Multi-decision points model to solve coupled-task scheduling problem with heterogeneous multi-AGV in manufacturing systems

Abstract

Automated guided vehicle (AGV) is widely used in automated manufacturing systems as a material handling tool. Although the task scheduling problem with isomorphic AGV has remained a very active research field through the years, too little work has been devoted to the task scheduling problems with heterogeneous AGVs. A coupled task with heterogeneous AGVs is a complex task that needs the cooperation of more than one type of AGVs. In this paper, a manufacturing system with two types of AGVs and three types of tasks is studied. To solve the coupled task scheduling problem with heterogeneous AGVs in this manufacturing system, we introduce two new methods based on the established mathematical model, namely, the decoupled scheduling strategy and coupled scheduling strategy with multi-decision model. The decoupled scheduling strategy is widely used in coupled task scheduling problems. However, there are some situations that the decoupled scheduling strategy cannot solve the problem well. To overcome the problem, the multi-decision point model solves the coupled task scheduling problem without decomposition. In order to ensure the searching speed and searching accuracy, a novel hybrid heuristic algorithm based on simulated annealing algorithm and tabu search algorithm is developed. The simulation experiment results show the proposed coupled scheduling algorithm has priority in coupled task scheduling problems.

Adaptive Balance Optimizer: A New Adaptive Metaheuristic and its Application in Solving Optimization Problem in Finance

“…The exploration mode consists of two searches. The guided search toward the global best solution is formalized using (3) while the limited random search is formalized using (5). s c denotes the solution candidate which is then evaluated using (4) that represents the strict acceptance approach.…”

“…s c denotes the solution candidate which is then evaluated using (4) that represents the strict acceptance approach. In (5), it is shown that the local search space gets narrow as the iteration increases.…”

“…An improved sparrow search algorithm (ISSA) was developed to improve the high-intensity focused ultrasound (HIFU) technology that is used for tumor treatment [4]. A combination of tabu search (TS) and simulated annealing (SA) is used to solve the coupled task scheduling of the heterogeneous multiple automated guided vehicles (AGV) in a manufacturing system [5]. Its popularity comes from two reasons.…”