International Transactions in Operational Research最新文献

In the storage location assignment problem under a picker-to-parts system (SLAP-FP), we assign warehouse space to products based on customer orders. The distance we have to travel to pick up customer orders and the frequency of restocking in the warehouse depend on the location of the products and their allocated space. The goal of SLAP-FP is to minimize the costs of restocking the products and picking the orders. This study introduces a novel constraint programming formulation to solve the SLAP-FP. The model uses logical rules and rational expressions to describe the problem concisely. Numerical results with standard solvers show that the proposed model significantly outperforms the previously known integer programming approach. Specifically, the constraint programming model is especially good at finding better solutions in large instances with many different products.

In this paper, we study a public bus line operating between a terminal and a city center for which there exists a clear demand imbalance between the two directions during peak hours. Despite the demand imbalance, traditional public bus systems typically operate with a fixed-route and fixed-timetable scheme in both directions. To increase the efficiency of service during peak hours, in this paper, we propose an on-demand operation that allows vehicles to take some shortcuts between the city center and the terminal, based on the passenger requests that are collected before the peak hours. We develop a variable neighborhood search (VNS) algorithm to optimize the operational decisions of this system. Based on the received requests, the VNS algorithm decides the following: (1) shortcut decisions: which shortcuts to take by each bus in each direction, (2) departure time decisions: when to depart from the city center and the terminal, and (3) passenger assignment decisions: which service serves each passenger. Experiments show that the total passenger travel time improves up to 45% on a real-size line compared to its traditional fixed-route operation. The performance of the system is also analyzed under different circumstances.

This paper presents a two-stage model for planning a renewable energy portfolio by balancing economic, social and environmental sustainability goals. The first stage addresses a multi-objective problem where conflictive impacts generated by the energy portfolios should be optimised according to the corresponding economic, social or environmental profile. A parametric goal programming with a fuzzy hierarchy model, an alternative to the weighted and lexicographic goal programming models, is built. The second stage, comprising three steps, identifies the best energy mix for each profile. First, preferential weights are determined economically and flexibly by the decision-maker (DM) using the extended best-worst method. Then, a valuation process is applied to each portfolio's deviation vectors, and the transformed deviation vectors are ranked using the technique for order preference by similarity to ideal solution (TOPSIS) method. Applied to Spain, the results reveal significant conflict in the social profile, especially regarding total employment, which shows the greatest disagreement with other impacts. In contrast, the economic and environmental profiles exhibit low divergence among their key impacts. This work suggests that policymaking in renewable energy requires a balanced approach and, sometimes, the acceptance of unavoidable trade-offs between objectives. Our proposal guides the search for energy portfolios with strong DM intervention.

This paper considers a last-mile delivery system in which drones are used to deliver parcels to customers within a predefined delivery-time limit, while drones can perform multiple multivisit trips. The effects of both drone speed and load weight are considered on energy consumption in all flight phases, including takeoff, cruise, hovering, and landing phases, using a realistic nonconvex formula. The problem determines the routes of the drones and their speeds during the cruise flight phases, with the aim of minimizing the total consumed energy. The paper initially examines the problem for the discrete case where the speeds are selected from a finite list of options. The initial formulation of the problem is a mixed-integer nonlinear program that cannot be solved efficiently. Using a novel approach that changes the problem's input space, a mixed-integer linear program is proposed and enhanced by valid inequalities. Utilizing an efficient preprocessing algorithm, the proposed linear formulation is able to solve problem instances with up to 100 customers. Next, the paper studies the problem for the continuous case in which the speeds can take values from a specified interval. It is first demonstrated how the continuous case can be solved optimally when no delivery-time limit is imposed. Then, it is shown that the continuous case can be solved approximately using the discrete case. A method for computing the approximation error is also presented, which can be used to determine an appropriate level of discretization.

A demand-responsive feeder system (DRFS) presents an alternative to traditional feeder bus systems (TFS) in areas with low demand. This paper introduces an optimization problem to support the planning and operation of a DRFS. A variable neighborhood search (VNS) is employed to optimize the DRFS, focusing on minimizing passengers' travel times. The performance of the VNS is compared with that obtained by solving the mathematical model using a commercial solver (CPLEX) across two networks: a small network for validation and a larger suburban network that simulates a TFS. The results indicate that the VNS is a viable and efficient alternative for optimizing DRFS operations, providing flexibility in route and departure time adjustments, and achieving significant reductions in passenger travel times. The results also demonstrate that the DRFS outperforms the TFS in low-demand areas. On average, the VNS improves upon CPLEX results, obtained within one hour of calculation time for the small network, by 2.3%. Using the same resources, the DRFS reduces the average passenger travel time by 9.6% compared to the TFS.