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Showing 3 results for Arasteh
Dr. Abdollah Arasteh,
Volume 7, Issue 1 (4-2016)
Abstract
Here, issues connected with characteristic stochastic practices are considered. In the first part, the plausibility of covering the arrangements of an improvement issue on subjective subgraphs is studied. The impulse for this strategy is a state where an advancement issue must be settled as often as possible for discretionary illustrations. Then, a preprocessing stage is considered that would quicken ensuing inquiries by discovering a settled scattered subgraph covering the answer for an arbitrary subgraph with a high likelihood. This outcome is grown to the basic instance of matroids, in addition to advancement issues taking into account the briefest way and resource covering sets. Next, a stochastic improvement model is considered where an answer is sequentially finished by picking an accumulation of “points”. Our crucial idea is the profit of adaptivity, which is investigated for an extraordinary sort of an issue. For the stochastic knapsack issue, the industrious upper and lower cutoff points of the “adaptivity hole” between ideal adaptive and non-adaptive methodologies are checked. Also, an algorithm is described that accomplishes a close ideal estimate. Finally, complicational results are shown to verify the optimal adaptive approaches.
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.
Mr. Sajjad Mohseni Andargoli, Dr. Abdollah Arasteh, Dr. Ali Divsalar,
Volume 16, Issue 2 (8-2025)
Abstract
The explosive growth of global e-commerce and the increasing complexity of last-mile logistics have made the strategic placement of smart lockers a critical concern in modern urban logistics systems. Conventional methods, which rely solely on Multi-Criteria Decision Making (MCDM) methods for obtaining solutions, suffer from several limitations when implemented in uncertain, significant, and multi-objective scenarios. This paper proposes a stochastic multi-objective optimisation model for the BWM, prioritising decision criteria, which is solved by combining a hybrid metaheuristic solution methodology. The proposed model optimizes both total cost and sustainability performance from economic, environmental, and social perspectives, as well as robustness to demand uncertainty. An empirical study using Babol City, Iran, is presented to test and demonstrate the proposed framework. Candidate locker location and demand areas were examined based on expert-elicited criteria weights, with the preparation of a multi-objective mixed-integer programming model. In order to alleviate the computation burden, a combined structure of NSGA-II and LNS (referred to as NSGA-II+LNS) was proposed, which outperforms classical evolutionary algorithms in terms of convergence into the Pareto frontier. Factual results indicate that factoring in economic affordability, accessibility, and environmental impact is key to optimal locker capacity design. Robust solutions under demand fluctuation can save up to 18% more on service reliability, providing strong deterministic answers. This article makes the following theoretical and practical contributions: (i) a novel sustainable-oriented, deterministic model for smart locker location is proposed; (ii) advanced metaheuristics are integrated with MCDM in urban logistics, whereas fewer studies have focused on integrating them; and (iii) policy implications are suggested not only to policymakers but also to logistics operators who want robust last-mile delivery strategies..
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