ABSTRACT
Conventionally, price discounts are offered to enhance demand, for example, to get rid of excess inventory. This paper, however, studies the possibility of using discounts in situations when total demand is non-sensitive to changes in prices. By introducing a discount in some periods, the supplier is able to give his customers the incentive to change their order patterns in a way that minimises his total cost. With the new order schemes, all parties are at least as well off as before. A mono-objective reformulation for this conflicting bi-objective problem is proposed and a mixed-integer-based heuristic is developed for the reformulated problem. The heuristic algorithm is based on a separation between determining the order/set-up pattern and the required discounts. Extensive numerical studies show that the application of the heuristic results in a benefit for the whole supply chain. To increase the supplier's profit, a modification of the heuristic which differs in the discount approximation used is developed
Acknowledgements
The authors are grateful to Anders Thorstenson, CORAL, Department of Economics and Business, Aarhus University, for his valuable comments and suggestions on the paper.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
Notes on contributors
Viktoryia Buhayenko
Viktoryia Buhayenko is a PhD student in Operations Research at CORAL, Department of Economics and Business, Aarhus University. She received her MSc degree in Industrial Logistics from Molde University College in 2012. Her research interests include pricing, channel coordination, mixed-integer programming and metaheuristic approaches.
Erik van Eikenhorst
Erik van Eikenhorst is a PhD student in Logistics at the Faculty of Economics, Informatics and Social Sciences, Molde University College. He got his MSc degree in Operations Research from the University of Edinburgh in 2009. His research interests include production planning, mixed-integer programming and mixed-integer-based heuristics.