Cell Formation Decisions Research Articles

Service level-based production smoothening model for robust cellular manufacturing system

This paper presents a new strategy addressed as “service level-based smooth production strategy” for the cell formation decisions of a robust cellular manufacturing system (CMS) to handle varying demands. In this strategy, a service level concept is adapted to fix smooth production level estimated to meet the desired demand satisfaction rate (DSR) or fill rate of the customers. Besides, the model considers cross-flow and scheduling aspects, which are not often addressed in CMS designs. A non-linear integer program is formulated to evolve CMS decisions for the specified DSR under fluctuating demands. Two solution methodologies of mathematical programming using ILOG CPLEX solver and genetic algorithm (GA) with adaptive mutation and crossover operators addressed in as adaptive genetic algorithm (AGA) are proposed. The model analysis carried out under various DSR reveals that the cell formation decisions are highly sensitive to DSR. This paper also discusses how this model can be used to compare robust and reconfigurable approaches to decide the suitable approach to handle fluctuating demands with minimum cost. The performance study of solution methodologies reveals that CPLEX and AGA are efficient in finding optimal solutions to smaller-size problems. However, AGA is comparatively efficient for larger-size problems with a reasonable computational time.

Optimizing expected holding cost in cellular manufacturing systems with stochastic extensions

This research introduces a new application of mathematical modelling for designing cellular manufacturing systems integrated with group scheduling aspects under uncertain conditions. The mixed 0–1 quadratic model is able to optimize group scheduling decisions under lower satisfaction of cell formation decisions. In this way, the required processing time of parts on machines is assumed to be uncertain and also is explained by a discrete set of scenarios. This model tries to optimize the expected scheduling cost in cells plus the subcontracting cost for exceptional operations. In addition, the objectives associated with the cell formation decisions are regarded as constraints by considering the bounded objective technique, one of the multi-objective decision-making techniques. The scheduling problem in a cellular manufacturing framework is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no intercellular transfer is needed. An efficient modified simulated annealing technique is proposed to solve this complex problem under an optimization rule as a subordinate section. This integrative combination algorithm is compared with global solutions and a heuristic algorithm introduced in the literature. Finally, the performance of the algorithm is verified through some test problems.

Grouping operations in cellular manufacturing considering alternative routings and the impact of run length on product quality

When a production lot is split into alternative routes, the production run in each route will be shortened. Merging sub-lots from different alternative routes to one selected route will result in a longer production run in the selected route. Such variation in product run length could have impacts on product quality. The paper formulates a mathematical programming model for optimal lot splitting into alternative routes to account for the impact of production run length on product quality in a cellular manufacturing environment. A genetic algorithm is developed to solve the proposed model efficiently. Numerical examples are presented to demonstrate the features of the proposed model and computational efficiency of the solution method. It further proposes extensions of the developed model and solution procedure to consider cell formation decisions when the impact of splitting production lots into alternative routes on product quality is considered.

Using penalty function and Tabu search to solve cell formation problems with fixed cell cost

In this paper, an integrated approach for manufacturing cell formation with fixed charge cost is presented. The solution of the problem includes not only cell formation decisions but also cell set-up decisions. A mixed integer non-linear programming model is formulated to solve the problem. The NP-hardness of the problem makes direct solution computationally prohibitive for real-life applications. A heursitic algorithm was developed to solve the problem efficiently based on the features of the model and model duality analysis. Tabu search was used to find the optimal or sub-optimal solutions of the problem. Numerical examples are presented. Scope and purpose Today's manufacturing industry is facing strong competition in providing high quality and low cost products to ever demanding consumers. Cellular manufacturing is one of the widely used approaches to improve manufacturing productivity to achieve this purpose. In cellular manufacturing, machine tools are grouped into different manufacturing cells for improved efficiency and flexibility. This research proposes a mathematical programming model to form the cells so that material handling cost, machine tool cost and eventually the overall production cost can be minimized. We also developed an efficient solution method to solve the complicated mathematical model.

Models of various cell structures in GT

This paper develops optimal 0–1 Integer Programming models to represent various cell structures that are used in Group Technology (GT) to make cell formation decisions. Most of the methods used in the literature are based on the cell structure of Independent Cells, where a part family is completely processed in a single machine cell. Three other cell structures will be considered: (1) using a common cell, (2) allowing some part families to have alternate routes, and (3) arranging the machine cells in an assembly line fashion.

A procedure for dealing with multiple objectives in cell formation decisions

AbstractA procedure for dealing with multiple objectives in cell formation decisions is proposed. Theoretical foundations from multiobjective decision making have been utilized to provide a framework for the study. The purpose of the study is to develop a procedure that provides managers with a means of developing multiple objective cell formation decisions as an alternative to intractable integer programming models. We use a best of random seeds heuristic to generate a large number of alternatives. Nondominated solution theory is used to develop a list of potentially useful alternatives. Finally, preference cone theory is used to aid decision maker selection from these nondominated alternatives. Each of these three technologies have been developed elsewhere. This study considers aspects of applying these technologies to cell formation decisions. The procedure and its theoretical underpinnings are explained step‐by‐step and illustrated in a four‐objective, four‐cell, 24‐machine, and 50‐part cell formation problem. The example contains 12 nondominated solutions and requires only four pairwise comparisons of nondominated solutions by a decision maker before convergence to the preferred solution. Computer processing requirements are not burdensome, and decision maker participation is expected to be efficient.

An Optimal Model for Cell Formation Decisions

This study develops an approach to cell formation decisions for cellular manufacturing layouts in group technology settings. An optimal 0–1 integer programming model is used to provide an analysis for determining which machines and parts should be assigned to cells in cellular manufacturing layouts. This approach minimizes the cost of manufacturing exceptional parts outside the cellular system, subject to machine capacity constraints. Part–machine matrices are partitioned into disconnected cells and use far fewer 0–1 variables than earlier approaches. Formulation of the model is described with a numerical example and computer solutions to realistic problems are obtained. The characteristics of computer run times, model performance, and applications of the model are discussed.