Integrating lot-sizing and pricing decisions in multi-channel markets with discrete menus of prices

This paper presents a model for coordinating lot-sizing and pricing decisions for manufacturers operating in multichannel markets with limited production capacity. Integrating these decisions is a significant challenge for manufacturers because it allows them to align their marketing and operational...

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Bibliographic Details
Published in:Computers & operations research Vol. 182; p. 107131
Main Authors: Terzi, Mourad, Ouazene, Yassine, Yalaoui, Alice, Yalaoui, Farouk
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.10.2025
Subjects:
Attraction demand models
Heuristics
Lot-sizing
Mixed-integer linear programming
Mixed-integer nonlinear programming
Multi-channel market
Pricing
Lot-sizing
Mixed-integer nonlinear programming
Multi-channel market
Heuristics
Pricing
Attraction demand models
Mixed-integer linear programming
ISSN:0305-0548
Online Access:Get full text
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Summary:This paper presents a model for coordinating lot-sizing and pricing decisions for manufacturers operating in multichannel markets with limited production capacity. Integrating these decisions is a significant challenge for manufacturers because it allows them to align their marketing and operational strategies effectively. This study addresses the application of attraction models to characterize consumers’ preferences and switching behavior between sales channels. Initially, the problem is formulated as a non-convex mixed-integer nonlinear programming (MINLP) model that incorporates production capacity constraints and a discrete menu of prices for each channel. To overcome the challenges of nonlinearity and nonconvexity, the problem is reformulated as an exact mixed-integer linear programming (MILP) model. Then, two MILP-based heuristics, the Fix-and-Relax and Double-Fix-and-Relax methods, are designed to solve the problem efficiently. An illustrative example demonstrates the model’s practical application and offers valuable insights for decision-makers in real-life scenarios. Furthermore, a comprehensive experimental study on 120 benchmark instances shows that the proposed solution methods achieve near-optimal solutions in less than one minute of computational time.
ISSN:0305-0548
DOI:10.1016/j.cor.2025.107131