Logistics Scheduling Research Articles

A Self-Learning Hyper-Heuristic Algorithm Based on a Genetic Algorithm: A Case Study on Prefabricated Modular Cabin Unit Logistics Scheduling in a Cruise Ship Manufacturer.

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%.

Application of energy sustainability model based on optical sensing technology in intelligent warehousing performance management in green manufacturing industry

As the global focus on sustainable development continues to grow, green manufacturing, as a way to optimize resource use and reduce environmental impact, needs to be combined with advanced technologies to improve its efficiency. This study aims to explore the application of energy sustainability model based on optical sensing technology in smart storage of green manufacturing industry, evaluate its impact on storage performance management, and further promote the development of green manufacturing industry. In this paper, an optical sensing system is constructed to monitor and analyze the energy consumption in storage environment in real time. In this study, a green manufacturing enterprise was selected as an example, and the optical sensor was used to monitor energy consumption, inventory status and environmental parameters in real time to ensure the real-time and accuracy of data. The collected data is analyzed to identify patterns and anomalies in energy use to optimize inventory management and logistics scheduling. Evaluate the energy efficiency of storage systems by setting energy efficiency indicators and compare them with traditional models to quantify improvements. On the basis of monitoring and analysis, the energy management strategy is continuously adjusted to enhance the adaptability and flexibility of the intelligent storage system. The research results show that the application of optical sensing technology can significantly improve the energy management performance in warehousing, through real-time monitoring and intelligent optimization, enterprises can achieve more efficient energy use, further promote the implementation of green manufacturing, and provide strong support for the realization of environmental and economic benefits.

A discrete Jaya algorithm for vehicle routing problems with uncertain demands

Vehicle Routing Problem with Uncertain Demands (VRPUD) is one of the research hotspots in the field of logistics scheduling. In this paper, a Discrete Jaya (DJaya) algorithm is presented for the VRPUD to minimize the total cost. A novel dividing-point-based coding scheme is designed to represent solutions with higher robustness. In addition, an efficient repair strategy is embedded into the decoding process to avoid the failure of producing feasible solutions. The best and worst solutions are employed to generate offspring solutions in DJaya. Several efficient local search methods are also presented to enhance the exploitation ability and increase the diversity of solutions. Based on the benchmark data sets of the VRPUD, numerical simulations are carried out for the proposed DJaya algorithm with uncertain demands. Computational results and comparisons with the state-of-the-art algorithms demonstrate the superiority of the proposed algorithm in solving VRPUD.

Optimization and benefit analysis of the supply chain for sweet sorghum bioenergy production in China

<p>Bio-liquid fuel is one of the effective ways to alleviate energy shortages and reduce greenhouse gas emissions. Sweet sorghum, as an energy crop used in fuel ethanol production, has enormous potential for development. However, due to seasonality, vulnerability, and logistical scheduling costs during transportation, the sweet sorghum ethanol supply chain faces significant difficulties and challenges in its development. To address the research gap, this paper proposes a multi-objective sustainable supply chain optimization model for Chinese sweet sorghum bioenergy based on mixed-integer linear programming. In this framework, the crop growth process model is first spatially extended to obtain the feedstock sources of the sweet sorghum ethanol supply chain. Then a site selection model for the feedstock collection stations and ethanol processing plants is constructed based on the geographic information system (GIS) to determine the candidate locations of the basic units of the sweet sorghum ethanol supply chain. Finally, a multi-objective supply chain optimization model based on the mixed-integer linear programming methodology is created to achieve the sweet sorghum ethanol supply chain layout and optimization at the national level. This framework takes into account the fuel ethanol market demand in China and the current state of the sweet sorghum ethanol supply chain industry, achieving national-level layout and optimization of the sweet sorghum ethanol supply chain. The total aboveground biomass (dry weight) of sweet sorghum on marginal land in China can be converted into approximately 37.212 million tons of fuel ethanol production, meeting the requirement to promote a nationwide 10% substitution rate for automotive ethanol gasoline. The lowest-cost supply chain layout scheme suggests establishing large ethanol processing plants near the Hu Huanyong Line, as this area benefits from abundant sweet sorghum yield on marginal lands and lower east-west direction transportation costs. However, the lowest carbon emission supply chain layout scheme recommends reducing the raw material supply area and increasing the ethanol transportation route to lower carbon emissions. The results of the sensitivity analyses indicate that lowering feedstock production and increasing ethanol demand increases the overall cost and carbon emissions of the sweet sorghum ethanol supply chain, while using rail transportation methods, increasing feedstock production, and decreasing ethanol demand reduces costs and carbon emissions.</p>

Risk-Aware Coordination of Logistics Scheduling and Energy Management for Maritime Mobile Microgrid Clusters

Risk-Aware Coordination of Logistics Scheduling and Energy Management for Maritime Mobile Microgrid Clusters

Deep Reinforcement Learning-Based Rescue Resource Distribution Scheduling of Storm Surge Inundation Emergency Logistics

A high-intensity storm surge hazard exposes residents and facilities to inundation danger. A continuously operating robust inundation emergency logistics system is vital to guarantee lives and support the control of hazardous materials. A massive inundation area, large number of affected facilities, and instantly changing flooding situation bring tremendous challenges to rescue resource allocation. In this article, a rescue resource distribution scheduling of storm surge inundation logistics is proposed to quantitatively formulate the rescue time minimization problem in emergency logistics. The mixed-integer linear programming (MILP) method is proposed for the emergency logistics scheduling model validation and optimality comparison. To enhance the efficiency of creating a good quality allocation strategy when facing large-scale problems, a deep reinforcement learning algorithm—deep deterministic policy gradient (DDPG)—is utilized to search the solutions. Based on a rescue resource scheduling model of storm surge inundation logistics targeting storm surge Mangkhut in September, 2018, a case study of the Futian District, Shenzhen, China, was conducted to verify the correctness and efficiency of the MILP and the DDPG. The optimal schedule solution designed by the MILP had a duration of 3.5138 h, while the solution calculated by DDPG had a duration of 5.065 h for the rescue. The execution time of DDPG was stable and under a second, while the execution time of the MILP was over two hours.

Group-Based Distributed Auction Algorithms for Multi-Robot Task Assignment

This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots’ total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots’ total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method. Note to Practitioners—This work presents two group-based distributed auction algorithms for a sufficiently large fleet of robots to efficiently transport a set of dynamically appearing dispersed packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity, and the objective is to minimize the robots’ total travel time to transport all the packages within the prescribed time-windows. The paper’s practical contributions are threefold: First, the multi-robot task assignment problem is formulated through a robot-group assignment strategy, which enables complex logistic scheduling for tasks grouped according to their distributions and time-windows. Second, we theoretically show that the multi-robot task assignment problem is an NP-hard problem, which implies the necessity for designing approximate task assignment algorithms. Third, the proposed group-based distributed auction algorithms are efficient and can be adapted for real scenarios.

A Buffer-Based Ant Colony System Approach for Dynamic Cold Chain Logistics Scheduling

Cold chain logistics (CCL) scheduling is an emerging research problem in the logistics industry in smart cities, which mainly concerns the distribution of perishable goods. As the quality loss of goods that occurs in the distribution process should be considered, the CCL scheduling problem is very challenging. Moreover, the problem is more challenging when the dynamic characteristics (e.g., the orders are unknown beforehand) of the real scheduling environment are considered. Therefore, this paper focuses on the dynamic CCL (DCCL) scheduling problem by establishing a practical DCCL model. In this model, a working day is divided into multiple time slices so that the dynamic new orders revealed in the working day can be scheduled in time. The objective of the DCCL model is to minimize the total distribution cost in a working day, which includes the transportation cost, the cost of order rejection penalty, and the cost of quality loss of goods. To solve the DCCL model, a buffer-based ant colony system (BACS) approach is proposed. The BACS approach is characterized by a buffering strategy that is carried out at the beginning of the scheduling in every time slice except the last one to temporarily buffer some non-urgent orders, so as to concentrate on scheduling the orders that are preferred to be delivered first. Besides, to further promote the performance of BACS, a periodic learning strategy is designed to avoid local optima. Comparison experiments are conducted on test instances with different problem scales. The results show that BACS is more preferred for solving the DCCL model when compared with the other five state-of-the-art and recent well-performing scheduling approaches.

Real Environment-Aware Multisource Data-Associated Cold Chain Logistics Scheduling: A Multiple Population-Based Multiobjective Ant Colony System Approach

Cold chain logistics (CCL) scheduling is important for smart cities as it directly affects the service quality and operating profits of logistics companies. However, traditional CCL models seldom reflect the real transportation environment, making the solutions hardly applicable to the real CCL scenes. Hence, this paper attempts to establish a multisource data-associated CCL model oriented to the real transportation environment. This environment is considered by employing the real-captured driving duration and distance between any two places. Three scheduling objectives (namely, quality losses, personnel and vehicle costs, and transportation costs) are taken into account. To efficiently solve the proposed multisource data-associated multiobjective CCL model, a multiple population-based multiobjective ant colony system (MPMOACS) approach is proposed. Based on the multiple populations for multiple objectives framework, the MPMOACS approach can optimize multiple objectives sufficiently, and thus obtain promising solutions distributed along the entire Pareto front. To further enhance the performance of the MPMOACS, a ranking-based local search strategy is also designed. Experiments are conducted on not only the existing benchmark instances but also a real environment-aware multisource dataset that is built based on real-captured transportation data of Guangzhou and Shenzhen, China. Compared with six state-of-the-art and very recent well-performing multiobjective optimization approaches, the proposed MPMOACS approach exhibits the overall best performance.

Patient-reported reasons for no-shows to screening mammogram appointments among African American women.

e18534 Background: Mammography screening significantly reduces breast-cancer related mortality; however, many women fail to undergo screening as recommended by national guidelines. No-shows are responsible for a significant proportion of delayed or missed cancer screening exams. Further, no-shows disproportionately affect underserved and minority populations. We previously identified a high no-show rate for screening mammograms among patients seeking care our institution. African American (AA) women were almost three times more likely to no-show than non-Hispanic white women. The racial disparity in no-shows persisted after adjustment for socioeconomic factors. The objective of this survey study was to identify reasons for missed mammogram screening appointments among AA women. Methods: We conducted a survey (via mail or telephone) of AA women who missed their screening mammogram appointment in summer 2021. Using a structured survey instrument, we collected information on patient-specific and health service barriers. Patient-specific barriers included procedure-related concerns (e.g., concern about discomfort), cognitive-emotional factors (e.g., fear of finding cancer), and changes in health status. Health service barriers included logistical factors (e.g., transportation), cost (e.g., lack of insurance) and scheduling problems (e.g., forgot about appointment or scheduled at an inconvenient time). Here we describe the most common reasons for missed appointments and compared women who reported patient-specific versus health service barriers. Results: 255 women who no-showed for their appointment were contacted and 91 participated in the study survey (35.6% response rate). Most respondents (90%) attributed their no-show to at least one of the listed barriers. Nineteen (7.5%) attributed their no-show to COVID-19, but only 1 person reported this as their only barrier. Scheduling issues were the most commonly reported barriers (57.8%), followed by transportation (38.9%). Three-quarters of respondents reported health service barriers, while only 40.7% reported patient-related barriers. The most common patient-related barriers were cognitive-emotional (25%), changes in health status (20.9%) and procedure-related concerns (15.6%). The majority of respondents (82.6%) were interested in rescheduling their mammogram. Conclusions: Most appointment no-shows among surveyed AA women resulted from potentially preventable scheduling and transportation issues. Relatively few respondents reported cognitive-emotional or procedure-related concerns. Further, the majority of respondents were interested in rescheduling their mammogram; which suggests that these women remain motivated to undergo breast cancer screening. Programs which address preventable health-service related issues may help these women keep their appointments.

Integrated Offsite Logistics Scheduling Approach for High-Rise Modular Building Projects

By maximizing offsite work and minimizing onsite activities, modular construction has become increasingly popular in high-rise, high-density cities. For successfully delivering high-rise modular buildings, a key issue is the effective scheduling of offsite logistics, consisting of module manufacturing, transportation, and storage. This paper proposes an integrated offsite logistics scheduling approach for high-rise modular building projects that addresses project-specific constraints and uncertainties, based on the stochastic Petri net (SPN) method. The proposed approach supports multi-criteria decision-making, that considers completion time, total cost, storage capacity utilization, and production process stability under what-if scenarios. Its efficacy and novelty for offsite logistics scheduling were demonstrated through a real-life project case study. The results indicate the need to (1) strengthen suppliers’ demand responsiveness by shortening module production time; (2) enhance manufacturing flexibility by increasing factory storage utilization; (3) improve supply chain synchronization; and (4) capitalize on the government’s role in offsite logistics enhancement. The findings methodologically innovate offsite logistics scheduling, and practically support contractors and module suppliers in successfully delivering high-rise modular building projects in high-density cities.

A Cluster-First Route-Second Constructive Heuristic Method for Emergency Logistics Scheduling in Urban Transport Networks

Advanced strategies for emergency logistics scheduling problems in urban transport networks have been a challenging topic for centuries. This study proposed a cluster-first route-second constructive heuristic method based on the continuous approximation (CA) for ‘one-to-many’ vehicle routing to dispatch commidities after an emergency. The objective of the study is to provide a replenish schedule and routing solution from the government/provider’s end in order to minimize the total motion cost, pipeline inventory cost, and holding cost with backorder for the disaster relief operation. The developed method can turn the complicated vehicle routing problem (VRP) into a relatively simple travel salesman problem (TSP) for pre-assigned customer sets. The CA is employed to determine the optimal replenish amount and inventory level for the route serving a given location. The Christofides method is then applied to solve the TSP for the selected cluster. Two clustering methods are investigated in this research: (1) a local-based approach where clustering and routing are determined; and (2) a K-mean clustering method where points are clustered upfront by the CA solution. A case study in Miami-Dade County in Florida to dispatch fuels from the depot to 72 gas stations is presented, demonstrating the proposed approach and comparing two clustering methods. The numerical results illustrate the effectiveness of the algorithms and conclude that the local-based clustering approach may yield a lower total cost with a higher motion cost.

Research on Fresh Product Logistics Transportation Scheduling Based on Deep Reinforcement Learning

With the improvement of the economic level, people’s quality of life continues to improve, the demand for fresh food is increasing, and the logistics of fresh products is also developing rapidly. We effectively balance the relationship between transportation costs and service levels in fresh product logistics and transportation businesses, improve the transportation capacity and efficiency of logistics transportation businesses, and improve the resource utilization of businesses. It is important for the development of the logistics transportation scheduling industry for fresh products. Significance. Based on this, this paper proposes a DNQ algorithm based on pointer network, which solves the single fresh product distribution service center-regional efficient logistics scheduling problem, and a feasible logistics transportation scheduling scheme can be obtained through simulation experiments. The simulation results show that the algorithm is superior to other common intelligent algorithms in terms of accuracy and stability, which proves that the algorithm is effective and feasible (the research results cannot be directly shown in the abstract and need to be supplemented) At the same time, it further explored the DNQ algorithm to improve the correction network, which can solve the complex problem of multiple fresh product distribution service centers-regional efficient logistics scheduling. It is a successful attempt to improve the solution algorithm. Complex logistics and transportation scheduling problems provide ideas and have good guidance and reference significance.

Cooperative Multiagent Attentional Communication for Large-Scale Task Space

With the rapid development of mobile robots, they have begun to be widely used in industrial manufacturing, logistics scheduling, intelligent medical, and other fields. For large-scale task space, the communication between multiagents is the key to affect cooperation productivity, and agents can coordinate more effectively with the help of dynamic communication. However, the traditional communication mechanism uses simple message aggregation and broadcast and, in some cases, lacks the distinction of the importance of information. Multiagent deep reinforcement learning (MDRL) is valid to solve the problem of informational coordination strategies. However, how different messages affect each agent’s decision-making process remains a challenging task for large-scale task. To solve this problem, we propose IMANet (Import Message Attention Network). It divides the decision-making process into two substages: communication and action, where communication is considered to be part of the environment. First, an attention mechanism based on query vectors is introduced. The correlation between the query vector agent’s own information and the current state information of other agents is estimated, and then, the results are used to distinguish the importance of information from other agents. Second, the LSTM network is used as the unit controller for each agent, and individual rewards are used to guide the agent training after communication. Finally, IMANet is evaluated on tasks on challenging multi-agent platforms, Predator and Prey (PP), and traffic junction. The results show that IMANet can improve the efficiency of learning and training, especially when applied to large-scale task space, with a success rate 12% higher than CommNet in baseline experiments.

Holistic Approaches to Reducing Salmonella Contamination in Poultry Industry

Salmonella are widely found in the poultry industry, which subsequently may pose a risk to animal and human health. The aim of this review is to highlight strategies for the prevention and control of Salmonella at each stage in the poultry production chain by monitoring risks from the farm to the retailer. Among the primary approaches for control of Salmonella at the farm level includes the administration of synthetic and natural compounds to live chickens (vaccination and antibiotic), litter management as well as fortification of feed and acidification of drinking water. In the poultry processing plant, multiple hurdle technology and different chilling conditions to reduce Salmonella were discussed. In the retail level, an effective monitoring program to control Salmonella contamination by good manufacturing practices and hazard analysis and critical control points has been reviewed. Overall, we conclude that these approaches play a role in reducing the dissemination of Salmonella in the poultry industry. However, there is no published data related to logistic scheduling of poultry processing.

Integrated cross-supplier order and logistic scheduling in cloud manufacturing

In the cloud manufacturing environment, integrated cross-supplier order and logistic scheduling can benefit both suppliers and third-party logistics, significantly reduce their production and transport costs to improve the overall efficiency of the supply chain. This paper aims to construct a hybrid solution with both cross-supplier order assignments and third-party logistics scheduling under a centralised scheduling mode of cloud manufacturing platform, which is defined as an integrated cross-supplier order and logistic scheduling (ICSOLS) problem. Since the problem is NP-hard, a strategy based on an improved shuffled frog-leaping algorithm was developed for solving the model. A numerical case was designed for demonstrating the modelling process and analysing results. Different scales of simulations are designed to verify validity and robustness. Results show the proposed approach and algorithm can solve the defined problem efficiently.

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period post-disaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a real-world instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.

Logistics Chain Optimization and Scheduling of Hospital Pharmacy Drugs Using Genetic Algorithms

In recent years, the health sector has faced increasingly important challenges. Due to the economic crisis and competitions, hospitals are facing many issues affecting the supply chain, such as budget cuts or lack thereof as well as insufficient human resources. Although essential for an excellent service, logistics take up a considerable part of the budget as challenges need to be addressed such as delays in drugs delivery, transportation and storage conditions, routing and scheduling. As to governance, each hospital is assigned to a specific region, which cannot be defined due to political, demographic, or geographic issues. This paper focuses on multi-depot vehicle routing problem (MDVRP) in healthcare logistics to feed the hospital pharmacies. The idea is to apply MDVRP's approach to the health sector, specifically hospital pharmacies. In this projection, hospitals are considered to present clients, and central pharmacies present deposits. This problem (the MDVRP) is known by this nature NP-hard. For that, the heuristic method was used as genetic algorithm to solve the problem. The paper is organized as follows, the first section discusses, compares, and proposes clustering methods for healthcare facilities with applying them on Moroccan hospitals case; the second section proposes a genetic algorithm to resolve the MDVRP with a simulation.

Transplantation of allogeneic cryopreserved hematopoietic cell grafts during the Covid-19 pandemic: A National Marrow Donor Program perspective.

Transplantation of allogeneic cryopreserved hematopoietic cell grafts during the Covid-19 pandemic: A National Marrow Donor Program perspective.

A hybrid many-objective competitive swarm optimization algorithm for large-scale multirobot task allocation problem

Large-scale multi-robot task allocation (MRTA) problem is an important part of intelligent logistics scheduling. And the load capacity of robot and picking station are important factors affecting the MRTA problem. In this paper, the MRTA problem is built as a many-objective optimization model with four objectives, which takes the load capacity of single robot, single picking station, all robots and all picking stations into account. To solve the model, a hybrid many-objective competitive swarm optimization (HMaCSO) algorithm is designed. The novel selection method employing two different measurement mechanisms will form the mating selection operation. Then the population will be updated by employing the competitive swarm optimization strategy. Meanwhile, the environment selection will play a role in choosing the excellent solution. To prove the superiority of our approach, there are two series of experiments are carried out. On the one hand, our approach is compared with other five famous many-objective algorithms on benchmark problem. On the other hand, the involved algorithms are applied in solving large-scale MRTA problem. Simulation results prove that the performance of our approach is superior than other algorithms.