POLAR COORDINATES APPLIED FOR SOLVING PART-MACHINE CELL FORMATION PROBLEM

Global market competition and fluctuating customers’ demands require manufacturing enterprises to focus on cost reduction and efficiency improvement to increase competitiveness and sustainability. The purpose of the research was the elaboration of the methodology and procedure of a new combined efficiency improvement method which basically applies Lean methods and also uses the facility layout design (FLD) method simultaneously, integrating the different advantages of these methods, which is even more efficient that applying each of the methods individually. The main significant added-value of the study is the elaboration of a new combined method, which results in even more significant improvement of efficiency and several KPIs, furthermore, cost reduction, which is confirmed by a real case study for the improvement of a manufacturing plant. In the case study, the application of 13 Lean methods and the FLD method (which aims at the minimisation of material workflow, travel distance of materials, material handling cost and space used for assembly) led to the improvement of 10 quantitative and 5 qualitative indicators: productivity; cycle-time; number of workstations and operators; WIP (work-in-process) inventories; space used for assembly; material workflow; travel distance of materials; material handling cost; labour cost; component supply; products’ quality; transparency; standardisation; workplace ergonomics.

Group technology (GT) is a manufacturing philosophy that attempts to reduce production cost by reducing the material handling and transportation cost. The GT cell formation by any known algorithm/heuristics results in much intercell movement known as exceptional elements. In such cases, fractional cell formation using reminder cells can be adopted successfully to minimize the number of exceptional elements. The fractional cell formation problem is solved using modified adaptive resonance theory1 network (ART1). The input to the modified ART1 is machine-part incidence matrix comprising of the binary digits 0 and 1. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or superior to other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.

A Combined Algorithm is proposed to form the machine cell and part family identification in the cellular manufacturing system. In the first phase, part families identification, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC). In second phase, Machine cell formation, by using Rank Order Clustering (ROC) and Modified Single Linkage Clustering (MOD-SLC), which is to assign machines into machine cells to produce part families. The above Proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is one of the most widely used measures of quality for Cellular Manufacturing Systems.

CV. SG is a company engaged in the distribution of bottled cooking oil, including the Minyakita product. However, an unstructured facility layout has led to various challenges, particularly in operational efficiency and material handling costs. Inefficient placement of goods and lack of attention to material movement distances have resulted in increased operational expenses and prolonged processing times. These inefficiencies can hinder productivity and add to the workload, especially in routine material handling activities involving frequent movement of goods.To address these issues, this study focuses on redesigning the warehouse layout with the primary objective of optimizing material movement distances and reducing material handling costs. The method employed in this research is quantitative analysis, which includes calculating transfer distances, movement frequency, material handling moments, and cost estimation.The findings indicate that the initial layout incurred a total material handling cost of IDR 2,860,000 per month, with a total movement distance of 300.80 meters. After the redesign, the proposed layout successfully reduced the movement distance to 241.17 meters, leading to a decrease in material handling costs to IDR 2,761,200 per month. Thus, the implementation of the new layout has proven to be more efficient in reducing costs and enhancing the effectiveness of material movement within the warehouse. Keywords: Layout, Material Handling, Warehouse

In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM) formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.

Modeling of a manufacturing cell design problem with fuzzy multi-objective parametric programming

A capacity constrained mathematical programming model for cellular manufacturing with exceptional elements

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 cellular manufacturing system is a well-known strategy which improves the production efficiency and cutting down the manufacturing cost. The main problem in designing cellular manufacturing system is cell formation, which is concerned with grouping parts with similar processing required into part families and associated machines into machines and minimises the exceptional elements. This paper presents the artificial immune system method to solve the manufacturing cell formation problems with the objective to minimise the exceptional elements. This method is applied to the existing datasets found in the literature and it is found to out performs other methods.

In automated batch type production systems, machine-part cell formation problems (CFP) have long drawn attention of researchers. The objective of CFP in cellular manufacturing system (CMS) is to identity machine cells and part families in order to minimize the intercellular movements of parts as well as maximize the utilization of machines. Optimum cell formation results reduction in total production times, in-process inventories, material handling cost, labor cost/times, paper works, number of machine set-ups, set-up times. It also simplifies process plans, management and improves product quality, productivity, utilization of resources. Since the modern manufacturing machines are generally multifunctional, so the processing of parts can be performed by number of alternative routes. The objectives of this study is to determine the optimal processing route and balanced machine cells (to minimize cell load variation) incorporation with parts volume, process sequence for minimum intercellular movements of parts. A genetic algorithm metaheuristic approach is presented for a benchmark CFP. Computational results show that the proposed approach gives better result comparatively with the well-known existing methods in terms of total intercellular movements.

Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability

Design of layouts for Cellular Manufacturing Systems (CMSs) has not received attention by researchers as much as cell formation in cellular manufacturing. In this paper a mathematical model, formulated as a multicriteria Quadratic Assignment Problem (QAP) is proposed for the design of layouts for CMSs with the objective of minimising the material handling cost (quantitative) and maximising the closeness rating (qualitative). A Genetic Algorithm (GA)-based tool called FUGEN, which makes use of crossover, mutation (inversion) and direct entry (elitism) with embedded fuzzy logic and Analytic Hierarchy Process (AHP) models, is developed for the design of layouts for CMSs. An attempt is made to make the model practical by considering the production volume, transfer batch size and operational sequence of parts. The proposed tool is validated using an example from the literature.

Cellular Manufacturing (CM) is an important application of Group Technology (GT) in which families of parts are produced in manufacturing cells or a group of various machines, which are physically close together and can entirely process a part family. The manufacturing system established based on such an idea is called Cellular Manufacturing System (CMS). A major problem associated with many CMSs is the existence of Exceptional Elements (EEs), i.e. bottleneck machines and exceptional parts. These are machines/parts that cannot be exclusively assigned to a machine cell/part family. In this paper a new model is presented for dealing with the EEs in the form of a Multi-objective Optimization Problem (MOP). This model aims to minimize: (1) intercellular parts movements, (2) total cost needed for machine duplication and part subcontracting, (3) the system's under-utilization, and (4) deviations among the cells' utilization. Attaining an ideal solution, which is optimal to all of the objectives is prohibited, as they conflict with each other. Hence, a Multi-Objective Genetic Algorithm (MOGA) is developed to provide the decision-maker with a set of non-dominated or Pareto-optimal solutions. Comparisons between the developed MOGA and three other MOGAs show its viability in three performance aspects, namely: quality, diversity and CPU time.

This paper presents an electromagnetism-like mechanism (EM) algorithm in order to solve cell formation problems. Based on the attraction-repulsion principle in electromagnetic theory, each combination of the part and machine groups was regarded as a charged particle. The total force calculation and the move along the total force calculation were then determined out to either maximize the cell group effectiveness or minimize the exceptional elements (EE) and the number of vacancies (NV). A comparison with other algorithms in the related literature found that the final result of the EM algorithm met the expected quality in terms of the exceptional elements and number of vacancies, regardless of whether or not new cells were added. Key words: Cellular manufacturing system, group technology, electromagnetic-like mechanism.

Businesses, including manufacturing companies, must reduce cost to survive in today’s economy. During the last several decades, to reduce cost, many companies began to focus on labor optimization and/or increasing productivity. This may require an increase in investment cost (e.g., equipment costs). Among the consequences of these policies is a significant reduction in direct labor cost. The necessity to reduce direct labor cost has made material handling cost more important than ever. Material handling cost reductions can be obtained by designing efficient layouts. Efficient layouts are obtained by solving machine and facility layout problems effectively. Commonly, facility and machine layout problems assume that product flows are either known or estimated before hand, and that the layout solution has enough capacity to satisfy all product demands. When product demands exceed capacities of planned production systems, it is important to select a feasible product mix that maximizes company profits. In addition, when identical machines are considered, determining material flows as well as the layout always lead to better solutions. Integrating these issues leads to the generalized machine layout problem (GMALP). More specifically, the GMALP is the designing of a facility layout by defining the product mix, selecting the number of machines to be used, assigning these machines to the plant floor, and assigning products to machines such that total profit is maximized. Moreover, the GMALP integrates the quadratic assignment problem (QAP) and the maximum profit multicommodity flow problem (MPMFP). Therefore, the GMALP is a computationally intractable problem. Consequently, a mixed integer nonlinear programming model was developed and used to solve small problem instances. In addition, construction algorithms and metaheuristics (i.e., tabu search and memetic algorithms) were developed for solving large GMALP instances in acceptable computation times. Also, a test dataset was used to evaluate the performances of the heuristics presented. Finally, the memetic algorithms performed better than the tabu search heuristics for the dataset.