Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm

Heuristics for the dynamic facility layout problem with unequal-area departments

The dynamic facility layout problem (DFLP) deals with the arrangement of facilities/departments in a factory for different periods so that the location of the facilities can be changed from one period to another one. Traditionally, this problem is formulated to minimize the sum of material handling and rearrangement costs in the planning horizon by assuming that all parameters are deterministic. In this paper, we assume that the material flow between departments and rearrangement costs are uncertain and, accordingly, develop the robust counterpart (RC) of the DFLP model. The model is computationally intractable; therefore, we propose a hybrid algorithm based on nested partitions (NP) and simulated annealing (SA) algorithms, namely NP-SA. Moreover, we develop a heuristic algorithm to compute the values of the additional variables used in the RC model. The numerical results indicate that the NP-SA algorithm is very effective in giving a good solution in a short time. Furthermore, a simulation study demonstrates that, on average, robust solutions are better than nominal solutions.

An artificial immune system based algorithm to solve unequal area facility layout problem

This paper proposes an integrated approach for dynamic facility layout problem considering the material handling equipment (MHE). The objectives of this problem are minimization of the fixed costs of MHE, minimization of material handling cost (MHC) and minimization of machine rearrangement costs (MRC). To be more realistic, MHE fixed costs, MRC and MHC, which might be of different importance to decision maker, are considered separately in three objective functions. An integrated model is proposed which is able to simultaneously select the MHE along with the arranging and re-arranging facilities. The model belongs to the class of multi-objective nonlinear mathematical programming models. Considering the NP-hard nature of the model and inspiring the existing water flow like algorithm, a novel Pareto-based meta-heuristic algorithm called multi-objective water flow like algorithm (MOWFA)is developed to solve the problem. We comprehensively discuss the parameter tuning of the algorithms utilizing Taguchi method. Finally, the performance of the proposed MOWFA is evaluated against two well-known meta-heuristic algorithms called non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA). Computational results indicate the greater efficiency of the algorithm compared to the two addressed algorithms for solving the given multi-objective problem.

Facility layout problems deal with layout of facilities or departments in a shop floor. This article studies unequal-area static facility layout problems in order to minimize the sum of the material handling costs and unequal-area dynamic facility layout problems so as to minimize the sum of the material handling costs and rearrangement costs. Unequal-area static and dynamic facility layout problems are NP-hard. Therefore, a modified particle swarm optimization was suggested to solve them where the departments have fixed shapes and areas throughout the time horizon. The modified particle swarm optimization was tested using the available problem instances chosen from the literature. The proposed algorithm applied two local search methods and the department swapping method to improve the quality of solutions and to prevent local optima for dynamic and static problems. It also utilized the period swapping method to improve the solutions for dynamic problems. The results showed that the proposed algorithm has created encouraging layouts in comparison with other approaches.

Hybrid ant systems for the dynamic facility layout problem

Most facility layout problems have departments with unequal areas and have significant rearrangement costs. This paper describes a model and improved algorithm which simultaneously handles these parameters. An existing algorithm solves the dynamic facilities layout problem while permitting the departments to have unequal areas. One part of the algorithm solves a mixed integer programming problem to find the desired block diagram layout. This large, complex problem could only be solved optimally for small problems. Therefore a preprocessing method was developed to prespecify certain obvious department pair orientations, which had previously required binary variables. The method uses estimated location, department sizes, and flow costs to determine the probable variable values. Then, a revised branch and bound strategy solves for the less obvious department pair orientations. Test results show a significant cost reduction on a variety of previously published problems, and feasible solutions to previously unsolved problems. The algorithm found a layout solution to the standard . CRAFT problem which has 10 5% lower costs than the previously best published layout.

The problem of rearranging manufacturing facilities over time is known as Dynamic Facility Layout Problem (DFLP). The objective is to minimize the sum of the material handling and the rearrangement costs. The problem is NP-hard and has begun to receive attention very recently. In this paper, an approximate algorithm/ heuristic for solving DFLP is presented. The proposed heuristics has been applied to eight different data sets of a problem set (containing 48 data sets) given by Balakrishnan and Cheng [1], and it has been found that the proposed heuristic provide good solutions having about 12% of deviation from the best known solution available in the published literature. Further, as a future scope of research work, an improvement heuristic can be developed or some meta-heuristic approach such as simulated annealing, and tabu search can be further applied to improve the solution quality obtained from the proposed approximate algorithm.

During the past few decades, developing efficient methods to solve dynamic facility layout problems has been focused on significantly by practitioners and researchers. More specifically meta-heuristic algorithms, especially genetic algorithm, have been proven to be increasingly helpful to generate sub-optimal solutions for large-scale dynamic facility layout problems. Nevertheless, the uncertainty of the manufacturing factors in addition to the scale of the layout problem calls for a mixed genetic algorithm–robust approach that could provide a single unlimited layout design. The present research aims to devise a customized permutation-based robust genetic algorithm in dynamic manufacturing environments that is expected to be generating a unique robust layout for all the manufacturing periods. The numerical outcomes of the proposed robust genetic algorithm indicate significant cost improvements compared to the conventional genetic algorithm methods and a selective number of other heuristic and meta-heuristic techniques.

An Improved Pigeon-inspired Optimization Algorithm for Solving Dynamic Facility Layout Problem with Uncertain Demand

This paper deals with a multi-objective unequal sized dynamic facility layout problem (DFLP) with pickup/drop-off locations. First, a mathematical model to obtain optimal solutions for small size instances of the problem is developed. Then, a multi-objective particle swarm optimisation (MOPSO) algorithm is implemented to find near optimal solutions. Two new heuristics to prevent overlapping of the departments and to reduce ‘unused gaps’ between the departments are introduced. The performance of the MOPSO is examined using some sets of available test problems in the literature and various random test problems in small, medium, and large sizes. The percentage of improvements on the initial solutions is calculated for small, medium and large size instances. Also, the generation metric and the space metric for non-dominated solutions are examined. These experiments show the good performance of the developed MOPSO and sensitivity analysis show the robustness of the obtained solutions.

Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities

Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re‐arrange machines enabling the minimisation of inter/intra‐ cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.

The competitions among industries will get increasing and grow rapidly along with the advan/cement of technology. The layout of company facilities must be considered well as it holds a vital role in determining the quality of production process. Sofi Garmen Firm has encountered a problem of far distance in material handling among departments that finally triggered disturbance of production activity and huge cost of material handling. Therefore, a design for relayouting production facilities became the research object. It aimed at designing the layout proposal through Systematic Layout Planning and Blocplan methods. To calculate the distance, the researcher employed Rectilinear and Euclidean. The result of both distance calculations in Systematic Layout Planning obtained 29,678.5 meters and 23,375 meters respectively, whereas the cost of material handling gained averagely IDR 1,129,356.5. Meanwhile, the results of distance calculations of Rectilinear and Euclidean in Blocplan earned 30,920 meters and 26,942.5 meters consecutively and the cost of material handling produced averagely IDR 1,237,564.5. Accordingly, the calculation results indicated that the recommendation yielded by Systematic Layout Planning became the most optimum alternative of layout.

This paper presents a new integrated mathematical model for a cellular manufacturing system and production planning. The aim of this model is to minimize machine purchasing, intra-cell material handling, cell reconfiguration and setup costs. The presented model forms the manufacturing cells and determines the quantity of machines and movements at each period of time that minimizes the aforementioned costs. It is so difficult to find an optimal solution in a reasonable time. Thus, we design and develop a meta-heuristic algorithm based on a genetic algorithm (GA). This proposed algorithm is evaluated, and the related results confirm the efficiency and effectiveness of our proposed GA to provide good solutions, especially for medium and large-sized problems.