International Journal of Industrial Engineering & Production Research

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This paper offers an approach that could be useful for diverse types of layout problems or even area allocation problems. By this approach there is no need to large number of discrete variables and only by few continues variables large-scale layout problems could be solved in polynomial time. This is resulted from dividing area into discrete and continuous dimensions. Also defining decision variables as starting and finishing point of departments in area makes it possible to model layout problem so. This paper also provides new technique that models basic constraints of layout problems.

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This article addresses a single row facility layout problem where the objective is to optimize the arrangement of some rectangular facilities with different dimensions on a line. Regarding the NP-Hard nature of the considered problem, a hybrid meta-heuristic algorithm based on simulated annealing has been proposed to obtain a near optimal solution. A number of test problems are randomly generated and the results obtained by the proposed hybrid meta-heuristic are compared with exact solutions. The results imply that the proposed hybrid method provides more efficient solutions for the large-sized problem instances.

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The present paper aims to investigate the effects of modularity and the layout of subsystems and parts of a complex system on its maintainability. For this purpose, four objective functions have been considered simultaneously: I) maximizing the level of accordance between system design and optimum modularity design,II) maximizing the level of accessibility and the maintenance space required,III) maximizing the providing of distance requirement and IV) minimizing the layout space. The first objective function has been put forward for the first time in the present paper and in it, the optimum system modularity design was determined using the Design Structure Matrix (DSM) technique.The second objective function is combined with the concept of Level of Repair Analysis (LoRA) and developed. Simultaneous optimization of the above-mentioned objective functions has not been considered in previous studies. The multi objective problem which has been put forward was applied on a laser range finder containing 17 subsystems and the modularity and optimum layout was determined using a multi objective particle swarm optimization (MOPSO) algorithm.

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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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It is indispensable that any manufacturing system is consistent with potential changes such as fluctuations in demand. The uncertainty also makes it more essential. Routing Flexibility (RF) is one of the necessities to any modern manufacturing system such as Flexible Manufacturing System (FMS). This paper suggests three mixed integer nonlinear programming models for the Unequal–Area Stochastic Dynamic Facility Layout Problems (UA–SDFLPs) by considering the Routing Flexibility. The models are proposed when the independent demands follow the random variable with the Poisson, Exponential, and Normal distributions. To validation of the proposed models, many small-sized test problems has solved that derived from a real case in literature. The large-sized test problems are solved by the Genetic Algorithm (GA) at a reasonable computational time. The obtained results indicate that the discussed models for the UA–SDFLPs are valid and the managers can take these models to the manufacturing floor to adapt to the potential changes in today's competitive market.
 

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