, PP and LA decisions. The objective function terms are to optimize the sale revenue and total costs of machine operating, machine overhead, inter-cell material handling, inventory holding, outsourcing, machine installation/uninstallation, products transportation, dispersing machines, establishing plants, and forming cells. A sample test problem is solved by CPLEX solver to show the achievement obtained by the characteristics incorporated into the model. Since the proposed model is NP-hard, two meta-heuristics algorithms as grey wolf optimization and genetic algorithm are developed. To verify the computational effectiveness of the employed meta-heuristics in comparison with that of CPLEX solver, its performance is tested using a number of test problems.

Robust optimization to design a dynamic cellular manufacturing system integrating group layout and workers assignment

In this paper, a robust optimisation approach is proposed to solve a mathematical model integrating cell formation, group layout and operators assignment decisions under a dynamic situation. The main aim of applying a robust approach is to obtain an optimal design of a cellular manufacturing system that is robust with respect to encountered uncertainties in part demands and processing times. The integrated model incorporates several design attributes including operations sequence, group layout, equal-area facilities multi-rows layout, flexible cell reconfiguration, operators hiring/firing and training, operator available time, limitations of cell size and uncertain processing times and part demands. Two illustrative numerical examples are solved to investigate the validity of the robust model. Regarding the NP-hardness of the proposed model, an efficient simulated annealing algorithm is implemented. Some test problems either generated randomly or taken from the literature are solved and the results are compared with the ones obtained using CPLEX. [Received: 31 May 2019; Accepted: 3 May 2020]

New integration of preventive maintenance and production planning with cell formation and group scheduling for dynamic cellular manufacturing systems

This paper proposes a new integration method for cell formation, group scheduling, production, and preventive maintenance (PM) planning problems in a dynamic cellular manufacturing system (CMS). The cell formation sub-problem aims to form part families and machine groups, which minimizes the inter-cell material handling, under-utilization, and relocation costs. The production planning aspect is a multi-item capacitated lot-sizing problem accompanied by sub-contracting decisions, while the group scheduling problem deals with the decisions on the sequential order of the parts and their corresponding completion times. The purpose of the maintenance sub-problem is to determine the availability of the system and the time when the noncyclical perfect PM must be implemented to reduce the number of corrective actions. Numerical examples are generated and solved by Bender’s decomposition pack in GAMS to evaluate the interactions of the proposed model. Statistical analysis, based on a nonparametric method, is also used to study the behavior of the model’s cost components in two different situations. It is shown that by adding the PM planning decisions to the tactical decisions of the dynamic CMS, the optimal configuration and production plans of the system are heavily affected. The results indicate that omitting the PM actions increases the number of sudden failures, which leads to a higher total cost. Finally, it is concluded that the boost in the total availability of the dynamic CMS is one of the main advantages of the proposed integrated method.

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.

A Hybrid GRASP Algorithm for an Integrated Production Planning and a Group Layout Design in a Dynamic Cellular Manufacturing System

Demand fluctuations influence the configuration of manufacturing workshops. Integration of optimal production planning via the replenishment organization, can significantly reduce the excessive++reconfigurations number in each period and, thereafter, the global managing costs. In this article, we discuss the joint machines Group layout design (GLD) and lot-sizing problem (LSP) in a dynamic cellular manufacturing system (DCMS). We propose a novel multi-period model to determine the best cell formation, necessary configurations over each period, and optimal production and inventory policy that minimizes intra and inter-cell material handling, holding costs, and multitasks machines relocation. We propose a novel mixed-integer programming (MIP) associated model which is then solved by using the commercial software Optimizer CPLEX. Additionally, we present a hybrid greedy randomized adaptive search procedure (GRASP) enhanced with a path relinking procedure (PR) to solve the problem. Computational results on several benchmarks and randomly generated instances show the effectiveness and the relevance of the proposed approach and highlight the integration value.

An Integrated Mathematical Programming Model for a Dynamic Cellular Manufacturing System with Limited Resources

This paper proposes an integrated integer nonlinear programming model for a concurrent cell formation and production planning problem in a dynamic cell-manufacturing system (DCMS) with limited resources to setup cells and to procure machines. The proposed model seeks to minimise the total costs associated with the production planning and the cell construction and formation under a dynamic system. To validate the model, it is first converted to a linear programming. Then, a numerical example is presented based on which the branch and bound method is used to solve it employing the Lingo 8 software. Besides, due to NP-hardness of the problem, two meta-heuristic algorithms namely a GA and a PSO are utilised to solve large-scale problems. In addition, the Taguchi method is used to calibrate the parameters of the meta-heuristics in order to find better quality solutions. Computational results confirm the efficiency and the effectiveness of the proposed solution algorithms.

An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources

An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources

Bi-objective robust optimization model for configuring cellular manufacturing system with variable machine reliability and parts demand: A real case study

In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the second one maximizes the machines reliability through minimizing machines failure. In addition, some routes are considered to produce each part based on operational requirements. An appropriate route is selected respect to the costs and operational time. Some parameters are considered under uncertainty in two categories. The first category such as demand is dependent on market condition and the uncontrolled competitive environment. The second one includes some parameters for production system and machines that are directly related to plans organized by production management. A robust optimization approach is used to deal with parameters uncertainty to produce feasible and optimal solutions. Furthermore, for validation and implementation of results in real world, a case study is investigated. Computational results show that the robust model reports better values for objective functions compared to the scenario-based model. In fact, Pareto-front which are resulted by robust model are dominated by scenario-based models’ Pareto front. Sensitivity analyses on main parameters of the problem are performed to drive some managerial insights that help corresponding decision makers to provide suitable and homogenous decisions in a production system.

Integrated optimization of dynamic cell formation and hierarchical production planning problems

The paper presents a non-linear mixed integer programming (MIP) model that integrates dynamic cell formation (DCF) and hierarchical production planning (HPP). In the model, the DCF problem optimizes reconfiguration of machine cells, with varying production quantities in different periods determined by the HPP model. The HPP problem, formulated as an integrated model, determines the optimal production plans that meet forecast demands in the planning horizon, with capacity limitations of the machine cells formed through the DCF model. Compared with prior studies that integrate DCF and production planning (PP) problems, this paper provides the most comprehensive options needed to meet demands in dynamic cellular manufacturing systems (DCMS). With the introduction of HPP, the model could incorporate more decision variables such as inventory, internal production, subcontracting and backlogging costs, inter- and intra-cell material handling costs, yet with less solution complexity. The model is solved with branch-and-bound method, and its complexity and solution results are analyzed using empirical data from a mold manufacturing plant, with the results of the model compared with those in the literature. Our analyses show that the proposed model is simpler and easier to solve, and that the total cost is insensitive to demand volatility and decreases inversely with the number of cells.

A genetic algorithm to integrate a comprehensive dynamic cellular manufacturing system with aggregate planning decisions

ABSTRACTIn this paper, to design a Cellular Manufacturing System (CMS) under a dynamic situation and make Aggregate Planning (AP) decisions simultaneously, a mixed-integer nonlinear programming (MINLP) model is designed. The distinctive features of the comprehensive Dynamic CMS (DCMS) model under consideration are: i) an extensive coverage of significant manufacturing characteristics in designing a DCMS in addition to the main strategies of AP, ii) integration of cost elements addressing structural, operational and planning issues in the design of DCMS, and iii) capable of developing better DCMS design decisions by incorporating more detailed and realistic parameters when compared to the literature. An illustrative numerical example is solved by CPLEX 12.6 to demonstrate the achievements obtained by the integrated model. Since the proposed model belongs to NP-hard category, a Genetic Algorithm (GA) improved by an elaborately designed matrix-based chromosome representation to represent all decision variables, as well as a sequential procedure generating initial solutions is developed. 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 GA is capable of evolving optimal or near-optimal solutions with relative gap less than 1% in a computationally satisfactory manner.Abbreviation: DCMS: Dynamic CMS; AP: Aggregate Planning; GA: Genetic Algorithm; MINLP: Mixed-integer nonlinear programming

Dynamic Cellular Manufacturing Systems and Their Solution Using Genetic Algorithm

Production planning and cell formation problems are two important parts of this system, which have mutual effects. In this work, a comprehensive, non-linear mathematical model with integer variables has been proposed for such problems for cell formation and production planning in a dynamic cellular manufacturing system. The proposed model is aimed at reduction of the expenses associated with production planning under dynamic conditions and the costs related to the construction and formation of cells including the costs of cell preparation and activation. The expenses investigated in this research include the costs of buying machinery, machine operation, intercellular material transfer, cell reconfiguration, activation of cells, maintenance of an inventory of assurance, maintenance of an end of the period inventory and part production as well as the unpredictable, variable costs of cell activation and buying machinery. Since the proposed model is an NP-Hard model, an effective genetic algorithm has been used. In order to evaluate and confirm the performance of the algorithms used, the results have been compared with those obtained from GAM software for different problems from the perspectives of response quality and solution time.

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.