Ignacio Eguia

Part-machine grouping using weighted similarity coefficients

The first step in the transition to cellular manufacturing is part-machine grouping. In this paper, grouping parts into families and machines into cells is done in two phases: by first grouping machines and then assigning parts. Limits both on the number of machines per cell and on the number of parts per family are considered. The number of cells is not fixed. A weighted sum of within-cell voids and out-of-cell operations is used to evaluate the part-machine grouping obtained. In Phase One, weighted similarity coefficients are computed and machines are clustered using a Tabu search algorithm. In Phase Two, part types are assigned to the previously formed groups using a linear minimum cost network flow model. The proposed approach is compared with three heuristics, namely ZODIAC, GRAFICS and MST, on a large number of problems.

Cell design and loading in the presence of alternative routing

This paper deals with the design and loading of a cellular manufacturing system in the presence of alternative routing. The problem is decomposed into a cell design phase performed once and a cell loading phase performed on a recurrent basis. Two alternatives for the cell design problem are proposed: either the process plans of each part type are treated as if they were separate part types; or aggregate part types are considered. In either case, a conventional cell formation method is used to group machines. The cell loading problem is modelled as a multi-period LP formulation that determines the quantity of each part type that will follow each alternative route in each period of the planning horizon in order to minimize total transportation and holding costs while keeping both machine and cell utilizations approximately balanced.

Cell design and multi-period machine loading in cellular reconfigurable manufacturing systems with alternative routing

This paper deals with the design and loading of Cellular Reconfigurable Manufacturing Systems in the presence of alternative routing and multiple time periods. These systems consist of multiple reconfigurable machining cells, each of which has Reconfigurable Machine Tools and Computer Numerical Control (CNC) machines. Each reconfigurable machine has a library of feasible auxiliary machine modules for achieving particular operational capabilities, while each CNC machine has an automatic tool changer and a tool magazine of a limited capacity. The proposed approach consists of two phases: the machine cell design phase which involves the grouping of machines into machine cells, and the cell loading phase that determines the routing mix and the tool and module allocation. In this paper, the cell design problem is modelled as an Integer Linear Programming formulation, considering the multiple process plans of each part type as if they were separate part types. Once the manufacturing cells are formed, a Mixed Integer Linear Programming model is developed for the cell loading problem, considering multi-period demands for the part types, and minimising transportation and holding costs while keeping the machine and cell utilisations in each period, and the system utilisation across periods, approximately balanced. An illustrative problem and experimental results are presented.

Cell formation and scheduling of part families for reconfigurable cellular manufacturing systems using Tabu search

A reconfigurable cellular manufacturing system (RCMS) consists of multiple reconfigurable machining cells, each of which has one or more reconfigurable machine tools (RMTs), a setup station, and an automatic material handling and storage system. As part of the RCMS design process, similar parts must be grouped into part families and the RMTs must be arranged to form parallel cell configurations. A RCMS is designed at the outset for rapid changes in its components, allowing the production of multiple part families in each parallel cell. This paper proposes a new approach to simultaneously solve the cell formation and the scheduling of part families for an effective working of a RCMS. A new mixed integer linear programming model is used to represent both problems at the same time with the objective of minimizing production costs. Two types of production costs are considered: reconfiguration (i.e. setup) costs for changing from one family to the next one, and under-utilization costs for not using the RMT resources. A small size example is used to illustrate this integrated methodology. Computational experiments have been carried out adapting some larger instances from the literature on cellular manufacturing systems. Solving large instances optimally becomes prohibitive in terms of computational effort. That is why an approximate method, based on a Tabu search (TS) algorithm, has also been developed. Results show the ability of this algorithm to find good-quality production schedules of part families in a RCMS without requiring long computing times. It can be concluded that a RCMS can attain manufacturing flexibility without losing cost-effectiveness and that the approach proposed in this paper can efficiently solve real-world problems.

Environmental Issues in Vehicle Routing Problems

In the last decade interest in environment preservation is increasing and environmental aspects play an important role in strategic and operational policies. Therefore, environmental targets are to be added to economic targets, to find the right balance between these two dimensions. Green logistics extend the traditional definition of logistics by explicitly considering external factors associated mainly with climate change, air pollution, noise, vibration and accidents. Among the logistical activities, the vehicle routing problem (VRP) is one of the most widely researched and has mainly focused on economic objectives, not considering explicitly environmental issues. In this chapter, a realistic variant of the VRP with heterogeneous vehicle fleets in which vehicles are characterized by different capacities and costs, has been considered and external costs have been estimated using international research projects, and have been included as part of a mixed-integer linear programming model to solve a realistic variant of the VRP. To solve medium to large-size VRP instances, heuristic approaches are necessary. An impressive number of heuristic have been proposed for the VRP in the literature. In this chapter, one heuristic is developed to find good solutions to the proposed eco-efficiency model: a savings heuristic when time windows are not considered. Since there are no instances for this problem variant, the algorithm is validated with benchmarking problems adapted from the literature, offering good solutions and quickness. The selection of eco-efficiency routes can help to reduce the emissions of air pollutants, noises and greenhouse gases, without losing competitiveness in transport companies.

A Genetic Algorithm for Solving a Production and Delivery Scheduling Problem with Time Windows

This paper deals with the problem of selecting and scheduling a set of orders to be processed by a manufacturing plant and immediately delivered to the customer site. Constraints to be considered are the limited production capacity, the available number of vehicles and the time windows within which orders must be served. We describe the problem relating it to similar problems studied in the literature. A genetic algorithm to solve the problem is developed and tested empirically with randomly generated problems. Comparisons with an exact procedure and a tabu search procedure show that the method finds very good-quality solutions.

Data envelopment analysis with multiple modes of functioning. Application to reconfigurable manufacturing systems

In principle, data envelopment analysis (DEA) does not consider the possibility, which can occur in practice, of a production system being able to operate in different modes of functioning. In this paper, a new DEA modelling approach is proposed in which the different modes of functioning are taken into account and included in the analysis. The observed input consumption and output production in each mode of functioning is used to derive a mode-specific technology. The overall DEA technology aggregates these mode-specific technologies according to their respective time allocations. The proposed model computes a target operating point for each mode of functioning so that the operation of the overall system is efficient. The proposed approach is applied to assess the technical, cost and allocative efficiency of a reconfigurable manufacturing system. The inputs considered are modules/tools usage, labour and energy consumption. The outputs are the number of units produced of each part type. The production possibility set is determined by previous observations of the system functioning, from which the best practices can be identified. Technical, cost and allocative efficiency scores can be computed. The proposed approach not only generates input cost savings but also lead time reductions.

Cell design and loading with alternative routing in cellular reconfigurable manufacturing systems

Abstract This paper deals with the design and loading of a Cellular Reconfigurable Manufacturing System (CRMS) in the presence of alternative routing. A CRMS consists of multiple reconfigurable machining cells, each of which has Reconfigurable Machine Tools (RMT) and Computer Numerical Control (CNC) machines, a setup station and an automatic material handling and storage system. Each RMT has a library of feasible auxiliary machine modules for achieving a particular operational capability of the RMT. Each CNC machine has an automatic tool changer and a tool magazine of a limited capacity. The machine cell design problem involves the grouping of machines into machine cells. This problem is performed once, prior to the cell loading, using two cell formation methods from the literature. Once the resulting cells are physically implemented, a mixed-integer linear programming problem is developed for determining the routing mix (i.e. which proportion of each part type to be assigned to each alternative route) and the tool and module allocation (i.e. how many auxiliary modules and tools of each available type to be assigned to each RMT and CNC machine within each formed cell) in order to minimize total intercellular movements of parts. An illustrative problem and experimental results are presented.

Managing Maturity States in a Collaborative Platform for the iDMU of Aeronautical Assembly Lines

Collaborative Engineering aims to integrate both functional and industrial design. This goal requires integrating the design processes, the design teams and using a single common software platform to hold all the stakeholders contributions. Airbus company coined the concept of the industrial Digital Mock Up (iDMU) as the necessary unique deliverable to perform the design process with a unique team. Previous virtual manufacturing projects confirmed the potential of the iDMU to improve the industrial design process in a collaborative engineering environment. This paper presents the methodology and preliminary results for the management of the maturity states of the iDMU with all product, process and resource information associated with the assembly of an aeronautical component. The methodology aims to evaluate the suitability of a PLM platform to implement the iDMU in the creation of a control mechanism that allows a collaborative work.

Facility Location Using Neural Networks

Facility location problems occur whenever more than one facility need to be assigned to an equal number of locations at a minimal cost. The quadratic assignment problem is an example within this class of problems. This paper presents a new self-organizing approach to solve quadratic assignment problems. Our neural approach uses neuron normalization as well as a conscience mechanism to consistently find good feasible solutions. To test our neural approach, a set of test problems from the literature has been used. Further research avenues are suggested.