EconPapers    
Economics at your fingertips  
 

Joint production planning, pricing and retailer selection with emission control based on Stackelberg game and nested genetic algorithm

Linda Zhang, Gang D.U., Jun W.U. and Yujie M.A.
Additional contact information
Linda Zhang: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique

Post-Print from HAL

Abstract: In practice, it is of paramount importance that firms make joint decisions in production planning, pricing and retailer selection while considering emission regulation. This is because the joint decisions can ensure firms to obtain higher profits while contributing to sustainable environments. However, due to the problem complexity, no models facilitating such decision making are available. This study aims to develop a model to help firms make optimal joint decisions. To model the situations where a manufacturer is the leader and the retailers are followers, we adopt the Stackelberg game theory and develop a 0–1 mixed nonlinear bilevel program to maximize the profits of both the manufacturer and his retailers. We further develop a nested genetic algorithm to solve the game model. Numerical examples demonstrate (i) the applicability of the game model and the algorithm and (ii) the robustness of the algorithm. Managerial insights are obtained, suggesting that (i) manufacturers need to identify the capacity ranges (called capacity traps) where capacity increases result in reduced profits when making decisions to optimize profits; (ii) retailers should make suitable, e.g., pricing decisions so that the manufacturers can include them in the supply chains; (iii) both manufacturers and retailers may not need to consider the carbon emission buying (or selling) price when making decisions.

Keywords: Stackelberg game; Nonlinear bilevel programming; Nested genetic algorithm; Emission control; Joint decision making (search for similar items in EconPapers)
Date: 2020-12-15
New Economics Papers: this item is included in nep-cmp, nep-ene, nep-env and nep-gth
Note: View the original document on HAL open archive server: https://hal.science/hal-03276837v1
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Expert Systems with Applications, 2020, 161, pp.113733. ⟨10.1016/j.eswa.2020.113733⟩

Downloads: (external link)
https://hal.science/hal-03276837v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03276837

DOI: 10.1016/j.eswa.2020.113733

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().

 
Page updated 2025-03-19
Handle: RePEc:hal:journl:hal-03276837