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Showing 2 results for Possibilistic Programming
Noori Darvish, Tavakkoli-Moghaddam, Javadian,
Volume 3, Issue 1 (4-2012)
Abstract
We consider an open shop scheduling problem. At first, a bi-objective possibilistic mixed-integer programming formulation is developed. The inherent uncertainty in processing times and due dates as fuzzy parameters, machine-dependent setup times and removal times are the special features of this model. The considered bi-objectives are to minimize the weighted mean tardiness and weighted mean completion times. After converting the original formulation into a single-objective crisp one by using an interactive approach and obtaining the Pareto-optimal solutions for small-sized instances, an efficient multi-objective particle swarm optimization (MOPSO) is proposed in order to achieve a good approximate Pareto-optimal set for medium and large-sized examples. This algorithm exploits new selection regimes of the literature for the global best and personal best. Furthermore, a modified decoding scheme is designed to reduce the search area in the solution space, and a local search algorithm is proposed to generate initial particle positions. Finally, the efficiency of the proposed MOPSO (PMOPSO) is shown by comparing with the common MOPSO (CMOPSO) by the use of the design of experiments (DOE) based on three comparison metrics.
Mr. Aidin Azari Marhabi, Dr. Abdollah Arasteh, Dr. Mohammad Mahdi Paydar,
Volume 10, Issue 1 (7-2019)
Abstract
This paper presents a structure that empower designing supervisory groups to survey the estimation of real options in projects of enormous scale, incompletely standardized frameworks actualized a couple of times over the medium term. Specific options writing is done using a methodology of planning the design and making prior decisions regarding the arrangements of specific options, with a recreation-based value measure designed to be near-current construction rehearsals and to resolve financial problems in particular cases. To study the case and demonstrate the actual application of this method, drug chain modeling at the tactical level was investigated. The physical and financial flow and their disturbance are simultaneously modulated. In order to complete the financial flow, financial ratios are also entered into the model. Problem uncertainty has been modeled using one of the most recent robust optimization approaches called Robust Possibilistic Programming (RPP) in combination with real options theory. The model was applied to a case study and its results were analyzed and validated by GAMS software. The results show that without violating the limitations of the problem, it shows appropriate decisions to deal with the problem.
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