Intra-cell Movements Research Articles

Simultaneous consideration of time and cost impacts of machine failures on cellular manufacturing systems

Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems.

Heuristic-based approach to solve layout design and workers’ assignment problem in the cellular manufacturing system

ABSTRACT The design process of a cellular manufacturing system (CMS) involves well-known NP-hard problems. Being an NP-hard, it is difficult to address these CMS-related problems optimally within a reasonable time. Therefore, this paper proposes a novel heuristic-based two-stage approach to tackle the CMS-related problems. In the first stage, a mathematical model is developed to concurrently solve cell formation and workers’ assignment problems (CFWAP). The worker’s selection is determined by their performance in operating a machine for a specific part, measured by the defect rate resulting from assigning the worker to a machine. The model’s objective is to minimize the weighted sum of the total inter-cell, intra-cell movements, operation, and defect costs. In the second stage, based on the output of the mathematical model in the first stage, a heuristic algorithm is developed to solve the problem related to the layout design of the cellular manufacturing system (LDCMS). The actual dimensions of machines and the aisle distances between them are considered in LDCMS to find out the best possible allocation of the machines and cells within the entire system. A comparison is conducted between numerical examples from the literature to show the efficiency and the validation of this two-stage heuristic approach. The results show that the proposed method can optimally tackle the aforementioned CMS-related problems within a reasonable time. To the best of our knowledge, this is the first study that integrates the CFWAP with LDCMS, considering all these practical and manufacturing features. Finally, it should be noted that the proposed approach has been applied as a real case study for a central workshop on die manufacturing for a food stove company in Egypt. The results depict a significant reduction in the company’s total associated costs by achieving up to 23% savings from applying the proposed method.

Optimization of Inter and Intra-Cell Layout in Cellular Manufacturing Systems using Simulated Software

In this article we discuss how to manage “The Cellular Manufacturing System” (CMS) and explained the use of the Group Technology and in what reason that permit crumbling an Assembling Frame work and how is its management easier way than entire manufacturing system. Secondly, we develop model design by single row intra cell layout in three ways, they are Linear L shape – S shape by using Automation Studio Software (ASS), and find the best layout among these two types of design using ASS with minimize the Inter and Intra cell movements in static and dynamic environment from model and choose for the exertion is critic by using ARENA simulation software to identify optimized layout under Inter and Intra – cell of both environment in CMS.

Sustainable dynamic cellular facility layout: a solution approach using simulated annealing-based meta-heuristic

The fiercely competitive business environment may require the manufacturing layouts to be modified across the entire planning horizon owing to addition or deletion of new/existing products, machines or processes. The existing layout may not be appropriate for the next time periods as product combination and part demand tend to vary under multi-time scenario. Also the increased awareness of environmental concerns and paucity of vital resources like electric energy has led organizations to rethink about their manufacturing strategies and design layouts which are both cost effective as well as environmentally sustainable. An appropriately planned layout not only helps in reduction of material handling distance but can also greatly contribute to enhancement of the energy efficiency of manufacturing systems and contribute to resource productivity and sustainable value creation. To address these issues in the facility layout design, this paper models a dynamic cellular facility layout problem incorporating the sustainability aspect by considering the minimization of net electric energy consumption along with material handling and rearrangement costs. The model presented in this work is a mixed integer non-linear program. The model aims to minimize the aggregated cost of overall material handling for both the inter and intra-cell movements simultaneously. Additionally the model also minimizes the net electrical energy consumption across the entire time horizon. Twenty five data sets corresponding to varying combinations of machines, time periods and cells have been taken from extant literature to validate the proposed model. LINGO 10 optimization software has been used to solve the proposed model. However, due to NP-Hard nature of cellular facility layout problem, the proposed model is computationally difficult to be solved in reasonable time using LINGO 10, particularly, for layouts pertaining to larger dimensions. To overcome these complexities, a meta-heuristic based on simulated annealing (SA) is also employed to solve the model. It is discerned from the experimental results that LINGO is not able to optimally solve the model whereas the SA optimally solves the model for larger dimensions as well in reasonable computational time.

Developing a cellular manufacturing model considering the alternative routes, tool assignment, and machine reliability

The cell formation (CF) is one of the most important steps in the design of a cellular manufacturing system (CMS), which it includes machines’ grouping in cells and part grouping as separate families, so that the costs are minimized. The various aspects of the problem should be considered in a CF. The machine reliability and the tool assigned to them are the most important problems which have to be modeled correctly. Another important aspect in CMS is material handling costs that they consist of inter-cell and intra-cell movement costs. Moreover, setup and tool replacement costs can be effective in CF decision making. It is obvious that CF cannot be completed without considering the number of demand. With considering of all of the above aspects, an extended linear integer programming is represented for solving the cell formation problem (CFP) in this study. The objective is to minimize the sum of inter-cell movement, intra-cell movement, tool replacement, machine breakdown, and setup costs. In the other terms, for states that cost of movement is higher than tool-changing cost, although a part can have the inter- and/or intra-cell movements, the model tries to find a solution which part is allocated to one cell and with changing the tools, processes of that part is completed. In addition, to validate the model and show its efficiency and performance, several examples are solved by branch and bound (B&B) method.

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.

Solving a mathematical model integrating unequal-area facilities layout and part scheduling in a cellular manufacturing system by a genetic algorithm.

In this article, a novel integrated mixed-integer nonlinear programming model is presented for designing a cellular manufacturing system (CMS) considering machine layout and part scheduling problems simultaneously as interrelated decisions. The integrated CMS model is formulated to incorporate several design features including part due date, material handling time, operation sequence, processing time, an intra-cell layout of unequal-area facilities, and part scheduling. The objective function is to minimize makespan, tardiness penalties, and material handling costs of inter-cell and intra-cell movements. Two numerical examples are solved by the Lingo software to illustrate the results obtained by the incorporated features. In order to assess the effects and importance of integration of machine layout and part scheduling in designing a CMS, two approaches, sequentially and concurrent are investigated and the improvement resulted from a concurrent approach is revealed. Also, due to the NP-hardness of the integrated model, an efficient genetic algorithm is designed. As a consequence, computational results of this study indicate that the best solutions found by GA are better than the solutions found by B&B in much less time for both sequential and concurrent approaches. Moreover, the comparisons between the objective function values (OFVs) obtained by sequential and concurrent approaches demonstrate that the OFV improvement is averagely around 17 % by GA and 14 % by B&B.

Robust optimization of a mathematical model to design a dynamic cell formation problem considering labor utilization

Cell formation (CF) problem is one of the most important decision problems in designing a cellular manufacturing system includes grouping machines into machine cells and parts into part families. Several factors should be considered in a cell formation problem. In this work, robust optimization of a mathematical model of a dynamic cell formation problem integrating CF, production planning and worker assignment is implemented with uncertain scenario-based data. The robust approach is used to reduce the effects of fluctuations of the uncertain parameters with regards to all possible future scenarios. In this research, miscellaneous cost parameters of the cell formation and demand fluctuations are subject to uncertainty and a mixed-integer nonlinear programming model is developed to formulate the related robust dynamic cell formation problem. The objective function seeks to minimize total costs including machine constant, machine procurement, machine relocation, machine operation, inter-cell and intra-cell movement, overtime, shifting labors between cells and inventory holding. Finally, a case study is carried out to display the robustness and effectiveness of the proposed model. The tradeoff between solution robustness and model robustness is also analyzed in the obtained results.

Multioptimization in a Cellular Manufacturing System Having Stochastic Parameters Considering Pricing

AbstractA mathematical model is developed to formulate a cellular manufacturing system with uncertain parameters. In this work, the processing times and demands are stochastic and estimated via expected value and standard deviation after sampling process. The objectives of the proposed mathematical model are to configure machines’ layout in cells so that the inter-cell and intra-cell movements are minimized, the bottlenecks are breakthrough and the profit is increased. Finally, the profit is maximized due to decreasing production cost. The applicability of the proposed mathematical program is illustrated using numerical examples. With respect to the large amount of computational efforts in larger sized problem, a heuristic methodology is developed as solution approach. The properties of the proposed heuristic method are the novel search algorithm and the allocation methodology.

Concurrent cell formation and layout design using a genetic algorithm under dynamic conditions

The fundamental function of a Cellular Manufacturing System (CMS) is based on the definition and recognition of a type of similarity among the parts that must be produced in a planning period. Cell Formation (CF) and cell machine layout design are two important steps in the implementation of the CMS. This paper represents a new mathematical nonlinear programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the presented model, the objective function accurately calculates the costs of inter and intra-cell movements of parts and calculates the cost of a cell restructure. Due to the problem complexity, the presented mathematical model is categorized in NP-hardness; thus, a Genetic Algorithm (GA) is used for solving this problem. Comparing the results from the GA and the results from solving a linear model through Lingo specifies that solving the presented model accurately for more than four machines in a logical time is impossible.

Two-stage method for solving cell formation and layout problems with alternative routings and machine capacity constraints

This paper presents a two-stage method which can be used for the precise evaluation of the material handling cost within the CF process. In the first stage, a mathematical model is presented for minimizing the weighted sum of the total inter- and intra-cell movement cost by considering alternative processing routings and under constraints on cell size and machine capacity. In this stage, depending on the weight proposed in the model, different machine cells can be formed. In the second stage, another mathematical model is presented for minimizing the total material handling cost by taking into account the dimensions of the machines, the widths of the cells and the aisle distances; the linear arrangement of machines within the cells as well as the cell layout are obtained in this stage. Each machine cell formed in the first stage is evaluated using the proposed model in the second stage. Finally, the solution with the lowest material handling cost is selected as the best solution. Several numerical examples adopted from the literature are solved by the CPLEX solver to verify the performance of the proposed method. Finally, the computational results are presented.

Services and Applications Based on Mobile User’s Location Detection and Prediction

Many wireless applications are deployed and available to customers via their mobile phones. Variety of these applications and services are based on determination of the current or future location of mobile user. Location based services (LBS) are one of the vital applications which are subdivided into two main categories: economical category and public category. Economic applications include mobile marketing, entertainment and tracking applications. Whereas, emergency cases, safety, traffic management, Muslims’ applications and public information applications are sort of public applications. The first part of the paper presents a new proposed system with developed procedure to recreate public and economic applications with high positioning accuracy and good authentication of users’ data. The developed system is created to enhance both location based services and network allocation resources within mobile network platform using either normal or GPS supported mobile equipment. The second part of the paper introduces future location prediction of mobile user dependent applications. New algorithm is developed depending on utilizing both intra-cell Movement Pattern algorithm (ICMP) [1] and hybrid uplink time Difference of Arrival and Assisted GPS technique (UTDOA_AGPS) [2]. It has been noticed that ICMP algorithm outperforms other future location prediction algorithms with high precision and within suitable time (less than 220) msec. However, UTDOA_AGPS guarantees high precession of mobile user independent of the surrounding environment. The proposed technique is used to enhance reliability and efficiency of location based services using cellular network platform.

A bi-objective mathematical model toward dynamic cell formation considering labor utilization

This paper addresses dynamic cell formation problem (DCFP) via a new bi-objective mathematical formulation. Although literature body of DCFP includes a vast number of research instances, human-related issues are mostly neglected as an important aspect of DCFP in the corresponding literature. In this paper, the first objective function seeks to minimize relevant costs of the problem including machine procurement and relocation costs, machine variable cost, inter-cell movement and intra-cell movement costs, overtime cost and labor shifting cost between cells, while labor utilization of the modeled DCFP is maximized through the second objective function. Due to NP-hardness of DCFP, an ant colony optimization (ACO) meta-heuristic is developed for the first time in the literature to tackle the problem. Also, authors enhance diversification of the developed algorithm is enhanced by hybridization of the proposed ACO algorithm with a genetic one. Finally, some numerical samples are generated randomly to validate the proposed model by which strengths of the developed algorithm is shown.

Solving a multi-criteria group scheduling problem for a cellular manufacturing system by scatter search

This article presents a group scheduling problem for manufacturing cells, in which parts may visit different cells. By addressing intra-cell scheduling, the sequence of parts within manufacturing cells is determined; however, in inter-cell scheduling, the sequence of cells is obtained. A novel mathematical model is presented for a multi-criteria group scheduling problem in a cellular manufacturing system with respect to a design objective function minimizing the makespan and costs of intra-cell movement, tardiness, and sequence-dependent setup, simultaneously. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Thus, we develop a meta-heuristic algorithm based on scatter search (SS) to solve the foregoing model. The related results confirm the efficiency and effectiveness of our proposed SS algorithm, which finds good solutions, especially for medium- and large-sized problems.

Dynamic cellular manufacturing systems design—a comprehensive model

This paper addresses the dynamic cell formation problem (DCF). In dynamic environment, the product demand and mix changes in each period of a multiperiod planning horizon. It causes need of reconfiguration of cells to respond to the product demand and mix change in each period. This paper proposes a mixed-integer nonlinear programming model to design the dynamic cellular manufacturing systems (DCMSs) under dynamic environment. The proposed model, to the best of the author’s knowledge, is the most comprehensive model to date with more integrated approach to the DCMSs. The proposed DCMS model integrates concurrently the important manufacturing attributes in existing models in a single model such as machine breakdown effect in terms of machine repair cost effect and production time loss cost effect to incorporate reliability modeling; production planning in terms of part inventory holding, part internal production cost, and part outsourcing; process batch size; transfer batch size for intracell travel; transfer batch size for intercell travel; lot splitting; alternative process plan, and routing and sequence of operation; multiple copies of identical copies; machine capacity, cutting tooling requirements, work load balancing, and machine in different cells constraint; machine in same cell constraint; and machine procurements and multiple period dynamic cell reconfiguration. Further, the objective of the proposed model is to minimize the sum of various costs such as intracell movement costs; intercell movement costs and machine procurement costs; setup cost; cutting tool consumption costs; machine operation costs; production planning-related costs such as internal part production cost, part holding costs, and subcontracting costs; system reconfiguration costs; and machine breakdown repair cost, production time loss cost due to machine breakdown, machine maintenance overheads, etc. ,in an integrated manner. Nonlinear terms of objective functions are transformed into linear terms to make mixed-integer linear programming model. The proposed model has been demonstrated with several problems, and results have been presented accordingly.

The economic cell number

The economic benefits brought by the cellular manufacturing have been reported. Cell formation is one of the most important problems in the design of a cellular manufacturing. In cell formation problems, the number of cells is a critical factor. However, it is impossible to know the number of cells before solving the cell formation problem. In fact, determining the correct number of cells in a cellular manufacturing system is still an unresolved problem. We introduce a mathematical model in this paper to find the economic number of cells (we call it “Economic Cell Number”, ECN) that minimizes the total sum of intracell and intercell movements costs without solving the cell formation problem. A numerical example is used to verify the effectiveness of the proposed ECN. Although the proposed simple model still cannot deal with many realistic cell formation problems, it plays as a first attempt to find the economic number of cells.

Design of a facility layout problem in cellular manufacturing systems with stochastic demands

This paper presents a new mathematical model to solve a facility layout problem in cellular manufacturing systems (CMSs) with stochastic demands. The objective is to minimize the total costs of inter and intra-cell movements in both machine and cell layout problems simultaneously. This model depends on the attitude of the decision maker towards uncertainty in such a way that the optimal layout in CMSs can be changed significantly. A number of instances are optimally solved by the Lingo software to validate and verify the proposed model. Finally, computational results are reported and analyzed.

A GENETIC ALGORITHM APPROACH FOR MACHINE CELL FORMATION

Cellular Manufacturing Systems (CMS) have been the key to the success of manufacturing industries in the recent past. Machine cell design, which involves formation of machine cells and component groups, represents the most important step in the design of CMS. Even though a tremendous amount of research has been conducted in this area, the gap between theoretical research and practice is widening primarily because of the lack of consideration of key production data such as production volumes, operations sequences, machine sequence inside the cells, processing times, setup times, and machine costs during the cell design stage. This paper discuses the development of a Genetic Algorithm Model (GAM), designed to assist in the formation of manufacturing cells. The GAM aims at the minimization of the material handling and the penalty costs while considering the effects of inter-cell, intra-cell, backtracking, and machine skipping movements.

A GENERALIZED CELL FORMATION PROBLEM IN DYNAMIC ENVIRONMENT WITH DIFFERENT INTER and INTRA-CELL BATCH SIZES

This paper presents a mixed-integer linear programming approach for cell formation problem (CFP) by assuming that the product mix and part demand change under a multi-period planning horizon. As a result, the best cell design for one period may not be efficient for subsequent periods and then reconfiguration of cells is required. The advantages of the proposed model are as follows: considering different inter and intra-cell movements, batch sizes, alternative process plan for part types, sequencing part-operation, machine relocation, cell size, and flexibility of cell number. The objective is to minimize the sum of the depreciation and relocation machine cost as well as inter and intra-cell movements cost.

Predictive call admission control for multimedia streaming in CDMA cellular environments

Third-generation (3G) wireless networks based on code-division multiple access (CDMA) have been engineered to provide mobile users with voice as well as advanced packet data services. Support of Internet-based multimedia streaming services is considered a critical factor for future deployment of such networks. Provisioning streaming services in a dependable way, however, requires supporting data transmission at relatively high data rates while maintaining session quality during both intracell and intercell user movements. In this paper, an adaptive bandwidth allocation and admission control scheme is proposed for managing network resources for a streaming service. Streaming requests are assumed to be served by an adaptive server capable of choosing suitable video streaming parameters (e.g., video resolution, frame rate, and encoding parameters) in response to possible requests from the wireless network to vary the currently allocated bandwidth. The devised admission control scheme exploits a priori knowledge of user mobility patterns to minimize the risk of running into an overload condition after acceptance of a new multimedia streaming connection. The obtained simulation results show that, compared to a nonpredictive admission control scheme, the proposed scheme achieves a lower forced-termination probability and higher throughput, while consuming less base-station transmission energy.