Mario Enea

Project Selection by Constrained Fuzzy AHP

The selection of a project among a set of possible alternatives is a difficult task decision makers have to face. Difficulties in selecting a project arise because of the different goals involved and because of the large number of attributes to consider. Our approach is based upon a fuzzy extension of the Analytic Hierarchy Process (AHP). This paper focuses on the constraints that have to be considered within fuzzy AHP in order to take in account all the available information. This study demonstrates that by considering all the information deriving from the constraints better results in terms of certainty and reliability can be achieved.

Fuzzy approach to the environmental impact evaluation

Because of the imprecision of the ecological impacts and the frequent lack of quantitative information, fuzzy theory provides a useful approach to the environmental impact evaluation. In this paper, the environmental parameters are defined through fuzzy numbers. By considering the quality of the global component as derived from primitive environmental components, for these primitive components the quality is generally derived from their fuzzy physical parameters. Suitable operators are proposed in order to estimate the global environmental component quality as a function of environmental primitive components quality that can decrease or increase the environmental quality. Also in fuzzy form the magnitude of the plant impact factor that strikes the environmental components is esteemed. This magnitude is a function of environmental parameters that can be fuzzy, as fuzzy can be the relation between these parameters and the magnitude of the impact factor. Therefore, through the matrix method, the total environmental impact (T.E.I.) in fuzzy terms is calculated, as in fuzzy form, the percentage of impact on every environmental component is calculated. A criterion is proposed in order to compare fuzzy T.E.I. for various sceneries. This criterion is based on a ranking method that considers the grade of prudence of the decision-maker and the acceptable risk. Finally, a case study is reported as an explanation of the method.

An Analytic Hierarchy Process for the Evaluation of Transport Policies to Reduce Climate Change Impacts

Transport is the sector with the fastest growth of greenhouse gases emissions, both in developed and in developing countries, leading to adverse climate change impacts. As the experts disagree on the occurrence of these impacts, by applying the analytic hierarchy process (AHP), we have faced the question on how to form transport policies when the experts have different opinions and beliefs. The opinions of experts have been investigated by a means of a survey questionnaire. The results show that tax schemes aiming at promoting environmental-friendly transport mode are the best policy. This incentives public and environmental-friendly transport modes, such as car sharing and car pooling.

A multi objective genetic algorithm for the facility layout problem based upon slicing structure encoding

This paper proposes a new multi objective genetic algorithm (MOGA) for solving unequal area facility layout problems (UA-FLPs). The genetic algorithm suggested is based upon the slicing structure where the relative locations of the facilities on the floor are represented by a location matrix encoded in two chromosomes. A block layout is constructed by partitioning the floor into a set of rectangular blocks using guillotine cuts satisfying the areas requirements of the departments. The procedure takes into account four objective functions (material handling costs, aspect ratio, closeness and distance requests) by means of a Pareto based evolutionary approach. The main advantage of the proposed formulation, with respect to existing referenced approaches (e.g. bay structure), is that the search space is considerably wide and the practicability of the layout designs is preserved, thus improving the quality of the solutions obtained.

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.

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.

ELECTRE III to dynamically support the decision maker about the periodic replacements configurations for a multi-component system

The problem tackled by the present paper concerns the selection of the elements of a repairable and stochastically deteriorating multi-component system to replace (replacements configuration) during each scheduled and periodical system stop within a finite optimization cycle, by ensuring the simultaneous minimization of both the expected total maintenance cost and the system unavailability. To solve the considered problem, a combined approach between multi-objective optimization problem (MOOP) and multi-criteria decision making (MCDM) resolution techniques is proposed. In particular, the @e constraint method is used to single out the optimal Pareto frontier whereas the ELECTRE III multi-criteria decision support method is proposed to support the selection of the replacements configuration that represents the best compromise among the considered objectives. The proposed approach is sequentially applied at each scheduled system stop by allowing the dynamic updating of the information about the decisional context in which the decision maker has to operate. To illustrate the whole procedure a numerical case study is solved for different hypothesized scenarios related to the importance attributed by the decision maker to the system unavailability and the maintenance cost objectives.

The expected value of the traceability information

Recent regulations on agri-food traceability prescribe traceability throughout the entire supply chain, in order to ensure consumers’ safety and product quality. This has led producers and retailers to consider the opportunity to improve the firms reputation and consumer confidence through the implementation of traceability systems designed not only to satisfy the legal requirements, but also to track the quality of the products through the supply chain for optimization purposes. However the actual implementation of such systems depends on the possibility of gathering specific information related to the product quality. Nowadays, innovative and non invasive technologies such as the Radio Frequency Identification (RFID) allow the automatic real time collection of data, thus enabling the development of effective traceability systems. In such context the expected value of traceability is a fundamental issue concerning the economic analysis of costs involved in such an investment and the optimal granularity level of implementation. This paper aims at evaluating the expected value of the implementation of traceability systems for perishable products like fruits and vegetables, and its profit. The study presents a mathematical stochastic approach for optimizing the supply chain profit and establishing the optimal granularity level (namely the Economic Traceability Lot) when a RFID solution is adopted. In particular, the supply chain profit in the presence of RFID traceability system has been calculated and compared with the expected profit in absence of such a system, and the results confirm the importance of the specific characteristics of the supply chain in determining the optimal configuration of the traceability system.