This paper establishes a location-routing optimization model of the distribution center for emergency supplies with the goals of system reaction time, total cost of consumption, psychological fear of the populace in disaster-affected locations, and material usage rate. Where the excess time, demand, and penalty coefficient are the components of the penalty cost in the total consumption cost, and where the psychological panic of those in the affected area is represented by the psychological perception function of panic developed in accordance with the prospect theory. An improved hybrid multi-objective cuckoo-large-neighborhood search algorithm was then designed to introduce tent mapping, nonlinear inertia weights, elite strategies, congestion operators, and dynamically adjusted discovery probabilities into the standard multi-objective cuckoo optimization algorithm, which generates a new solution using a large-neighborhood search algorithm after discarding part of the solution with the discovery probability, and then accepts the current nondominated solution with dynamic probabilities. The paper uses the improved algorithm to solve Christofides69, an arithmetic example from the standard dataset of the LRP problem, and the results show that the solution provided by the improved algorithm outperforms the solutions provided by the standard multi-objective cuckoo search algorithm and the NSGA-II algorithm in terms of the total cost of dissipation, the level of psychological panic of the people in the affected area, the rate of utilization of the supplies, and the number of distribution centers open. Finally, the improved algorithm was used to analyze cases of different sizes separately, and it was found that the algorithm yielded better results and was therefore able to demonstrate its effectiveness.

This study aims to optimize a hybrid multi-item fabricating-shipping integrated system incorporating scrap, adjustable rate, and postponement. In present-day competitive market environments, there is a clear client demand trend for various goods, shorter lead time, and expected quality. To satisfy the client’s needs, the management of manufacturing firms requires an effective and efficient plan to fabricate various high-quality goods in an expedited period, under limited capacity, and with minimal operating expenses. Inspired by facilitating production management to determine the best fabricating scheme/plan to achieve their operational goals, this work proposes an exploratory postponement model with quality assurance and uptime reduction strategies for their decision-making. By employing a two-phase making scheme, the required standard components are first made in the 1st phase, and multiple finished merchandise is fabricated in the 2nd phase. The study suggests strategies of contracting out a part of the common parts’ batch and adopting an adjusted/expedited making rate in the 2nd phase to considerably reduce both phases’ production uptimes. During both fabricating processes, the screening tasks identify/remove scrapped/faulty goods to ensure each finished batch’s quality. Equal-amount multiple shipments of end merchandise are transported to the clients in fixed time-interval. Optimization methodology and mathematical analyses support us in deriving the model’s expected annual operating cost and deciding the optimal production-transportation policy. A numerical illustration helps verify our model’s applicability and reveals important managerial insights into the studied problem to facilitate management in decision-making.

This research delves into the software testing process and its environmental factors to uncover the core elements influencing software reliability. Specifically, it focuses on the learning and negligent aspects of the software reliability growth model. The learning factor accelerates reliability growth, leading to an S-shaped curve in the mean value function, while the negligent factor highlights the occurrence of imperfect debugging. The study also uses Brownian motion and stochastic differential equations to establish statistical confidence intervals for reliability and costs. These intervals aid software managers in assessing potential release risks at various confidence levels, allowing them to make informed decisions considering resource constraints and desired system reliability levels across different scenarios.

The One-Dimensional Cutting Stock Problem consists in cutting long bars into smaller ones, to satisfy customers’ demand, minimizing waste and cost. In this paper the standard problem is extended with the inclusion of additional constraints that are generally neglected in scientific literature, although relevant in many industrial applications. We also modified the standard objective function, by assuming that bars may have a different economical value and a different processing or shipping priority. Moreover, in line with business requirements, among solutions that generate the same cutting waste, we prefer the ones that generate a low number of leftovers, especially if leftovers are long, so that the likelihood of their reuse is high. To solve the problem, we propose a Simulated Annealing based heuristic, which exploits a specific neighbor search. The heuristic is implemented in a parametric way that allows the user to set the priorities of the bars and to choose the specific sub-set of constraints he or she wants to consider. The heuristic is finally tested on many problem instances, and it is compared to three benchmarks and to one commercial software. The outcomes of this comparative analysis demonstrate both its quality and effectiveness.

Safety is a top concern for the civil aviation industry, and the risk of safety accidents will increase due to pilot fatigue. To ensure the safety of civil aviation, this paper proposes a method to solve the crew scheduling problem considering pilot fatigue. In order to reflect individual differences and fatigue levels of pilots, an improved three-stage alertness calculation model is first proposed based on subjective and objective perspectives to represent pilots’ alertness levels and fatigue working duration quantitatively. Then, for the crew scheduling problem considering pilot fatigue, a mixed integer programming model is constructed to simultaneously achieve the optimization objectives of reducing the overall scheduling cost and crew fatigue working duration. Next, since the actual crew scheduling problem is large-scale, a solution algorithm based on a column generation framework is developed to improve the quality and efficiency of solving the large-scale crew scheduling problem. Furthermore, in the case study, we collected actual data from an airline company to validate the effectiveness of our proposed method. Finally, through multiple experimental comparisons and analyses, to balance the two optimization objectives mentioned above, it is more reasonable to handle pilot fatigue working duration with soft constraints. Sensitivity analysis reveals the variation rules of the crew cost and fatigue, providing some valuable managerial insights for the crew scheduling problem considering pilot fatigue.

Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

The dual point policy is an important policy in the field of China's new energy vehicle industry, various factors such as point trading prices and technological innovation costs were included in the profit game model to explore the effects of cost contract model and revenue contract model on the optimal profit of new energy vehicle supply entities after collaborative decision-making. Research has found that the dual credit policy for China's new energy industry has a promoting effect on collaborative innovation among entities in the new energy vehicle supply chain; Compared with decentralized decision-making situations, the integration of cost sharing contracts or revenue sharing contracts can more effectively stimulate the innovation vitality of new energy battery suppliers and enhance their technological innovation level; Under the cost sharing contract and the benefit sharing contract, the optimal profit after collaborative decision-making between new energy vehicle manufacturers and new energy battery suppliers is greater than the optimal profit during decentralized decision-making, while the optimal profit of new energy vehicle supply chain entities under the benefit sharing contract is slightly higher than the optimal profit of new energy vehicle supply chain entities under the cost sharing contract.

In the contemporary era, green development has become integral to modern industrial supply chains. Accelerating the green transformation of the supply chain in the express delivery industry poses a significant challenge in China. To address this challenge, we establish a trilateral evolutionary game model that considers the interdependent constraints involving the government, vehicle suppliers, and courier companies. This model aims to explore the optimal stable decisions for each stakeholder and the entire supply chain system. Through numerical simulations, we analyze the impact of key parameters on the stability of strategies and find that there are four Evolutionary Stable Strategies (ESS) in the system. Economic factors play a dual role: income-related factors encourage the adoption of green strategies by stakeholders, whereas cost-related factors extend the time required for stakeholders to transition to green strategies. For sustained production and utilization of new energy vehicles, the government must utilize a balanced system of rewards and penalties effectively. Vehicle suppliers and courier companies should collaborate for mutually beneficial outcomes, jointly fostering the green transformation of the supply chain with a focus on cost reduction and efficiency improvement. This study offers theoretical insights and methodological support for decision-makers in green supply chain management.

It is crucial to identify alternative energy sources owing to the ever-increasing demand for energy and the other environmental problems associated with using fossil fuels. Biomass as a source of bioenergy is considered a promising alternative to fossil fuels. This study aims to optimize the biomass supply chain by developing an integrated model incorporating typical tactical supply chain decisions based on market or demand selection decisions. To this end, a novel mixed-integer linear programming (MILP) model is proposed to maximize the profit of the corresponding biomass supply chain and to commercialize electricity production by selecting electricity demand and making supply chain decisions regarding power plant operations, biomass feedstock purchase and storage, and biomass transport trucks. Owing to the intricacy of the MILP model, a fix-and-optimize-based solution strategy is developed and validated by applying it to several instances of a real-world case study. The results demonstrate that the proposed strategy can significantly reduce computational time while preserving high solution quality. Additionally, it helps improve planning and decision-making as it reveals the effect of essential biomass logistics characteristics on routing outcomes.

Helicopters and drones are widely used in military and post-disaster reconnaissance. But less attention has been paid to collaborative reconnaissance between the two, especially when drones can be launched and retrieved multiple times. We propose a synchronous routing problem of helicopter and heterogeneous multi-drone for reconnaissance, which is a new variant of the orienteering problem (OP), where the drones can visit multiple mission nodes and can reconnoiter the retrieval nodes in a single trip, with the goal of maximizing the information collected. The problem is formulated as a mixed integer linear programming (MILP) model, and then an adaptive simulated annealing algorithm (A-SA) is designed to solve the problem. Specifically, a universal high-efficiency heuristics solution evaluation method based on segment sorting is proposed. The time complexity of this method is O(n). The numerical experiments illustrate the accuracy and efficiency of the algorithm. The results also show that allowing the drones to conduct reconnaissance on the retrieval nodes can positively impact the solution.