Optimization of economic lot scheduling problem with backordering and shelf-life considerations using calibrated metaheuristic algorithms

• Optimization of the economic lot scheduling problem with shelf life and backordering.
• It is allowed to produce each item more than once in every cycle.
• Allowing multiple setups leads to improving solutions.
• Four metaheuristic methods GA, SA, PSO, and ABC were used to solve the problem.
• Metaheuristic methods outperformed other reported procedures in the literature.

This paper addresses the optimization of economic lot scheduling problem, where multiple items are produced on a single machine in a cyclical pattern. It is assumed that each item can be produced more than once in every cycle, each product has a shelf-life restriction, and backordering is permitted. The aim is to determine the optimal production rate, production frequency, cycle time, as well as a feasible manufacturing schedule for the family of items, and to minimize the long-run average costs. Efficient search procedures are presented to obtain the optimum solutions by employing four well-known metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), simulated annealing (SA), and artificial bee colony (ABC). Furthermore, to make the algorithms more effective, Taguchi method is employed to tune various parameters of the proposed algorithms. The computational performance and statistical optimization results show the effectiveness and superiority of the metaheuristic algorithms over other reported methods in the literature.