2,086 research outputs found
A Hybrid Method for The Closed-loop Supply Chain to Minimize Total Logistics Costs
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
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
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
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
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
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
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
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Policy options for food system transformation in Africa and the role of science, technology and innovation
As recognized by the Science, Technology and Innovation Strategy for Africa – 2024 (STISA-2024), science, technology and innovation (STI) offer many opportunities for addressing the main constraints to embracing transformation in Africa, while important lessons can be learned from successful interventions, including policy and institutional innovations, from those African countries that have already made significant progress towards food system transformation. This chapter identifies opportunities for African countries and the region to take proactive steps to harness the potential of the food and agriculture sector so as to ensure future food and nutrition security by applying STI solutions and by drawing on transformational policy and institutional innovations across the continent. Potential game-changing solutions and innovations for food system transformation serving people and ecology apply to (a) raising production efficiency and restoring and sustainably managing degraded resources; (b) finding innovation in the storage, processing and packaging of foods; (c) improving human nutrition and health; (d) addressing equity and vulnerability at the community and ecosystem levels; and (e) establishing preparedness and accountability systems. To be effective in these areas will require institutional coordination; clear, food safety and health-conscious regulatory environments; greater and timely access to information; and transparent monitoring and accountability systems
Stochastic Cyclic Inventory Routing with Supply Uncertainty: A Case in Green-Hydrogen Logistics
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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Role of Formal and Informal Institutions in Advancing Sustainable Environmental Practices in SMEs of Pakistan's Textile Sector
Economies around the globe have established formal institutions to protect their natural environments (Klewitz et al., 2012, Wahga et al., 2018b), but parallel to them are 'proto-institutions' that also make an important contribution towards sustainable development. A proto-institution, an institution in the making, comprises rules, practices, and technologies that are partially diffused and weakly entrenched but poised to become widely institutionalised (Lawrence et al., 2002, p. 283). This qualitative study examines how proto-institutions in Pakistan's textile sector emerged and played a role in promoting sustainable environmental practices. Stakeholder Theory and Institutional Theory were combined to guide data collection and analysis. Primary data were collected through in-depth interviews, field observations and a field journal, whereas secondary data came from archival records and industry-specific publications. NVIVO 12 was used to sort and prepare data for analysis. Grounded analysis (Gioia et al., 2013, Easterby-Smith et al., 2015) revealed that institutional voids (Mair and Marti, 2009) and institutional gaps (Kolk, 2014) impeded the ability of formal institutions to assist the textile sector and ensure compliance with the established Punjab Environmental Quality Standards (PEQS). Due to these voids and gaps, textile manufacturers and stakeholders collaborated in various ways, resulting in the emergence of proto-institutions. These proto-institutions address the 'knowledge gap' by conducting informative seminars, capacity building workshops, and the production of best practice manuals. They bridge the 'cleaner production gap' by devolving internationally tested cleaner production solutions and assisting with their implementation. In addition, they take steps to close the 'compliance gap' by building the capacity of firms and public institutions. They fill the 'R&D gap' through commercial research into inputs, processes, and product development. They also provide firms with financial assistance through matching grants that help firms overcome their 'financial assistance gap' and acquire international certifications for market entry into global markets and undertake business development services. In doing so, these proto-institutions imposed iii normative and mimetic pressure on firms to adopt green practices while coexisting with formal institutions as compensatory institutions to create environmentally compliant isomorphs (firms). These findings add to the insights about institutional work processes and roles of proto-institutions, by presenting evidence from a previously under-research context: promoting sustainability in a SMEs dominated manufacturing sector of a developing country. In terms of practice, these findings are helpful information for textile manufacturers who are yet unknown to the benefits they could reap by adopting sustainable practices and processes in their manufacturing concerns. The information about collaboration is helpful for stakeholders looking to form new partnerships for responsible production. This study also suggests policymakers to both encourage and collaborate with proto-institutions to accomplish national and international commitments such as SDG 12 - Sustainable Consumption and Production, and race to net zero in textiles. Furthermore, the context specific factors that are affecting the emergence and development of proto-institutions in Pakistan’s textile sector could also help policymakers in Pakistan and alike developing countries to overcome institutional gaps and voids in their formal institutional arrangements and better promote sustainable production in their key manufacturing sectors
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