Column Generation‐Based Approach for Solving Large‐Scale Ready Mixed Concrete Delivery Dispatching Problems. (4th December 2015)

• Record Type: Journal Article
• Authors: Maghrebi, Mojtaba; Periaraj, Vivek; Waller, S. Travis; Sammut, Claude
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 31:Number 2(2016:Feb.)
• Page Start: 145
• Page End: 159
• Publication Date: 2015-12-04
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12182
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: Ready mix concrete (RMC) dispatching forms a critical component of the construction supply chain. However, optimization approaches within the RMC dispatching continue to evolve due to the specific size, constraints, and objectives required of the application domain. In this article, we develop a column generation algorithm for vehicle routing problems (VRPs) with time window constraints as applied to RMC dispatching problems and examine the performance of the approach for this specific application domain. The objective of the problem is to find the minimum cost routes for a fleet of capacitated vehicles serving concrete to customers with known demand from depots within the allowable time window. The VRP is specified to cover the concrete delivery problem by adding additional constraints that reflect real situations. The introduced model is amenable to the Dantzig–Wolfe reformulation for solving pricing problems using a two‐staged methodology as proposed in this article. Further, under the mild assumption of homogeneity of the vehicles, the pricing sub‐problem can be viewed as a minimum‐cost multi‐commodity flow problem and solved in polynomial time using efficient network simplex method implementations. A large‐scale field collect data set is used for evaluating the model and the proposed solution method, with and without time window constraints. In addition, the method is compared with the exact solution found via enumeration. The results show that on average the proposed methodology attains near optimal solutions for many of the large sized models but is 10 times faster than branch‐and‐cut.