The cyclic production routing problem

Abstract

This paper introduces the Cyclic Production Routing Problem (CPRP). The CPRP is an extension of the well-known NP-hard Production Routing Problem (PRP), which is a hard-to-solve combinatorial optimisation problem with numerous practical applications in the field of freight transportation, logistics and supply chain management. Under the PRP setting, a manufacturer is responsible for determining production decisions, as well as the timing and quantity of replenishment services offered to a set of geographically dispersed customers over a multi-period time horizon. The problem calls for jointly optimising the production, inventory, distribution and routing decisions. In this paper, the basic PRP model is modified to generate repeatable cyclic production and delivery schedules. A two-commodity flow formulation is proposed along with valid inequalities. Extensive comparisons between the basic PRP and the proposed cyclic variant on well-known benchmark instances are provided. The new variant is significantly harder to solve, especially when the vehicle fleet is limited. From a managerial perspective, the generation of cyclic production-routing schedules significantly increases all costs, whereas the number of vehicle routes required to implement a cyclic schedule is higher.

Keywords:

• Supply chain management
• production routing
• cyclic
• limited vehicle fleet
• two-commodity flow

Acknowledgements

The authors would like to thank four anonymous reviewers and the editor for their constructive comments that significantly improved the manuscript quality.

Disclosure statement

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

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials. Additional data are available from the authors upon reasonable request.

Additional information

Funding

This work was supported by Hellenic Foundation for Research and Innovation [project number 02562].

Notes on contributors

Eleftherios G. Manousakis

Eleftherios G. Manousakis completed his doctoral studies at the Department of Management Science & Technology of Athens University of Economics and Business (AUEB), Athens, Greece. He has received a B.Sc. in Management Science from AUEB, and an MSc in Computer Science with specialisation in Machine Learning from the University of Edinburgh, Edinburgh, Scotland, UK. He has short teaching experience at undergraduate and postgraduate programmes. He is a researcher at the Operations Research & Decision Systems centre (ORDeS) in AUEB. Also, he has been a research assistant at the Transportation Systems and Logistics Laboratory (TRANSLOG) in AUEB and at the Hellenic Ministry of Development and Competitiveness. His main research interests are quantitative decision-making in supply chain management, inventory and production management, freight transportation and logistics with focus on the development of: (a) innovative mathematical programming models for realistic operational research problems and (b) computational/artificial intelligence optimisation methods for large scale optimisation problems. His main fields of academic interest are Operations Research, Machine Learning and the promising intersection of the two. His work has been published in high-level journals in the areas of Management Science, Operations Research, and Supply Chain Management and has received several citations. He has participated in several national and international conferences on the respective scientific fields, and he has also taken part in various research projects on the areas of interest.

Christos D. Tarantilis

Dr. Christos D. Tarantilis is a Full-Professor at the Department of Management Science & Technology of Athens University of Economics and Business (AUEB). He has served as the Head of the Department of Management Science and Technology, the Director of the ‘Operations Research and Decision Systems’ group and member of the steering committee for four postgraduate programmes at AUEB. He is also member of the editorial board (e.g. the ‘Networks’ journal-Wiley) and guest editor in well-known academic journals, chairman and member of the scientific committee of international scientific conferences. Prof. Tarantilis has more than 60 publications in well-respected international journals in the field of operations research, transportation planning and logistics. His publications have received more than 6000 citations according to the Google Scholar. In addition, both undergraduate and postgraduate/MBA students have recognised his teaching ability and this is well reflected by his numerous teaching awards the last fifteen years. Prof. Tarantilis has participated in various research projects both at a national and international level.

Emmanouil E. Zachariadis

Emmanouil E. Zachariadis is an Assistant Professor of Management Science & Quantitative Methods at the Athens University of Economics and Business, School of Business, Department of Management Science and Technology. He has received a Diploma in Chemical Engineering from the National Technical University of Athens (NTUA), and an MSc in Computing Science from the Imperial College of London. His doctoral studies aimed at solving operational research problems via computational optimisation methods and were carried out at the School of Chemical Engineering of NTUA. His research interests focus on: (a) Development of mathematical programming models and computational optimisation methods for operational research problems; (b) Management of transportation networks, freight transportation & distribution logistics systems, (c) Production and service operations management, and (d) Study and minimisation of the environmental impact of transportation logistics. His publication record includes more than 25 articles in journals in the areas of Management Science, Operations Research, and Supply Chain Management. He has successfully participated in several European and National research projects on the above-mentioned scientific fields. His teaching experience involves both undergraduate and postgraduate courses in the areas of quantitative methods, operational research and supply chain optimisation.