International Transactions in Operational Research最新文献

This study focuses on postdisaster damage assessment operations supported by a set of drones. We propose a multistage framework, consisting of two phases applied iteratively to rapidly gather damage information within an assessment period. In the initial phase, the problem involves determining areas to be scanned by each drone and the optimal sequence for visiting these selected areas. We have adapted an electric vehicle routing formulation and devised a variable neighborhood descent heuristic for this phase. In the second phase, information collected from the scanned areas is employed to predict the damage status of the unscanned areas. We have introduced a novel, fast, and easily implementable imputation policy for this purpose. To evaluate the performance of our approach in real-life disasters, we develop a case study for the expected 7.5 magnitude earthquake in Istanbul, Turkey. Our numerical study demonstrates a significant improvement in response time and priority-based metrics.

As the prefabrication construction method plays an increasingly important role in the construction of cross-sea bridges, coordinating the schedule between off-site prefabrication and on-site assembly becomes crucial and challenging. This paper studies a just-in-time single-machine scheduling problem with due windows and release dates, which originates from the production and supply process of large segments of precast steel box girders in bridge construction. To solve this problem, we equivalently decompose it into the independent and same type of subproblems and formulate a mixed-integer linear mathematical programming model. Furthermore, we propose a branch-and-bound algorithm with the initialization of a novel linear early and tardy dispatching rule with due windows to solve the problem exactly. The dispatching rule, based on the local dominance criteria with due windows, is designed and used to improve the upper bound. Moreover, a lower bound is obtained by further decomposing and relaxing the subproblems. Finally, the parameter of the dispatching rule is calibrated, and the best search strategy of the branch-and-bound algorithm is determined. The comprehensive computational experiments carried out show the efficiency and effectiveness of the proposed branch-and-bound algorithm.

In the prize-collecting geometric enclosure problem (PCGEP), a set $�S$ of points in the plane is given, each with an associated benefit. The goal is to find a simple polygon $�P$ with vertices in $�S$ that maximizes the sum of the benefits of the points of $�S$ enclosed by $�P$ minus the perimeter of $�P$ multiplied by a given nonnegative cost. The PCGEP is NP-complete and has applications to land surveying for exploration or preservation of natural resources. In this paper, we develop the first heuristic, called PCGEP-GR, for the PCGEP and revisit a previously proposed integer linear programming (ILP) model to solve it to optimality. We conducted a comprehensive experimental study of that heuristic and an exact algorithm based on the ILP model. We show that a new set of constraints, together with the previous set, is necessary to guarantee the correctness of the ILP model and introduce preprocessing strategies that allow us to prove optimality 40% faster on average. The proposed heuristic is able to reach the optimum in 32% of our benchmark instances and, for those with unknown optima, PCGEP-GR was found better than or at least as good solutions as the ones obtained by the cplex ILP solver in 54% of the cases. Notwithstanding these positive results, the design of effective heuristics for the PCGEP proved to be very challenging, which also led us to obtain a result that provides the theoretical foundation for future advances in the study of this problem.

The dial-a-ride problem (DARP) involves designing vehicle routes to fulfill the door-to-door transportation requests of users where the goal is to minimize costs while satisfying transportation requests. In this paper, we introduce the rich heterogeneous DARP, which extends the generalized heterogeneous DARP to consider a fleet of buses and taxis, multiple depots, time windows at pickup and delivery locations, maximum ride and waiting times, and the possibility of an accompanying person. Our approach is based on a real service in Barcelona, and we also consider the variation in trip duration based on the time of day and day of the week. A predictive model is developed using machine learning techniques to estimate trip durations accurately. We apply our proposal to the daily door-to-door transportation of people with reduced mobility in Barcelona and demonstrate its superiority in terms of costs and quality of service by using the Gurobi optimizer. Additionally, we provide an analysis of the consequences of varying certain features on the costs and quality of service.

Sustainable development is today a major concern, and within this supply chain’ environmental and social performances need to be considered. The social pillar of the supply chain is not easy to measure, and its quantification is a researcher's challenge. In addition, quantifying the social impact of a monetary unit, easily understood by decision-makers, is an important step towards achieving global sustainable supply chain performance. This work intends to contribute to this challenge and develops a monetization approach for assessing supply chain's social performance, considering three main relevant social indicators: job creation, accidents and training.  These are then integrated into an optimization-based decision tool that can be used to design and plan a supply chain based on an informed decision. This operation research–based tool considers the economic and the social pillars independently or simultaneously, allowing the development of different analyses to reflect on the different trade-offs that could be considered when designing and planning sustainable supply chains. A case study is utilized to demonstrate the tool's applicability, and a number of conclusions and insights are provided on how to strengthen supply chain sustainability.

Advances in quantum computing with applications in combinatorial optimization have evolved at an increasing rate in recent years. The quadratic unconstrained binary optimization (QUBO) model is at the center of these developments and has become recognized as an effective alternative method for representing a wide variety of combinatorial optimization problems. Additional momentum has resulted from the arrival of quantum computers and their ability to solve the Ising spin glass problem, another form of the QUBO model. This paper highlights advances in solving QUBO models and extensions to more general polynomial unconstrained binary optimization (PUBO) models as important alternatives to traditional approaches. Computational experience is provided that compares the performance of unique quantum-inspired metaheuristic solvers—the Next Generation Quantum (NGQ) solver for QUBO models and the NGQ-PUBO solver for PUBO models—with the performance of CPLEX and the Dwave quantum advantage solver. Extensive results, including experiments with a set of large set partitioning problems representing the largest QUBO models reported in the literature to date, along with maximum diversity and max cut problem sets, disclose that our solvers outperform both CPLEX and Dwave by a wide margin in terms of both computational time and solution quality.

Drone operation, a new driving force for logistics innovation, is struggling to overcome practical challenges. One of the concerns for drone utilization is limited flight ranges, and different concepts of facilities are continually developed to support drone delivery. These new facilities prompt the need to integrate decision-making across different phases. In particular, the deployment of facilities that complement the physical limitations of drones and the scheduling of drones to perform delivery tasks are closely related. Therefore, we developed a scheduling-location problem with drones, a new methodology for integrating operational and strategic planning decisions. The integrated decision-making determines the location of the drone facilities by not only considering the critical distance of facilities but also by taking into account whether the delivery schedule is implemented. In our model, additional drone facilities are sometimes opened considering available drones due to the feasibility of the delivery schedule. An extended formulation and a restricted master heuristic are proposed to solve problems time-efficiently. Computational results show that the restricted master heuristic outperforms the mathematical model in finding solutions for large-scale instances. The developed model and heuristic algorithm provide drone delivery services even in areas that are not easily reachable by drones due to being located far from the warehouse and can be effectively applied to humanitarian logistics.