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Saeid Rezaei

Saeid Rezaei

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0003-1362-7294
Education: PhD.
ScopusId: 56982653700
HIndex: 0/00
Faculty: Engineering
Address: Arak University
Phone: 32625734

Research

Title
Competitive planning of partnership supply networks focusing on sustainable multi-agent transportation and virtual alliance: A matheuristic approach
Type
JournalPaper
Keywords
Supply chain management, Competitive matheuristic approach, Sustainable multi-agent transportation, Virtual alliance, Benders Decomposition, Particle Swarm Optimization
Year
2021
Journal Journal of Cleaner Production
DOI
Researchers Saeid Rezaei

Abstract

The partnership supply of market needs is one of the most regarded orientations in recent years. In this paper, a competitive model is designed in partnership supply networks including parent firms (brands), manufacturing plants, governmental logistics company and franchised stores. In the raised scheme, the involved supply networks seek to develop their upstream and downstream partnerships (respectively with manufacturing plants and franchised stores) in a competitive environment. Besides this cross-network competition, the downstream partners of each network develop some virtual alliances. In such alliances, each of the involved members, further pursuing its own interests, aims to increase the overall share of the parent brand. Even more striking, a multi-agent transportation platform is implemented in the distribution of products to meet the environmental sustainability requirements. The state-owned logistics company, as the market leader of the cited distributed structure, dictates its strategy to the supply networks seeking to gain the most possible partnership interactions. Hence, in this paper, a multi-level competition-oriented problem is considered in the partnership supply networks and formulated upon multiple hierarchical stages. Due to the NP-hardness of the problem and given its structural features, a matheuristic approach based on a combination of the Benders Decomposition method and Particle Swarm Optimization algorithm is developed. The proposed solving approach is compared against a gaming-based heuristic and also a pure Benders Decomposition. The results of the evaluations indicate the superiority of the suggested hybrid approach, especially in large-scale instances.