Solving an extended multi-row facility layout problem with fuzzy clearances using GA

Safarzadeh, Soroush; Koosha, Hamidreza

doi:10.1016/j.asoc.2017.09.003

Cited by 45 publications

(19 citation statements)

References 45 publications

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“…Heuristic algorithms were proposed in [18], and a nonlinear formulation was given in [16] and solved using a genetic algorithm (GA). A GA was also proposed by [31] but they do not enforce the strict row structure as we do here. An SDPbased approach was introduced in [24], and to the best of our knowledge, this is the only global optimization approach for the general row FLP with more than two rows.…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical optimization approach for facility layout on several rows

Anjos

Vieira

2020

Optim Lett

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The facility layout problem is concerned with finding an arrangement of non-overlapping indivisible departments within a facility so as to minimize the total expected flow cost. In this paper we consider the special case of multi-row layout in which all the departments are to be placed in three or more rows, and our focus is on, for the first time, solutions for large instances. We first propose a new mixed integer linear programming formulation that uses continuous variables to represent the departments’ location in both x and y coordinates, where x represents the position of a department within a row and y represents the row assigned to the department. We prove that this formulation always achieves an optimal solution with integer values of y, but it is limited to solving instances with up to 13 departments. This limitation motivates the application of a two-stage optimization algorithm that combines two mathematical optimization models by taking the output of the first-stage model as the input of the second-stage model. This algorithm is, to the best of our knowledge, the first one in the literature reporting solutions for instances with up to 100 departments.

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Section: Literature Reviewmentioning

confidence: 99%

Mathematical optimization approach for facility layout on several rows

Anjos

Vieira

2020

Optim Lett

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“…The in-plant supply of manufacturing processes is based on complex material handling systems, which were optimized with a hybrid multi-objective artificial immune systems-based algorithm [42]. Manufacturing processes are typical uncertain environments, where fuzzy modelling and fuzzy optimization offers suitable tools [43,44] and the fuzzy approach can easily integrate with other analytical or heuristic algorithms [45]. Researchers used an integrated data-driven stochastic degradation model to find the optimal maintenance strategy in chemical and manufacturing processes, where unit failures are caused due to equipment degradation [46].…”

Section: Content Analysismentioning

confidence: 99%

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

et al. 2019

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In the context of Industry 4.0, the matrix production concept represents revolutionary solutions from a technological and logistics point of view. In a matrix production system, flexible, configurable production and assembly cells are arranged in a grid layout, and the in-plant supply is based on autonomous vehicles. Adaptable and flexible material handling solutions are required to perform the dynamically changing supply-demands of standardized and categorized manufacturing and assembly cells. Within the frame of this paper, the authors describe the in-plant supply process of matrix production and the optimization potential in these processes. After a systematic literature review, this paper introduces the structure of matrix production as a cyber-physical system focusing on logistics aspects. A mathematical model of this in-plant supply process is described including extended and real-time optimization from routing, assignment, and scheduling points of view. The optimization problem described in the model is an NP-hard problem. There are no known efficient analytical methods to find the best solution for this kind of problem; therefore, we use heuristics to find a suitable solution for the above-described problem. Next, a sequential black hole–floral pollination heuristic algorithm is described. The scenario analysis, which focuses on the clustering and routing aspects of supply demands in a matrix production system, validates the model and evaluates its performance to increase cost-efficiency and warrants environmental awareness of the in-plant supply in matrix production.

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“…S [15,20,25], [3,5,7] 960.18 RL [10,20,20], [2,5,5] 731.57 RR [20,20,30], [5,5,8] 1,234.52 SL [13,20,23], [2,5,7] 884.74 SR [17,20,27], [3,5,8] 1,067.63 a. Crisp number Figure 6: The relationship between various fuzzy data sets and the total cost.…”

Section: %mentioning

confidence: 99%

“…These factors are also time-dependent, such as customer orders and product growth, so the cost of material handling between machines usually changes. Different methods [9][10][11][12] have been presented for solving these kinds of problems; however, uncertain and ambiguous data inputs have been paid little attention [13]. Therefore, the fuzzy set theory is applied in this paper.…”

Section: Introductionmentioning

confidence: 99%

Effects of Using Fuzzy Material Handling Inputs in the Genetic Algorithm for Machine Layout

Boongsood¹,

Jadram²

2019

Adv. sci. technol. eng. syst. j.

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This study introduces the implementation of fuzzy set theory to solve machine layout design issues, in order to handle vague information, using a genetic algorithm with tournament selection as the selection operator. The material handling inputs, including frequency and volume of materials that move between machines, were the parameters regarded as fuzzy numbers. The experimental results came from 2 case studies in a manufacturing system. In the first case, examining the difference in shapes of the triangular membership functions of input data, the total distances were reduced from 38.45 m to 29.72 m, a 22.71% reduction in distance. In the second case, examining the uncertainty of fuzzy data by an expert, the total distances were reduced from 103.45 m to 82.45 m, a 20.03% reduction in distance. It was found that given the uncertainty in input data, a shorter total material handling distance might not give a lower cost. The selection operator of tournament selection can compete effectively to converge to near the optimum solution. This can, therefore, be an alternative technique in managing manufacture.

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Solving an extended multi-row facility layout problem with fuzzy clearances using GA

Cited by 45 publications

References 45 publications

Mathematical optimization approach for facility layout on several rows

Mathematical optimization approach for facility layout on several rows

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

Effects of Using Fuzzy Material Handling Inputs in the Genetic Algorithm for Machine Layout

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