The two-echelon capacitated vehicle routing problem: models and math-based heuristics

Multiechelon distribution systems are quite common in supply-chain and logistics. They are used by public administrations in their transportation and traffic planning strategies, as well as by companies, to model own distribution systems. In the literature, most of the studies address issues relating to the movement of flows throughout the system from their origins to their final destinations. Another recent trend is to focus on the management of the vehicle fleets required to provide transportation among different echelons. The aim of this paper is twofold. First, it introduces the family of two-echelon vehicle routing problems (VRPs), a term that broadly covers such settings, where the delivery from one or more depots to customers is managed by routing and consolidating freight through intermediate depots. Second, it considers in detail the basic version of two-echelon VRPs, the two-echelon capacitated VRP, which is an extension of the classical VRP in which the delivery is compulsorily delivered through intermediate depots, named satellites. A mathematical model for two-echelon capacitated VRP, some valid inequalities, and two math-heuristics based on the model are presented. Computational results of up to 50 customers and four satellites show the effectiveness of the methods developed

Demurrage costs optimization in ports using binary mathematical programming models

Binary programming, demurrage optimization, scheduling,

A Generic Model and Hybrid Algorithm for Hoist Scheduling Problems

. This paper presents a robust approach to solve Hoist Scheduling Problems (HSPs) based on an integration of Constraint Logic Programming (CLP) and Mixed Integer Programming (MIP). By contrast with previous dedicated models and algorithms for solving classes of HSPs, we define only one model and run different solvers. The robust approach is achieved by using a CLP formalism. We show that our models for different classes of industrial HSPs are all based on the same generic model. In our hybrid algorithm search is separated from the handling of constraints. Constraint handling is performed by constraint propagation and linear constraint solving. Search is applied by labelling of boolean and integer variables. Computational experience shows that the hybrid algorithm, combining CLP and MIP solvers, solves classes of HSPs which cannot be handled by previous dedicated algorithms. For example, the hybrid algorithm derives an optimal solution, and proves its optimality, for multiple-hoists sch..