An optimization approach and a heuristic procedure to schedule battery charging processes for stackers of palletized cargo.

Franco Basso*, Leonardo D. Epstein, Raúl Pezoa, Mauricio Varas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This paper proposes an approach to develop schedules for charging batteries in a battery center. This problem arises in warehouses and logistics centers that attempt to provide uninterrupted operations with battery powered machinery such as stackers. These operations often require recharging batteries on location, a process that involves two decisions: determining charging start-times and assigning batteries to chargers. Prices of grid-provided electric energy vary by hour of the day and can be almost negligible for energy available from photovoltaic solar collectors. Thus, efficient schedules should recharge batteries during time intervals with low tariffs while minimizing the time batteries spend in queue waiting for an available charger. In this situation, batteries arrive at the battery center during the operation. The model assumes that the counts of arriving batteries in time bands are known. The objective is to determine a charging schedule that minimizes a weighted sum of the costs of energy and delays. We develop a MIP model that incorporates the main features of the battery charging process. Unfortunately, computation times to solve these MIPs are too long to be practical. To overcome this limitation, we develop a constructive heuristic that finds a feasible solution in a matter of seconds, even for large-sized instances, with a relatively low GAP of 9.67%.

Original languageEnglish
Pages (from-to)9-18
Number of pages10
JournalComputers and Industrial Engineering
StatePublished - Jul 2019

Bibliographical note

Funding Information:
We gratefully acknowledge financial support from the Complex Engineering Systems Institute, ISCI (grant CONICYT FB0816). Raúl Pezoa thanks doctoral scholarship to CONICYT-PFCHA/Doctorado Nacional/2018-21181528.

Publisher Copyright:
© 2019 Elsevier Ltd


  • Energy efficiency
  • Greedy heuristic
  • MIP modeling
  • Scheduling
  • Warehouse operations


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