Solving to optimality the uncapacitated fixed-charge network flow problem
Computers and Operations Research
Revised-modified penalties for fixed charge transportation problems
Management Science
Network design techniques using adapted genetic algorithms
Advances in Engineering Software
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
On the use of genetic algorithms to solve location problems
Computers and Operations Research - Location analysis
A Bundle Algorithm Approach for the Aircraft Schedule Recovery Problem During Hub Closures
Transportation Science
Solving a Time-Space Network Formulation for the Convoy Movement Problem
Operations Research
A survey on benders decomposition applied to fixed-charge network design problems
Computers and Operations Research
Solving Real-Life Locomotive-Scheduling Problems
Transportation Science
An Integrated Model and Solution Approach for Fleet Sizing with Heterogeneous Assets
Transportation Science
Adaptive dynamic cost updating procedure for solving fixed charge network flow problems
Computational Optimization and Applications
A solution approach to the fixed charge network flow problem using a dynamic slope scaling procedure
Operations Research Letters
Journal of Combinatorial Optimization
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The nodes and arcs of a network configuration replicated over time is a common structure found in many applications, particularly in the area of logistics. A common cost structure for flows in arcs for such problems involves both a fixed and variable cost. Combining the two concepts results in the uncapacitated time-space fixed-charge network flow problem. These problems can be modeled as mixed binary linear programs and can be solved with commercial software. To create these models for uncapacitated arcs requires determining artificial arc capacities that are sufficiently large so that the solution space has not been altered but are small enough that the linear programming relaxations are tight. In this investigation, we present a strategy for determining these artificial arc capacities for any time-space fixed-charge network flow problem. In extensive empirical tests, we provide statistical evidence that the strategy is superior to the usual techniques applied to this class of problem. Many of the most difficult problems were solved in only 5% of the computational time required by standard techniques.