International Journal of Industrial Engineering & Production Research

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Dynamic cellular manufacturing systems,   Mixed-integer non-linear programming,   Production planning, Manufacturing attributes

This paper presents a novel mixed-integer non-linear programming model for the design of a dynamic cellular manufacturing system (DCMS) based on production planning (PP) decisions and several manufacturing attributes. Such an integrated DCMS model with an extensive coverage of important design features has not been proposed yet and incorporates several manufacturing attributes including alternative process routings, operation sequence, processing time, production volume of parts, purchasing machine, duplicate machines, machine depot, machine capacity, lot splitting, material flow conservation equations, inflation coefficient, cell workload balancing, budget constraints for cell construction and machine procurement, varying number of formed cells, worker capacity, holding inventories and backorders, outsourcing part-operations, warehouse capacity, and cell reconfiguration. The objective of the integrated model is to minimize the total costs of cell construction, cell unemployment, machine overhead and machine processing, part-operations setup and production, outsourcing, backorders, inventory holding, material handling between system and warehouse, intra-cell and inter-cell movements, purchasing new machines, and machine relocation/installation/uninstallation. A comprehensive numerical example taken from the literature is solved by the Lingo software to illustrate the performance of the proposed model in handling the PP decisions and to investigate the incorporated manufacturing attributes in an integrated DCMS .

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The fundamental function of a cellular manufacturing system (CMS) is based on definition and recognition of a type of similarity among parts that should be produced in a planning period. Cell formation (CF) and cell layout design are two important steps in implementation of the CMS. This paper represents a new nonlinear mathematical programming model for dynamic cell formation that employs the rectilinear distance notion to determine the layout in the continuous space. In the proposed model, machines are considered unreliable with a stochastic time between failures. The objective function calculates the costs of inter and intra-cell movements of parts and the cost due to the existence of exceptional elements (EEs), cell reconfigurations and machine breakdowns. 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. Several crossover and mutation strategies are adjusted for GA and parameters are calibrated based on Taguchi experimental design method. The great efficiency of the proposed GA is then demonstrated via comparing with particle swarm optimization (PSO) and the optimum solution via GAMS considering several small/medium and large-sized problems. 

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There is still a great deal of attention in cellular manufacturing systems and proposing capable metaheuristics to better solve these complicated optimization models. In this study, machines are considered unreliable that life span of them follows a Weibull distribution. The intra and inter-cell movements for both parts and machines are determined using batch sizes for transferring parts are related to the distance traveled through a rectilinear distance. The objectives minimize the total cost of parts relocations and maximize the processing routes reliability due to alternative process routing. To solve the proposed problem, Genetic Algorithm (GA) and two recent nature-inspired algorithms including Keshtel Algorithm (KA) and Red Deer Algorithm (RDA) are employed. In addition, the main innovation of this paper is to propose a novel hybrid metaheuristic algorithm based on the benefits of aforementioned algorithms. Some numerical instances are defined and solved by the proposed algorithms and also validated by the outputs of exact solver. A real case study is also utilized to validate the proposed solution and modeling algorithms. The results indicate that the proposed hybrid algorithm is more appropriate than the exact solver and outperforms the performance of individual ones.

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Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.