Machine Duplication Research Articles

Retract-Complete Similarity Measure Mathematical Model For Cell Layout Design

The presented mathematical model is used to form machine cells, optimize costs of exceptional elements and design the shop floor layout for various demands of components. The complete similarity measure algorithm forms machine cells and part families in a refined form. Later, exceptional elements are eliminated in linear programming optimization model by using machine duplication and part subcontract. Then the shop floor layout is designed to have optimized material movements between cells and within a cell. The performance evaluation of cell formation algorithm is done on case studies of various batch sizes to give the process capability compared with other similar methods. The result from a linear programming optimization model is cost savings, machines duplicated, parts subcontracted, inter intra cellular movements. Finally, the output of inbound facility design is the floor layout which has machine cell clusters with optimized floor area.

CELLULAR MANUFACTURING LAYOUT DESIGN USING HEURISTIC CLUSTERING ALGORITHM AND LPP MODEL

This mathematical model forms machine cells, optimises the costs of unassigned machines and components, and designs the shop floor cell layout to have minimal movement of materials. The complete similarity measure algorithm forms machine cells and part families in a refined form. Later, exceptional elements are eliminated in the optimisation model by using machine duplication and sub-contracting of parts. Then the shop floor layout is designed to have optimised material movements between and within cells. An evaluation of the cell formation algorithm’ performance is done on the benchmark problems of various batch sizes to reveal the process’s capability compared with other similar methods. The data of machining times are acquired and tabulated in a part incidence matrix, which is used as input for the algorithm. The results from the linear programming optimisation model are that costs are saved, machines are duplicated, parts are sub-contracted, and there are inter- and intra- cellular movements. Finally, the output of the inbound facility design is the floor layout, which has machine cell clusters within the optimised floor area.

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.

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.

Flexible Flowshop Design in Cellular Manufacturing Systems

This paper explores the flowshop cell design in the cellular manufacturing system to minimize the number of machines, considering shifting the bottleneck machine and the annual demand. In a flowshop cell, products are processed through a series of production stages. Based on the number of machines per stage, the cell can be classified as either a production line (flowshop) or flexible flowshop. Two steps are followed in this study to design flexible flowshop cell. First, the un-capacitated mathematical model distributes the products into families and their corresponding machines into cells (product family formulation). Second, two non-linear mathematical models are proposed to design flexible flowshop. The first non-linear mathematical model is developed to build a flexible flowshop cell based on production rates. In this context, the number of machines in the system is determined, considering the machine duplication possibilities per stage for all products simultaneously, based on minimizing the total mean capacity required. The minimum number of machines per system is determined using a second non-linear mathematical model, with the objective to minimize the number of machines in the cell. In the two proposed nonlinear mathematical models, there is a limitation for the number of machine types. The results show that, as machine duplication is allowed, the number of machines per cell increases. Therefore, flexible flowshop design contributes to minimize the total number of machines in the system. In this way, the number of cells is reduced while production rates increase, and the total number of machines and total mean capacity required decrease.

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.

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.

An adjustable grouping genetic algorithm for the design of cellular manufacturing system integrating structural and operational parameters

This paper presents non-linear and linear formulations for the design of a Cellular Manufacturing Systems (CMS) modeled integrating structural and operational decision parameters, and a Genetic Algorithm (GA) based on self-regulating adaptive operators. The proposed CMS model evolves the structural design decisions of number of cells, and parts – machines assignment to cells, along with operational decisions of scheduling under machine duplications and alternate routings/cross-flow environments. The distinctive features of the CMS model under consideration are: i) integration of cost elements addressing both structural and operational issues in the design of CMS; ii) capable of evolving better CMS design decisions in terms of operational cost when compared to the literature part-machine grouping decisions; iii) suitable for variety of manufacturing system designs by relaxing the model constraints. Besides, this paper proposes a new variant of Grouping Genetic Algorithm namely Adjustable Grouping Genetic Algorithm (AGGA) that has features to adjust the coding suitable for machine duplication environment of the proposed CMS model and regulate genetic parameters towards convergence. It is shown, through comparisons with Simulated Annealing (SA) algorithm, Simple Genetic Algorithm (SGA) and also optimal solutions obtained via mathematical model relaxed to fixed number of cells, that AGGA is capable of evolving optimal or near optimal solutions in a computationally efficient manner.

A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication

Cell formation is one of the first and most important steps in designing a cellular manufacturing system. It consist of grouping parts with similar design features or processing requirements into part families and associated machines into machine cells. In this study, a bi-objective cell formation problem considering alternative process routings and machine duplication is presented. Manufacturing factors such as part demands, processing times and machine capacities are incorporated in the problem. The objectives of the problem include the minimization of the total dissimilarity between the parts and the minimization of the total investment needed for the acquisition of machines. A normalized weighted sum method is applied to unify the objective functions. Due to the computational complexity of the problem, a hybrid method combining genetic algorithm and dynamic programming is developed to solve it. In the proposed method, the dynamic programming is implemented to evaluate the fitness value of chromosomes in the genetic algorithm. Computational experiments are conducted to examine the performance of the hybrid method. The computations showed promising results in terms of both solution quality and computation time.

Dynamic cellular manufacturing system design considering alternative routing and part operation tradeoff using simulated annealing based genetic algorithm

In this paper, an integrated mathematical model of multi-period cell formation and part operation tradeoff in a dynamic cellular manufacturing system is proposed in consideration with multiple part process route. This paper puts emphasize on the production flexibility (production/subcontracting part operation) to satisfy the product demand requirement in different period segments of planning horizon considering production capacity shortage and/or sudden machine breakdown. The proposed model simultaneously generates machine cells and part families and selects the optimum process route instead of the user specifying predetermined routes. Conventional optimization method for the optimal cell formation problem requires substantial amount of time and memory space. Hence a simulated annealing based genetic algorithm is proposed to explore the solution regions efficiently and to expedite the solution search space. To evaluate the computability of the proposed algorithm, different problem scenarios are adopted from literature. The results approve the effectiveness of the proposed approach in designing the manufacturing cell and minimization of the overall cost, considering various manufacturing aspects such as production volume, multiple process route, production capacity, machine duplication, system reconfiguration, material handling and subcontracting part operation.

A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training

This paper presents a comprehensive multi-objective mixed integer mathematical programming model which considers cell formation and production planning problems simultaneously. This comprehensive model includes dynamic system reconfiguration, multi period production planning, operation sequence, alternative process plans for part types, machine and worker flexibility, duplicate machines, machine capacity, available time of workers and worker assignment. The aim of the proposed model is to minimize inter and intra-cell movement costs, machine and reconfiguration costs, setup costs, production planning costs (holding, backorder and subcontracting costs) and workers hiring, firing, training and salary costs, as well as minimizing summation of machines idle times as a second objective. Due to NP-hardness of the problem, a recent and efficient meta-heuristic algorithm namely multi-objective vibration damping optimization (MOVDO) is designed for finding Pareto-optimal frontier. In order to check the efficiency of the developed algorithm, it is compared with two salient multi-objective genetic algorithms named NSGAII and NRGA. Finally, by generating some test problems in small and large scales and using some multi-objective comparison metrics, the algorithms are compared and analyzed statistically.

Concurrent design of cell formation and scheduling with consideration of duplicate machines and alternative process routings

Concurrent design of cell formation and scheduling is an effective method for better implementing cellular manufacturing. To address the integrated cell formation and scheduling problem, a nonlinear mixed integer programming mathematical model is developed in this paper. This newly proposed model features the simultaneous consideration of many design attributes, such as duplicate machines, alternative process routings, reentrant parts and variable cell number. Several linearization techniques are proposed to transform it into a mixed integer linear programming formulation. An improved genetic algorithm (IGA) is developed to solve large-scale problems efficiently. To remove redundancy between two chromosomes, a cell renumbering procedure is applied in IGA. An illustrative example problem is solved and the results show that the integration of cell formation and scheduling can remarkably reduce the flowtime of cellular manufacturing systems. A set of thirteen test problems with various scale is used to further evaluate the performance of IGA. Comparison of the results obtained by IGA with those obtained by Lingo and CPLEX reveals the better effectiveness and efficiency of IGA.

An integrated mathematical model for solving dynamic cell formation problem considering operator assignment and inter/intra cell layouts

The aim of this paper is to present an integrated mathematical model to solve the dynamic cell formation problem considering operator assignment and inter/intra cell layouts problems with machine duplication, simultaneously. The proposed model includes three objectives which the first objective seeks to minimize inter/intra cell part movements and machine relocation, the second objective minimizes machine and operator related costs and the third objective maximizes consecutive forward flows ratio. In order to validate the proposed model, a numerical example is presented and solved by the sum weighted method. Due to NP-hardness of the model, two meta-heuristics namely multi-objective simulated annealing (MOSA) and multi-objective vibration damping optimization (MOVDO) present to solve the proposed model. Finally, two algorithms have been compared using multi-objective criteria.

Design of Cellular Manufacturing Systems Considering Dynamic Production Planning and Worker Assignments

This article presents a comprehensive mathematical model for the design and analysis of Dynamic Cellular Manufacturing Systems (DCMS). The proposed DCMS model considers several manufacturing attributes such as multi period production planning, dynamic system reconfiguration, duplicate machines, machine capacity, the available time for workers, worker assignments, and machine procurement. The objective is to minimize total costs; consisting of holding cost, outsourcing cost, inter-cell material handling cost, maintenance and overhead cost, machine relocation cost. While a study of published articles in the area of Cellular Manufacturing Systems (CMS) shows that workforce management issues have not sufficiently been addressed in the literature, the model presented also incorporates CMS workforce management issues such as salaries, hiring and firing costs of workers in addition to the manufacturing attributes. In-depth discussions on the results for two numerical examples are presented to illustrate applications of the proposed model. The model developed aims to raise the envelope by expanding and improving several CMS models previously presented in the literature.

A comprehensive mathematical model for dynamic cellular manufacturing system design and Linear Programming embedded hybrid solution techniques

Considering the ever changing market conditions, it is essential to design responsive and flexible manufacturing systems. This study addresses the multi-period Dynamic Cellular Manufacturing System (DCMS) design problem and introduces a new mathematical model. The objective function of the mathematical model considers inter-cell and intra-cell material handling, machine purchasing, layout reconfiguration, variable and constant machine costs. Machine duplication, machine capacities, operation sequences, alternative processing routes of the products, varying demands of products and lot splitting are among the most important issues addressed by the mathematical model. It makes decisions on many system related issues, including cell formation, inter- and intra-cell layout, product routing and product flow between machines. Due to the complexity of the problem, we suggest two heuristic solution approaches that combine Simulated Annealing (SA) with Linear Programming and Genetic Algorithm (GA) with Linear Programming. The developed approaches were tested using a data set from the literature. In addition, randomly generated test problems were also used to investigate the performance of the hybrid heuristic approaches. A problem specific lower bound mathematical model was also proposed to observe the solution quality of the developed approaches. The suggested approaches outperformed the previous study in terms of both computational time and the solution quality by reducing the overall system cost.

Cell formation design with improved similarity coefficient method and decomposed mathematical model

This research develops a new methodology for a cell formation problem in the cellular manufacturing system. The methodology includes two phases. In the first phase, an improved similarity coefficient method, which considers the operation sequence and the number of repeated operations firstly in related researches, is proposed to identify part families. A new decomposed mathematical model is presented in the second phase, which considers some crucial operational aspects such as alternative routing, machine capacity, part demand, operation time, and lot splitting, to assign machines into part families for minimum machine cost, operation cost, and inter-cell movement cost. The model puts emphasis on the effect of trade-off between machine duplication and material inter-cell movement on performance of the cell formation to optimize machine utilization and workload balance. This paper also provides a concrete production schedule with optimum system utilization for cell formation. Test problems and sensitivity analyses are carried out to reveal the effectiveness and feasibility of the proposed methodology.

A dynamic programming–enhanced simulated annealing algorithm for solving bi-objective cell formation problem with duplicate machines

Article history: Received June 10, 2014 Accepted October 22, 2014 Available online October 22 2014 Cell formation process is one of the first and the most important steps in designing cellular manufacturing systems. It consists of identifying part families according to the similarities in the design, shape, and presses of parts and dedicating machines to each part family based on the operations required by the parts. In this study, a hybrid method based on a combination of simulated annealing algorithm and dynamic programming was developed to solve a biobjective cell formation problem with duplicate machines. In the proposed hybrid method, each solution was represented as a permutation of parts, which is created by simulated annealing algorithm, and dynamic programming was used to partition this permutation into part families and determine the number of machines in each cell such that the total dissimilarity between the parts and the total machine investment cost are minimized. The performance of the algorithm was evaluated by performing numerical experiments in different sizes. Our computational experiments indicated that the results were very encouraging in terms of computational time and solution quality. Growing Science Ltd. All rights reserved. 5 © 201

Machine cell formation: using genetic algorithm-based heuristic considering alternative route

In this paper, genetic algorithm-based heuristic is proposed to solve the cell formation problem. The proposed algorithm selects the optimum part route with certain set of machines before clustering the machine cell. A heuristic is applied within the genetic algorithm for assignment of parts to independent cell. Further, trade-off between part inter-cell movement and machine duplication is permitted to optimise the machine cell design. The proposed model simultaneously considers relevant production data such as production volume, alternative part process route, operation sequence and process time. Conventional optimisation method for the optimal cell formation problem requires significant amount of time and large memory space. Hence, a genetic algorithm-based heuristic method has been developed for solving the proposed model. To evaluate the computational performance of the proposed approach, the algorithm is tested on four benchmark problems collected from literature. The results approve the effectiveness of the proposed method in the manufacturing cell formation.

Effective two-phase p-median approach for the balanced cell formation in the design of cellular manufacturing system

In this paper, we present an effective two-phase p-median approach for the balanced cell formation (CF) in the design of cellular manufacturing system. In phase 1, the p-median mathematical model of machine CF, which adopts a linear integer programming formulation, is developed. Our formulation uses a new similarity coefficient based on the generalised nonbinary part-machine incidence matrix (PMIM) which incorporates realistic manufacturing aspects such as setup time, processing time, operation sequences and lot size of parts and duplicate machine types. In phase 2, a systematic part assignment procedure based on the new classification scheme of part types is established in pursuit of balancing the workload among machine cells. New efficiency measures for evaluating the quality of the binary and nonbinary PMIM-based block diagonal solutions are proposed to judge the degree of cell load imbalance. Computational experiments with moderately intermediate-sized data-sets selected from the literature show effectiveness of our two-phase p-median approach for the balanced CF.

A capacity constrained mathematical programming model for cellular manufacturing with exceptional elements

Most of the research on cellular manufacturing focuses on cell formation, the initial step of setting up a cellular manufacturing system. Numerous methods exist for organizing efficient manufacturing cells for existing equipment and parts. However, when cell redesign is not possible or desired, opportunities still exist for further optimization and cost savings with the existing cell formation. Exceptional elements (EEs) in cellular manufacturing are bottleneck machines and exceptional parts that span two or more manufacturing cells. This paper develops a mathematical programming model that retains the original cell formation, which is assumed to be optimal in the long term, and minimizes total costs of a cellular manufacturing system with exceptional elements through (1) intercellular transfer, (2) machine duplication, and (3) subcontracting while taking machine capacities into account to avoid capacity violations.