International Transactions in Operational Research最新文献

Tourism is a very complex industry and represents one of the most profitable activity in the world. A tourist product requires the execution of several multifaceted activities. Indeed, transportation, accommodation, entertainment, food, and beverages represent only some of the products/services required by a tourist. It is evident that a single enterprise cannot provide all the components of a tourist product, but several interrelated actors should collaborate. This leads to a very complex supply tourist chain that needs to be optimized. The relevant theories and methods of logistics can be used to efficiently manage all the flows that are generated in a tourist chain. The definition of appropriate policies, at the different nodes of the chain, can improve the performance of all the actors involved in tourism logistics. In this paper, we concentrate our attention on tour operators that are relevant in the touristic logistic chain since they are involved in several activities. We introduce different revenue management policies to support tour operators in the decision of accepting the most profitable tourist requests. A request consists of flights and hotel booking, characterized by a starting time of the trip and the length of stay at the destination. We allow for various combinations of flight legs and multiple categories of hotels to accommodate a variety of customer preferences and needs. A computational study is carried out by considering different scenarios, and the performance of the considered revenue management policies is analyzed in detail.

The increasing use of drones has led to growing interest in their potential applications in disaster response. In this study, we examine the role of drones in disaster management under three categories of operation types, which are information collection, delivery, and communication network recovery. Focusing on an operations research (OR) perspective, we analyze and summarize the important characteristics of different problem structures and highlight the solution approaches. We also identify gaps and limitations in the current literature and propose future directions for further investigation. Overall, the study provides a comprehensive summary and valuable insights for researchers and practitioners interested in exploring the use of drones for humanitarian purposes and developing OR-based approaches to support decision-making.

We present two mixed integer linear programming (MILP) formulations for a well-known integrated network, timber landing location, and routing problem that arises in forest management. The models seek to jointly optimize the construction and maintenance schedule of forest road networks with landing site selection and transportation routing for timber production. This problem is, in general, difficult to solve as it contains the so-called fixed charge network flow problem, which is known to be NP-hard. One of the proposed MILP formulations considers 3-index continuous variables to represent timber flows on road segments in each period. The presence of Big-M constraints leads to weak linear relaxation bounds. Disaggregating flow variables, according to timber origin, results in a novel 4-index formulation with very tight linear relaxation bounds. Nevertheless, the number of variables increases prohibitively. This research makes use of spatial constraints common to Smallholding Forested Landscapes to develop a solution approach that reduces the number of flow variables in the new 4-index model. Results from a real-world case study located in Northwest Portugal show that, with the 4-index formulation, the proposed solution approach makes it possible to obtain optimal solutions in a short computational time.

This paper investigates blockchain technology (BT) adoption strategy in a platform dual-channel supply chain, wherein the supplier establishes a direct selling channel (DC) in addition to the existing reselling channel (RC) of the e-retailer. Both the supplier and the e-retailer are risk-averse, and they have access to consumers through a common online platform. The online platform possesses the capability to introduce BT and can opt to introduce it to neither, one (DC or RC) or both channels. Game models for the four strategies are developed, and the corresponding equilibrium outcomes are obtained using backward induction. Our analysis reveals that when competition intensity is high and both perceived risk aversion level and BT's unit cost are low, the online platform prefers only DC with adopting BT. Otherwise, it prefers both channels with adopting BT. The supplier shares the same preference as the online platform for BT adoption strategy. Interestingly, the e-retailer prefers only DC with adopting BT only when BT's unit cost is high; otherwise, she prefers solely RC with adopting BT. Furthermore, we enhance the basic model by modifying parameter configurations and the sequence game, and verify that our main findings in these extensions remain robust.

Effective planning and execution of humanitarian aid logistics activities ensure that disaster-related losses are minimized. This study addresses a tactical-level pre-disaster humanitarian logistics problem where a decision-maker decides on cross-dock locations by taking potential vehicle routes into account. A decision support model is proposed for the location selection and distribution operations in humanitarian logistics with explicit fuel consumption estimation. In the addressed problem, the demand amount of each node depends on probabilistic disaster scenarios. Probabilities of whether each arc/road is open or closed and heterogeneous vehicle fleet in terms of vehicle sizes are also respected. The model is formulated as probabilistic bi-objective mixed integer linear programming, whose objectives are minimization of the total cost (i.e., fuel cost, vehicle fixed cost, and fixed opening cost) and total travel time. To the best of our knowledge, the proposed decision support model is unique in terms of the features considered simultaneously. The applicability of the model is demonstrated by the case study and subsequent numerical analyses of a possible earthquake in the Kartal district of Istanbul. The proposed model is shown to have the potential to support decision-makers in preparation for a disaster. A solution approach based on a clustering algorithm has been also proposed to solve larger instances of the problem. The effectiveness of this heuristic has been demonstrated through its application to larger-scale problems.