An integrated model of cellular manufacturing and supplier selection considering product quality

Today’s business environment has forced manufacturers and plants to produce high-quality products at low cost and the shortest possible delivery time. To cope with this challenge, manufacturing organizations need to optimize the manufacturing and other functions that are in logical association with each other. Therefore, manufacturing system design and supplier selection process are linked together as two major and interrelated decisions involved in viability of production firm. As a matter of fact, production and purchasing functions interact in the form of an organization’s overall operation and jointly determine corporate success. In this research, we tried to show the relationship between designing cellular manufacturing system (CMS) and supplier selection process by providing product quality considerations as well as the imprecise nature of some input parameters including parts demands and defects rates. A unified fuzzy mixed integer linear programming model is developed to make the interrelated cell formation and supplier selection decisions simultaneously and to obtain the advantages of this integrated approach with product quality and consequently reduction of total cost. Computational results also display the efficiency of proposed mathematical model for simultaneous consideration of cellular manufacturing design and supplier selection as compared to when these two decisions separately taken into account

Modeling and analysis of the generalized warehouse location problem with staircase costs

The Capacitated Warehouse Location consists of determining the number and locations of capacitated warehouses on a set of potential sites such that demands of predefined customers are met. Two typical assumptions in modeling this problem are: the capacity of warehouses is constant and that warehouses are able to truly satisfy customer demands. However, while these kinds of assumptions define a well structured problem from the mathematical modeling perspective, they are not realistic. In this thesis we relaxed such constraints based on the fact that warehouses can be built in various sizes and also warehouses can put in orders for unsatisfied customers' demand directly to the manufacturing plant with additional costs. This flexibility can lead to best decision making ability for managers and supply chain specialists to decide between higher capacity level with higher fixed and variable costs at the warehouse or direct ordering from the manufacturing plant. A new non linear integer programming formulation with staircase costs for multiple commodities in supply chain network is presented, and new method for linearizing the model is described. Computational results indicate that reasonably good solution can be obtained by the proposed linear model. Also for solving larger problems we developed a Tabu Search algorithm. The comparisons of the result between nonlinear/linear model and the Tabu Search algorithm are also presented

A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521

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

Operation-Level Sequence- Dependent Setup Time Reduction In Dynamic Cellular Manufacturing Systems

In closed job shop, in which a fixed number of products are produced on a repetitive basis, when there are significant sequence dependent setup times and costs involved, cell formation (CF) problem should consider minimizing the sequence-dependent setup times in order to minimize the production cost. Setup time reduction in CMS has gained little to modest attention in the literature. This could be attributed to the fact that the fundamental problem in cell formation in CMS has been mainly about material handling and machine utilization while setup time was presumed to normally decrease as a result of grouping similar parts in a manufacturing cell. Despite more than three decades of history of CMS’s it has been relatively recent that setup time has been included in cell formation problems and found a place in the existing models. Sequence-dependent setup time in the literature has been dealt with mostly for scheduling part-families in a single manufacturing cell or in allocation of parts to cells in a pure flow shop. In this thesis, the issue of setup time has been extended to the members of a part family and to its lowest level which is operation-level and incorporated in general cell formation problem in a dynamic CMS. In this thesis we have developed a multi-period integer programming CF model to address the reduction of the sequence-dependent setup time as well as considering the dynamic nature of today’s manufacturing environment in CMS, where the product mix demanded would change in different time periods. Due to time complexity of the problem, a two stage solution approach has been adopted. First a GA-based heuristic was developed that provides near optimal solutions for single-period problems of the global model. The performance of the GA-based heuristic was successfully evaluated versus optimization software. Second, a dynamic programming (DP)-based heuristic was developed that reintegrates the single-period solutions into a multi-period solution. The performance of the DP-based heuristic was also evaluated against optimization softwar

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