TY - GEN
T1 - k-Times Bin Packing and its Application to Fair Electricity Distribution
AU - Baghel, Dinesh Kumar
AU - Ravsky, Alex
AU - Segal-Halevi, Erel
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Given items of different sizes and a fixed bin capacity, the bin-packing problem is to pack these items into a minimum number of bins such that the sum of item sizes in a bin does not exceed the capacity. We define a new variant called k-times bin-packing (kBP), where the goal is to pack the items such that each item appears exactly k times, in k different bins. We generalize some existing approximation algorithms for bin-packing to solve kBP, and analyze their performance ratio. The study of kBP is motivated by the problem of fair electricity distribution. In many developing countries, the total electricity demand is higher than the supply capacity. We prove that every electricity division problem can be solved by k-times bin-packing for some finite k. We also show that k-times bin-packing can be used to distribute the electricity in a fair and efficient way. Particularly, we implement generalizations of the First-Fit and First-Fit Decreasing bin-packing algorithms to solve kBP, and apply the generalizations to real electricity demand data. We show that our generalizations outperform existing heuristic solutions to the same problem. Due to space constraints, several parts of the paper were moved to appendices. All appendices are available in the full version [1].
AB - Given items of different sizes and a fixed bin capacity, the bin-packing problem is to pack these items into a minimum number of bins such that the sum of item sizes in a bin does not exceed the capacity. We define a new variant called k-times bin-packing (kBP), where the goal is to pack the items such that each item appears exactly k times, in k different bins. We generalize some existing approximation algorithms for bin-packing to solve kBP, and analyze their performance ratio. The study of kBP is motivated by the problem of fair electricity distribution. In many developing countries, the total electricity demand is higher than the supply capacity. We prove that every electricity division problem can be solved by k-times bin-packing for some finite k. We also show that k-times bin-packing can be used to distribute the electricity in a fair and efficient way. Particularly, we implement generalizations of the First-Fit and First-Fit Decreasing bin-packing algorithms to solve kBP, and apply the generalizations to real electricity demand data. We show that our generalizations outperform existing heuristic solutions to the same problem. Due to space constraints, several parts of the paper were moved to appendices. All appendices are available in the full version [1].
KW - Approximation algorithms
KW - bin-packing
KW - egalitarian metric
KW - electricity distribution
KW - fair division
KW - Fernandez de la Vega-Lueker algorithm
KW - First-Fit
KW - First-Fit Decreasing
KW - Karmarkar-Karp algorithms
KW - Next-Fit
KW - utilitarian metric
KW - utility difference
UR - http://www.scopus.com/inward/record.url?scp=85204396703&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-71033-9_27
DO - 10.1007/978-3-031-71033-9_27
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AN - SCOPUS:85204396703
SN - 9783031710322
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 483
EP - 500
BT - Algorithmic Game Theory - 17th International Symposium, SAGT 2024, Proceedings
A2 - Schäfer, Guido
A2 - Ventre, Carmine
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Symposium on Algorithmic Game Theory, SAGT 2024
Y2 - 3 September 2024 through 6 September 2024
ER -