A hybrid approach to solve a raw material collecting vehicle routing problem

Abstract

PurposeThe purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.Design/methodology/approachTo model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).FindingsThe findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.Research limitations/implicationsThe data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.Practical implicationsThis study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.Originality/valueThis study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.