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.

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.

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.

In countries like Greece where long-term fiscal imbalances and bailouts are to a large extent attributed to particularly high pension expenditures as a share of Gross Domestic Product (GDP), (e.g., 15.5

The Charging Station (CS) infrastructure for electric vehicles is a critical aspect of promoting the widespread adoption of this technology. The selection of appropriate locations for new charging stations is a complex problem that requires a systematic approach. The area of interest is divided in cells with each one having a charging demand level and they represent potential locations for CS installation. This research introduces the Budget-Constrained Charging Station Location Problem (BC-CSLP). The goal is to find the optimal cells for the installation of CSs to maximize charging coverage under a maximum budget constraint. The problem considers different types of CSs and multiple CSs per cell. A mathematical formulation for the BC-CSLP is presented. A hybrid genetic algorithm is developed to optimize the solve the BC-CSLP. The effectiveness of the algorithm is evaluated on the closely related facility location problem. In addition, a case study was carried out in the city center of Chania, Greece. The results indicate that the proposed genetic algorithm is an effective method for solving this problem, and the identified locations are well-suited for the installation of new charging stations.

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.

Timely planning and scheduling of railway infrastructure maintenance interventions are crucial for increased safety, improved availability, and reduced cost. We propose a data‐driven decision‐support framework integrating track condition predictions with tactical maintenance planning and operational scheduling. The framework acknowledges prediction uncertainties by using a Wiener process‐based prediction model at the tactical level. We also develop planning and scheduling algorithms at the operational level. One algorithm focuses on cost‐optimisation, and one algorithm considers the multi‐component characteristics of the railway track by grouping track segments near each other for one maintenance activity. The proposed framework's performance is evaluated using track geometry measurement data from a 34 km railway section in northern Sweden, focusing on the tamping maintenance action. We analyse maintenance costs and demonstrate potential efficiency increases by applying the decision‐support framework.

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.

Supply chain management experiences inefficiencies due to several reasons, such as the lack of information sharing and coordination among supply chain participants. Moreover, these participants may not share their information with other parties in the supply chain due to trust issues and considering such information as sensitive asset. This behavior may hinder the supply chain efficiency and motivate the occurrence of bullwhip effect (BWE). This phenomenon has been intensively investigated in manufacturing industries. In addition, information sharing has been considered the main remedy to eliminate BWE in literature. However, few attempts examined the effect of information sharing in reducing this phenomenon in service supply chain (SSC). Digitalization and computerization have the potential to convert and reshape the supply chains in all kind of businesses and improve the coordination among supply chain partners by exchanging real-time information. Blockchain as a disruptive technology has many distinct features like disintermediation and decentralization that provide integrity, visibility and security for the supply chain. To bridge this gap, this paper aims at proposing a blockchain architecture to mitigate BWE in SSC by improving end-to-end visibility among supply chain partners through sharing backlog information. The proposed supply chain-based blockchain enables SSC partners to share securely and transparent backlog information mitigating thus BWE.

In view of uncertainties caused by sudden accidents (SAs) and affecting retailers' demand in many districts, it is difficult for suppliers to determine when and how many products to procure/produce. Considering a supply chain consisting of two types of competing suppliers and multi-retailer, this work studies the suppliers' optimal emergency procurement/production decision (EPD) with information updating. Firstly, a probability evolution model with information updating to describe the probability of the retailers' procurement behaviour and the occurrence probability of supply disruption (SD) is inferred. Secondly, suppliers' EPDs regarding retailers' procurement behaviour and occurrence probability of SD are discussed and a real-time updated emergency decision-making model (EDM) is proposed based on Stackelberg game and Bayesian inference. Thirdly, the value of information updating and the critical factors that affect the suppliers' optimal EPD are quantitatively analysed. Numerical examples are finally provided to verify the EDM. Results indicate that information is the premise and foundation for the suppliers to deal with SA effectively; suppliers can easily determine when and how many products to procure/produce based on the proposed EDM; it is demonstrated that for any chosen supplier strategy, there exists a corresponding optimal procurement/production quantity for the suppliers that maximises the expected profits. Moreover, the suppliers' EPD with information updating is affected by cost parameters, with the rank of information collection cost coefficient, unit procurement/production cost, unit sales price, unit holding cost and unit shortage cost, from apparently to slightly.

Estimations of the amount of lithium-ion batteries reaching their end-of-life in 2025 and the amount being recycled indicates large deviations. To enable an efficient recycling process a well-defined and efficient supply chain network for the recovery of discarded lithium-ion batteries must be put in place. This includes analyzing the needs and restrictions of such a network. The aim of this paper is to provide decision support tools, to analyze input, and optimize a future supply chain for discarded lithium-ion batteries. A mixed integer programming model is developed and applied to the Swedish market. The findings show that several aspects will affect a reverse supply chain for discarded lithium-ion batteries, many of which are still uncertain and hard to predict.

Green supply chain management is concerned with the integration of environmental criteria and sustainability issues in the management of the supply chain. Within this framework, firms applying internally green strategy have, naturally, the interest to pressure purchases of goods and services from suppliers that are themselves green, at least to a certain extent. Thus, Supplier Evaluation and Selection is crucial due to its big impact on business function. The prospect of applying green principles has consequently become an important feature of a supplier’s overall performance. Hence, green supplier selection and evaluation, although a relatively new research subject, has grown quite rapidly. It develops and studies the decision and evaluation models based on environmental criteria. Its main tools are based on multi-criteria decision making approaches. Our objective, in this paper, is to review journal articles published in the period 2012–2019 on this topic, in order to identify the most widely applied approaches for green supplier evaluation and selection and the most cited green criteria.

This article is intended to honor the late Professor Hoàng Tuy, a highly respected researcher and author in the field of Global Optimization. It summarizes our interaction with him and some of his many contributions to the field of optimization as well as his difficult road to success and international recognition.

Constrained by production capacity and the pressure to reduce emissions, many original equipment manufacturers (OEMs) authorize third-party remanufacturers (TPRs) to remanufacture patented products. We investigate the operational decisions of OEMs and authorized TPRs under carbon cap-and-trade regulations in a two-echelon supply chain. We first formulate an operational decision model for OEMs before a TPR enters. Then, for the cases of centralized and decentralized decision making, we formulate an operational decision-making model for the TPR and, subsequently, establish one for the OEM after the TPR enters. We further analyze the effects of carbon emissions cap, trading price of carbon permits, yield rate, and consumer willingness to pay (WTP) on optimal decisions. Our results indicate: whether TPRs accept authorization remanufacturing depending on the ratio of carbon emissions cap to carbon emissions for producing per remanufactured product; royalty rate is negatively affected by trading price of carbon permits and per remanufactured product’ carbon emissions other than that for per new product, and can offset the threat caused by TPRs; the implementation of carbon cap-and-trade regulations causes OEMs to charge TPRs lower royalty rate; centralized decision making increases the total profit of the supply chain and delivers superior environmental benefits. As yield rate and WTP increase, the total profit increases, increasingly sensitive to WTP.

In this paper a Permutation Flowshop Scheduling Problem is solved using a hybridization of the Firefly algorithm with Variable Neighborhood Search algorithm. The Permutation Flowshop Scheduling Problem (PFSP) is one of the most computationally complex problems. It belongs to the class of combinatorial optimization problems characterized as NP-hard. In order to find high quality solutions in reasonable computational time, heuristic and metaheuristic algorithms have been used for solving the problem. The proposed method, Hybrid Firefly Variable Neighborhood Search algorithm, uses in the local search phase of the algorithm a number of local search algorithms, 1-0 relocate, 1-1 exchange and 2-opt. In order to test the effectiveness and efficiency of the proposed method we used a set of benchmark instances of different sizes from the literature.

Clonal Selection Algorithm is a very powerful Nature Inspired Algorithm that has been applied in a number of different kind of optimization problems since the time it was first published. Also, in recent years a growing number of optimization models have been proposed that are trying to reduce the energy consumption in vehicle routing. In this paper, a new variant of Clonal Selection Algorithm, the Parallel Multi-Start Multiobjective Clonal Selection Algorithm (PMS-MOCSA) is proposed for the solution of a Vehicle Routing Problem variant, the Multiobjective Energy Reduction Multi-Depot Vehicle Routing Problem (MERMDVRP). In the formulation four different scenarios are proposed where the distances between the customers and the depots are either symmetric or asymmetric and the customers have either demand or pickup. The algorithm is compared with two other multiobjective algorithms, the Parallel Multi-Start Non-dominated Sorting Differential Evolution (PMS-NSDE) and the Parallel Multi-Start Non-dominated Sorting Genetic Algorithm II (PMS-NSGA II) for a number of benchmark instances.

In this paper, a new variant of the Particle Swarm Optimization (PSO) algorithm is proposed for the solution of the Vehicle Routing Problem with Time Windows (VRPTW). Three different adaptive strategies are used in the proposed Multi-Adaptive Particle Swarm Optimization (MAPSO) algorithm. The first adaptive strategy concerns the use of a Greedy Randomized Adaptive Search Procedure (GRASP) that is applied when the initial solutions are produced and when a new solution is created during the iterations of the algorithm. The second adaptive strategy concerns the adaptiveness in the movement of the particles from one solution to another where a new adaptive strategy, the Adaptive Combinatorial Neighborhood Topology, is used. Finally, there is an adaptiveness in all parameters of the Particle Swarm Optimization algorithm. The algorithm starts with random values of the parameters and based on some conditions all parameters are adapted during the iterations. The algorithm was tested in the two classic sets of benchmark instances, the one that includes 56 instances with 100 nodes and the other that includes 300 instances with number of nodes varying between 200 and 1000. The algorithm was compared with other versions of PSO and with the best performing algorithms from the literature. (C) 2019 Elsevier Inc. All rights reserved.