International Transactions in Operational Research最新文献

In the context of sustainability, which has become fundamental today, we aim to optimize (reduce) the energy consumption due to the use of pumps that bring water from all the different reservoirs to the nodes of the distribution network. The proposed model allows us, thanks to the use of smart meters and new 5G technologies, to determine the optimal strategies (i.e., the optimal flows of water pumped by each pump of the network), taking into account the different quality indices of each reservoir and guaranteeing the water quality conditions required by law. We formulate the nonlinear optimization problem as a game in which each reservoir manager acts in a noncooperative manner while satisfying some shared constraints. Hence, a variational formulation is also proposed for all the reservoirs simultaneously, with existence and uniqueness results. Finally, some simulations highlight how the optimization strategy is useful.

Conflicts and wars can significantly disrupt global agricultural supply chains, with severe impacts on food security worldwide. War risk insurance, on the other hand, can mitigate farmers' losses. In this paper, we use the theory of variational inequalities to construct a new multicommodity international trade network equilibrium model with exchange rates on general transportation networks joining supply markets with demand markets under different wartime scenarios. The model incorporates capacities on production and transportation, as well as potential commodity losses in transportation. We then propose formulae for integrated crop and cargo war risk insurance premiums that are supply market and commodity specific and incorporate them into the model, along with government subsidies for the premiums. A series of numerical examples, both illustrative and algorithmically solved, focusing on the ongoing war on Ukraine, reveal the critical role of integrated war risk insurance and government support in sustaining agricultural commodity trade flows and supply market prices as well as protecting the revenue of farmers.

The double-ended matching queueing model has wide applicability across various real-life scenarios. In this paper, we analyze fluid models of bilateral matching queues with time-varying arrivals. The model contains fluid contents with general patience time distributions in a task queue and a worker queue, aiming to approximate the characteristics of bilateral queueing systems. We first prove the existence and uniqueness of the solution to the fluid model. When the arrival rates of both tasks and workers are constant, we determine the steady-state queue lengths and abandonment proportions for both sides of the queueing system. Furthermore, we demonstrate that the solution converges to an equilibrium state starting from any initial state. For situations involving time-varying arrival rates, satisfactory results from numerical experiments validate the reliability of the fluid model in describing the queueing system. Finally, we explore the matching problems within bilateral queueing systems and provide tailored solutions for various scenarios.

The surge in e-commerce retailing and the prosperity of urban life led to a growing demand for last-mile delivery services. With the rise of the platform economy, emerging third-party delivery platforms (e.g., Cainiao Station) have gained popularity. Couriers can leave parcels at a station near the consumer's residence. Compared to delivery at home, the relay station service (RSS) enhances delivery capacity but also incurs additional costs. In our study, we aim to study the pricing and service mode strategies of last-mile service providers under different competitive scenarios. We construct queuing game models to analyze the mode and pricing decisions of last-mile service providers under monopoly and competition scenarios. The results reveal that in the monopoly case, there exists a threshold of the relay fee, below which the service provider chooses RSS. The threshold increases in the potential demand and decreases in the probability that consumers will stay in their residences. In the case of duopoly competition with a relatively low relay fee, both competing service providers choose the same mode when the potential demand is either abundant or scarce. However, when the demand is moderate, adopting differentiated modes yields higher profits. This offers fresh insights for providers on competitive positioning, indicating the possibility of targeting specific market segments through differentiated service modes.

This work addresses the problem of assigning preventive maintenance jobs in a 52-week planning horizon. Given a set of machines that need preventive maintenance, a set of maintenance jobs in these machines, a set of work teams, and a planning horizon, the problem consists of assigning each job to a work team in a given instant of the planning horizon, aiming to minimize the cost with work teams and the cost of performing the unscheduled jobs using outsourced teams. We propose an iterated local search (ILS)-based algorithm specialized for this problem. Using real instances, the ILS algorithm achieved the best results in 81% of the instances, outperforming literature algorithms. However, these algorithms only treat the deterministic version of the problem and do not consider the uncertainty in the job duration that may occur in an industry environment. Not considering this aspect can produce an inefficient schedule with many unscheduled jobs. So, this work also proposes a simheuristic-based algorithm (SIM-ILS) capable of capturing this issue. We tested it in three scenarios, which differ in the level of uncertainty regarding the job duration, and compared their results with those provided by the stochastically evaluated ILS solutions. SIM-ILS found the best solution in 61% of the tests. Therefore, the SIM-ILS can be used to support decision-making in different industrial environments, from environments with low variability in job duration to those with high variability.