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Modular integrated construction (MiC) is an innovative construction method that is widely used in practice, but high transportation cost is an obstacle to its wider implementation. This paper is motivated by the real-world need for a scientific approach for MiC transport scheduling, and addresses the practical problem of how to achieve on-time door-to-door MiC delivery in an economical manner. An innovative integer programming model is formulated with the objective of minimising the total transportation costs, including trailer rental, fuel costs, and worker salaries. A case study based on Hong Kong data validates that the developed model can be efficiently solved for problems of practical size. The results also demonstrate that the derived MiC schedule plan can reduce transportation costs by 58.7% compared with a just-in-time (JIT) strategy, thus bringing great economic benefits to off-site construction practitioners. Useful managerial implications related to on-site MiC storage areas are derived through sensitivity analysis. Overall, this paper sets the ground for the application of mathematical programming methodology to the off-site construction field. Future research may be devoted to MiC transport scheduling under uncertainty. [ABSTRACT FROM AUTHOR]

The complexity of decision-making for companies buying transportation services has increased due to the presence of more options and pricing schedules for transportation. Many companies make transportation and inventory decisions in an uncoordinated way and select only one transportation mode, missing opportunities for logistics cost savings. The experimental study in this paper is based on a real-world decision problem faced by a Scandinavian company that distributes fast-moving consumer goods and wants to determine its transportation strategy. We propose a novel multi-mode lot-sizing model with dynamic deterministic demand to illustrate the cost impact of accurately modelling piecewise-linear transportation costs and allowing a more flexible usage of transportation modes when planning order replenishments. We compare three transportation strategies with increasing degrees of flexibility: two single mode strategies, where one strategy is more flexible than the other, and a multi-mode strategy. We conclude that managers can significantly reduce costs by increasing the flexibility of mode selection in transportation strategies. [ABSTRACT FROM AUTHOR]

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As the value and importance of urban air mobility (UAM) are being recognized, there is growing attention towards UAM. To ensure that urban air traffic can serve passengers to the greatest extent while ensuring safety and generating revenue, there is an urgent need for a transportation scheduling plan based on safety considerations. The region of Beijing–Tianjin–Hebei was selected as the case study in this research. A real-time demand transportation scheduling model for a single day was constructed, with the total service population and total cost as objective functions, and safety intervals, eVTOL performance, and passenger maximum waiting time as constraints. A Joint Optimization of Cost and Scheduling Particle Swarm Optimization (JOCS-PSO) algorithm was utilized to obtain the optimal solution. The optimal solution obtained in this study can serve 138,610,575 passengers during eVTOLs' entire lifecycle (15 years) with a total cost of CNY 368.57 hundred million, with the cost of CNY 265.9 per passenger. Although it is higher than the driving cost, it saves 1–1.5 h and thus has high cost effectiveness during rush hours. [ABSTRACT FROM AUTHOR]

3D printing technology is fundamentally transforming how companies are run and managed today. This work analyzes the problem of integrated production and transportation of automotive spare parts in the context of 3D printing. We consider two sets of customers – scheduled and breakdown maintenance – serviced by the same resource but with distinct delivery modes. We propose a mixed-integer programming (MIP) formulation for optimally solving small problem instances. We develop an exact approach based on column generation (CG) for large cases by reformulating the MIP model as a set-covering problem through Dantzig-Wolfe decomposition. We design and deploy two acceleration strategies for the quicker convergence of the CG approach. Through extensive computational experiments, we establish the superiority and suitability of the proposed solution methodology for solving real-life problems at terse computational times. [ABSTRACT FROM AUTHOR]

We study an integrated optimisation problem with blending, scheduling, and routing components for a melted material blending production system. The problem is formulated as a mixed-integer linear programming model that considers the blending machine environment, due dates, target amounts, required chemical compositions of the products, and ready times of the materials in containers. This model aimed to determine the container pairings, blending plants for container pairs, and schedules for blending operations while minimising the total end time of material usage, total penalty for violating component specifications, and employee workload. Further, we propose a three-stage approach that involves solving a relaxed problem and then resolving the problem with fixed variables. We developed a combinatorial Benders decomposition algorithm with a minimal infeasible subsystem identification algorithm for the blending scheduling problem. The experimental results indicate that the proposed method can find high-quality solutions within a reasonable amount of time. [ABSTRACT FROM AUTHOR]

Purpose: Production and transportation of precast components, as two continuous service stages of a precast plant, play an important role in meeting customer needs and controlling costs. However, there is still a lack of production and transportation scheduling methods that comprehensively consider delivery timeliness and transportation economy. This article aims to study the integrated scheduling optimization problem of in-plant flowshop production and off-plant transportation under the consideration of practical constraints of customer order delivery time window, and seek an optimal scheduling method that balances delivery timeliness and transportation economy. Design/methodology/approach: In this study, an integrated scheduling optimization model of flowshop production and transportation for precast components with delivery time windows is established, which describes the relationship between production and transportation and handles transportation constraints under the premise of balancing delivery timeliness and transportation economy. Then a genetic algorithm is designed to solve this model. It realizes the integrated scheduling of production and transportation through double-layer chromosome coding. A program is designed to realize the solution process. Finally, the validity of the model is proved by the calculation of actual enterprise data. Findings: The optimized scheduling scheme can not only meet the on-time delivery, but also improve the truck loading rate and reduce the total cost, composed of early cost in plant, delivery penalty cost and transportation cost. In the model validation, the optimal scheduling scheme uses one less truck than the traditional EDD scheme (saving 20% of the transportation cost), and the total cost can be saved by 17.22%. Originality/value: This study clarifies the relationship between the production and transportation of precast components and establishes the integrated scheduling optimization model and its solution algorithm. Different from previous studies, the proposed optimization model can balance the timeliness and economy of production and transportation for precast components. [ABSTRACT FROM AUTHOR]

This paper focuses on linear programming problems in a rough interval environment. By introducing four linear programming problems, an attempt is being made to propose some results on optimal value of a linear programming problem with rough interval parameters. To obtain optimal solutions of a linear programming problem with rough interval data, constraints of the four proposed linear problems are applied. In this regard, firstly, the largest and the smallest feasible spaces for a linear constraint set with rough interval coefficients and parameters are introduced. Then, a rough interval for optimal value of such problems is obtained. Further, an upper approximation interval and a lower approximation interval as the optimal solutions of linear programming problems with rough interval parameters are achieved. Moreover, two solution concepts, surely and possibly solutions, are defined. Some numerical examples demonstrate the validity of the results. In particular, a scheduling problem and a fixed-charge transportation problem (FCTP) under rough interval uncertainty are investigated. [ABSTRACT FROM AUTHOR]

With trends of urbanisation on the rise, providing adequate housing to individuals remains a complex issue to be addressed. Often, the slow output of relevant housing policies, coupled with quickly increasing housing costs, leaves individuals with the burden of finding housing that is affordable and in a safe location. In this paper, we unveil how transit service to employment hubs, not just housing policies, can prevent individuals from improving their housing conditions. We approach this question in three steps, applying the workflow to 20 cities in the United States of America. First, we propose a comprehensive framework to quantify housing insecurity and assign a housing demographic to each neighbourhood. Second, we use transit-pedestrian networks and public transit timetables (GTFS feeds) to estimate the time it takes to travel between two neighbourhoods using public transportation. Third, we apply geospatial autocorrelation to identify employment hotspots for each housing demographic. Finally, we use stochastic modelling to highlight how commuting to areas associated with better housing conditions results in transit commute times of over an hour in 15 cities. Ultimately, we consider the compounded burdens that come with housing insecurity, by having poor transit access to employment areas. In doing so, we highlight the importance of understanding how negative outcomes of housing insecurity coincide with various urban mechanisms, particularly emphasising the role that public transportation plays in locking vulnerable demographics into a cycle of poverty. [ABSTRACT FROM AUTHOR]

The growth of container throughput and the impact of COVID-19 have made container ports increasingly congested, leading to uncertain cargo transit times and high freight rates. Shipping parties want to know in advance the congestion of each port in the next phase to adjust their plans, but there are few studies involving congestion prediction, and this study hopes to make some additions. Three indicators are extracted from Automatic Identification System (AIS) data to represent the port congestion status for the sake of generalizability. And those indicators are used to predict port congestion status and further to predict container ships' time in port using the eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) algorithms. The established models show that those indicators can improve prediction accuracy of port time compared to not considering port status. The findings also show that port congestion status contributes significantly to determining port time and makes it fluctuate by up to nearly 50 hours. The prediction results could help shipping companies to change their transportation schedules early to bypass ports with long waiting times or to arrange ships to enter the port earlier for delivering cargos on schedule. [ABSTRACT FROM AUTHOR]

In response to the European Union (EU)'s sanctions on Russian oil products, tanker shipping firms may adopt two strategies to reoptimize their shipping networks. The first strategy is to switch the flag states of tankers that are not eligible to operate on certain routes. The second strategy is to reposition tankers based on their flag states, i.e., moving those tankers that are eligible from other groups to specified routes. To help tanker shipping firms minimize the total operating cost during the planning horizon in the context of EU oil sanctions, including costs of fleet repositioning, flag switching, and fuel, this study investigates an integrated problem of fleet repositioning, flag switching, transportation scheduling, and speed optimization considering the dynamic relationships among fuel consumption, speed, and load. By formulating the problem as a nonlinear integer programming model and applying various linearization techniques to convert the nonlinear model into a linear optimization model solvable by off-the-shelf linear optimization solvers, this study demonstrates the practical application potential of the proposed model, with the longest solution time of less than two hours for a numerical instance with seven routes. Furthermore, through sensitivity analyses on important factors including unit fuel prices, crude oil transportation demand, and the tanker repositioning cost, this study provides managerial insights into the operations management of tanker shipping firms. [ABSTRACT FROM AUTHOR]

This paper studies a new variant of integrated production scheduling and vehicle routing problem where production of customer orders are performed under job-shop environment and order deliveries are made by a heterogeneous fleet of vehicles, each of which is allowed to take multiple trips. Two conflicting objectives are considered, namely minimisation of the total amount of CO2 emitted by the vehicles and minimisation of maximum tardiness resulting from late deliveries. To this end, we present a bi-objective mixed-integer programming formulation. Augmented ε-Constraint (Augmecon) method is implemented to find Pareto optimal solutions. Due to problem complexity, Augmecon cannot provide solutions even with small-sized problems. Thus, we adopt Pareto Local Search (PLS) and non-dominated sorting genetic algorithm-II (NSGA-II) for practical sized instances. For small-sized instances involving 5, 6, and 7 customers, experimental results indicate that CPU time of Augmecon are 11, 84, and 524 sec, respectively with an average number of Pareto efficient solution of 3.5. In terms of hypervolume index, Augmecon shows the best performance, followed by NSGA-II with 11.32% and PLS with 20.75% degradation for small-sized instances. For medium and large-sized instances, PLS shows worse performance than NSGA-II by 16.87% and 40.48%. [ABSTRACT FROM AUTHOR]

The coal loading operation of the coal preparation plant of an open pit coal mine causes chaos in coal mine vehicle scheduling due to the unreasonable arrival times of outgoing and container transportation vehicles. To further reduce the length of time that vehicle transportation equipment waits for each other and to reduce the total cost of container transportation, the optimisation model of container transportation vehicle scheduling in an open pit coal mine is constructed to minimise the minimum sum of the shortest time of container reversal and the lowest cost of container transportation. To accurately measure the total cost of container backward transportation, waiting time and unit waiting time cost parameters are introduced, and the total cost of container transportation is measured using the transportation cost and the waiting time cost transformation method. An improved grey wolf algorithm is proposed to speed up the convergence of the algorithm and improve the quality of the solution. When employing the genetic algorithm (GA) and grey wolf optimisation algorithm (GWO) for optimising the scheduling of container transport vehicles in coal mines, it is noted that while the GA can achieve the global optimum, its convergence speed is relatively slow. Conversely, the GWO converges more quickly, but it tends to be trapped in local optima. To accelerate the convergence speed of the algorithm and improve the solution quality, a hybrid GA−GWO algorithm is proposed, which introduces three genetic operations of selection, crossover, and mutation of GA into the GWO algorithm to prevent the algorithm from falling into the local optimum due to the fall; at the same time, it introduces hunting and attacking operations into the elite retention strategy of GA, which improves the stability of the algorithm's global convergence. Analysis indicates that, compared to SA, GWO, and GA, the hybrid algorithm enhances optimisation speed by 43.1%, 46.2%, and 43.7%, increases optimisation accuracy by 4.12%, 6.1%, and 3.2%, respectively, and reduces the total container reversal time by 35.46, 22, and 31 h. The total cost of container transportation is reduced by 2437 RMB, 3512 RMB, and 1334 RMB, respectively. [ABSTRACT FROM AUTHOR]

Modeling external and empty freight trips helps in better planning and scheduling of transportation operations. This can lead to more effective route planning, load consolidation, and overall optimization of transportation networks. In this study external and empty truck trips modeled using Freight Analysis Framework (FAF) and Highway Performance Measurement System (HPMS) data with generating trip outputs from POLARIS agent-based simulation framework. The model then applied to the Austin metropolitan area, Texas, USA. The model estimated single and multi-unit AADT values with 91% and 93.4% accuracy compared with the HPMS volume, respectively. The estimated error – 9% and 6.6% - is due to the shortest path routing that may prefer faster routes on certain highways. Truck trips on large volume links tend to be overestimated while short links underestimate truck trips to some extent. [ABSTRACT FROM AUTHOR]

Dynamic flexible job-shop scheduling is traditionally a challenge in real-world manufacturing systems, especially considering the constraints of transportation resources and transportation time. To address the dynamic optimisation problem in flexible manufacturing systems, this paper proposes a novel proactive-reactive methodology to adapt to the dynamic changes in working environments and addresses the joint scheduling problem for machine tools and transportation resources. The joint optimisation model is first formulated as a mixed-integer programming model considering production efficiency and transportation constraints. The flowchart of the dynamic scheduling system is then designed for dynamic decision-making, and a novel particle swarm optimisation algorithm integrated with genetic operators is developed to respond to dynamic events and generate the reschedule plan in time. Finally, several numerical experiments and case studies in reality are applied to verify the efficiency of the developed methodology. Common dispatching rules and heuristic methods are also applied to test and evaluate the efficiency of the developed algorithm. Computational results demonstrate that the developed methods and decision models are efficient for dynamic job-shop scheduling problems in flexible manufacturing systems, which can acquire rather a good effect in practical production. [ABSTRACT FROM AUTHOR]

An intensified competition forces warehouses to handle more orders in shorter time windows. This complicates the timely retrieval of these customer orders. Planning order picking operations, thereby aiming to increase efficiency, inevitably results in balancing concerns, such as imbalances among pick areas, pickers or time periods. Reducing workload imbalances, therefore anticipating on workload peaks, results in a more stable order picking process. However, there exist several measures that can be used to evaluate and correct existing imbalances. This study contributes to academic literature by analysing, explaining and evaluating the effectiveness of various workload balancing approaches (e.g. Rawlsian's approach, range, mean-based) in order picking operations, more specifically in the context of balancing workload over time in case of restricted time windows for retrieving customer orders. Results show that the effect of warehouse layout characteristics and customer order parameters on the effectiveness of balancing measures is very limited. However, the underlying managerial reason (e.g. workforce allocation, transportation schedule or human well-being) for solving the operational workload balancing problem does significantly impact the effectiveness and choice of an appropriate balancing measure. [ABSTRACT FROM AUTHOR]

Industry 4.0 technologies, such as artificial intelligence, the internet of things and 3D printing are aiding the manufacturers by complementing their skilled workforce and transforming the way factories are run. This paper studies an integrated production and transportation scheduling problem in the context of the spare parts supply chain by integrating 3D printing with JIT delivery systems. This work aims to find a synchronised production and distribution schedule that minimises the weighted sum of delivery times and transportation costs. Based on the characteristics of the problem, we propose a new set-covering formulation. An enhanced branch-and-price algorithm is designed to solve the problem instances to optimality. To expedite the column generation process, two acceleration strategies are also used. The computational results are in favour of the proposed algorithm and the acceleration strategies. Further, the results indicate that integrating the production and transportation scheduling decisions leads to an average savings of about 16.27% of the total costs. [ABSTRACT FROM AUTHOR]

The traveling salesman problem with time windows (TSPTW) has wide practical applications in transportation and scheduling operations. We study the TSPTW in the deterministic case as well as under travel time uncertainty. We consider the three classical TSPTW variants with the objectives of minimizing cost, completion time, and tour duration. In addition, a new TSPTW variant that maximizes the minimum slack of a tour, i.e., the smallest time buffer between the arrival time and the end of the time window over all nodes visited on the tour, is introduced. We further address a variant of the TSPTW in which the arc travel times are uncertain. This problem is modeled by means of an uncertainty set, and the goal is to determine a tour that remains feasible in the worst case within a budget stipulating the sum of all travel time deviations from their nominal values. To solve all targeted problem variants, we develop a two-phase general variable neighborhood search (GVNS) that applies an efficient move evaluation approach within the local search. Extensive numerical experiments on benchmark instances from the literature show that our GVNS finds high-quality solutions that are competitive with those obtained by the state-of-the-art heuristics for all problem variants. [ABSTRACT FROM AUTHOR]

Ride-hailing services have expanded the role of shared mobility in passenger transportation systems, creating new markets and creative planning solutions for major urban centers. In this paper, we consider their use for the first-mile or last-mile passenger transportation in coordination with a mass transit service to provide a seamless multimodal transportation experience for the user. A system that provides passengers with predictable information on travel and waiting times in their commutes is immensely valuable. We envision that the passengers will inform the system of their desired travel and arrival windows so that the system can jointly optimize the schedules of passengers. The problem we study balances minimizing travel time and the number of trips taken by the last-mile vehicles, so that long-term planning, maintenance, and environmental impact are all taken into account. We focus on the case where the last-mile service aggregates passengers by destination. We show that this problem is NP-hard, and we propose a decision diagram–based branch-and-price decomposition model that can solve instances of real-world size (10,000 passengers spread over an hour, 50 last-mile destinations, 600 last-mile vehicles) in computational time (∼1 minute) that is orders of magnitude faster than the solution times of other methods appearing in the literature. Our experiments also indicate that aggregating passengers by destination on the last-mile service provides high-quality solutions to more general settings. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2019.0163) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2019.0163). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Reasonable matching of capacity resources and transported cargoes is the key to realizing intelligent scheduling of less-than-truck-load (LTL) logistics. In practice, there are many types and numbers of participating objects involved in LTL logistics, such as customers, orders, trucks, unitized implements, etc. This results in a complex and large number of matching schemes where truck assignments interact with customer order service sequencing. For the truck–cargo online matching problem under real-time demand, it is necessary to comprehensively consider the online matching process of multi-node orders and the scheduling of multi-types of trucks. Combined with the actual operation scenario, a mixed-integer nonlinear programming model is introduced, and an online matching algorithm with a double-layer nested time window is designed to solve it. By solving the model in a small numerical case using Gurobi and the online matching algorithm, the validity of the model and the effectiveness of the algorithm are verified. The results indicate that the online matching algorithm can obtain optimization results with a lower gap while outperforming in terms of computation time. Relying on the realistic large-scale case for empirical analysis, the optimization results in a significant reduction in the number of trips for smaller types of trucks, and the average truck loading efficiency has reached close to 95%. The experimental results demonstrate the general applicability and effectiveness of the algorithm. Thus, it helps to realize the on-demand allocation of capacity resources and the timely response of transportation scheduling of LTL logistics hubs. [ABSTRACT FROM AUTHOR]

Uncertainties, such as road restrictions at shipyards and the irregular shape of blocks, pose challenges for transporter scheduling. Efficient scheduling of multiple transporters is critical to improving transportation efficiency. The digital twin (DT) technology offers numerous benefits, enabling interactions between the virtual and real worlds, real-time mapping, and dynamic performance evaluation. Based on DT technology, this study proposes a dynamic scheduling approach for cooperative transportation utilizing multiple transporters. The scheduling problem for multiple transporters is addressed and modeled in this study, considering factors such as block size and transporter loading. To solve this problem, a framework of DT-based multiple transporters system is established in a virtual environment. By inputting block information into this system, a solution is generated using transporter scheduling rules and interference detection methods. Experimental comparisons are conducted in this paper, exploring various scenarios with different number of tasks and the application of DT. The results demonstrate that the proposed approach effectively enhances transportation efficiency and improves ship construction efficiency. Hence, this study expands the application of DT technology in dynamic scheduling of transportation in shipyards and provides new ideas for shipbuilding company managers. [ABSTRACT FROM AUTHOR]

Distribution centers (DCs) run more efficiently if their inbound deliveries are spread out evenly over the days of the week so that volume is aligned with receiving capacity. However, delivery dates are often scheduled at the time of placing purchase orders without an efficient process for spreading deliveries evenly, and there has been little research on how to optimally do this. This paper terms this process Master Purchase Receipt Scheduling (MPRS) and introduces a novel MPRS algorithm developed by New Horizon Soft, LLC using data from a major U.S. restaurant chain, where the algorithm is now in use. The approach optimizes order delivery dates, accounting for vendor shipping calendars, shipment sizes, and transportation uncertainty. Results from a Monte Carlo simulation validate the effectiveness and robustness of the algorithm. By addressing the complexities of DC operations, this innovation promises to reduce receiving bottlenecks, increase efficiency and capacity utilization, and foster additional research in this vital area. [ABSTRACT FROM AUTHOR]

The current public transportation systems predominantly rely on rigid schedules and service patterns, leading to suboptimal resource allocation that impacts both passengers and transit operators. This inefficiency results in the wastage of resources and dissatisfaction among users. The unsatisfactory passenger experience significantly contributes to the declining ridership, thereby diminishing revenue for transit operators. To specifically address these challenges encountered by Lhasa's public transportation system, we propose a multi-objective model for bus departure timetables. The model aims to synchronize the costs of passenger waiting time and bus operation costs concurrently, accounting for diverse constraints such as actual travel times, operational bus numbers, bus capacity limits, and arrival time distributions. In this research, we establish a multi-objective optimization model with the primary goal of maximizing passenger satisfaction while concurrently optimizing the revenue of the transit company. Implemented in Lhasa, China, we use the Non-Dominated Sorting Genetic Algorithm-Ⅱ to derive Pareto fronts relevant for analysis. The research findings demonstrate a reduction in the frequency of departures by one bus within a one-hour timeframe. Additionally, a substantial 37% decrease is observed in both the count of buses not arriving at stations and the number of passengers waiting at these stations compared to previous timetables. These results suggest promising potential for significant benefits to both the transit company and passengers within the public transportation system. [ABSTRACT FROM AUTHOR]

The development goal of the intelligent cluster control system for the beam yard warehousing equipment is to achieve the intelligent and automated operations throughout the entire process from the precast beam pedestal to the outbound dock within the yard. Based on a certain highway expansion project, which is the first in China to adopt the three-dimensional composite expansion mode for highways. This paper addresses the issue of multiple beam transportation vehicles operating simultaneously in the storage system, which can easily lead to conflicts and deadlock situations. The Hopcroft-Tarjan algorithm is employed to establish path orientation strategies for the beam warehousing district. An entry and exit scheduling model for beam transportation, with the goal of minimizing transportation time, is developed, and a hybrid genetic algorithm (IH-GA) is designed to solve this model. Results show that the optimized hybrid genetic algorithm has a faster convergence rate and higher optimization efficiency. It can effectively reduce the time required for beam transportation operations and enhance the efficiency of beam scheduling. [ABSTRACT FROM AUTHOR]

Increasing disasters in recent years have necessitated the development of emergency logistics plans. Evacuation planning plays an important role in emergency logistics management, particularly when it comes to addressing transit-dependent populations that are often neglected in previous studies. This systematic literature review explores the current state of transit-based evacuation planning and examines the current gaps. We focused on transit-based evacuation planning problems that used optimisation and modelling approaches. This review conducts an extensive analysis of relevant studies to provide a comprehensive overview, identify research gaps, and outline future directions in the evacuation planning body of knowledge. Using an integrated systematic review methodology, a thorough search of the Scopus and Web of Science databases was conducted, resulting in a total of 538 articles. These articles were screened and evaluated based on predetermined inclusion and exclusion criteria, ultimately yielding 82 studies for final analysis. The findings highlight the growing importance of optimisation and modelling approaches within transit-based evacuation planning. Studies emphasize the integration of public transportation networks into evacuation strategies to enhance operational efficiency, optimize resource allocation, and ensure evacuee safety. Transit-based evacuation planning is vital for both those without personal vehicles, making evacuation more equitable, and vehicle owners, particularly in earthquakes where vehicles might be inaccessible or trapped, demonstrating its wide usefulness in all emergency scenarios. Various optimisation and modelling approaches have been employed in transit-based evacuation planning studies to simulate and analyse the flow of evacuees and vehicles during emergencies. Transit-based evacuation planning exhibits unique characteristics within disaster management, including the consideration of spatial and temporal dynamics of transit systems, integration of social and demographic factors, and involvement of multiple stakeholders. Spatial and temporal dynamics encompass transportation schedules, capacities, and routes, while social and demographic factors involve variables such as income, age, and mobility status. Stakeholder engagement facilitates collaborative decision-making and effective plan development. However, transit-based evacuation planning faces challenges that require further research and development. Data availability and accuracy, model validation, stakeholder coordination, and the integration of uncertainty and dynamic factors pose significant hurdles. Addressing these challenges necessitates advances in data collection, robust modelling frameworks, and improved communication and coordination mechanisms among stakeholders. Addressing these gaps requires interdisciplinary collaborations and advances in data analytics and modelling techniques. [ABSTRACT FROM AUTHOR]

We study the following scheduling problem with transportation. Given a set of n jobs that need to be processed on a single machine, we need to deliver the finished jobs to one of the two destinations using a single transporter. The goal is to minimize the time that all the jobs are delivered to their destination, such that the transporter returns. We propose a 11 / 6 + ε -approximation algorithm which improves upon the best-known approximation ratio of 2. [ABSTRACT FROM AUTHOR]

The concept of public transport with periodic timetables has been slowly spreading in the Czech Republic for many years. Together with the announcement of tenders for the operation of compulsory transport according to the European Parliament's Regulations 1370/2007 and 2338/2016, circular integrations occur in many regions, which usually represent the adjustment of public transport to a concept corresponding to current trends and needs. [ABSTRACT FROM AUTHOR]

Quality public transport is an essential part of global mobility. In general, there is an effort to ensure that as many transport users as possible use public transport. The public transport subsystems also include rail transport. Nowadays, traveling by train is not only a matter of overcoming longer distances in national and international transport, but also a matter of daily commuting to work, school or service. Suburban rail transport is usually part of integrated transport systems, the principle of which is the possibility to use several types of means of transport in one journey per ticket. An indicator of the proper functioning of these systems is the existence of quality and convenient transfer dates times and reliable transfer links that minimize waiting times and total time spent on transportation. Within these systems, there is no network-specific maximum transfer time, which usually depends on the technical solution of the transfer point, the distance that passengers have to cover between vehicles or check-in technology (for example, front door boarding, turnstiles, etc.). the ideal value of the transfer time in terms of the probability that the passenger will not miss the connection and at the same time represents different aspects of the view of this issue. This is on the specific route Ostrava - Prague, which is the busiest in the Czech Republic. Within it, two reference links will be selected (one for each direction), the mean value of the delay in the stations with connections and other statistical indicators will be determined. Furthermore, a simulation of train delays is created using the Monte Carlo method, from which the probability of a connection is determined and subsequently the degree of dissatisfaction with the route is determined while maintaining the current connections. From which a transfer time is subsequently identified in which the probability of passing is as low as possible, but at the same time in normal situations passengers do not have to wait too long. [ABSTRACT FROM AUTHOR]

With city logistics becoming more and more important, increasing attention has been paid to the 'last-mile delivery' in urban areas. We investigate a novel crowdsource-enabled integrated production and transportation scheduling problem in the paper. The problem is first formulated into a mixed-integer linear program and its strong NP-hardness is proved. To better understand this complex problem, two sub-problems: a production and transportation scheduling problem and a crowdsourced bid selection problem are analysed. Based on problem properties, a Genetic Algorithm (GA) and a lower bound (LB) are developed to solve the original problem. Experimental results with up to 100 customers show that the GA outperforms the well-known commercial MIP solver CPLEX. Especially, (1) the GA can yield near-optimal solutions for all the tested instances with an average gap of 10.17% from the lower bound, while CPLEX provides feasible solutions only for instances with no more than 30 customers; (2) the average computation time of the GA is only 0.93% of that required by CPLEX; Besides, sensitivity analysis demonstrates advantages of introducing crowdsourced delivery into city logistics. [ABSTRACT FROM AUTHOR]

While the procurement decision is generally made by individual buyers, this study investigates how a group of buyers can make a shared decision. We call this collaborative approach, co-procurement. A mathematical model is formulated for the decision of procurement from multiple suppliers. The model is solved for individual buyers. The outcome shows the optimal number of items a buyer should buy from different suppliers such that the total cost is minimised for that buyer. Next, it is investigated how a group of buyers could make this decision together. The proposed model takes into account transaction costs of collaboration, to determine the optimal size of the collaboration and the involved parties. The idea is new in the old direction of procurement and it introduces the concept of transaction costs in this area and analyses its impact on the optimal collaboration size and mix. A case study from Dutch Food Valley is provided to investigate the benefits of co-procurement and validate the developed structure. The results indicate that co-procurement can bring considerable cost-savings through consolidation of orders and more efficient transportation schedules. A sensitivity analysis is conducted to determine the impact of changes in the transaction cost in favour of the co-procurement. [ABSTRACT FROM AUTHOR]

The problem of cargoes transportation scheduling in the transport network represented by an undirected multigraph is considered. Transportations between vertices are provided at predefined time intervals. The iterative algorithm to search for a solution approximate to the optimal one by criterion value is proposed in the problem under consideration. The algorithm is constructed on the base of solutions of mixed integer linear programming problems. The applicability of the algorithm is tested by the example with more than 90 million binary variables. [ABSTRACT FROM AUTHOR]

In many flexible job shop scheduling problems, transportation scheduling problems are involved, increasing the difficulty in problem-solving. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us address this problem. First, the flexible job shop scheduling problem with transportation constraints is modeled as a state transition diagram and a multi-objective function, where there are ten states in total for state transition, and the multi-objective function considers the makespan, average processing waiting time, and average transportation waiting time. Second, a novel artificial Physarum polycephalum colony algorithm is designed herein with two main operations: expansion and contraction. In the expansion operation, each mycelium can cross with any other mycelia and generate more offspring mycelia, of which each includes multiple pieces of parental information, so the population expands to more than twice its original size. In the contraction operation, a fast grouping section algorithm is designed to randomly group all mycelia according to the original population size, where each group selects the best fitness one to survive, but the other mycelia are absorbed to disappear, so the population size recovers to the original size. After multiple iterations, the proposed algorithm can find the optimal solution to the flexible job shop scheduling problem. Third, a series of computational experiments are conducted on several benchmark instances, and a selection of mainstream algorithms is employed for comparison. These experiments revealed that the proposed method outperformed many state-of-the-art algorithms and is very promising in helping us to solve these complex problems. [ABSTRACT FROM AUTHOR]

Accurate traffic estimation on urban networks is a prerequisite for efficient traffic detection, congestion warning, and transportation schedule. The current estimation methods can be roughly divided into model-driven and the data-driven methods. The estimation accuracy of the model-driven methods cannot satisfy certain applications. Meanwhile, the data-driven methods have the disadvantages of poor generalization ability and weak interpretability. To overcome these challenges, this paper proposes a framework named the physics-informed spatiotemporal graph convolution neural network (PSTGCN) based on physics-informed deep learning theories. The PSTGCN uses a spatiotemporal graph convolution neural network combined with traffic flow models to estimate the traffic state. The proposed model not only considers the temporal and spatial dependence of traffic flow but also abides by the internal law of traffic flow. Furthermore, the estimation objects of the proposedmodel are multiple variables that comprehensively represent the traffic state. Experiments on real-world traffic data reveal the error of the PSTGCN is reduced by 38.39% compared to the baselines. Also, the PSTGCN can achieve a similar prediction effect as the baselines by using half of the global spatial information. These results demonstrate that the PSTGCN outperforms the state-of-the-art models in urban traffic estimation and is robust under variable road conditions and data scales. [ABSTRACT FROM AUTHOR]

Trajectory prediction of vehicles is of great importance to various smart city applications ranging from transportation scheduling, vehicle navigation, to location-based advertisements. Existing methods all focus on modeling spatiotemporal relations with explicit contextual semantics from labeled trajectory data, and rarely consider the effective use of large amounts of available unlabeled trajectory data with the assistance of contrastive learning and pre-training techniques. To this end, we develop a novel Pretrained-based Contrastive Learning Network (PreCLN) for vehicle trajectory prediction. Specifically, we propose a dual-view trajectory contrastive learning framework to achieve self-supervised pre-training. A Transformer-based trajectory encoder is designed to effectively capture the long-term spatiotemporal dependencies in trajectories to embed input trajectories into fixed-length representation vectors. Moreover, three auxiliary pre-training tasks, i.e., trajectory imputation, trajectory destination prediction, and trajectory-user linking, are used to assist the training of PreCLN with the dual-view trajectory contrastive learning framework. After pre-training, the result trajectory encoder is used to generate trajectory representations for future trajectory prediction. Extensive experiments on two real-world large-scale trajectory datasets demonstrate the significant superiority of PreCLN against state-of-the-art trajectory prediction baselines in terms of all evaluation metrics. [ABSTRACT FROM AUTHOR]

A two-agent production and transportation coordinated scheduling problem in a single-machine environment is suggested to compete for one machine from different downstream production links or various consumers. The jobs of two agents compete for the processing position on a machine, and after the processed, they compete for the transport position on a transport vehicle to be transported to two agents. The two agents have different objective functions. The objective function of the first agent is the sum of the makespan and the total transportation time, whereas the objective function of the second agent is the sum of the total completion time and the total transportation time. Given the competition between two agents for machine resources and transportation resources, a noncooperative game model with agents as game players is established. The job processing position and transportation position corresponding to the two agents are mapped as strategies, and the corresponding objective function is the utility function. To solve the game model, an approximate Nash equilibrium solution algorithm based on an improved genetic algorithm (NE-IGA) is proposed. The genetic operation based on processing sequence and transportation sequence, as well as the fitness function based on Nash equilibrium definition, are designed based on the features of the two-agent production and transportation coordination scheduling problem. The effectiveness of the proposed algorithm is demonstrated through numerical experiments of various sizes. When compared to heuristic rules such as the Longest Processing Time first (LPT) and the Shortest Processing Time first (SPT), the objective function values of the two agents are reduced by 4.3% and 2.6% on average. [ABSTRACT FROM AUTHOR]

In a flexible job shop problem with transportation (FJSPT), a typical flexible manufacturing system comprises transporters that pick up and deliver jobs for processing at flexible job shops. This problem has grown in importance through the wide use of automated transporters in Industry 4.0. In this article, a two-phase iterative mathematical programming-based heuristic is proposed to minimize makespan using a machine-operation assignment centric decomposition scheme. The first phase approximates the FJSPT through an augmented flexible job shop scheduling problem (FJSP + T) that reduces the solution space while serving as a heuristic in locating good machine-operation assignments. In the second phase, a job shop scheduling problem with transportation (JSPT) network is constructed from these assignments and solved for the makespan. Compared to prior JSPT implementations, the proposed JSPT model considers job pre-emption, which is instrumental in enabling this FJSPT implementation to outperform certain established benchmarks, confirming the importance of considering job pre-emption. Results indicate that the proposed approach is effective, robust, and competitive. [ABSTRACT FROM AUTHOR]

This paper addresses cell part scheduling (CPS) problem with transportation capacity constraint. In the problem, parts may need to visit different cells, they have to be transferred by automated guided vehicle (AGV). The objective is to minimise the over-all process make-span. An integer nonlinear programming (INLP) model is formulated to allocate the machines, AGVs and schedule all parts. A reasonable transportation mode is presented, and an auction-based heuristic approach is proposed to solve the problem, which focuses on dealing with cooperation between machines and AGVs during processing and transferring parts. The auction consists of two aspects: auction for AGV and auction for machine. In both auctions, AGVs and machines act as auctioneers respectively, and parts act as bidders. A new improved disjunctive graph model is developed to optimise the feasible solutions obtained by auction-based approach. Numerical experiments were conducted to test the auction-based approach and improved disjunctive graph model. The results demonstrate the effectiveness of proposed auction-based approach and improved disjunctive graph model, also indicate influence of the capacity of AGV on scheduling parts. [ABSTRACT FROM AUTHOR]

This work addresses the flexible job shop scheduling problem with transportation (FJSPT), which can be seen as an extension of both the flexible job shop scheduling problem (FJSP) and the job shop scheduling problem with transportation (JSPT). Regarding the former case, the FJSPT additionally considers that the jobs need to be transported to the machines on which they are processed on, while in the latter, the specific machine processing each operation also needs to be decided. The FJSPT is NP-hard since it extends NP-hard problems. Good-quality solutions are efficiently found by an operation-based multistart biased random key genetic algorithm (BRKGA) coupled with greedy heuristics to select the machine processing each operation and the vehicles transporting the jobs to operations. The proposed approach outperforms state-of-the-art solution approaches since it finds very good quality solutions in a short time. Such solutions are optimal for most problem instances. In addition, the approach is robust, which is a very important characteristic in practical applications. Finally, due to its modular structure, the multistart BRKGA can be easily adapted to solve other similar scheduling problems, as shown in the computational experiments reported in this paper. [ABSTRACT FROM AUTHOR]

Purpose. To improve selection methods of operation schedules as well as means of auxiliary transportation facilities involving adaptation of their parameters to the specific application environment while preparing new extraction pillars for coal mining. Methodology. The current methods for substantiating the operational parameters of the means of internal mine transport do not take into account the impact of mining-geological and technological factors, which change inadequately in space and time, on their productivity. The recommended method for selecting transport and technological schemes and increasing their adaptability in the real conditions of the mine environment is based on the comprehensive use of methods of expert evaluation of the operational indicators of the new generation of auxiliary transport means and modeling of the conditions of their interaction with arch support and side rocks at the stages of mining and their operation when preparing new mining pillars for cleaning mining. Findings. The expert evaluation concerning technological features of mining field preparation as well as assembling and dismantling operations has determined that for the specific conditions of Western Donbas mines, the suspended diesel monorail locomotive and self-propelled machines are the most promising options. Gear clutch rail diesel locomotives are recommended as the auxiliary transport for the accelerated preparation of extraction pillar and introduction of a longwall into operation. Originality. New approaches have been substantiated to improve selection of the efficient means of auxiliary transportation facilities and identify their operational parameters for the well-timed preparation of extraction pillars for their removal under the intensified underground mining conditions. A transportation schedule has been proposed to supply heavy tonnage loads by means of the suspended diesel rail locomotives as well as methods to calculate the expected loads on suspension members of the monorail road taking into consideration physico-mechanical characteristics of roof rocks within a development mine working. Practical value. It has been proved that under the conditions of the intensified mining operations, the recommended engineering solutions meet the requirements of the well-timed preparation of new extraction pillars. In addition, they are considered as a new tendency to improve current schemes of auxiliary transportation facilities for regional mines and provide operational reliability of the available underground transport equipment. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Truck platooning has been identified as an emerging and promising operational technology with the advantages of fuel consumption savings and carbon emissions reductions. We formulate the truck platooning routing optimization problem as a multi-commodity network flow problem from a transportation optimization and scheduling perspective. Based on fuel consumption savings generated through the reduction of aerodynamic drag by the formation of truck platooning, the route of each truck is also set to be a decision variable needing settlement to facilitate the formation of truck platooning to maximize fuel consumption savings. Considering fuel consumption and detour costs, we construct a truck platooning routing optimization model with minimum overall system fuel consumption as the optimization objective. The output of the routing optimization model could both reflect the composition of each truck platooning on each link and directly show the routings of each truck. To explore the impact of the restrictions on the number of trucks in truck platooning on overall fuel consumption savings, road networks are constructed and the truck platooning routing optimization model is solved by the commercial solver CPLEX. Compared to individual trucks, 8% or 12% fuel consumption savings are achieved, respectively, with the number of trucks being restricted or not restricted in truck platooning. Considering the different fuel reduction rates of the following trucks in platooning on the system performance in terms of the total fuel cost, a sensitivity analysis is also conducted. The results also show that the ideal truck platooning routing plan can be obtained by the proposed model, and the study provides a theoretical reference for the promotion and application of truck platooning. [ABSTRACT FROM AUTHOR]

Ship–infrastructure cooperation, i.e., infrastructure scheduling, is significant for optimizing the utilization of spatial-temporal resources of infrastructures and improving the efficiency and safety of waterborne transportation systems. This paper carries out a systematic review of the scheduling problems of the infrastructures in waterborne transportation systems, including locks, terminals, berths, and waterway intersections. The infrastructure scheduling problems are linked to the classical optimization problems, and a generalized infrastructure scheduling problem is formulated. For lock scheduling, the ship placement sub-problem aims at minimizing the number of lockages, which is a kind of classic 2D bin packing problem; the lockage scheduling sub-problem deals with chamber assignment and lockage operation planning, which is modeled as a single or parallel machine scheduling problem. For berth and terminal scheduling, the idea of queuing theory (for discrete terminal) and 2D bin packing (for continuous terminal) are usually applied. Most research aims at minimizing the waiting time of ships and focuses on the continuous dynamic terminal scheduling problems. As a special infrastructure, the waterway intersection receives little attention. Most research focuses on traffic conflicts and capacity problems. Future research directions are provided based on the review results and problems of infrastructure scheduling in practice. [ABSTRACT FROM AUTHOR]

With the widespread adoption of intelligent transportation equipment such as AGVs in the manufacturing field, the flexible job shop scheduling considering limited transportation resources has increasingly attracted attention. However, current research does not consider various dynamic disturbances in real production scenarios, resulting in lower executability of scheduling solutions. To solve this problem, a dynamic flexible job shop scheduling model with limited transportation resources is established, aiming to minimize makespan and total energy consumption. Considering three types of disturbances: job cancellation, machine breakdown, and AGV breakdown, the corresponding event-driven rescheduling strategy is proposed, and a rescheduling instability index is designed to measure the performance of the rescheduling strategy. A Q -Learning-based NSGA-II algorithm (QNSGA-II) is proposed. By learning the feedback historical search experience, it adaptively selects the appropriate neighborhood structures for local search; and a hybrid initialization strategy tailored to the problem characteristics is designed to improve the optimization performance of the algorithm. Through simulation experiments, the effectiveness of the rescheduling strategies and the superiority of the QNSGA-II algorithm in solving such problems are validated. [ABSTRACT FROM AUTHOR]

This paper deals with an extension of the integrated production and transportation scheduling problem (PTSP) by considering multiple vehicles (PTSPm) for optimisation of supply chains. The problem reflects a real concern for industry since production and transportation subproblems are commonly addressed independently or sequentially, which leads to suboptimal solutions. The problem includes specific capacity constraints, the short lifespan of products and the special case of the single vehicle that has already been studied in the literature. A greedy randomised adaptive search procedure (GRASP) with an evolutionary local search (ELS) is proposed to solve the instances with a single vehicle as a special case. The method has been proven to be more effective than those published and provides shorter computational times with new best solutions for the single vehicle case. A new set of instances with multiple vehicles is introduced to favour equitable future research. Our study extends previous research using an indirect resolution approach and provides an algorithm to solve a wide range of one-machine scheduling problems with the proper coordination of single or multiple vehicles. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The real-life assembly production often has transportation between fabrication and assembly, and the capacity of transportation machine is often considered; however, the previous works are mainly about two-stage distributed assembly scheduling problems. In this study, a distributed energy-efficient assembly scheduling problem (DEASP) with transportation capacity is investigated, in which dedicated parallel machines with symmetry under the given conditions, transportation machines and an assembly machine are used. An adaptive imperialist competitive algorithm (AICA) is proposed to minimize makespan and total energy consumption. A heuristic and an energy-saving rule are used to produce initial solutions. An adaptive assimilation with adaptive global search and an adaptive revolution are implemented, in which neighborhood structures are chosen dynamically, and revolution probability and search times are decided by using the solution quality. The features of the problem are also used effectively. Computational experiments are conducted on a number of instances. The computational results demonstrate that the new strategies of AICA are effective and efficient and AICA can provide promising results for the considered DEASP. [ABSTRACT FROM AUTHOR]

The energy-saving scheduling problem (ESSP) has gained increasing attention of researchers in the manufacturing field. However, there is a lack of studies on ESSPs in the assembly job shop environment. In contrast with traditional scheduling problems, the assembly job shop scheduling problem (AJSP) adds the additional consideration of hierarchical precedence constraints between different jobs of each final product. This paper focuses on developing a methodology for an energy-saving assembly job shop scheduling problem with job transportation times. Firstly, a mathematical model is constructed with the objective of minimizing total energy consumption. Secondly, an improved elephant herding optimization (IEHO) is proposed by considering the problem's characteristics. Finally, thirty-two different instances are designed to verify the performance of the proposed algorithm. Computational results and statistical data demonstrate that the IEHO has advantages over other algorithms in terms of the solving accuracy for the considered problem. [ABSTRACT FROM AUTHOR]