Planning with multiple objectives

Wiedemann, Paul

doi:10.1016/0305-0483(78)90097-x

Cited by 3 publications

(1 citation statement)

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“…Despite rapid technological advancements in software and hardware performance, many problems featuring numerous evaluation criteria or objective functions (Wiedemann 1978), multiple constraints of different nature and hundreds to thousands of decision variables remain challenging to solve (Carrizosa et al 2019). Several methods have been developed to solve multi-objective linear programming problems, each of which features strengths and weaknesses (Sylva and Crema 2007).…”

Section: Introductionmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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Systems can be unstructured, uncertain and complex, and their optimisation often requires operational research techniques. In this study, we introduce AUGMECON-R, a robust variant of the augmented ε-constraint algorithm, for solving multiobjective linear programming problems, by drawing from the weaknesses of AUG-MECON 2, one of the most widely used improvements of the ε-constraint method. These weaknesses can be summarised in the ineffective handling of the true nadir points of the objective functions and, most notably, in the significant amount of time required to apply it as more objective functions are added to a problem. We subsequently apply AUGMECON-R in comparison with its predecessor, in both a set of reference problems from the literature and a series of significantly more complex problems of four to six objective functions. Our findings suggest that the proposed method greatly outperforms its predecessor, by solving significantly less models in emphatically less time and allowing easy and timely solution of hard or practically impossible, in terms of time and processing requirements, problems of numerous objective functions. AUGMECON-R, furthermore, solves the limitation of unknown nadir points, by using very low or zero-value lower bounds without surging the time and resources required.

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Section: Introductionmentioning

confidence: 99%

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

et al. 2020

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Robustness as a corporate objective under uncertainty

Dorward

Wiedemann

1981

Manage Decis Econ

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This paper examines the nature of the objective function of the firm when operating under conditions of uncertainty. Robustness is presented as a purposeful maximand for decision making both under conditions of certainty and uncertainty ‐ a robust decision being one in which the decision maker retains the maximum flexibility with regard to future decisions after an initial decision has been made. Its incorporation within a managerial objective function provides a measurable scale for making choices between alternative courses of action, including under conditions of internal organizational conflict and environmental reaction by other decision makers.

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Sailing through the storm – performance of Russian manufacturing subsidiaries of multinational corporations in the first year of the COVID-19 pandemic

Gurkov

Filinov

2022

Post-Communist Economies

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Planning with multiple objectives

Cited by 3 publications

References 1 publication

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems

Robustness as a corporate objective under uncertainty

Sailing through the storm – performance of Russian manufacturing subsidiaries of multinational corporations in the first year of the COVID-19 pandemic

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