Enhancing Benders decomposition algorithm to solve a combat logistics problem

Mohammad Marufuzzaman (), Farjana Nur, Amy E. Bednar and Mark Cowan
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Mohammad Marufuzzaman: Mississippi State University
Farjana Nur: Mississippi State University
Amy E. Bednar: U.S. Army Engineer Research and Development Center
Mark Cowan: U.S. Army Engineer Research and Development Center

OR Spectrum: Quantitative Approaches in Management, 2020, vol. 42, issue 1, No 5, 198 pages

Abstract: Abstract This paper proposes a multi-time period, two-stage stochastic programming model for the design and management of a typical combat logistics problem. The design shall minimize the total path setup cost, commodity preposition and processing costs, and expected transportation, storage, and shortage costs across all possible path failure scenarios. Due to the complexity associated with solving the model, we propose an accelerated Benders decomposition algorithm to solve the model in a realistic-size network problem within a reasonable amount of time. The Benders decomposition algorithm incorporates several algorithmic improvements such as pareto-optimal cuts, multi-cuts, knapsack inequalities, integer cuts, input ordering, mean-value cuts, and the rolling horizon heuristic. Computational experiments are performed to assess the efficiency of different enhancement techniques within the Benders decomposition algorithm.

Keywords: Combat logistics; Benders decomposition algorithm; Input ordering; Multi-cut; Mean-value cut (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s00291-019-00571-y

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