We study a vehicle routing problem that originates from a Nordic distribution company and includes the essential decision-making components of the company’s logistics operations. The problem considers customer deliveries from a depot using heavy depot vehicles, swap bodies, optional switch points, and lighter local vehicles; a feature is that deliveries are made by both depot and local vehicles. The problem has earlier been solved by a fast metaheuristic, which does however not give any quality guarantee. To assess the solution quality, two strong formulations of the problem based on the column generation approach are developed. In both of these the computational complexity is mitigated through an enumeration of the switch point options. The formulations are evaluated with respect to the quality of the linear programming lower bounds in relation to the bounds obtained from a compact formulation. The strong lower bounding quality enables a significant reduction of the optimality gap compared to the compact formulation. Further, the bounds verify the high quality of the metaheuristic solutions, and for several problem instances the optimality gap is even closed.

Effective information sharing is essential for improving the efficiency and effectiveness of supply chains. However, the specific impact of various information sharing factors on mitigating the bullwhip effect has not been extensively investigated in existing literature. This study addresses this gap by systematically reviewing and analyzing research from 2015 to 2024, focusing on how different information sharing factors contribute to bullwhip effect mitigation. We reviewed 57 articles to gain insights into five critical factors: what information is shared, how it is shared, with whom it is shared, why it is shared, and the direction of information sharing. Our analysis indicates that “why to share” and “what to share” are the most frequently studied dimensions, while “how to share,” “with whom to share,” and “the direction of information sharing” have been less explored. This study underscores the need for further research on these less examined factors and enhances understanding of their role in mitigating the bullwhip effect. Additionally, it provides a future research agenda to direct further investigations in this domain.

This paper studies the Hierarchical Multi-Switch Multi-Echelon VRP (HMSME-VRP), a newly introduced VRP variant based on a real-world case involving High Capacity Vehicles (HCV). The problem originates from the policies of a distribution company in the Nordic countries where HCVs of up to 34.5 m and up to 76 tons are allowed. The HMSME-VRP offer a new way to model distribution problems to cover large geographical areas without substantial costs in infrastructure. Furthermore, it adds complexity to the standard VRP and, as such, remains NP-hard and difficult to solve to optimality. Indeed, it has been demonstrated that only very small instances can be solved to optimality by a commercial solver. Thus, in order to handle instances of real-world size, we propose two General Variable Neighborhood Search (GVNS) procedures, the second of which is adaptive, utilizing an intelligent reordering mechanism. In order to evaluate the proposed procedures, 48 benchmark instances of various sizes and characteristics are generated and made publicly available, comprising of clustered, random, and semi-clustered customers. The computational results show that both GVNS procedures outperform the exact solver. Additionally, the adaptive version outperforms the conventional version based on both average and best solutions. Furthermore, we present a statistical analysis to verify the superiority of the adaptive version.

This contributed volume guides researchers and practitioners on resource collaborative management of supply chains and manufacturing enterprises within an industrial internet technological environment. The book comprises 10 chapters that cover two major topics in the subject of logistics 4.0, namely the utilization of both disruptive technologies and optimization techniques in smart logistic management. With global research on the book's topic expanding rapidly across various directions and disciplines, it provides a structured framework for international experts to showcase outstanding work and unique approaches. Researchers and students will find the comprehensive outline on collaborative optimization and management of smart manufacturing and production, warehousing, inventory, logistics, transportation, integrated supply chain, and supply network within the industrial internet platform a beneficial guide to understanding current and future practical problems that arise in manufacturing and supply chain management. 

This paper introduces the Hierarchical Multi-Switch Multi-Echelon Vehicle Routing Problem, a new variant of the well-known Vehicle Routing Problem. It is a real-world problem originating from the policies of a Nordic distribution company. The problem includes a single depot, a non-predetermined hierarchy of intermediate facilities, and two different fleets, consisting of homogeneous original and homogeneous local vehicles, which are pulling swap-bodies. Original vehicles with attached swap-bodies depart from the central depot. They can either visit customers directly if only one swap-body is attached or visit one or two consecutive switch points in order to transfer one or two loaded swap-bodies to a corresponding number of local vehicles, which are subsequently routed to customers while the original vehicle itself proceeds to serve customers with the remaining loaded swap-body. A mixed-integer formulation of the problem is proposed. A short bibliographic review, relations, shared characteristics, and differences of the proposed variant and several known VRP variants are analyzed and discussed. The solution of an illustrative instance is presented in order to demonstrate the solution concept for the problem as well as to compare with solution concepts for previously stated VRP variants. Computational experiments on small instances that could be solved within one hour are also presented. The problem is computationally hard to solve. Thus, the development of heuristics and metaheuristics is an important future task in order to enable solution of real case instances or instances of realistic sizes.

The facility location problem and the vehicle routing problem are highly interdependent and critical parts of any efficient and cost-effective supply chain. The location of facilities heavily affects the design of distribution routes between the facilities and various demand nodes. Within locational analysis, the location-routing problem is a mathematical optimization problem that considers the underlying issues of vehicle routing and simultaneously optimizes the location of facilities and the design of distribution routes. Since, in real-life applications, it is common that decision-makers encounter more than one, often conflicting objectives, the problem can be stated in term of multi-objective optimization. This paper reviews 80 journal articles published in the field of bi- and multi-objective location-routing problems between 2014 and 2020. Included papers are classified based on several factors covering model assumptions and characteristics, objectives, solution approaches, and application area. For each application area, individual papers are presented and discussed. The paper concludes with remarks and suggestions for future research.