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Multicriteria multigoal decision making - the fuzzy paradigm
Abstract
In real life, the imprecise and subjective nature of information makes decision making rather complex and incosistent. This paper presents a Fuzzy Decision Support Expert System that enables more efficient and consistant decision making. The linguistic thought process of the decision maker are qualified and quantified using fuzzy logic and approximate reasoning. The system integrates multicriteria decision rules and various measures of decision factors to make the inference. The mechanism of this system is illustrated by addressing the Aggregate Production Planning Problem. Holt's[1] HMMS Paint Factory data is used with the rule base proposed by Rinks[2]. Comparison of results with Rinks and Turksen[3] shows that the proposed system can be used successfully to generate a near-optimum solution. The capability of the system in handling diverse problem domains that require group decision making with imprecise and incomplete information in a multigoal environment is also discussed.
... Indeed, the pioneering works of applying fuzzy logic to manufacturing seemed to have originated in these streams (e.g. Satyadasa and Chen, 1992;Ward et al., 1992;Grabot, 1993). Other topics such as machine control (Narayanaswamy et al., 1996), process control (Jeffries et al., 2003) and cellular operations (Torkul et al., 2006) have seen continual increases in application of fuzzy tools during the past decade. ...
... Scheduling and aggregate planning Ward et al. (1992) Applies fuzzy logic using rinks to relate cost, inventory, production, and work force Satyadasa and Chen (1992) Presents how fuzzy logic that leads to more efficient and consistent decision making Chan et al. (1997) Apllies fuzzy scheduling rules in flexible manufacturing systems Chan et al. (2003) Applies fuzzy scheduling rules in flexible manufacturing systems by evaluating multiple performance measures Macchiaroli et al. (1999) Uses fuzzy logic to develop scheduling of no-wait manufacturing processes Hsu and Lin (1999) Combines fuzzy logic and genetic algorithm, suggesting superiority of fuzzy approach Wang et al. (1999) Applies fuzzy variables in JIT production planning (2001) Uses fuzzy logic to develop a dispatching system for fleet of automated guided vehicles Cha and Jung (2003) Applies fuzzy logic to a multilevel scheduling model for manufacturing Tedford and Lowe (2003) Suggests for adaptable fuzzy logic system enhanced by genetic algorithm Bilkay et al. (2004) Applies fuzzy logic for re-generating schedules in case of a machine breakdown Canbolat and Gundogar (2004) Applies a fuzzy logic-based algorithm for scheduling using combinatory rules Monfared and Yang (2005) Uses a multilevel fuzzy logic in developing an integrated intelligent scheduling system Uses fuzzy logic for optimizing electrical discharge machining process Srinoi et al. (2006) Uses fuzzy logic approach scheduling in flexible manufacturing systems Bonfatti et al. (2006) Uses fuzzy logic to develop a load-oriented control system for job-shop scheduling Mula et al. (2006) Applies fuzzy programming to production planning in a capacity constrained MRP Araz and Salum (2010) Applies fuzzy logic to dual resource constrained manufacturing Product mix Bhattacharya and Vasant (2007) Applies fuzzy logic to include decision makers' biases in product mix scheduling Karakas, Koyuncu Erol (2010) Applies fuzzy logic in product mix scheduling using expanded ABC approach Hasuike and Ishii (2009) Applies fuzzy logic to include ambiguity of future returns in product mix decisions ...
... Cet aspect multidimensionnel se retrouve à tous les niveaux du système de production, à savoir : le choix des équipements (Lotfi, 1995, Oeljbruns et al., 1995, Myint et al., 1994, l'aménagement (Fortenberry et al., 1985, Rosenblatt, 1979, Urban, 1987, Malakooti et al., 1989, la sélection des sous traitants (Abhir, 1996), la planification de la production (Kaplic, et al. 1992, Satyadas et al. (1992, Zeleny, 1986), la gestion de projets et l'ordonnancement (Bausch, 1992, Belton et al., 1996, Gravel et al., 1992, la gestion des stocks (Chaudhry et al., 1991, Patrovi et al., 1994, Agrell, 1995, le contrôle de la qualité (Ravindran et al., 1986, Schnider et al., 1989, la maintenance etc. ...
RÉSUMÉ : Ce travail aborde le problème du classement des équipements de production par ordre de priorité dans le contexte de mise en oeuvre d'un système de gestion et de suivi de la maintenance au sein d'une entreprise industrielle. Une approche multicritère est proposée pour classer les équipements d'une entreprise tunisienne oeuvrant dans le domaine de l'emballage flexible. Une famille de critères est proposée et une approche de surclassement de synthèse est adoptée pour effectuer le classement. Les résultats obtenus permettent aisément de dégager un lot de machines jugées stratégiques pour la maintenance. Par ailleurs, une étude de sensibilité est effectuée permettant d'apprécier l'effet de la variation des poids accordés à chaque critère sur le classement obtenu.
... This includes the subjectivity and imprecision in the decision-making process itself (e.g., uncertainty in the decision makers' preferences). Frequently, techniques from Fuzzy Logic are used to represent these uncertainties [6], [10]. ...
This paper presents a distributed system facilitating robust decision-making under uncertainty in complex situations often found in strategic emergency management. The construction of decision-relevant scenarios, each being a consistent, coherent and plausible description of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision-making. The construction of scenarios is targeted at assessing decision alternatives avoiding time-consuming processing of irrelevant information. The scenarios are constructed in a distributed setting ensuring that each decision can be founded on a coherent and consistent set of assessments and assumptions provided by the best (human or artificial) experts available within limited time. Our theoretical framework is illustrated by means of an emergency management example.
... A25.2: Establish an externally focused knowledge delivery mechanism to provide filtered knowledge to strategic relationship partners. [17]. MCDM methods include simple additive weighting, conjunction/disjunction, outranking, analytical hierarchical process, and maxi-min. ...
Presents the cognizant enterprise maturity model (CEMM). The model
provides tripartite usage of calibration, capability assessment and
maturity advancement. The entry point is an organizational and
departmental profiler that provides relevance measures based on fuzzy
multicriteria group decision-making capabilities to key maturity areas
(KMAs) identified for the five-level maturity model. These relevance
measures allow organizations to weigh and score the KMAs and to nurture
the model based on their own experience and on industry experience. Each
KMA comes with a set of goals and abilities. CEMM relies on four
criteria: (1) cognizant environments, (2) knowledge utilization and
leverage, (3) the SEI's software CMM (capability maturity model) and
people CMM, and (4) an adaptive enterprise, and it distinguishes between
information and knowledge management (KM)
Aggregate Production Planning (APP) aims at balancing the forecast demand for product families with the available production resources, thus allowing changes to be made in the system and the best action strategy to be found. APP decisions are strategic decisions for the organization, which take an intermediate time horizon which spans 12 months, in general. As different issues may influence business success, this kind of decision problem will take multiple objectives into account, when a strategic view is considered. Conflicts between objectives frequently increase the complexity of APP problems. In order to deal with this kind of problem, this paper proposes a multi-criteria decision model considering the five objectives related to manufacturing strategy, namely: quality, speed, dependability, flexibility, and cost. By applying a multi-objective optimizing model which considers the organization’s strategical objectives, and by using the NSGA-II algorithm, a subset of representative solutions of the Pareto Frontier is obtained, thereby enabling the application of the FITradeoff method. A multi-criteria method, such as the FITradeoff, assists Decision Makers (DMs) in choosing a production plan more suitable to their objectives. The results show that the FITradeoff method contributes to the evaluation of solutions by aiding the DM to consider a significant set of alternatives while at the same time reducing the cognitive effort that he/she needs to make.
Keywords:
aggregate production planning; manufacturing strategy; FITradeoff method; Multi-Criteria Decision-Making (MCDM); NSGA-II
This work deals with the problem of classifying equipment with respect to their importance for maintenance purpose. A multi-criteria approach is adopted to classify a Tunisian packaging company production equipment. The obtained results clearly display a group of machines considered as strategic and which consequently deserve more attention and deeper analysis as far as their maintenance is concerned. Ce travail aborde le problème du classement des équipements de production par ordre de priorité dans le contexte de mise en oeuvre d'un système de gestion et de suivi de la maintenance au sein d'une entreprise industrielle. Une approche multicritère est proposée pour classer les équipements d'une entreprise manufacturière. Une famille de critères est proposée et une approche de surclassement de synthèse est adoptée pour effectuer le classement. Les résultats obtenus permettent aisément de dégager un lot de machines jugées stratégiques pour la maintenance. Par ailleurs, une étude de sensibilité est effectuée permettant d'apprécier l’effet de la variation des poids accordés à chaque critère sur le classement obtenu.
Research has been carried out to investigate the use of genetic algorithms (GAs) as a common solution technique for solving the range of problems that arise when designing and planning manufacturing operations. A variety of problem areas have been selected that are representative of the range of problem types found in manufacturing decision-making, i.e. assortment planning, aggregate planning, lot sizing within material requirements planning environments, line balancing and facilities layout. Part 1 of this paper reported how typical solutions for each problem area were coded in terms of a genetic algorithm structure and how suitable objective functions were constructed. In addition, comparisons of performance were carried out between GA solution methods and traditional solution methods. Part 2 of this paper now describes the GA experiments undertaken during the identification of suitable GA operators and operator parameter values. These experiments have enabled underlying relationships between problem characteristics and performance of individual operator types and parameter values to be identified. From this work a set of guidelines has been identified for selecting appropriate genetic algorithm structures for specific types of operations management decision area.
An aggregate plan is a statement of future resources and finished goods inventory levels that must be made available to enable a manufacturing organization to meet its forecast sales demand. The aggregate planning process must enable the strategic business objectives of a manufacturing organization to be quickly converted into short-term operational plans that are economical to implement. This link between strategy and the day-to-day activities of an organization is becoming increasingly important in enabling organizations to cope with the faster rate at which business objectives must be changed in order to remain competitive.
This paper initially examines the aggregate planning process and describes the basic tasks involved. Current methods of setting aggregate plans are then compared and the main limitations of such techniques highlighted. Using an aggregate planning example, the paper then explains how genetic algorithms can be used to develop such plans and illustrates how this type of algorithm provides the means by which the limitations of existing aggregate planning techniques can be overcome.
Research has been carried out to investigate the use of genetic algorithms (GAs) as a common solution technique for solving the range of problems that arise when designing and planning manufacturing operations. A variety of problem areas have been selected that are representative of the range of problem types found in manufacturing decision-making, i.e. assortment planning, aggregate planning, lot sizing within material requirements planning environments, line balancing and facilities layout. In Part 1 of this paper, for each problem area, a typical solution has been coded in terms of a GA structure, a suitable objective function has been constructed and experiments have been performed in order to identify suitable GA operators and operator parameter values. The potential suitability of GAs to solve these problem types has then been determined by comparing the best solution generated using a GA to a solution derived using a traditional solution technique. Part 2 of this paper focuses on the range of G A experiments undertaken to investigate the underlying relationships between problem characteristics and the performance of individual operator types and their parameter values.
This paper presents a distributed system facilitating robust decision-making under uncertainty in complex situations often found in strategic emergency management. The construction of decision-relevant scenarios, each being a consistent, coherent and plausible description of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision-making. The construction of scenarios is targeted at assessing decision alternatives avoiding time-consuming processing of irrelevant information. The scenarios are constructed in a distributed setting ensuring that each decision can be founded on a coherent and consistent set of assessments and assumptions provided by the best (human or artificial) experts available within limited time. Our theoretical framework is illustrated by means of an emergency management example.
The decision problems involved in setting the aggregate production rate of a factory and setting the size of its work force are frequently both complex and difficult. The quality of these decisions can be of great importance to the profitability of an individual company, and when viewed on a national scale these decisions have a significant influence on the efficiency of the economy as a whole. This paper reports some of the findings of a research team that has been developing new methods to enable production executives to make better decisions and to make them more easily than they can with prevailing procedures. With the cooperation of a manufacturing concern, the new methods have been developed in the context of a set of concrete production scheduling problems that were found in a factory operated by the company.
The new method which is presented in this paper, involves: (1) formalizing and quantifying the decision problem (using a quadratic approximation to the criterion function) and (2), calculating a generalized optimal solution of the problem in the form of a (linear) decision rule. Like a rule of thumb, an optimal decision rule prescribes a course of action when it is applied to a particular set of circumstances; but, unlike most rules of thumb, an optimal decision rule prescribes courses of action for which the claim can be made that the decisions are “the best possible,” the meaning of “best” being clearly specified. The ultimate test, of course, must be whether the new decision methods do or do not outperform prevailing decision methods when full allowance is made for the cost of obtaining the optimal decisions.
Approximate reasoning for production planning could effectively be based on an interval-valued representation of linguistic variables. An interval-valued representation provides a framework that: (i) incorporates a dispersion about membership values, and (ii) produces a robust result compared to classical results. The proposed representation and inference is applied to a set of production problems in prototype studies. The result of one such prototype study is summarized.
By decision-making in a fuzzy environment is meant a decision process in which the goals and/or the constraints, but not necessarily the system under control, are fuzzy in nature. This means that the goals and/or the constraints constitute classes of alternatives whose boundaries are not sharply defined.
An example of a fuzzy constraint is: “The cost of A should not be substantially higher than α,” where α is a specified constant. Similarly, an example of a fuzzy goal is: “x should be in the vicinity of x 0 ,” where x 0 is a constant. The italicized words are the sources of fuzziness in these examples.
Fuzzy goals and fuzzy constraints can be defined precisely as fuzzy sets in the space of alternatives. A fuzzy decision, then, may be viewed as an intersection of the given goals and constraints. A maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value.
The use of these concepts is illustrated by examples involving multistage decision processes in which the system under control is either deterministic or stochastic. By using dynamic programming, the determination of a maximizing decision is reduced to the solution of a system of functional equations. A reverse-flow technique is described for the solution of a functional equation arising in connection with a decision process in which the termination time is defined implicitly by the condition that the process stops when the system under control enters a specified set of states in its state space.