Hybrid bi-objective economic lot scheduling problem with feasible production plan equipped with an efficient adjunct search technique

Abstract

In this research, the economic lot scheduling problem (ELSP), as an NP-hard problem in terms of a bi-objective approach considering deteriorating items and shortage, is studied. The goal is to simultaneously minimise ‘setup and inventory holding costs, comprising deterioration’, and ‘total amount of units facing shortage throughout every period. Two policies besides a heuristic method are employed simultaneously, named extended basic period and Power-of-Two (PoT), to make sure of having feasible replenishment cycles. For handling the considered problem, three multi-objective techniques are employed: non-dominated sorting genetic algorithm II (NSGAII), non-dominated ranking genetic algorithm (NRGA), and a multi-objective procedure hybridized of NSGAII and particle swarm optimization (PSO), called PSNSGAII. Also, three metrics are used to assess the quality of the algorithms’ outputs, including spacing, mean ideal distance, and spread. Experimental results, including extensive conducted parametric and non-parametric statistical analyses, prove that the proposed hybrid PSNSGAII algorithm can well meet discussed criteria compared to other employed algorithms in both ‘quality of solutions’ and ‘diversity’ in almost all small, medium and large test instances. Finally, some useful managerial and practical insights are presented.

KEYWORDS:

• Economic lot scheduling problem (ELSP)
• deterioration
• backorder
• non-dominated sorting genetic algorithm-II (NSGA-II)
• hybrid search
• particle swarm optimisation (PSO)

Data availability statement

The authors confirm that the generation scheme of data supporting the findings of this study is available within the article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Vahid Kayvanfar

Vahid Kayvanfar has just finished his Postdoctoral Fellowship at Sharif University of Technology. His expertise mainly falls within logistics & supply chain (including sustainability), healthcare, and applied optimization. He is currently an editorial board member in some journals such as Mathematical Problems in Engineering, and serves as reviewer in more than 40 Web of Science journals. He has published more than 30 peer reviewed journal papers, 20 peer reviewed conference proceedings and two books. He has won several National and International awards, such as ARAP scholarship from A*STAR in Singapore in 2016, and Iran National Elite Foundation awards during 2012–2016.

M. Zandieh

M. Zandieh accomplished his B.Sc. in industrial engineering at Amirkabir University of Technology, Tehran, Iran (1994–1998), and M.Sc. in industrial engineering at Sharif University of Technology, Tehran, Iran (1998–2000). He obtained his Ph.D. in industrial engineering from Amirkabir University of Technology, Tehran, Iran (2000–2006). Currently, he is a professor at industrial management and information technology department, Shahid Beheshti University, Tehran, Iran. His research interests are production and operations management, production planning and scheduling, financial engineering, quality engineering, applied operations research, simulation, and artificial intelligence techniques in the areas of manufacturing systems design.

Mehrdad Arashpour

Associate Professor Mehrdad Arashpour is the Head of Construction Engineering Discipline at Monash University. His lab (ASCII) undertakes research on artificial intelligence, robotics, prefabricated structures and vision technologies. He is one of the 13 worldwide members of the Working Commission on Off-site Construction and Infrastructure Task Groups established by the International Council for Building (CIB). Arashpour has many years of managerial experience in the civil infrastructure sector in Australasia and Europe.