Forest management optimisation models and habitat diversity: a goal programming approach

This paper proposes an analytical framework, based upon Goal Programming, to incorporate an operational measure of habitat diversity into a forest management optimisation model. First of all, the trade-off curve between the proposed measure of habitat diversity and financial returns is determined. Then, the measure of habitat diversity is integrated in conjunction with other relevant criteria into a compact forest management model. The way in which both models work is explained with the help of a simple but illustrative case study.     

Fuzzy goal programming for inventory management: A bacterial foraging approach

An efficient inventory planning approach in today’s global trading regime is necessary not only for increasing the profit margin, but also to maintain system flexibility for achieving higher customer satisfaction. Such an approach should hence be comprised of a prudent inventory policy and clear satisfaction of stakeholder’s goals. Relative significance given to various objectives in a supply chain network varies with product as well as time. In this paper, a model is proposed to fill this void for a single product inventory control of a supply chain consisting of three echelons. A generic modification proposed to the membership functions of the fuzzy goal-programming approach is used to mathematically map the aspiration levels of the decision maker. The bacterial foraging algorithm has been modified with enhancement of the algorithms’ capability to map integer solution spaces and utilised to solve resulting fuzzy multi-objective function. An illustrative example comprehensively covers various decision scenarios and highlights the underlying managerial insights.     

Product life cycle analysis: a goal programming approach

This paper considers the role of Life-Cycle Costing (LCC), Time-Based Competition (TBC), and other relevant goals at the three planning-horizon levels of strategic, intermediate, and tactical with respect to time and cost of products to market in a concurrent engineering environment. A conceptual framework has been developed in which the general areas of LCC and TBC are delineated. Based on the conceptual framework, the specific components of LCC and TBC at each level of planning are developed and presented. Subsequently, a Preemptive Goal Programming (PGP) model is developed. This model uses the components of LCC, TBC, and other relevant goals as the decision variables. Then, a case study of a company that has developed LCC and TBC is considered. The PGP model is then applied to the company's operations by using the actual data obtained from the company in order to capture the essence of the conceptual framework. This example explains and validates the PGP model in practice and provides managerial implications of the solution. Finally, a set of conclusions and an assessment of the results are presented.     

Strategic financial management in a multinational financial conglomerate: A multiple goal stochastic programming approach

The paper discusses a multi-stage stochastic programming approach to the strategic financial management of a multi-company financial conglomerate. The planning system creates a comprehensive strategy which simultaneously covers a number of future scenarios within a multi-period planning horizon. Multiple conflicting goals may be specified for the group level, company level or individual business area level, and the decision maker’s preferences are allowed to change over time to reflect changing operating conditions and trade-off relationships between the goals. Special features include, among other things, full market valuation throughout the model, integrated treatment of different types of risks, explicit modelling of various types of intra-group transactions and relationships, extensive structures to deal with distressed assets and the covering of losses within the group, as well as consideration of potential portfolio effects of a diversified group structure on the cost of funding.     

Fuzzy goal programming approach to multilevel programming problems

The purpose of this paper is to propose a procedure for solving multilevel programming problems in a large hierarchical decentralized organization through linear fuzzy goal programming approach. Here, the tolerance membership functions for the fuzzily described objectives of all levels as well as the control vectors of the higher level decision makers are defined by determining individual optimal solution of each of the level decision makers. Since the objectives are potentially conflicting in nature, a possible relaxation of the higher level decision is considered for avoiding decision deadlock. Then fuzzy goal programming approach is used for achieving highest degree of each of the membership goals by minimizing negative deviational variables. Sensitivity analysis with variation of tolerance values on decision vectors is performed to present how the solution is sensitive to the change of tolerance values. The efficiency of our concept is ascertained by comparing results with other fuzzy programming approaches.     

Multi-product production planning: A goal programming approach

Cost minimization multi-product production problems with static production resource usage and internal product flow requirements have been solved by linear programming (LP) with input/output analysis. If the problem is complicated by interval resource estimates, interval linear programming (ILP) can be used. The solution of realistic problems by the above method is cumbersome. This paper suggests that linear goal programming (LGP) can be used to model a multi-product production system. LGP's unique modeling capabilities are used to solve a production planning problem with variable resource parameters. Input/output analysis is used to determine the technological coefficients for the goal constraints and is also used to derive an information sub-model that is used to reduce the number of variable resource goal constraints. Preliminary findings suggest that the LGP approach is more cost-efficient (in terms of CPU time) and in addition provides valuable information for aggregate planning.     

A method for solving fuzzy goal programming problems based on MINMAX approach

Narasimhan incorporated fuzzy set theory within goal programming formulation in 1980. Since then numerous research has been carried out in this field. One of the well-known models for solving fuzzy goal programming problems was proposed by Hannan in 1981. In this paper the conventional MINMAX approach in goal programming is applied to solve fuzzy goal programming problems. It is proved that the proposed model is an extension to Hannan model that deals with unbalanced triangular linear membership functions. In addition, it is shown that the new model is equivalent to a model proposed in 1991 by Yang et al. Moreover, a weighted model of the new approach is introduced and is compared with Kim and Whang’s model presented in 1998. A numerical example is given to demonstrate the validity and strengths of the new models.     

Interactive fuzzy goal programming approach for bilevel programming problem

This paper presents an interactive fuzzy goal programming (FGP) approach for bilevel programming problems with the characteristics of dynamic programming (DP).     

A goal programming approach to energy forecasting

Goal programming besides, being a broadly used decision tool, is shown in this paper to be a powerful forecasting device too. It is used as the main component of an energy demand forecasting system, performing a function of an integrator of information on the future growth of energy demand. This information comes from a variety of sources. The most important is a set of econometric models, based on different approaches, which explain past behaviour of the energy demand system. Other sources of information used are statements on government energy policy, feasibility or technical reports and, finally, the forecaster's guess. Thus GP is intended to relax many of the assumptions of conventional econometric forecasting, which render it inadequate under the prevailing conditions of quickly changing energy structures.An implementation of the system on the Greek economy is also described in this paper. Four scenarios of energy demand growth up to 1990 are studied. The levels of energy requirements in these scenarios vary as a consequence of the patterns of economic development and rates of increase in the price of oil.     

Solving generalized goal programming problems using a visual interactive approach

In this paper we describe how generalized goal programming problems can be solved using our Visual Interactive Approach (Korhonen and Laakso, 1986). The method enables the decision maker to evaluate attainable solutions corresponding to his aspiration levels using computer graphics. Goals are divided into flexible and inflexible goals. This classification can be altered during the interactive process. If the problem is entirely linear, the approach can be implemented using any standard linear optimization package with a parametric optimization routine. In addition, a graphical display device is required.     

Solving multi-level multi-objective linear programming problems through fuzzy goal programming approach

In this paper, two new algorithms are presented to solve multi-level multi-objective linear programming (ML-MOLP) problems through the fuzzy goal programming (FGP) approach. The membership functions for the defined fuzzy goals of all objective functions at all levels are developed in the model formulation of the problem; so also are the membership functions for vectors of fuzzy goals of the decision variables, controlled by decision makers at the top levels. Then the fuzzy goal programming approach is used to achieve the highest degree of each of the membership goals by minimizing their deviational variables and thereby obtain the most satisfactory solution for all decision makers.     

A fuzzy goal programming approach to portfolio selection

Portfolio selection is a usual multiobjective problem. This paper will try to deal with the optimum portfolio for a private investor, taking into account three criteria: return, risk and liquidity. These objectives, in general, are not crisp from the point of view of the investor, so we will deal with them in fuzzy terms. The problem formulation is a goal programming (G.P.) one, where the goals and the constraints are fuzzy. We will apply a fuzzy G.P. approach to the above problem to obtain a solution. Then, we will offer the investor help in handling the results.     

Financial portfolio management through the goal programming model: Current state-of-the-art

Since Markowitz (1952) formulated the portfolio selection problem, many researchers have developed models aggregating simultaneously several conflicting attributes such as: the return on investment, risk and liquidity. The portfolio manager generally seeks the best combination of stocks/assets that meets his/her investment objectives. The Goal Programming (GP) model is widely applied to finance and portfolio management. The aim of this paper is to present the different variants of the GP model that have been applied to the financial portfolio selection problem from the 1970s to nowadays.     

A goal programming approach to simple U-line balancing problem

In this paper, a goal programming model for the simple U-line balancing (ULB) problem is developed. The model is based on the integer programming formulation developed by Urban [Urban, Note: Optimal balancing of U-shaped assembly lines, Management Science 44(5) (1998) 738–741] for the ULB problem and the goal model of Deckro and Rangachari [Deckro, Rangachari, A goal approach to assembly line balancing, Computers and Operations Research 17 (1990) 509–521] developed for the traditional single model assembly line balancing (ALB) problem. The proposed model which is the first multi-criteria decision making approach to the U-line version provides increased flexibility to the decision maker since several conflicting goals can be simultaneously considered.     

Solving a multi-objective multi-skilled manpower scheduling model by a fuzzy goal programming approach

Manpower scheduling is an intricate problem in production and service environments with the purpose of generating fair schedules that consider employers’ objectives and employees’ preferences as much as possible. However, sometimes, vagueness of information related to employers’ objectives and employees’ preferences leads to the fuzzy nature of the problem. This paper presents a multi-objective manpower scheduling model regarding the lack of clarity on the target values of employers’ objectives and employees’ preferences. Hence, a fuzzy goal programming model is developed for the presented model. Afterwards, two fuzzy solution approaches are used to convert the fuzzy goal programming model to two single-objective models. Finally, the results obtained by both single-objective models are compared with each other to select the solution that has the greatest degree of the satisfaction level of employers’ objectives and employees’ preferences.     

Multi-choice goal programming formulation based on the conic scalarizing function

The multi-choice goal programming allows the decision maker to set multi-choice aspiration levels for each goal to avoid underestimation of the decision. In this paper, we propose an alternative multi-choice goal programming formulation based on the conic scalarizing function with three contributions: (1) the alternative formulation allows the decision maker to set multi-choice aspiration levels for each goal to obtain an efficient solution in the global region, (2) the proposed formulation reduces auxiliary constraints and additional variables, and (3) the proposed model guarantees to obtain a properly efficient (in the sense of Benson) point. Finally, to demonstrate the usefulness of the proposed formulation, illustrative examples and test problems are included.     

A goal programming approach to group decision making based on multiplicative preference relations and fuzzy preference relations

This paper analyses the mechanisms through which binding finance constraints can induce debt-constrained firms to improve technical efficiency to guarantee positive profits. This hypothesis is tested on a sample of firms belonging to the Italian manufacturing. Technical efficiency scores are computed by estimating parametric production frontiers using the one stage approach as in Battese and Coelli [Battese, G., Coelli, T., 1995. A model for technical efficiency effects in a stochastic frontier production function for panel data. Empirical Economics 20, 325–332]. The results support the hypothesis that a restriction in the availability of financial resources can affect positively efficiency.     

Resource allocation in the North Sea demersal fisheries: A goal programming approach

The management of a fishery is a complex task generally involving multiple, often conflicting, objectives. These objectives typically include economic, biological and social goals such as improving the income of fishers, reducing the catch of depleted species and maintaining employment.Multicriteria decision making (MCDM) techniques appear wellsuited to such a management problem, allowing compromises between conflicting objectives to be analysed in a structured framework. In comparison to other fields, such as water resource planning, forestry and agriculture, there have been few applications of MCDM to fisheries.In this paper, a goal programming model of the North Sea demersal fishery is presented. The model is used to demonstrate the potential applicability of this type of approach to the analysis and development of fisheries management plans with multiple objectives. Alternative scenarios are considered for the problem, and tradeoffs between given objectives are also highlighted and discussed.     

A goal programming model for bank assets and liabilities management

Assets and liabilities management is one of the most important issues in bank strategic planning. In the past, this problem has often been addressed through conventional mathematical programming, i.e. linear programming.However, bank management typically involves several conflicting goals, such as the maximisation of returns, minimisation of risk, expansion of deposits and loans, etc. The complexity of this problem can be captured more adequately by multiobjective mathematical programming. This paper discusses the construction and application at the Commercial Bank of Greece of a goal programming model that takes into account the essential institutional, financial, legal and bank policy considerations.     

Carbon tax based on the emission factor: a bilevel programming approach

We present a bilevel programming approach to design an effective carbon tax scheme based on the production emission factor, used as an intensity measure, for a competitive market with multiple players. At the upper level, the government sets a target emission factor for the industry and taxes firms if they exceed that target. At the lower level, the industry sets output levels that maximize social welfare. The bilevel model is transformed to a linear MIP by replacing the lower level optimization problem by its KKT conditions, and linearizing the complementarity slackness conditions. We test the model in the context of the cement industry. The results show that the proposed model finds the optimal tax rate that induces firms to switch to less carbon-intensive fuels and reduces the overall emissions.