Liquefied natural gas (LNG) is increasingly viewed as a promising fuel for dual-fuel ships due to its cost-effectiveness, low emissions, and alignment with regulatory requirements. However, the high methane content of...
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Liquefied natural gas (LNG) is increasingly viewed as a promising fuel for dual-fuel ships due to its cost-effectiveness, low emissions, and alignment with regulatory requirements. However, the high methane content of LNG, ranging from 85% to 95%, presents a significant challenge because of the phenomenon of methane slip whereby unburned methane escapes from the engine's combustion chamber and other parts of the storage and transportation systems. Methane slip, which peaks at low ship speeds and decreases at higher speeds, can lead to substantial environmental pollution if it is not properly managed. This study rigorously examines the impact of sailing speed on methane slip rates and recognizes the complexities of fuel usage in dual-fuel ships. We develop a nonlinear mixed-integer programming model designed for container shipping companies that aims to optimize fleet composition, sailing speed, and fuel usage strategies. The objective of the model is to minimize total operational costs, including fuel expenses and taxes related to carbon emissions and methane slip. To address the computational challenges posed by the model's nonlinearity, we propose a tailored solution method that uses sailing time as a proxy for speed, discretizing these times for effective implementation. The validity of this method is supported by theoretical guarantees and demonstrated through numerical experiments. Our computational results indicate that accounting for methane slip in the operational management of dual-fuel ships can help mitigate financial losses under certain conditions.
Online retail pharmacies usually price their products differently from traditional drugstores. Based on real-time consumer behaviors, this paper proposes a dynamic bundle pricing strategy to maximize the pharmacy'...
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Online retail pharmacies usually price their products differently from traditional drugstores. Based on real-time consumer behaviors, this paper proposes a dynamic bundle pricing strategy to maximize the pharmacy's profit. Given free shipping thresholds and consumer budgets, propose a mixed-integernonlinearprogramming model and a heuristic to sequentially price customized bundles. We further conduct a numerical study using the data from a leading e-pharmacy in China. Our computational results indicate that the proposed model not only improves the e-pharmacy's profit by attracting more customers but noticeably contributes consumer surplus. Through sensitivity analysis, our model is proved to be robust under various scenarios.
Airlines need to expand their flight networks with developing new routes and introducing more flights to increase their market share. In this work, we propose a two-stage stochastic mixedintegernonlinear program (MI...
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Airlines need to expand their flight networks with developing new routes and introducing more flights to increase their market share. In this work, we propose a two-stage stochastic mixedintegernonlinear program (MINLP), which expands an existing flight schedule by operating new flights either with existing fleet resources or a leased aircraft while considering the impact of departure time decisions on the probability distribution of random demand. Moreover, our study helps an airline to link a strategic decision of leasing an aircraft to the tactical aircraft assignment decisions by considering fuel efficiency and seat capacity of the aircraft alternatives in response to new passenger demand. However, the large number of scenarios, nonlinear fuel burn function and nonlinearities due to the decision dependent probabilities become main challenges of solving the problem. In order to deal with the computational requirements of a two-stage stochastic MINLP with decision dependent probabilities, we propose strong conic quadratic and McCormick inequalities, and an exact scenario group wise decomposition algorithm along with a new bounding method. In our computational results, we clearly demonstrate the effectiveness of proposed decomposition algorithm and the strength of the reformulations.
We study a special environmental producer responsibility policy for the Chinese electronics industry that is based on awarding a per unit subsidy to qualified returned electronic products and ensuring a minimum produc...
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We study a special environmental producer responsibility policy for the Chinese electronics industry that is based on awarding a per unit subsidy to qualified returned electronic products and ensuring a minimum producer collection volume while allowing larger collection volumes. Based on a real application from a Chinese electronics company that produces LCD TVs, our paper studies the optimal design of the product's reverse supply chain when there is flexibility in settling the inspection locations of the returned products and flexibility in the volume of returned products collected. The problem is modeled as a nonlinearmixed-integer program and an efficient outer approximation-based solution approach is proposed. Analytical results and extensive numerical experiments based on this real application are conducted. Observations novel to the reverse logistics literature are related to the testing location decisions (upstream or downstream) and the optimal collection volumes of returned products. Particularly, we show how the government can stimulate the collection amount of returned products by increasing the unit subsidy and we also find that the company's marginal benefit from improving the subsidy increases in a superlinear fashion. Furthermore, the highest collection volumes may not occur at the highest quality level of returned products for capacitated remanufacturers. The company can also be incentivized to increase the collection of returned products by permitting flexible testing locations. We also observe how the optimal testing locations vary for different levels of unit subsidy and different ratios of qualified and non-qualified returned products. Finally, conclusions and future research directions are provided.
Burstable billing is widely adopted by colocation data center providers to charge their users for data transferring. This paper proposes a cost-aware traffic management approach for a colocation data center user under...
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Burstable billing is widely adopted by colocation data center providers to charge their users for data transferring. This paper proposes a cost-aware traffic management approach for a colocation data center user under burstable billing where it is charged based on the 95th percentile bandwidth usage. To do this, we first develop a tractable mathematical expression to calculate the 95th percentile usage of a user. Then, we develop an optimization problem to maximize the user's surplus based on both deterministic and stochastic predictions of the user's demand. We show that the resulted optimization problem, while non-convex by nature, can be efficiently solved or approximated using a convex program. We also show that the proposed approach can also be applied in a more general scenario where the user gets services from multiple service providers. Using real-world workload traces, we show that the proposed approach can reduce a colocation data center user's IP transit cost by 26 percent and increase its total surplus by 23 percent, compared to the current practice of allocating bandwidth on-demand.
We address the bilevel optimization problem of identifying the most critical attacks to an alternating current (AC) power flow network. The upper-level binary maximization problem consists of choosing an attack that i...
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We address the bilevel optimization problem of identifying the most critical attacks to an alternating current (AC) power flow network. The upper-level binary maximization problem consists of choosing an attack that is treated as a parameter in the lower-level defender minimization problem. Instances of the lower-level global minimization problem by themselves are NP-hard due to the nonconvex AC power flow constraints, and bilevel solution approaches commonly apply a convex relaxation or approximation to allow for tractable bilevel reformulations at the cost of underestimating some power system vulnerabilities. Our main contribution is to provide an alternative branch-and-bound algorithm whose upper bounding mechanism (in a maximization context) is based on a reformulation that avoids relaxation of the AC power flow constraints in the lower-level defender problem. Lower bounding is provided with semidefinite programming (SDP) relaxed solutions to the lower-level problem. We establish finite termination with guarantees of either a globally optimal solution to the original bilevel problem, or a globally optimal solution to the SDP-relaxed bilevel problem which is included in a vetted list of upper-level attack solutions, at least one of which is a globally optimal solution to the bilevel problem. We demonstrate through computational experiments applied to IEEE case instances both the relevance of our contribution, and the effectiveness of our contributed algorithm for identifying power system vulnerabilities without resorting to convex relaxations of the lower-level problem. We conclude with a discussion of future extensions and improvements.
Supply chain managers have realized that competition between supply chains has replaced competition between companies. In addition, with increasing disruptions and uncertainty in planning, companies need to be able to...
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Supply chain managers have realized that competition between supply chains has replaced competition between companies. In addition, with increasing disruptions and uncertainty in planning, companies need to be able to make informed decisions at risk. Coordination in the resilient supply chain and appropriate selection of suppliers play a key role in risky situations. In previous research, mainly the impact of resilience strategies in the decentralized supply chain has been investigated and ignored the reliability of suppliers in the decision-making process. Therefore, we provide an effective framework for selecting reliable suppliers and order allocation, which increases the supply chain's benefits by considering the risk reduction strategies and coordination between the buyer and the supplier. Thus, we optimized the problem of supplier selection and order allocation in a centralized supply chain using mixed-integernonlinearprogramming models and risk reduction strategies. These strategies are protected suppliers, back-up suppliers, reserving additional capacity, emergency stock, and geographical separation. Also, by considering the failure mode and effects analysis technique and the risk priority number constraint, suppliers' reliability has been considered. A numerical example is solved with the exact method. In addition, the application of the proposed models in a case study has been investigated by the Grasshopper optimization algorithm. Based on the sensitivity analysis results, we found that the simultaneous use of risk reduction strategies in the models significantly reduces supply chain costs and increases its benefits. Also, considering the reliability constraints causes supply chain managers to choose suppliers with more desirable reliability.
The single transferable vote (STV) is a system of preferential voting for multiseat elections. Each ballot cast by a voter is a (potentially partial) ranking over a set of candidates. No techniques currently exist for...
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The single transferable vote (STV) is a system of preferential voting for multiseat elections. Each ballot cast by a voter is a (potentially partial) ranking over a set of candidates. No techniques currently exist for computing the margin of victory (MOV) in STV elections. The MOV is the smallest number of ballot manipulations (changes, additions, and deletions) required to bring about a change in the set of elected candidates. Knowing the MOV gives insight into how much time and money should be spent on auditing the election, and whether uncovered mistakes (such as ballot box losses) throw the election result into doubt-requiring a costly repeat election-or can be safely ignored. We present algorithms for computing lower and upper bounds on the MOV in STV elections. In small instances, these algorithms are able to compute exact margins.
The cannibalization effect between new and remanufactured products impacts market demand and further influences supply chain design, which makes supply chain operations complex. This article studies the impact of cann...
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The cannibalization effect between new and remanufactured products impacts market demand and further influences supply chain design, which makes supply chain operations complex. This article studies the impact of cannibalization between new and remanufactured products on supply chain network design and operations by considering a joint pricing-location-inventory problem. A three-level supply chain network that consists of multi-distribution centers and retailers is considered. New and remanufactured products are supplied simultaneously. The problem is formulated as a nonlinearmixed-integer program and is then transformed into a conic quadratic mixed-integer program. An outer approximation-based solution approach is developed to solve the program. Extensive numerical experiments are conducted to explore the performance of the algorithm and the effects of market cannibalization on the supply chain network design and operations.
The penetration rate of Electric Vehicles is increasing as expected. To meet the charging needs, numerous charging stations are built, which are small because of space and grid limitation in city. Such a high density ...
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ISBN:
(纸本)9781728135557
The penetration rate of Electric Vehicles is increasing as expected. To meet the charging needs, numerous charging stations are built, which are small because of space and grid limitation in city. Such a high density distribution of charging stations offers EV users more choices when seeking for charging service. In order to offer users better choices with global knowledge, recommendation systems are developed. However, the cost generated from recommendation system is not considered when designing the charging network, which result in an increase of overall system cost. Therefore, this paper mainly focuses on the charging network design problem under generalized recommendation systems to find a better co-working solution. We first formulate the EV charging network design as a cost minimization problem, considering construction cost, incentive cost for users' to transfer in the charging network, and cost for the loss of users leaving the system, which is a nonlinearmixed-integer optimization problem. In order to solve such problem, we propose a branch and bound algorithm. By combining local search heuristic and cutting plane process, the exact optimal solution can be obtained efficiently. To demonstrate the superiority of the scheme proposed by us, simulation are conducted.
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