Designing cellular manufacturing system under risk conditions

This paper develops a mathematical modeling to design a cellular manufacturing system. In addition some of the total or portion of the demand of the part types can be subcontracted.. In order to designing the optimal CMS, we needs to detrmined a plan to produce and subcontract parts at a minimum cost and to mitigate the impact of sub-contracting risk.Thus we propose a mixed integer programming approach to decision making and incorporate subcontracting risk . To control the risk of sub-contracting (cost) , the two popular percentile measures of risk are applied: value-at-risk and conditional value-at-risk. This model is capable of optimizing production cost of parts and calculating value-at-risk of subcontracting cost simultaneously. A numerical example is solved to verify the performance of the proposed model.Keywords: Cellular manufacturing system, sub contract, Risk management , Conditionalvalue-at-ris

The evolution of cell formation problem methodologies based on recent studies (1997-2008): review and directions for future research

This paper presents a literature review of the cell formation (CF) problem concentrating on formulations proposed in the last decade. It refers to a number of solution approaches that have been employed for CF such as mathematical programming, heuristic and metaheuristic methodologies and artificial intelligence strategies. A comparison and evaluation of all methodologies is attempted and some shortcomings are highlighted. Finally, suggestions for future research are proposed useful for CF researchers

Meta-heuristics in cellular manufacturing: A state-of-the-art review

Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area

A Mathematical Approach to the Design of Cellular Manufacturing System Considering Dynamic Production Planning and Worker Assignments

Due to increasing international competition, shorter product life-cycles, variable demand, diverse customer needs and customized products, manufacturers are forced from mass production to the production of a large product mix. Traditional manufacturing systems, such as job shops and flow lines, cannot provide such requirements efficiently coupled with flexibility to handle these changes. Cellular Manufacturing (CM) is an alternate manufacturing system combining the high throughput rates of line layouts with the flexibility offered by functional layouts (job shops). The benefits include reduced set-up times, material handling, in-process inventory, better product quality, and faster response time. The benefits of CM can only be achieved by sufficiently incorporating the real-life structural and operational features of a manufacturing plant when creating the cellular layout. This research presents integrated CM models, with an extensive coverage of important manufacturing structural and operational features. The proposed Dynamic Cellular Manufacturing Systems (DCMSs) model considers several manufacturing attributes such as multiperiod production planning, dynamic system relocation, duplicate machines, machine capacities, available time for workers, worker assignments, and machine breakdowns. The objective is to minimize total manufacturing cost comprised of holding cost, outsourcing cost, intercell material handling cost, maintenance and overhead cost, machine relocation cost as well as salary, hiring, and firing costs of the workers. Numerical examples are presented to show the performance of the model

Conception de systèmes manufacturiers cellulaires dans un environnement de chaînes d'approvisionnement

Classical cellular manufacturing system (CMS) design are performed on a single plant and are no more appropriate in an environment where material procurement and Customer delivery processes issues with the selection of multi-plant production system locations have to be considered simultaneously. Integration of these issues is a crucial challenge to supply chain managers. This thesis deals with the multi-plant CMS design in a supply chain environment. This context is established with considering the supply or the customer delivery processes, and with linked manufacturing plants. Three problems are examined. The first problem considers linked multi-plant CMS design integrated to material supply process. The solution approach is based on a proposed linearised model where the total system design cost to minimise combines the cost of the cellular configuration set on existing plants and the costs linked to supplier process selection. Experimentation demonstrates the potential benefits gained through increasing routing flexibility over plants on investment costs and the effect of integrating the supply process in a multi-plant cellular manufacturing configuration. The second problem combines multi-plant CMS design to manufacturing plant selection and customer demand allocation decisions. We propose a mathematical model and develop a simulated annealing based approach which aims to select the manufacturing plants to open and define the machine cells locations and structures and assign customer’s demand. We demonstrate the efficiency of the solution approach and show the cost savings due to integrated multi-stage multi-plant CMS with customer allocation decisions, compared to a sequential decision process. The third problem examines a context of multi-period customer demand where multi-plant production planning decisions, part transfer between plants, system reconfiguration and dynamic customer allocation decisions are coupled with dynamic multi-plant CMS configuration. It is shown that considering these issues in an integrated model reduces total design cost and enhances manufacturing and delivery flexibility