2,086 research outputs found

    A Hybrid Method for The Closed-loop Supply Chain to Minimize Total Logistics Costs

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    This is the final version. Available on open access from the International Journal of Technology via the DOI in this recordCrow search algorithm for binary optimization (BinCSA) is currently used in some ideal models of the uncapacitated facility location problem (UFLP), but studies on its use in real-world supply chain cases remain limited. Therefore, this study aimed to address the gap by introducing a hybrid method that combined the BinCSA with an exact method to solve a CLSC problem, including location allocation, transportation, and supplier selection challenges. The initial sections of the study included theoretical foundations and experimental results of the BinCSA. Subsequently, how the BinCSA works in the proposed hybrid method was discussed, and the computational results were showed to evaluate the performance of the proposed method

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A new matheheuristic approach based on Chu-Beasley genetic approach for the multi-depot electric vehicle routing problem

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    Operations with Electric Vehicles (EVs) on logistic companies and power utilities are increasingly related due to the charging stations representing the point of standard coupling between transportation and power networks. From this perspective, the Multi-depot Electric Vehicle Routing Problem (MDEVRP) is addressed in this research, considering a novel hybrid matheheuristic approach combining exact approaches and a Chu-Beasley Genetic Algorithm. An existing conflict is shown in three objectives handled through the experimentations: routing cost, cost of charging stations, and increased cost due to energy losses. EVs driving range is chosen as the parameter to perform the sensitivity analysis of the proposed MDEVRP. A 25-customer transportation network conforms to a newly designed test instance for methodology validation, spatially combined with a 33 nodes power distribution system

    Towards electric bus system: planning, operating and evaluating

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    The green transformation of public transportation is an indispensable way to achieve carbon neutrality. Governments and authorities are vigorously implementing electric bus procurement and charging infrastructure deployment programs. At this primary but urgent stage, how to reasonably plan the procurement of electric buses, how to arrange the operation of the heterogeneous fleet, and how to locate and scale the infrastructure are urgent issues to be solved. For a smooth transition to full electrification, this thesis aims to propose systematic guidance for the fleet and charging facilities, to ensure life-cycle efficiency and energy conservation from the planning to the operational phase.One of the most important issues in the operational phase is the charge scheduling for electric buses, a new issue that is not present in the conventional transit system. How to take into account the charging location and time duration in bus scheduling and not cause additional load peaks to the grid is the first issue being addressed. A charging schedule optimization model is constructed for opportunity charging with battery wear and charging costs as optimization objectives. Besides, the uncertainty in energy consumption poses new challenges to daily operations. This thesis further specifies the daily charging schedules with the consideration of energy consumption uncertainty while safeguarding the punctuality of bus services.In the context of e-mobility systems, battery sizing, charging station deployment, and bus scheduling emerge as crucial factors. Traditionally these elements have been approached and organized separately with battery sizing and charging facility deployment termed planning phase problems and bus scheduling belonging to operational phase issues. However, the integrated optimization of the three problems has advantages in terms of life-cycle costs and emissions. Therefore, a consolidated optimization model is proposed to collaboratively optimize the three problems and a life-cycle costs analysis framework is developed to examine the performance of the system from both economic and environmental aspects. To improve the attractiveness and utilization of electric public transportation resources, two new solutions have been proposed in terms of charging strategy (vehicle-to-vehicle charging) and operational efficiency (mixed-flow transport). Vehicle-to-vehicle charging allows energy to be continuously transmitted along the road, reducing reliance on the accessibility and deployment of charging facilities. Mixed flow transport mode balances the directional travel demands and facilities the parcel delivery while ensuring the punctuality and safety of passenger transport

    Covering problem with minimum radius enclosing circle

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    This study extends the classical smallest enclosing circle problem in location science to optimize healthcare communication hubs. Given a set of demand points and potential groups, we identify the optimal number of subgroups to cover all points and the circle enclosing them with minimum radius. The center of this circle serves as the communication hub location, minimizing the distance between demand points and facilities subject to customer demand. We develop a nonconvex-nonlinear optimization model and propose a quadratic programming-based approximation algorithm to solve it. Tested on various hypothetical and real scenarios, our model effectively reduces the facility setup cost and identifies the optimal communication hub location

    Towards the reduction of greenhouse gas emissions : models and algorithms for ridesharing and carbon capture and storage

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    Avec la ratification de l'Accord de Paris, les pays se sont engagés à limiter le réchauffement climatique bien en dessous de 2, de préférence à 1,5 degrés Celsius, par rapport aux niveaux préindustriels. À cette fin, les émissions anthropiques de gaz à effet de serre (GES, tels que CO2) doivent être réduites pour atteindre des émissions nettes de carbone nulles d'ici 2050. Cet objectif ambitieux peut être atteint grâce à différentes stratégies d'atténuation des GES, telles que l'électrification, les changements de comportement des consommateurs, l'amélioration de l'efficacité énergétique des procédés, l'utilisation de substituts aux combustibles fossiles (tels que la bioénergie ou l'hydrogène), le captage et le stockage du carbone (CSC), entre autres. Cette thèse vise à contribuer à deux de ces stratégies : le covoiturage (qui appartient à la catégorie des changements de comportement du consommateur) et la capture et le stockage du carbone. Cette thèse fournit des modèles mathématiques et d'optimisation et des algorithmes pour la planification opérationnelle et tactique des systèmes de covoiturage, et des heuristiques pour la planification stratégique d'un réseau de captage et de stockage du carbone. Dans le covoiturage, les émissions sont réduites lorsque les individus voyagent ensemble au lieu de conduire seuls. Dans ce contexte, cette thèse fournit de nouveaux modèles mathématiques pour représenter les systèmes de covoiturage, allant des problèmes d'affectation stochastique à deux étapes aux problèmes d'empaquetage d'ensembles stochastiques à deux étapes qui peuvent représenter un large éventail de systèmes de covoiturage. Ces modèles aident les décideurs dans leur planification opérationnelle des covoiturages, où les conducteurs et les passagers doivent être jumelés pour le covoiturage à court terme. De plus, cette thèse explore la planification tactique des systèmes de covoiturage en comparant différents modes de fonctionnement du covoiturage et les paramètres de la plateforme (par exemple, le partage des revenus et les pénalités). De nouvelles caractéristiques de problèmes sont étudiées, telles que l'incertitude du conducteur et du passager, la flexibilité de réappariement et la réservation de l'offre de conducteur via les frais de réservation et les pénalités. En particulier, la flexibilité de réappariement peut augmenter l'efficacité d'une plateforme de covoiturage, et la réservation de l'offre de conducteurs via les frais de réservation et les pénalités peut augmenter la satisfaction des utilisateurs grâce à une compensation garantie si un covoiturage n'est pas fourni. Des expériences computationnelles détaillées sont menées et des informations managériales sont fournies. Malgré la possibilité de réduction des émissions grâce au covoiturage et à d'autres stratégies d'atténuation, des études macroéconomiques mondiales montrent que même si plusieurs stratégies d'atténuation des GES sont utilisées simultanément, il ne sera probablement pas possible d'atteindre des émissions nettes nulles d'ici 2050 sans le CSC. Ici, le CO2 est capturé à partir des sites émetteurs et transporté vers des réservoirs géologiques, où il est injecté pour un stockage à long terme. Cette thèse considère un problème de planification stratégique multipériode pour l'optimisation d'une chaîne de valeur CSC. Ce problème est un problème combiné de localisation des installations et de conception du réseau où une infrastructure CSC est prévue pour les prochaines décennies. En raison des défis informatiques associés à ce problème, une heuristique est introduite, qui est capable de trouver de meilleures solutions qu'un solveur commercial de programmation mathématique, pour une fraction du temps de calcul. Cette heuristique comporte des phases d'intensification et de diversification, une génération améliorée de solutions réalisables par programmation dynamique, et une étape finale de raffinement basée sur un modèle restreint. Dans l'ensemble, les contributions de cette thèse sur le covoiturage et le CSC fournissent des modèles de programmation mathématique, des algorithmes et des informations managériales qui peuvent aider les praticiens et les parties prenantes à planifier des émissions nettes nulles.With the ratification of the Paris Agreement, countries committed to limiting global warming to well below 2, preferably to 1.5 degrees Celsius, compared to pre-industrial levels. To this end, anthropogenic greenhouse gas (GHG) emissions (such as CO2) must be reduced to reach net-zero carbon emissions by 2050. This ambitious target may be met by means of different GHG mitigation strategies, such as electrification, changes in consumer behavior, improving the energy efficiency of processes, using substitutes for fossil fuels (such as bioenergy or hydrogen), and carbon capture and storage (CCS). This thesis aims at contributing to two of these strategies: ridesharing (which belongs to the category of changes in consumer behavior) and carbon capture and storage. This thesis provides mathematical and optimization models and algorithms for the operational and tactical planning of ridesharing systems, and heuristics for the strategic planning of a carbon capture and storage network. In ridesharing, emissions are reduced when individuals travel together instead of driving alone. In this context, this thesis provides novel mathematical models to represent ridesharing systems, ranging from two-stage stochastic assignment problems to two-stage stochastic set packing problems that can represent a wide variety of ridesharing systems. These models aid decision makers in their operational planning of rideshares, where drivers and riders have to be matched for ridesharing on the short-term. Additionally, this thesis explores the tactical planning of ridesharing systems by comparing different modes of ridesharing operation and platform parameters (e.g., revenue share and penalties). Novel problem characteristics are studied, such as driver and rider uncertainty, rematching flexibility, and reservation of driver supply through booking fees and penalties. In particular, rematching flexibility may increase the efficiency of a ridesharing platform, and the reservation of driver supply through booking fees and penalties may increase user satisfaction through guaranteed compensation if a rideshare is not provided. Extensive computational experiments are conducted and managerial insights are given. Despite the opportunity to reduce emissions through ridesharing and other mitigation strategies, global macroeconomic studies show that even if several GHG mitigation strategies are used simultaneously, achieving net-zero emissions by 2050 will likely not be possible without CCS. Here, CO2 is captured from emitter sites and transported to geological reservoirs, where it is injected for long-term storage. This thesis considers a multiperiod strategic planning problem for the optimization of a CCS value chain. This problem is a combined facility location and network design problem where a CCS infrastructure is planned for the next decades. Due to the computational challenges associated with that problem, a slope scaling heuristic is introduced, which is capable of finding better solutions than a state-of-the-art general-purpose mathematical programming solver, at a fraction of the computational time. This heuristic has intensification and diversification phases, improved generation of feasible solutions through dynamic programming, and a final refining step based on a restricted model. Overall, the contributions of this thesis on ridesharing and CCS provide mathematical programming models, algorithms, and managerial insights that may help practitioners and stakeholders plan for net-zero emissions

    A Precedence Constrained Knapsack Problem with Uncertain Item Weights for Personalized Learning Systems

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    This paper studies a unique precedence constrained knapsack problem in which there are two methods available to place an item in the knapsack. Whether or not an item weight is uncertain depends on which one of the two methods is selected. This knapsack problem models students’ decisions on choosing subjects to study in hybrid personalized learning systems in which students can study either under teacher supervision or in an unsupervised self-study mode by using online tools. We incorporate the uncertainty in the problem using a chance-constrained programming framework. Under the assumption that uncertain item weights are independently and normally distributed, we focus on the deterministic reformulation in which the capacity constraint involves a nonlinear and convex function of the decision variables. By using the first-order linear approximations of this function, we propose an exact cutting plane method that iteratively adds feasibility cuts. To supplement this, we develop novel approximate cutting plane methods that converge quickly to high-quality feasible solutions. To improve the computational efficiency of our methods, we introduce new pre-processing procedures to eliminate items beforehand and cover cuts to refine the feasibility space. Our computational experiments on small and large problem instances show that the optimality gaps of our approximate methods are very small overall, and that they are even able to find solutions with no optimality gaps as the number of items increases in the instances. Moreover, our experiments demonstrate that our pre-processing methods are particularly effective when the precedence relations are dense, and that our cover cuts may significantly speed up our exact cutting plane approach in challenging instances

    Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics

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    Hydrogen can be produced from water, using electricity. The hydrogen can subsequently be kept in inventory in large quantities, unlike the electricity itself. This enables solar and wind energy generation to occur asynchronously from its usage. For this reason, hydrogen is expected to be a key ingredient for reaching a climate-neutral economy. However, the logistics for hydrogen are complex. Inventory policies must be determined for multiple locations in the network, and transportation of hydrogen from the production location to customers must be scheduled. At the same time, production patterns of hydrogen are intermittent, which affects the possibilities to realize the planned transportation and inventory levels. To provide policies for efficient transportation and storage of hydrogen, this paper proposes a parameterized cost function approximation approach to the stochastic cyclic inventory routing problem. Firstly, our approach includes a parameterized mixed integer programming (MIP) model which yields fixed and repetitive schedules for vehicle transportation of hydrogen. Secondly, buying and selling decisions in case of underproduction or overproduction are optimized further via a Markov decision process (MDP) model, taking into account the uncertainties in production and demand quantities. To jointly optimize the parameterized MIP and the MDP model, our approach includes an algorithm that searches the parameter space by iteratively solving the MIP and MDP models. We conduct computational experiments to validate our model in various problem settings and show that it provides near-optimal solutions. Moreover, we test our approach on an expert-reviewed case study at two hydrogen production locations in the Netherlands. We offer insights for the stakeholders in the region and analyze the impact of various problem elements in these case studies
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