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Goal Programming
Published in Albert G. Holzman, Mathematical Programming, 2020
Goal programming is a powerful and flexible technique that can be applied to a variety of decision problems involving multiple objectives. It should, however, be pointed out that goal programming is by no means a panacea for contemporary decision problems. The fact is that goal programming is applicable only under certain specified assumptions and conditions. Most goal programming applications have thus far been limited to well-defined deterministic problems. Furthermore, the primary analysis has been limited to the identification of an optimal solution that optimizes goal attainment to the extent possible within specified constraints. In order to develop goal programming as a universal technique for modem decision analysis, many refinements and further research are necessary. In this section, several areas where research is being conducted will be discussed.
Multiple Criteria Decision Making in Environmentally Conscious Manufacturing and Product Recovery
Published in Surendra M. Gupta, Mehmet Ali Ilgin, Multiple Criteria Decision Making Applications in Environmentally Conscious Manufacturing and Product Recovery, 2017
Surendra M. Gupta, Mehmet Ali Ilgin
Goal programming is an extension of linear programming, due to its ability to handle multiple and often conflicting objectives (Ignizio1976). Two variants of goal programming are prevalent in the literature. The first one is known as lexicographic or preemptive goal programming, while the second one is termed weighted or nonpreemptive goal programming. Preemptive goal programming assumes that all goals can be clearly prioritized and that satisfying a higher-priority goal should carry more importance than satisfying a lower-priority goal. Nonpreemptive goal programming assumes that all goals should be pursued. However, in this case, all deviations from the goals are multiplied by some weights (based on their relative importance) and summed up to form a single utility function that is optimized.
Disassembly Line Balancing
Published in Surendra M. Gupta, A. J. D. (Fred) Lambert, Environment Conscious Manufacturing, 2007
Seamus M. McGovern, Surendra M. Gupta
Except for some minor variation in the greedy or hill-climbing approach, all of the combinatorial optimization techniques described here use a similar methodology to address the multicriteria aspects of DLBP. Since measure of balance is the primary consideration in this chapter, additional objectives are only considered subsequently, that is, the methodologies first seek to select the best performing measure of balance solution as given by Formula 6.4; equal balance solutions are then evaluated for hazardous part removal positions as per Formula 6.13; equal balance and hazard measure solutions are evaluated for high-demand part removal positions as measured by Formula 6.21; and equal balance, hazard measure, and high-demand part removal position solutions are evaluated for the number of direction changes as measured by Formula 6.26. Based on lexicographic goal programming, this priority ranking approach was selected over a weighting scheme for several reasons. These include the simplicity of implementing lexicographic goal programming, ease in re-ranking the priorities, ease in expanding or reducing the number of priorities, because other weighting methods can be readily addressed at a later time, and primarily to enable unencumbered efficacy analysis of the solution-generating methodologies and instances.
Review of recent developments in short-term mine planning and IPCC with a research agenda
Published in Mining Technology, 2023
Nasib Al Habib, Eugene Ben-Awuah, Hooman Askari-Nasab
Linear programming only focuses on a single linear objective function with linear constraints. Goal programming is an extension of linear programming capable of handling multiple and conflicting objectives. Therefore, the model's objective function is usually a combination of multiple objectives. It does not get a single optimal solution. Still, it generates the so-called Pareto optimal solutions, a set of points in a multi-dimensional space, where each point represents an optimal or efficient solution in terms of multiple objectives. Upadhyay and Askari-Nasab (2016, 2017) used goal programming for a simulation-based short-term planning optimisation model to illustrate how proactive decisions can be made in a dynamic environment of mining and operational plans and how they can be synced with long-term planning to reduce opportunity cost, maximise production, and equipment utilisation.
Review on multi-criteria decision analysis in sustainable manufacturing decision making
Published in International Journal of Sustainable Engineering, 2021
Anbesh Jamwal, Rajeev Agrawal, Monica Sharma, Vikas Kumar
Goal programming is defined as an optimisation technique to solve manufacturing problems with multiple objectives. These objectives are generally incommensurable and conflict with each other in the decision-making horizons. At present, Goal programming has a wide range of application areas in SM or green manufacturing. Mokhtari and Hasani (2017) proposed a multi-objective cleaner production-transportation model for planning in the manufacturing plants supported by fuzzy logic. Computational experiments-, as well as real-life case studies, were done for evaluation of the proposed algorithm. Barbosa and Gomes (2015) used the goal programming and AHP technique for the assessment of efficiency and sustainability of the Brazilian chemical industries. Total of 4 variables with 21 performance indicator was considered for the study. In which goal programming was adopted for the continuous improvement of the process. It is found that goal programming is less subjective with a straight forward procedure. Tian et al. (2018) adopted the integrated AHP, GRA, and TOPSIS approach for the green performance evaluation of electromechanical products design to facilitate green manufacturing. The finding of the study reported that the selection of green design alternatives for green manufacturing is very important to facilitate green manufacturing in the industries. Drawbacks of the TOPSIS method are presented in Table 5. TOPSIS method is highly preferred for the selection of the strategies. The drawbacks of the TOPSIS can be eliminated by using integrate different hybrid approaches.
Optimization of truck-shovel allocation in open-pit mines under uncertainty: a chance-constrained goal programming approach
Published in Mining Technology, 2021
Mehrnaz Mohtasham, Hossein Mirzaei-Nasirabad, Behrooz Alizadeh
Multi-criteria decision-making methods such as goal programming models use normalization techniques to combine criteria with numerical data. However, since the goals of the model have different dimensions, it is essential to normalize the goals before performing mathematical operations and solving the optimization model. Generally, there are different types of normalizations in statistics; in this research, we use the Euclidean distance to normalize each and every goal of the objective function, which is done by dividing each of them to the square root of the sum of the coefficients of the decision variables in the goal constraint of the desired objective function. Equations (15)–(18) represent the norm of the four desired goals of the modified model, respectively. After normalizing the objectives, the normalized goals are multiplied by the weights to reach the desired priority. The selection of the weights performs a key role in the weighted goal programming model because it paves the way for changing the values of the objective function. Priority weights for particular goals could be estimated based on the technical conditions and production schedule of an open-pit mine by applying methods such as Analytical Hierarchy Process (AHP) or experience of experts’ knowledge.