Giacomo Maria Galante

The facility layout problem approached using a fuzzy model and a genetic search

The problem of facility layout design is discussed, taking into account the uncertainty of production scenarios and the finite production capacity of the departments. The uncertain production demand is modelled by a fuzzy number, and constrained arithmetic operators are used in order to calculate the fuzzy material handling costs. By using a ranking criterion, the layout that represents the minimum fuzzy cost is selected. A flexible bay structure is adopted as a physical model of the system while an effective genetic algorithm is implemented to search for a near optimal solution in a fuzzy contest. Constraints on the aspect ratio of the departments are taken into account using a penalty function introduced into the fitness function of the genetic algorithm. The efficiency of the genetic algorithm proposed is tested in a deterministic context and the possibility of applying the fuzzy approach to a medium-large layout problem is explored.

A multi-objective approach to solid waste management.

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy).

An integrated approach to the facilities and material handling system design

The facility layout problem involves the optimal location of manufacturing facilities into a workshop. The classical approach to the layout design is carried out in two separate steps: the first step is the construction of the block layout, i.e. the location of the departments into the workshop, and the second step is the design of the material handling system. The separate optimization of these two aspects of the problem leads to solutions that can be far from the total optimum. In this paper, an integrated approach to the facilities and material handling system design is proposed. Referring to a physical model, named the bay structure , and to a unidirectional AGV system, a genetic approach is proposed to individuate the locations of the departments, the positions of the pickup/delivery stations and the direction of the flow-path. The minimization of material handling cost is adopted as optimality criterion.

Determination of Pareto frontier in multi-objective maintenance optimization

Abstract The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series–parallel system.

An exact algorithm for preventive maintenance planning of series–parallel systems

Reliability is a meaningful parameter in assessing the performance of systems such as chemical processing facilities, power plant, aircrafts, ships, etc. In the literature, reliability optimization is widely considered during the system design phase and it is carried out by an opportune selection of both system components and redundancy. On the other hand, the problem of maintaining a required level of reliability by an opportune maintenance policy has been poorly examined. The paper tackles this problem for a system whose major components can be maintained only during a planned system downtime. An exact algorithm is proposed in order to single out the set of components that must be maintained to guarantee a required reliability level up to the next planned stop with the minimum cost. In order to verify the algorithm effectiveness, it has been applied to a complex real case regarding ship maintenance.

Multi-objective human resources allocation in R&D projects planning

In a R&D department, several projects may have to be implemented simultaneously within a certain period of time by a limited number of human resources with diverse skills. This paper proposes an optimisation model for the allocation of multi-skilled human resources to R&D projects, considering individual workers as entities having different knowledge, experience and ability. The model focuses on three fundamental aspects of human resources: the different skill levels, the learning process and the social relationships existing in working teams. The resolution approach for the multi-objective problem consists of two steps: firstly, a set of non-dominated solutions is obtained by exploring the optimal Pareto frontier and secondly, based on further information, the ELECTRE III method is utilised to select the best compromise with regards to the considered objectives. The uncertainty associated to each solution is modelled by fuzzy numbers and used in establishing the threshold values of ELECTRE III, while the weights of the objectives are determined taking into account the influence that each objective has on the others.

A predictive maintenance policy with imperfect monitoring

For many systems, failure is a very dangerous or costly event. To reduce the occurrence of this event, it is necessary to implement a preventive maintenance policy to replace the critical elements before failure. Since elements do not often exhibit incipient faults, they are replaced before a complete exploiting of their useful life. To conjugate the objective of exploiting elements for almost all their useful life with the objective to avoid failure, condition based and, more recently, predictive maintenance policies have been proposed. This paper deals with this topic and proposes a procedure for the computation of the maintenance time that minimizes the global maintenance cost. By adopting a stochastic model for the degradation process and by hypothesizing the use of an imperfect monitoring system, the procedure updates by a Bayesian approach, the a-priori information, using the data coming from the monitoring system. The convenience in adopting the proposed policy, with respect to the classical preventive one, is explored by simulation, showing how it depends on some parameters characterizing the problem.

Tool-life modelling as a stochastic process

Abstract In a previous paper [G. Galante, A. Lombardo, A. Passannanti, Proceedings of XXXVII Scientific Meeting of the Italian Statistical Society, 1994, p. 553] the Authors proposed to model cutting tool wear behaviour as a stochastic process with independent Gaussian increments plus drift. Such a model implies that the tool-life, i.e. the time to reach a fixed value of flank wear, has an inverse Gaussian probability distribution. The model has several practical and theoretical advantages. In fact, it is based on an easily and cheaply experimentally verifiable wear behaviour hypothesis, it is more flexible because it is not limited to a particular wear level and, finally, the data are better exploited for the estimation of the distribution parameters. In the present paper that model is verified under different working conditions. Moreover, by varying the cutting parameters, a relation between these and the tool-life is obtained. It is shown that the well-known Taylors equation can be considered as a first order approximation of the proposed model.

A Multi-Objective Approach to Optimize a Periodic Maintenance Policy

The present paper proposes a multi-objective approach to find out an optimal periodic maintenance policy for a repairable and stochastically deteriorating multi-component system over a finite time horizon. The tackled problem concerns the determination of the system elements to replace at each scheduled and periodical system inspection by ensuring the simultaneous minimization of both the expected total maintenance cost and the expected global system unavailability time. It is assumed that in the case of system elements failure they are instantaneously detected and repaired by means of minimal repair actions in order to rapidly restore the system. A nonlinear integer mathematical programming model is developed to solve the treated multi-objective problem, whereas the Pareto optimal frontier is described by the Lexicographic Goal Programming and the e-constraint methods. To explain the whole procedure, a case study is solved and the related considerations are given.

Minimizing the cycle time in serial manufacturing systems with multiple dual-gripper robots

Robots are being used more and more extensively as material-handling systems for automated manufacturing systems. This is especially true for dual-gripper robots whose in-process buffer (the robots second gripper) constitutes a further element of flexibility. When the number of stations to be served is high and the set of activities the robot must execute is great, the system throughput may depend on robot capability rather than on process times. In such conditions, the use of several robots leads to an increase in system productivity. Obviously, the design and the management of such a handling system becomes more complex: the minimum number of robots required, the work stations to be served by each of them and the robot move cycles must be all determined so as to minimize the cycle time of a multi-robot serial system. Since the aim of minimizing the cycle time could lead to a non-univocal configuration, a secondary objective may be pursued. To this aim, the classic case of a single dual-gripper robotic cell is preliminarily revisited, using a practical rather than a theoretical approach, to show that, under the conditions of minimum cycle time, it is possible to take into account both the reduction of the WIP and that of the length of the transitory periods.