Using Benders Decomposition for Solving Ready Mixed Concrete Dispatching Problems

Waller

Sammut

2016

J. Comput. Civ. Eng.

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Finding a practical solution for the allocation of resources in ready-mixed concrete (RMC) is a challenging issue. In the literature, heuristic methods have been mostly used for solving the RMC problem. The introduced methods are intended to find a solution in one stage but the amount of infeasible allocations in their initial solutions is their main challenge, as these infeasible solutions need postprocessing efforts. This paper introduces a sequential heuristic method that can solve RMC problems in two separate stages without any need for postprocessing. It was found that the depot-allocation problem is more complicated than truck allocation and the combination of these two subproblems threatens the efficiency of the solution. Another contribution of this paper is proposing a new formulation for minimizing the number of trucks. A genetic algorithm (GA) has been selected for implementing the proposed idea and for evaluating the large-scale data-set model. The data set covers an active RMC for a period of 1 month. The comprehensive tests show that sequential GA is more robust than traditional GA when it converges 10 times faster with achieved solution at 30% less cost.

Section: Literature Reviewmentioning

confidence: 99%

Sequential Meta-Heuristic Approach for Solving Large-Scale Ready-Mixed Concrete–Dispatching Problems

Waller

Sammut

2016

J. Comput. Civ. Eng.

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“…Asbach et al. () made the mathematical modeling much simpler by dividing the depots and customers into sub‐depots and sub‐customers and most recently Benders Decomposition (Maghrebi et al., ), Machine learning approach (Maghrebi and Waller, ; Maghrebi et al., ; Maghrebi et al., ,c), assessing experts’ decisions in RMC dispatching room (Maghrebi et al., ), new formulation (Maghrebi et al., ), and Lagrangian approach were applied (Narayanan et al., ) to achieve solutions with a slight optimality gap but within a practical time. Most recently, Maghrebi et al.…”

Section: Literature Surveymentioning

confidence: 99%

“…Yan et al (2012) used decomposition and relaxation techniques coupled with a mathematical solver to solve the problem, and Payr and Schmid (2009) applied variable neighborhood search to deal with RMC optimization problems. Asbach et al (2009) made the mathematical modeling much simpler by dividing the depots and customers into sub-depots and sub-customers and most recently Benders Decomposition (Maghrebi et al, 2014a), Machine learning approach (Maghrebi and Waller, 2014;Maghrebi et al, 2013a;Maghrebi et al, 2015a,c), assessing experts' decisions in RMC dispatching room (Maghrebi et al, 2014c), new formulation (Maghrebi et al, 2014b), and Lagrangian approach were applied (Narayanan et al, 2015) to achieve solutions with a slight optimality gap but within a practical time. Most recently, assessed the optimality gap of experts' decision in RMC by comparing their decisions with IP/MIP and two heuristic methods.…”

Section: Literature Surveymentioning

confidence: 99%

Column Generation‐Based Approach for Solving Large‐Scale Ready Mixed Concrete Delivery Dispatching Problems

Computer aided Civil Eng

Periaraj

Waller

et al. 2015

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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.

“…While this approach is shown to reduce the number of side constraints in the obtained MIP, the number of decision variables may increase significantly. This decomposition approach was subsequently used along with different solution methods for the multidepot RMCDP (13)(14)(15)(16)(17)(18). The present paper builds on this research and introduces a novel solution method based on Lagrangian relaxation.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Lagrangian Relaxation to Solve Ready Mixed Concrete Dispatching Problems

Narayanan

Rey

Transportation Research Record: Journal of the Transportation R

et al. 2015

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The logistics and planning problem of delivering ready mixed concrete (RMC) to a set of demand customers from multiple depots is addressed. The RMC dispatching problem (RMCDP) is closely related to the vehicle routing problem, with the difference that a truck may visit demand nodes in the RMCDP more than once. This class of routing problems can be represented by using mixed-integer programming (MIP) and is known to be NP-hard. Solving RMC delivery problems is often achieved through heuristics and metaheuristic-based methods as exact solution approaches are often unable to find optimal solutions efficiently, in particular when multiple depots are represented in the model. Although a variety of methods are available to solve MIP models, in this paper an attempt is made to solve the RMCDP by using a Lagrangian relaxation technique. Namely, a solution algorithm based on Lagrangian relaxation is derived to reduce the complexity of the initial MIP model and show that the proposed relaxation is able to provide promising computation results on a realistic data set representative of an active RMCDP in the region of Adelaide, Australia.