A Metaheuristic Search Technique for Solving the Warehouse Stock Management Problem and the Routing Problem in a Real Co
In many transport companies, one of the main objectives is to optimize the travel cost of their fleet. Other objectives are related to delivery time, fuel savings, etc. However warehouse stock management is not properly considered. Warehouse stock control
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Abstract. In many transport companies, one of the main objectives is to optimize the travel cost of their fleet. Other objectives are related to delivery time, fuel savings, etc. However warehouse stock management is not properly considered. Warehouse stock control is based on the correct allocation of resources to each order. In this paper, we combine the warehouse stock management problem and the routing problem to be applied in a real company that allows negative stock in their warehouses. The proposed multi-objective problem is modeled and solved by the greedy randomized adaptive search (GRASP) algorithm. The results shows that the proposed algorithm outperforms the current search technique used by the company mainly in stock balancing, improving the negative average stock by up to 82%. Keywords: Warehouse stock control
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· Metaheuristic · GRASP
Introduction
Nowadays, transport companies focus on obtaining automatic forecasts and order planning within a given time frame. Many different techniques can be found in the literature to solve this kind of problem. There is a set of problems that brings together many of the cases of graphics-related problems in the context of transport design. The multi-objective transportation network design provides a framework that lists all types of transportation problems together. In this context, a first level taxonomy is developed in which methods and techniques are grouped by mathematical structure or the purpose of the problem formulation [4]. The Multi-objective Transportation Network Design (MTND) is set of definitions that try to address all different cases in transportation problems. In this paper, we focus on two types of problems, the vehicle routing problems (VRP) and the assignment problems (AP). VRPs can be represented as theoretical problems in graphs. Given a complete network G = {V, A} in which V is a list of vertices and A is a list of arcs, most problems represent the zero vertex as the starting point and the rest of c Springer Nature Switzerland AG 2020 M. Bramer and R. Ellis (Eds.): SGAI-AI 2020, LNAI 12498, pp. 187–201, 2020. https://doi.org/10.1007/978-3-030-63799-6_15
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the vertices as customers to be passed through to deliver an order. The list of arcs is made up of i, j pairs that connect two vertices. These arcs have a cost associated with going from the i vertex to the j vertex [5]. From this point on, the wide development of solutions for diverse problems has generated an extensive amount literature. VRP problems are mainly multi-target problems in which there are certain features that combine with each other to deal with other types of problems. However, there is a more specific set of real-life problems that traditional VRP approaches cannot solve, such as: the Open VRP, the Dynamic VRP, the Time-Dependent VRP problems. In the Open VRP (OVRP) problems, the main feature that interests us is that the vehicles are not forced to return to the starting point, so problems of this type seek to minimize the number of vehicles used and t
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