TY - GEN
T1 - The Multi-period Petrol Station Replenishment Problem
T2 - 11th International Conference on Computational Logistics, ICCL 2020
AU - Boers, Luke
AU - Atasoy, Bilge
AU - Homem de Almeida Correia, G.
AU - Negenborn, Rudy R.
N1 - Accepted Author Manuscript
PY - 2020
Y1 - 2020
N2 - We present a “rich” Petrol Station Replenishment Problem (PSRP) with real-life characteristics that represents the complexities involved in actual operations. The planning is optimised over multiple days and therefore, the new variant can be classified as the Multi-Period Petrol Station Replenishment Problem (MP-PSRP). A Mixed Integer Linear Programming (MILP) formulation is developed and a decomposition heuristic is proposed as a solution algorithm, which is evaluated with a case study from a real-life petrol distributor in Denmark. To determine delivery quantities, the heuristic uses the newly introduced simultaneous dry run inventory policy. A procedure is applied to improve the initial solution. A commercial solver is able to find feasible solutions only for instances with up to 20 stations and 7 days for the MILP model where optimality is guaranteed for instances up to 10 stations and 5 days. The heuristic on the other hand provides feasible solutions for the full case study of 59 stations and 14 days, within a time limit of 2 h.
AB - We present a “rich” Petrol Station Replenishment Problem (PSRP) with real-life characteristics that represents the complexities involved in actual operations. The planning is optimised over multiple days and therefore, the new variant can be classified as the Multi-Period Petrol Station Replenishment Problem (MP-PSRP). A Mixed Integer Linear Programming (MILP) formulation is developed and a decomposition heuristic is proposed as a solution algorithm, which is evaluated with a case study from a real-life petrol distributor in Denmark. To determine delivery quantities, the heuristic uses the newly introduced simultaneous dry run inventory policy. A procedure is applied to improve the initial solution. A commercial solver is able to find feasible solutions only for instances with up to 20 stations and 7 days for the MILP model where optimality is guaranteed for instances up to 10 stations and 5 days. The heuristic on the other hand provides feasible solutions for the full case study of 59 stations and 14 days, within a time limit of 2 h.
KW - Decomposition heuristic
KW - Inventory routing
KW - Petrol Station Replenishment
KW - Simultaneous dry run inventory policy
UR - http://www.scopus.com/inward/record.url?scp=85092217933&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59747-4_39
DO - 10.1007/978-3-030-59747-4_39
M3 - Conference contribution
AN - SCOPUS:85092217933
SN - 978-3-030-59746-7
T3 - Lecture Notes in Computer Science
SP - 600
EP - 615
BT - Computational Logistics
A2 - Lalla-Ruiz, Eduardo
A2 - Mes, Martijn
A2 - Voß, Stefan
PB - Springer
CY - Cham, Switzerland
Y2 - 28 September 2020 through 30 September 2020
ER -