A gamified teaching–learning based optimization algorithm for a three-echelon supply chain scheduling problem in a two-stage assembly flow shop environment

Abstract

The supply chain scheduling problem, which is an integrated production scheduling and distribution through the batch delivery system, has been studied by researchers for more than two decades. So far, all researches in this regard have studied the two-echelon supply chain network. The first novelty of this research is that, for the first time, the three-echelon supply chain scheduling problem, including supplier, manufacturer and customer, is investigated considering two separated batch delivery systems for transportation, simultaneously. To do so, a two-stage assembly flow shop environment is used. In the first stage, there are a number of decentralized suppliers who are responsible for manufacturing components. The components are dispatched to the second stage (assembly stage) with the first batch delivery system and after assembly, the orders are dispatched to the customer through secondary batch delivery system. The purpose of this problem is to minimize the cost of total completion time plus batch delivery costs. First, a mixed integer linear programming model (MILP) is presented that is able to solve small-size instances in a logical time. The second novelty of this research is that, to solve large-size instances, a novel gamified teaching–learning based optimization (GTLBO) algorithm is developed with applying a number of dominance rules and gamification tools. The computational results indicate the superiority of the new TLBO variant over its standard form and two well-known methods, i.e. GA and PSO, especially by applying the dominance rules.