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In a virtual cell formation system, machine types are formed for one period during which the machine types of a cluster process the corresponding operations of part types. However, there is one major difference between a virtual cellular manufacturing and a cellular manufacturing and that is the fact that machine types of the same cluster are not necessarily brought to a physical proximity in virtual cellular manufacturing, contrary to a cellular manufacturing. Depending on the variations in the demand for part types, the virtual cells are clustered periodically and merged according to the demand for new part types in a planning horizon. On the other hand, optimal raw material quantity to purchase from qualified suppliers has gained importance recently; this phenomenon is greatly influenced by the significant portion of raw materials costs in a finished product. Consequently, most firms have to pay out most of their revenues on transportation and purchasing. In this paper, the dynamic virtual cellular manufacturing is formulated through a mathematical model developed for this purpose; it is also of utmost importance to consider machine layout and quantity of raw material purchased from qualified suppliers. To solve the real-sized problems of proposed model, a hybrid metaheuristic algorithm is extended. The results demonstrate that the proposed hybrid genetic algorithm is promising.

The cell formation problem determines decomposition of the manufacturing cells of a production system. Machines are assigned to the cells to process one or more part families so that each cell is operated independently and the inter-cellular movements are minimized. This paper proposes a new algorithm for grouping problems (bin packing, graph coloring, scheduling, etc.) which is a grouping version of an almost new algorithm (league championship algorithm (LCA)), and we used it to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 35 most widely used benchmark problem instances from literature and compare the performance of grouping LCA (GLCA) and several well-known algorithms published. The proposed algorithm can reach the best solution for 29 of the 35 benchmark problems and differs with the best-known solution of three benchmark problems only with 0.7 % average gap. We also used a new method to find the number of initial cells. The results show that GLCA may hopefully be a new approach for such kinds of difficult-to-solve problems. Moreover, a real-world industrial case is provided to show how the proposed algorithm works. Considering the performance of the GLCA algorithm on all test problems, the proposed algorithm should thus be useful to both practitioners and researchers.

In this paper, a multiproduct multiperiod capacitated production system that has been organized as a flow shop is considered. The main novelty of the paper is proposing a more efficient mathematical model for the problem of integrating lot sizing and scheduling with sequence-dependent setups. In comparison to the former model, because of fewer continuous and binary decision variables and constraints in the proposed model, this model is very easier to solve. Comparison between two models proves the superiority of the proposed model. Two mixed integer programming-based approaches with rolling horizon framework have been used to solve this model. To evaluate the performance of the proposed model and solution method, problems of different scales have been studied. The used algorithms search the solution space for both lot-sizing and scheduling problems and find a combination of production planning and scheduling that is feasible and close to optimum.

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.

Cell formation (CF) is a key step in group technology (GT). This combinatorial optimization problem is NP-complete. So, meta-heuristic algorithms have been extensively adopted to efficiently solve the CF problem. Particle swarm optimization (PSO) is a modern evolutionary computation technique based on a population mechanism. Since Kennedy and Eberhart invented the PSO, the challenge has been to employ the algorithm to different problem areas other than those that the inventors originally focused on. This paper investigates the first applications of this emerging novel optimization algorithm into the CF problem, and a newly developed PSO-based optimization algorithm for it is elaborated. Forming manufacturing cells lead to process each part family within a machine group with reduction intracellular travel of parts and setup time. A maximum number of machines in a cell and the maximum number of cells are imposed. Some published results in various problem sizes have been used as benchmarks to assess the proposed algorithm. Overall, the advantages of the proposed PSO are that it is rapidly converging towards an optimum, there are fewer parameters to adjust, it is simple to compute, it is easy to implement, it is free from the complex computation, and it is very efficient to use in CF with a wide variety of machine/part matrices.

This study proposes a nonparametric method based on adaptive neural network (ANN) technique for measuring efficiency of decision making units (DMUs) in one period case as a complementary tool for the ANN-based efficiency methods in the previous studies. In previous studies, there are needed to have large volume of data, and so the proposed method in this study is more applicable because it can be used for the cases which have no historical data. In fact, a limitative weakness of the ANN-based efficiency methods about applying them for these cases is removed. So, it can be a competitive method to the other common tools for measuring efficiency. By noting the importance of flexible manufacturing system, this study presents a decision-making model for optimization of operators’ allocation in cellular manufacturing system by computer simulation. The methodology is illustrated through its application on a previously reported dataset. It was found out that ANN provides more robust results and identifies more efficient units than the conventional methods since better performance patterns are explored.

As we know fuzzy modeling is one of the most powerful techniques to extract experts’ knowledge in the form of fuzzy if-then rules. In this research work, a new method to fuzzy modeling is proposed in which the main goal is to construct a fuzzy rule-base of the type of Mamdani. In the proposed method, fuzzy c-means (FCM) clustering is used for structure identification and two optimization problems are used for parameter identification. The proposed method is used to simulate experts’ knowledge for performance evaluation of tenants in incubators. The authors have implemented their proposed method in a real numerical example successfully.

This paper proposes a nonlinear integer model of cell formation under dynamic conditions. The cell formation (CF) problem is a portion of a cellular manufacturing strategy (CMS), in which the parts and machines are clustered with the aim of minimizing the material handling cost. In most previous research the cell formation problem has always been under static conditions in which cells are formed for a single-period planning horizon where product mix and demand are constant. In contrast, in dynamic conditions, a multi-period planning horizon is considered, where the product mix and demand in each period is different. This occurs in seasonally or monthly production. As a result, the best cell design for one period may not be efficient for subsequent periods. To verify the presented model, different problems have been solved and results are reported. Where the cell formation problem belongs to NP class, the use of a novel approach is necessary. In this research, we apply a neural approach based on mean filed theory for solving the proposed model. In this approach, the network weights are updated by an interaction procedure. The proposed model is solved by LINGO software and an optimum solution is obtained. Comparison of optimum and neural approach solutions shows the efficiency of the presented neural network approach.

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers.