A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy

Abstract

A flexible Cell Scheduling Problem (CSP) under Time-Of-Use (TOU) electricity tariffs is developed in this study. To apply a kind of energy-conscious policy, overconsumption cost of on-peak period electricity, limitations on total energy consumption by all facilities, setup time available on each cell, part defect (pert) percentage, and the total number of Automated Guided Vehicles (AGV) were considered. Additionally, an Ant Colony Optimization (ACO) algorithm was employed to find a near-optimum solution to the proposed Mixed Integer Linear Programming (MILP) model with the objective of minimizing the total cost of CSP model. Since no benchmark is available in the literature, a lower bound was implemented as well to validate the result achieved. Moreover, to improve the quality of the results obtained by meta-heuristic algorithms, two hybrid algorithms (HGA and HACO) were proposed to solve the model. For parameter tuning of algorithms, Taguchi experimental design method was applied. Then, numerical examples were presented to prove the application of the proposed methodology. Our results were compared with the lower bound, confirming consequently that HACO is capable of finding better and near-optimum solutions.