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.

An effective resource allocation technique is required for each Ready Mixed Concrete (RMC). Finding the optimum solution for large scale RMC dispatching problems with available computing facilities is intractable. Two kinds of techniques have been implemented to deal with this problem: (i) evolutionary techniques and (ii) numerical techniques. For the purposes of this paper we selected a technique from each category and compared them under the same conditions. Robust Genetic Algorithm (Robust-GA) and Column Generation (CG) were selected and tested with different sizes of real RMC problems. The results show that on average CG solutions are obtained with a 20% reduced cost. However, Robust-GA converges 40% faster than CG, while the number of unassigned customers for both the techniques is almost the same.

Large scale dispatching problems are technically characterized as classical NP-hard problems which means that they cannot be solved optimally with existing methods in a polynomial time. Benders decomposition is recommended for solving large scale Mixed Integer Programming (MIP). In this paper we use the Bender Decomposition technique for reformulating the Ready Mixed Concrete Dispatching Problem (RMCDP). Benders decomposition involves separating the original RMCDP formulation into the master (lower bound) and sub-problems (upper bound). The master problem only deals with integer variables and the sub problem is usually a linear programming problem. Benders optimally cuts and Benders feasibility cuts are added to the master problem upon solving the sub-problem at each iteration.. The proposed method is tested on a single real instance and results are reported.