Showing papers on "Goal programming published in 2005"

Multiple criteria decision analysis: state of the art surveys

Abstract: In two volumes, this new edition presents the state of the art in Multiple Criteria Decision Analysis (MCDA). Reflecting the explosive growth in the field seen during the last several years, the editors not only present surveys of the foundations of MCDA, but look as well at many new areas and new applications. Individual chapter authors are among the most prestigious names in MCDA research, and combined their chapters bring the field completely up to date. Part I of the book considers the history and current state of MCDA, with surveys that cover the early history of MCDA and an overview that discusses the “pre-theoretical” assumptions of MCDA. Part II then presents the foundations of MCDA, with individual chapters that provide a very exhaustive review of preference modeling, along with a chapter devoted to the axiomatic basis of the different models that multiple criteria preferences. Part III looks at outranking methods, with three chapters that consider the ELECTRE methods, PROMETHEE methods, and a look at the rich literature of other outranking methods. Part IV, on Multiattribute Utility and Value Theories (MAUT), presents chapters on the fundamentals of this approach, the very well known UTA methods, the Analytic Hierarchy Process (AHP) and its more recent extension, the Analytic Network Process (ANP), as well as a chapter on MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique). Part V looks at Non-Classical MCDA Approaches, with chapters on risk and uncertainty in MCDA, the decision rule approach to MCDA, the fuzzy integral approach, the verbal decision methods, and a tentative assessment of the role of fuzzy sets in decision analysis. Part VI, on Multiobjective Optimization, contains chapters on recent developments of vector and set optimization, the state of the art in continuous multiobjective programming, multiobjective combinatorial optimization, fuzzy multicriteria optimization, a review of the field of goal programming, interactive methods for solving multiobjective optimization problems, and relationships between MCDA and evolutionary multiobjective optimization (EMO). Part VII, on Applications, selects some of the most significant areas, including contributions of MCDA in finance, energy planning problems, telecommunication network planning and design, sustainable development, and portfolio analysis. Finally, Part VIII, on MCDM software, presents well known MCDA software packages.

Multiobjective Optimization and Multiple Constraint Handling with Evolutionary Algorithms

Abstract: In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including constraint satisfaction, lexicographic optimization, and a form of goal programming. Then, the ranking of an arbitrary number of candidates is considered, and the ef- fect of preference changes on the cost surface seen by an evolutionary algorithm is illustrated graphically for a simple problem. The formulation of a multiobjective genetic algorithm based on the pro- posed decision strategy is also discussed. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Finally, an application to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine is described, which il- lustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The two instances of the problem studied demonstrate the need for pref- erence articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample ef- fectively. It is shown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to sup- ply preference information to the GA.

Application of fuzzy goal programming technique to land use planning in agricultural system

Abstract: This paper presents how fuzzy goal programming can be efficiently used for modelling and solving land-use planning problems in agricultural systems for optimal production of several seasonal crops in a planning year. In the model formulation of the problem, utilization of total cultivable land, supply of productive resources, aspiration levels of various production of crops as well as the total expected profit from the farm are fuzzily described. In the decision-making situation, minimization of the under-deviational variables of the membership goals with highest membership value (unity) as their achievement levels defined for the membership functions of the fuzzy goals of the problem on the basis of the priorities of importance of achieving the aspired levels of the fuzzy goals to the extent possible is considered. As a study region, the District Nadia, West Bengal, India is taken into account. To expound the potential use of the approach, the model solution is compared with the existing cropping plan of the District as well as a solution of the problem obtained by using the additive fuzzy goal programming model studied by Tiwari et al. (Fuzzy sets and systems 24(1987)27.) previously.

Optimization of machining techniques — A retrospective and literature review

Abstract: In this paper an attempt is made to review the literature on optimizing machining parameters in turning processes. Various conventional techniques employed for machining optimization include geometric programming, geometric plus linear programming, goal programming, sequential unconstrained minimization technique, dynamic programming etc. The latest techniques for optimization include fuzzy logic, scatter search technique, genetic algorithm, Taguchi technique and response surface methodology.

Design of virtual manufacturing cells: a mathematical programming approach

Abstract: In this paper, a new type of virtual cellular manufacturing (CM) system is considered, and a multi-objective design procedure is developed for designing such cells in real time. Retaining the functional layout, virtual cells are addressed as temporary groupings of machines, jobs and workers to realize the benefits of CM. The virtual cells are created periodically, for instance every week or every month, depending on changes in demand volumes and mix, as new jobs accumulate during a planning period. The procedure includes labor grouping considerations in addition to part-machine grouping. The procedure is based on interactive goal programming methods. Factors such as capacity constraints, cell size restrictions, minimization of load imbalances, minimization of inter-cell movements of parts, provision of flexibility, etc. are considered. In labor grouping, the functionally specialized labor pools are partitioned and regrouped into virtual cells. Factors such as ensuring balanced loads for workers, minimization of inter-cell movements of workers, providing adequate levels of labor flexibility, etc. are considered in a pragmatic manner.

Manufacturing supply chain design and evaluation

Abstract: Successful supply chain management calls for robust supply chain design and evaluation tools. Many published papers focused on high level strategic aspects of supply chain design and the results are usually generic guidelines for business executives rather than specific tools for plant managers. In this paper, we present a decision-based methodology for supply chain design that a plant manager can use to select suppliers. The methodology utilizes the techniques of analytic hierarchy process and preemptive goal programming. Supply chain operations reference model level I performance metrics are incorporated into the methodology as the decision criteria. In addition, a set of performance metrics is developed to evaluate the overall supply chain effectiveness, which allows direct comparison of different supply chain designs.

On min-norm and min-max methods of multi-objective optimization

Abstract: This paper examines Pareto optimality of solutions to multi-objective problems scalarized in the min-norm, compromise programming, generalized goal programming, or unrestricted min-max formulations. Issues addressed include, among others, uniqueness in solution or objective space, penalization for over-achievement of goals, min-max reformulation of goal programming, inferiority in Tchebycheff-norm minimization, strength and weakness of weighted-bound optimization, “quasi-satisficing” decision-making, just attaining or even over-passing the goals, trading off by modifying weights or goals, non-convex Pareto frontier. New general necessary and sufficient conditions for both Pareto optimality and weak Pareto optimality are presented. Various formulations are compared in theoretical performance with respect to the goal-point location. Ideas for advanced goal programming and interactive decision-making are introduced.

Fuzzy goal-programming model with an artificial immune system (AIS) approach for a machine tool selection and operation allocation problem in a flexible manufacturing system

Abstract: Some of the important planning problems that need realistic modelling and a quicker solution, especially in automated manufacturing systems, have recently assumed greater significance. In real-life industrial applications, the existing models considering deterministic situations fail as the true language adopted by foremen and technicians are fuzzy in nature. Thus, to map the situation on the shop floor to arrive at a real-time solution of this kind of tactical planning problem, it is essential to adopt fuzzy-based multi-objective goals so as to express the target desired by the management of business enterprises. This paper presents a fuzzy goal-programming approach to model the machine tool selection and operation allocation problem of flexible manufacturing systems. The model is optimized using an approach based on artificial immune systems and the results of the computational experiments are reported.

Integrating linear physical programming within collaborative optimization for multiobjective multidisciplinary design optimization

Abstract: Multidisciplinary design optimization (MDO) is a concurrent engineering design tool for large-scale, complex systems design that can be affected through the optimal design of several smaller functional units or subsystems. Due to the multiobjective nature of most MDO problems, recent work has focused on formulating the MDO problem to resolve tradeoffs between multiple, conflicting objectives. In this paper, we describe the novel integration of linear physical programming within the collaborative optimization framework, which enables designers to formulate multiple system-level objectives in terms of physically meaningful parameters. The proposed formulation extends our previous multiobjective formulation of collaborative optimization, which uses goal programming at the system and subsystem levels to enable multiple objectives to be considered at both levels during optimization. The proposed framework is demonstrated using a racecar design example that consists of two subsystem level analyses — force and aerodynamics — and incorporates two system-level objectives: (1) minimize lap time and (2) maximize normalized weight distribution. The aerodynamics subsystem also seeks to minimize rearwheel downforce as a secondary objective. The racecar design example is presented in detail to provide a benchmark problem for other researchers. It is solved using the proposed formulation and compared against a traditional formulation without collaborative optimization or linear physical programming. The proposed framework capitalizes on the disciplinary organization encountered during large-scale systems design.

A stochastic linear goal programming approach to multistage portfolio management based on scenario generation via linear programming

Abstract: A stochastic linear goal programming model for multistage portfolio management is proposed. The model takes into account both the investment goal and risk control at each stage. A scenario generation method is proposed that acts as the basis of the portfolio management model. In particular, by matching the moments and fitting the descriptive features of the asset returns, a linear programming model is used to generate the single-stage scenarios. Scenarios for multistage portfolio management are generated by incorporating this single-stage method with the time-series model for the asset returns. Meanwhile, no arbitrage opportunity exists in the proposed method. A real case is solved via the goal programming model and the scenario generation approach which demonstrates the effectiveness of the model. We also comment on some practical issues of the approach.

An η-approximation method in nonlinear vector optimization

Abstract: In this paper, a new approach for a solution of a nonlinear multiobjective programming problem is introduced. An equivalent η -approximated vector optimization problem is constructed by a modification of the objective and the constraint functions in the original multiobjective programming problem. The connection between (weak) efficient points in the original multiobjective programming problem and its equivalent η -approximated vector optimization problem is proved. In this way, optimality conditions for nonlinear constrained multiobjective programming problems having invex and/or generalized invex objective and constraint functions (with respect to the same functions η ) are obtained.

Decision process for multiobjective, multi-item production-inventory system via interactive fuzzy satisficing technique

Abstract: Multiobjective and single-objective inventory models of stochastically deteriorating items are developed in which demand is a function of inventory level and selling price of the commodity. Production rate depends upon the quality level of the items produced and unit production cost is a function of production rate. Deterioration depends upon both the quality of the item and duration of time for storage. The time-related deterioration function follows a two-parameter Weibull distribution in time. In these models, results are derived for both without shortages and partially backlogged shortages. Here, objectives for profit maximization for each item are separately formulated with different goals and compromise solutions of the multiobjective production/inventory problems are obtained by goal programming method. The models are illustrated with numerical examples and results for different formulations are compared. The results for the models assuming them to be a single house integrated business are also obtained using a gradient-based optimization technique and compared with those obtained from the respective decentralized models. Taking man-machine interaction into consideration, interactive solutions are derived for one of the said models-multiobjective model with shortages using interactive fuzzy satisficing method. Pareto optimum and satisficing results are derived for some numerical data.

Preemptive goal programming using simulated annealing

Abstract: Goal programming is a commonly used technique for modelling and solving multiple objective optimization problems. It has been successfully applied to many diverse real-life problems in engineering design and optimization. One of the first attempts is made in this article to solve preemptive goal programming (PGP) problems by using a simulated annealing (SA) algorithm. The developed algorithm can be applied to non-linear, linear, integer and combinatorial goal programs. However, the main concentration is on non-linear programs, mainly due to the difficulty in solving these programs with the classical approaches. Several test problems are solved in order to test the suitability of SA in solving preemptive goal programs. It is observed that the SA algorithm is a suitable candidate to solve goal programs. The method can easily be applied to any kind of PGP problem.

Concurrent Optimization of Product Module Selection and Assembly Line Configuration: A Multi-Objective Approach

Abstract: Both modular product design and reconfigurable manufacturing have a great potential to enhance responsiveness to market changes and to reduce production cost. However, the two issues have thus far mostly been investigated separately, thereby causing possible mismatch between the modular product structure and the manufacturing or assembly system. Therefore, the potential benefits of product modularity may not be materialized due to such mismatch. For this reason, this paper presents a concurrent approach to the product module selection and assembly line design problems to provide a set of harmonic solutions to the two problems and hence avoid the mismatch between design and manufacturing. The integrated nature of the problem leads to several noncommensurable and often conflicting objectives. The modified Chebyshev goal programming approach is applied to solve the multi-objective problem. A genetic algorithm is further developed to provide quick and near-optimum solutions. The proposed approach and the solution procedure have been applied to an ABS motor problem. The performance of the genetic algorithm has also been examined.

Infeasibility in automated test assembly models: A comparison study of different methods

Abstract: Several techniques exist to automatically put together a test meeting a number of specifications. In an item bank, the items are stored with their characteristics. A test is constructed by selecting a set of items that fulfills the specifications set by the test assembler. Test assembly problems are often formulated in terms of a model consisting of restrictions and an objective to be maximized or minimized. A problem arises when it is impossible to construct a test from the item pool that meets all specifications, that is, when the model is not feasible. Several methods exist to handle these infeasibility problems. In this article, test assembly models resulting from two practical testing programs were reconstructed to be infeasible. These models were analyzed using methods that forced a solution (Goal Programming, Multiple-Goal Programming, Greedy Heuristic), that analyzed the causes (Relaxed and Ordered Deletion Algorithm (RODA), Integer Randomized Deletion Algorithm (IRDA), Set Covering (SC), and Item Sampling), or that analyzed the causes and used this information to force a solution (Irreducible Infeasible Set-Solver). Specialized methods such as the IRDA and the Irreducible Infeasible Set-Solver performed best. Recommendations about the use of different methods are given.

Design parameter estimation using a modified qfd method to improve customer perception

Abstract: This article presents an integrated approach to optimize cost while respecting the customer perception of a product using a modified Quality Function Deployment (QFD) method. This QFD method helps a design team to determine the effect of various design strategies for customer satisfaction. The new QFD method uses a two-phased approach for finding an optimum design strategy. During the first phase, the design team sets goals for customer perception for each customer attribute and relates them to those of its competitors (benchmarking); then, in the second phase, a goal-based model with a separated, mixed integer structure is used to minimize cost while respecting customer desires. The model defines fixed cost as a major improvement in design solutions such as changing parts, materials, or operational mechanisms. It also defines variable cost as a minor improvement in the current design solution. An illustrative example is given to demonstrate the use of the method, and a sensitivity analysis for budget lim...

Aspect-oriented programming beyond dependency injection

Abstract: Dependency injection is a hot topic among industrial developers using component frameworks. This paper first mentions that dependency injection and aspect-oriented programming share the same goal, which is to reduce dependency among components for better reus- ability. However, existing aspect-oriented programming languages/ frameworks, in particular, AspectJ, are not perfectly suitable for expressing inter-component dependency with a simple and straightforward representation. Their limited kinds of implicit construction of aspect instances (or implementations) cannot fully express inter-component dependency. This paper points out this fact and proposes our aspect-oriented programming system named GluonJ to address this problem. GluonJ allows developers to explicitly construct and associate an aspect implementation with aspect targets.

Goal programming and cognitive biases in decision making

Abstract: The study of cognitive biases in decision-making has largely arisen within the context of the subjective expected utility school of decision analysis. Many of the behavioural patterns that have been discovered do seem to be relevant to broader areas of multicriteria decision analysis (MCDA). In this paper, we look specifically at the judgemental inputs required in implementing goal programming (GP) models. The potential relevance of some of the known cognitive biases in this context are identified, and their impact studied by means of simulation experiments. It is found, inter alia, that biases due to anchoring and adjustment and to avoidance of sure loss can lead to substantial degradation in the performance of GP algorithms. Suggestions for practice and recommendations for follow-up research are derived from the simulation results.

Combining goals in the logistics bio-energy chains

Abstract: Biomass is becoming more important as an energy source. A biomass-to-energy chain is a complex network in which much logistic options are available. The optimal choice of a biomass type, type of power plant, locations, transport, storage and pre-treatments is difficult to make and becomes more difficult when taking into account: seasonal fluctuations in supply and demand; moisture losses due to drying and dry-matter losses due to biological processes. A decision support tool can be helpful with this kind of problems. Bioloco is a computer model for the optimisation of the biomass-to-energy chain. Bioloco helps to determine the best set-up of bio-energy chains on base of one of six optimisation criteria. 'Bioloco light' is a user-friendlier version of Bioloco. Through the use of goal programming several objectives can be combined. The best compromise is then determined between different types of goals (financial and energetic).

Using Stochastic Goal Programming: Some Applications to Management and a Case of Industrial Production

Abstract: This paper deals with stochastic goal programming as a method capable of providing “satisficing” solutions in the uncertainty case from the standard expected utility perspective. By extending recent results on ARA-based weighted achievement function and goal structures leading to mean-variance optimization, the paper combines random with non-random goals, and explains the role of ARA coefficients in the model. Eight applications to real world problems in managerial environments are described. A case study in the textile industry, the choice of fibers to make blends in yam production, is developed from empirical information and numerically solved.