Dry bulk river–sea intermodal transport is a critical consideration when connecting inland waterways and oceanic shipping, yet its efficiency hinges on precise vessel arrival time matching. The challenge of vessel arrival time matching has been exacerbated by existing research gaps. Current studies often focus on single vessel types or static scenarios, lacking integrated optimization of dynamic coordination between sea-going and river vessels, and failing to unify time and cost objectives. To address this, we develop a multiobjective scheduling model incorporating real-time arrival data from the dry bulk river–sea intermodal information platform to minimize total port time and operational costs. A heuristic genetic algorithm with adaptive weight adjustment (λ) is designed, achieving convergence within 200 iterations and a solution time of 33 seconds. This algorithm is validated under balanced conditions (λ=0.5) and is shown to yield 108.53 hours of total port time and 278,165.2 yuan in operational costs. Sensitivity analysis reveals a significant tradeoff: λ is reduced from 0.9 to 0.1, leading to an increase in port time by 1.42% but a reduction in costs of 3.03%. This reflects an improved flexibility in cost optimization as a result of resource manipulability. In contrast, port time is constrained by physical limits, such as loading/unloading efficiency. The framework developed provides practical decisional support for ports, with higher λ values (0.7–0.9) enabling rapid turnover in congestion and lower values (0.1–0.3) prioritizing cost economy. Future work should extend this approach to stochastic environments and incorporate multistakeholder coordination using game theory approaches.

As an important part of the fresh produce business format, fresh food instant delivery encounters numerous challenges. Issues like high losses, complex cold chains and time sensitivity lead to increased costs. Additionally, the living space of end-delivery personnel is under pressure and the talent market is saturated. The platform algorithms focus on the interests of themselves and customers while relatively overlooking those of delivery personnel, which affects the overall operation quality, resulting in a significant reduction in delivery efficiency and a remarkable decline in service quality, and further leading to the loss of user stickiness. Therefore, optimizing the fresh food delivery route and considering the interests of multiple parties to improve efficiency and service quality is a crucial research issue in the field of fresh food instant delivery. This paper designs a three-objective static model for fresh food instant delivery aiming at minimizing the total cost, maximizing customer satisfaction and maximizing riders satisfaction. Considering the dynamic changes of orders during the actual operation process and in combination with the dynamics of newly added orders, a multi-objective dynamic model with the goals of minimizing the total cost, minimizing the average customer dissatisfaction and maximizing the income fairness of riders is further established. Based on the constructed models and by incorporating the SPBO strategy, the NSGA-III algorithm is improved and designed to make it more adaptable to the multi-objective optimization requirements in the fresh food instant delivery scenario. This study selects five operational points within a specific region of a fresh food self-operated platform and the order data from a particular day as research cases to obtain the relevant parameters required for the model and conduct case analysis. Based on the platform's business priorities and development needs, appropriate Pareto solutions are selected. Additionally, the feasibility and effectiveness of the improved algorithm are verified through algorithmic comparison. The research aims to provide valuable references and insightful implications for the management decisions of relevant fresh food self-operated platforms, as well as to continuously optimize the management and service of the instant delivery process.

The parallel batch-processing machine scheduling problem is widely present in industries such as manufacturing, service, and healthcare, and becomes more complex when incorporating flexible preventive maintenance (FPM). This paper presents a mixed-integer programming (MIP) model and a multi-objective artificial bee colony (MOABC) algorithm to tackle the unrelated parallel batch-processing machine scheduling problem with flexible preventive maintenance (UPBPM-FPM). The objective is to simultaneously minimize the makespan, earliness and tardiness, and total energy consumption, providing a comprehensive solution to optimize both scheduling efficiency and energy use while incorporating preventive maintenance considerations. The MOABC algorithm integrates three key innovations: (1) a novel processing power-feature information (PP-FI) heuristic to generate high-quality initial solutions, (2) a hybrid selection strategy combining the hypervolume index and roulette wheel approach to improve diversity and convergence, and (3) a set of random and goal-oriented neighborhood search methods to enhance Pareto frontier. Experimental results demonstrate that the MOABC algorithm outperforms three classical algorithms, NSGA-III, ABC, and PSO, in terms of convergence, diversity, and robustness of the Pareto solutions. This study provides a robust framework for energy-efficient scheduling in complex manufacturing environments.

Sustainable production is not limited to environmental concerns only; It also provides economic benefits for businesses. Businesses that adopt sustainability principles can gain advantages in matters such as cost savings, competitive advantage, risk management, legal compliance and corporate reputation. Therefore, sustainability is no longer an option but a strategic imperative for businesses. For this reason, studies on energy-sensitive scheduling have started to increase recently. Another important factor in sustainable manufacturing is the reduction of scrap. Rework operations are required to reduce scrap. In this study, the multi-objective flexible job shop scheduling problem (MO-FJSP) that considers energy efficiency is discussed. The created model aims to minimize the energy consumption, total machine workload and makespan. In this study, new job arrivals are considered as dynamic events. Another dynamic event added to the model is the addition of rework processes between operations to reduce the scrap rate when a scrap decision is made during the production stages. The enhanced NSGA II algorithm was applied to solve this problem. The enhanced NSGA II algorithm was applied to test instances and its performance was compared using some of the multi-objective performance indicators. These experimental results prove the effectiveness of the proposed solution method.

With the continuous upgrading of industrial technology and information technology, consumers can deeply participate in the whole life cycle of products and realize customized production. These unprecedented changes have brought consumers and manufacturers closer together, resulting in the intelligent business model of "Internet + Customized Production" and "Customer to Manufacturer (C2M)". C2M has been adopted by more and more companies. However, the transition from traditional business models to C2M is a problem that every company must face and solve. Personalized orders of many varieties and small lots put enormous pressure on the production of mainly labor-intensive electronic assembly companies. The theoretical findings of Industry 4.0 and Lean Manufacturing show that people play a central role in assembly operations. As an important element of the production system, worker scheduling has a direct impact on delivery time and cost. Worker scheduling requires not only matching people to jobs, but also considering flexible employment. According to the "Learning Curve" theory, workers with learning potential can continuously enrich their skills and work efficiency will show dynamic changes. Therefore, under the condition of shortest order delivery time and lowest cost, worker scheduling considering the learning effect becomes a challenge for enterprise decision makers. Firstly, the production method of manufacturing industry in C2M environment is studied. Then, based on single-skill task and multi-skill task, respectively, a learning curve-based model of dynamic change in worker skill level is constructed. And this model is used as the input of the assembly line worker scheduling model. Secondly, an Elite Non-dominant Sorting Whale Optimization Algorithm (ENS-WOA) is designed for this multi-objective optimization problem. The correctness and feasibility of the proposed algorithm are verified by selecting classical arithmetic cases for experimental comparison with other algorithms. Finally, the established worker efficiency change model, worker scheduling model and the proposed algorithm are applied to optimize the assembly line of water pump products of Company B, which is being transformed to C2M, and solved by MATLAB software. The results show that the model proposed in this paper is effective, stable and practical compared with the worker costs and delivery period required to complete the order in the original assembly line. Worker costs were reduced by 29.02% and orders were completed approximately 10 days earlier.

This paper analyzes practical production characteristics, including customer's stringent quality requirements and uncertain processing time in aircraft shaft parts manufacturing. Considering the above characteristics, we propose a multi-objective fuzzy aircraft shaft parts production scheduling problem considering the maximization of production quality. We define this problem as a multi-objective fuzzy flexible job shop scheduling problem (MO-fFJSP) with fuzzy processing time. To address this problem, we developed an improved multi-objective spider monkey optimization (IMOSMO) algorithm. IMOSMO integrates strategies such as genetic operators, variable neighborhood search and Pareto optimization theory on the framework of the conventional Spider Monkey Optimization (SMO) framework and discretize the continuous SMO algorithm to solve MO-fFJSP. To enhance the efficiency of the algorithm, we further adjust the sequence of the local leader learning phase and the global leader learning phase within the proposed IMOSMO framework. We conduct a comparative analysis between the performance of IMOSMO and NSGA-Ⅱ using 28 cases of varying scales. The computational results demonstrate the superiority of our algorithm over NSGA-Ⅱ in terms of both solution diversity and quality. Moreover, the performance of the proposed algorithm upgrades as the problem scale increases.

The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

The challenge presented by simultaneous buffer and service rate allocation in manufacturing systems represents a difficult non-deterministic polynomial problem. Previous studies solved this problem by iteratively utilizing a generative method and an evaluative method. However, it typically takes a long computation time for the evaluative method to achieve high evaluation accuracy, while the satisfactory solution quality realized by the generative method requires a certain number of iterations. In this study, a data-driven hybrid approach is developed by integrating a tabu search–non-dominated sorting genetic algorithm II with a whale optimization algorithm–gradient boosting regression tree to maximize the throughput and minimize the average buffer level of a manufacturing system subject to a total buffer capacity and total service rate. The former algorithm effectively searches for candidate simultaneous allocation solutions by integrating global and local search strategies. The prediction models built by the latter algorithm efficiently evaluate the candidate solutions. Numerical examples demonstrate the efficacy of the proposed approach. The proposed approach improves the solution efficiency of simultaneous allocation, contributing to dynamic production resource reconfiguration of manufacturing systems.

Crew pairing is the primary cost checkpoint in airline crew scheduling. Because the crew cost comes second after the fuel cost, a substantial cost saving can be gained from effective crew pairing. In this paper, the cockpit crew pairing problem (CCPP) of a budget airline was studied. Unlike the conventional CCPP that focuses solely on the cost component, many more objectives deemed to be no less important than cost minimisation were also taken into consideration. The adaptive non-dominated sorting differential algorithm III (ANSDE III) was proposed to optimise the CCPP against many objectives simultaneously. The performance of ANSDE III was compared against the NSGA III, MOEA/D, and MODE algorithms under several Pareto optimal measurements, where ANSDE III outperformed the others in every metric.

Optimizing the trade-off between crucial decisions has been a prominent issue to help decision-makers for synchronizing the production scheduling and distribution planning in supply chain management. In this article, a bi-objective integrated scheduling problem of production and distribution is addressed in a production environment with identical parallel machines. Besides, two objective functions are considered as measures for customer satisfaction and reduction of the manufacturer’s costs. The first objective is considered aiming at minimizing the total weighted tardiness and total operation time. The second objective is considered aiming at minimizing the total cost of the company’s reputational damage due to the number of tardy orders, total earliness penalty, and total batch delivery costs. First, a mathematical programming model is developed for the problem. Then, two well-known meta-heuristic algorithms are designed to spot near-optimal solutions since the problem is strongly NP-hard. A multi-objective particle swarm optimization (MOPSO) is designed using a mutation function, followed by a non-dominated sorting genetic algorithm (NSGA-II) with a one-point crossover operator and a heuristic mutation operator. The experiments on MOPSO and NSGA-II are carried out on small, medium, and large scale problems. Moreover, the performance of the two algorithms is compared according to some comparing criteria. The computational results reveal that the NSGA-II performs highly better than the MOPSO algorithm in small scale problems. In the case of medium and large scale problems, the efficiency of the MOPSO algorithm was significantly improved. Nevertheless, the NSGA-II performs robustly in the most important criteria.