Transportation Scheduling Problem Research Articles

Fleet repositioning in the tramp ship routing and scheduling problem with bunker optimization: A matheuristic solution approach

This paper investigates an important planning problem faced by dry bulk shipping operators, referred to as the Tramp Ship Routing and Scheduling Problem with Bunker Optimization (TSRSPBO). The problem is to maximize the overall profit of a fleet of vessels by selecting cargoes and determining ship routes and schedules. We consider this problem under a set of practically relevant features such as flexibility in cargo quantities, as well as bunkering decisions on where to procure fuel and how much. As a particularly novel feature, we address the regional allocation of vessels at the end of the planning period to be well prepared for meeting (uncertain) future demand. To incorporate this, we consider the TSRSPBO as a two-stage stochastic programming problem, where cargo selection, routing, and bunkering decisions are solved in the first-stage problem, and the recourse cost of fleet repositioning is considered in the second stage. We present arc flow and path flow formulations, where the latter employs a priori generation of feasible routes as input. For solving realistically sized instances, we propose a matheuristic based on an Adaptive Large Neighborhood Search (ALNS) framework that iteratively generates columns and solves the path flow model. Computational experiments based on real data show that this matheuristic finds high-quality solutions for large test instances with 120 cargoes, 30 vessels, and ten bunker ports in less than one hour. Also, considering the TSRSPBO as a two-stage stochastic problem achieves the highest profits and is solved almost as quickly as the deterministic problem variant.

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm.

Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model's effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

Ship scheduling problem in an anchorage-to-quay channel with water discharge restrictions

The optimization of ship scheduling in water-land transshipment plays a crucial part in enhancing transportation efficiency and alleviating traffic congestion on inland waterways, particularly when the service capacity of locks is insufficient. This study focuses on the ship scheduling problem in an anchorage-to-quay channel, where the navigation of ships is influenced by the water discharge during flood seasons. A new mixed-integer linear programming (MILP) model is formulated for this problem, in which the interconnected decision of ships’ navigation and berthing activities, the variation of water discharge in the waterway channel, and the change of navigation scenarios (i.e., normal or flood impact) are explicitly taken into account. Then a multi-population genetic algorithm (MPGA) is applied to solve this model. Numerical experiments conducted for the Three Gorges Dam (TGD) demonstrated that the proposed heuristic method can tackle the ship scheduling problem within a reasonable time, which is also effective in assisting ships to match acceptable water discharge time window by slightly adjusting the departure time of ships at the anchorage or quay. Furthermore, the proposed method can ensure the navigational safety of ships and improve the efficiency of ship transportation in different scenarios, and quantitatively evaluate the water discharge impact.

Accident black spot clustering oriented maritime search and rescue resource allocation and optimization

Efficient and rapid deployment of maritime search and rescue(MSAR) resources is a prerequisite for maritime emergency search and rescue, in order to improve the efficiency and accuracy of MSAR. This paper proposes an integrated approach for emergency resource allocation. The approach encompasses three main steps: identifying accident black spots, assessing high-risk areas, and optimizing the outcomes through a synergistic combination of an optimization algorithm and reinforcement learning. In the initial step, the paper introduces the iterative self-organizing data analysis technology (ISODATA) for identifying accident spots at sea. A comparative analysis is conducted with other clustering algorithms, highlighting the superiority of ISODATA in effectively conducting dense clustering. This can effectively carry out dense clustering, instead of the situation where the data spots are too dispersed or obvious anomalies that affect the clustering. Furthermore, this approach incorporates entropy weighting to reassess the significance of accident spots by considering both the distance and the frequency of accidents. This integrated approach enhances the allocation of search and rescue forces, ensuring more efficient resource utilization. To address the MSAR vessel scheduling problem at sea, the paper employs the non-dominated sorting genetic algorithm II combined with reinforcement learning (NSGAII-RL). Comparative evaluations against other optimization algorithms reveal that the proposed approach can save a minimum of 7% in search and rescue time, leading to enhanced stability and improved efficiency in large-scale MSAR operations. Overall, the integrated approach presented in this paper offers a robust solution to the ship scheduling problem in maritime search and rescue operations. Its effectiveness is demonstrated through improved resource allocation, enhanced timeliness, and higher efficiency in responding to maritime accidents.

Integrated production and transportation scheduling problem under nonlinear cost structures

We consider an integrated production and transportation scheduling problem arising in several make-to-order settings, where customer orders have release times and pre-specified delivery time windows. These orders are first processed in a plant and, thereafter, delivered to their customer sites by transporters (e.g., freight trains, ships, or air flights) with fixed departure times and nonlinear transportation cost functions. If the processing of an order is completed but not immediately delivered, it will be stored temporarily, incurring inventory holding costs. The objective is finding an integrated schedule for production and transportation such that the total cost of inventory holding and transportation is minimized. In this study, we consider the following two cases: one where partial delivery is not allowed, and the other where partial delivery is allowed. For each case, we propose an exact algorithm to find optimal solutions for small-scale instances, and a heuristic algorithm to find near-optimal solutions for large-scale instances. For the randomly generated test instances, the computational results reveal that the exact algorithms significantly outperform a commercial optimization solver in terms of the computational times and the number of instances solved within a specified time limit, and the heuristic algorithms are capable of generating near-optimal solutions within a reasonable computational time. Finally, we incorporate considerations of sequence-dependent setup time into the problem and apply the approaches for designing the algorithms to address the more general problem.

Unmanned Driving Technology in Coal Mine Based on Semantic Information Method

Aiming at the transportation scheduling problem of unmanned trucks in open-pit mines, an optimization model for transportation scheduling of unmanned trucks in open-pit mines is established, taking the minimum sum of fuel cost, fixed start-up cost, fault maintenance cost and network base station construction and maintenance cost as the objective function, and taking mining amount, crushing amount in crushing field, truck number and truck transportation work as the bundle reduction conditions. By adjusting the position of the encapsulated individual and the optimal encapsulated individual through the parameter position adaptive updating mechanism, the search range of the solution space can be enlarged, so that the algorithm can jump out of the local optimum. This paper analyzes the influence of vehicle clearance on coordinated traffic at intersections under clearance theory, and puts forward the minimum vehicle clearance to meet coordinated traffic. Generate trajectories with behavioral semantics through hierarchical online selection of primitives; Based on the model predictive control algorithm, the motion control module fully considers the execution ability, tracking deviation and control stability of the platform drive motor, and updates the weight coefficient of the control objective function in real time with the help of the semantic attributes of the behavioral motion primitives given by the motion planning, so as to reduce the lateral error, heading error and speed error of the trajectory tracking. Finally, a real vehicle is verified by a medium-sized hybrid unmanned crawler platform, which proves that the proposed intersection coordinated traffic model is feasible in theory, reflecting that the traffic density is a factor that affects the transportation efficiency. Under the condition of meeting the production requirements, reduce the intersection conflict and improve the traffic capacity of the intersection by adopting large-load unmanned mine trucks as much as possible, reducing the number of unmanned mine trucks and the interference between equipment.

Branch-And-Price Algorithm for the Tramp Ship Routing and Scheduling Problem Considering Ship Speed and Payload

In recent years, increasing fuel prices, depressed market conditions and air pollution issues have brought huge challenges to the tramp shipping industry. This work investigates the tramp ship routing and scheduling problem considering ship speeds and payloads, aiming at minimizing ship fuel consumption. A mixed integer non-linear programming model with a discretized speed variable and a set partitioning model for this problem is established, and a branch-and-price algorithm is proposed to solve the problem. Through the column generation approach, the problem at each branch-and-bound node is decomposed into a linear programming master problem and a pricing problem of the elementary shortest path with resource constraints. A labeling algorithm is adopted for solving the pricing problem. Multiple groups of instances are generated to test the effectiveness of the proposed model and algorithm and analyze the impacts of ship speed, payload, and speed discretization on the solution. Computational experiments are conducted, which verifies the proposed scheduling routing method for tramp ships and confirms that adopting the proposed model can effectively reduce fuel consumption of tramp ships which can not only deepen the theory of tramp routing and scheduling, but also provide theoretical guidance to tramp ship company. The branch-and-price algorithm can effectively solve large-scale tramp ship routing and scheduling problems. Reasonable number of speed discretization points can bring a desirable trade-off between solution accuracy and algorithm runtime.

Determining the best ship loading strategy during military deployments

PurposeThis study aims to examine ship loading strategies during large-scale military deployments. Ships are usually loaded to a stowage goal of about 65% of the ship's capacity. The authors identify how much cargo to load onto ships for each sailing and propose lower stowage goals that could improve the delivery of forces during the deployment.Design/methodology/approachThe authors construct several mixed integer programs to identify optimal ship loading strategies that minimize delivery timelines for notional, but realistic, problem variables. The authors study the relative importance of these variables using experimental designs, regressions, correlations and chi-square tests of the empirical results.FindingsThe research specifies the conditions during which ships should be light loaded, i.e. loaded to less than 65% of total capacity. Empirical results show cargo delivered up to 16% faster with a light-loaded strategy compared to fully loaded ships.Research limitations/implicationsThis work assumes deterministic sailing times and ship loading times. Also, all timing aspects of the problem are estimated to the nearest natural number of days.Practical implicationsThis research provides important new insights about optimal ship loading strategies, which were not previously quantified. More importantly, logistics planners could use these insights to reduce sealift delivery timelines during military deployments.Originality/valueMost ship routing and scheduling problems minimize costs as the primary goal. This research identifies the situations in which ships transporting military forces should be light loaded, thereby trading efficiency for effectiveness, to enable faster overall delivery of unit equipment to theater seaports.

A robust ant colony metaheuristic for urban freight transport scheduling using passenger rail network

This paper considers a promising alternative for freight transport using urban passenger rail networks. It addresses a freight rail transport scheduling problem (FRTSP), which is one of the key operational issues to integrate freight into passenger transport in urban areas. In this paper, we develop a robust ant colony optimization (ACO) metaheuristic to tackle large-size instances. Taguchi method is used to set control parameters to make the algorithm robust. Computational results clearly highlight the proposed ACO algorithm’s capability of yielding near-optimal solutions in short computation times, even in large-size instances. They also show that the proposed algorithm dominates classic ACO variants, even when the parameters of these variants are also set using Taguchi method.

An effective and robust genetic algorithm for urban freight transport scheduling using passenger rail network

This paper considers freight transport in urban areas using passenger rail networks as an eco-friendly alternative to current predominantly fossil-fueled trucks. It addresses one of the essential problems, called freight rail transport scheduling problem (FRTSP), where the objective is to minimize the total waiting time of the parcels at their departure stations which is equivalent to minimizing the space occupancy at the stations and maximizing the turnover of the freight transport. In this paper, we focus on designing an effective and robust method to solve the FRTSP. For this purpose, a genetic algorithm is proposed whose performances, as it is widely known, highly depend on the length of chromosomes and control parameters. In this genetic algorithm, the length of chromosomes is reduced owing to a mathematical formulation with fewer integer decision variables representing a parcel-to-train assignment. We prove that as long as a parcel-to-train assignment is known, it takes a polynomial time to check whether this assignment can lead to a feasible solution and, if yes, solve the remaining problem by transforming it into computing the longest distances in a digraph. This result makes it possible to only consider parcel-to-train assignment variables in chromosomes, while efficiently determining the values of the other variables in fitness evaluation. In order to achieve robustness regarding all instances, the control parameters of the genetic algorithm are set with Taguchi method. Computational results show that the method developed in this way is very effective. For small-size instances, it yields optimal solutions within considerably shorter computation times than a commonplace optimization solver does. For large-size instances, it outperforms such a solver, not only providing much higher-quality solutions but also consuming much shorter computation times. This method thus meets very well operational requirements where high-quality solutions are expected while computation time is very limited.

Study on optimization of bus scheduling

In order to respond to the national policy, energy saving and emission reduction, from the perspective of transportation, we should drive fewer private cars and take more buses and subways. Good bus scheduling can better meet the interests of passengers and bus companies. Through the analysis of passenger flow big data, a more reasonable scheduling scheme can be provided for bus operation. The optimization of bus scheduling can improve the service quality and benefit passengers, and also reduce the input cost of bus companies. This paper analyzes the existing public transportation scheduling problems, analyzes the current research results of public transportation scheduling, and finally makes a prospect for public transportation scheduling.

A column generation based approach for an integrated production and transportation scheduling problem with dual delivery modes

ABSTRACT 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.

Bi-objective green in-house transportation scheduling and fleet size determination in mixed-model assembly lines with mobile robots

PurposeFacing the challenge of increasing energy cost and requirement of reducing the emissions, identifying the potential factors of them in the manufacturing factories is an important prerequisite to control energy consumption. This paper aims to present a bi-objective green in-house transportation scheduling and fleet size determination problem (BOGIHTS&FSDP) in automobile assembly line to schedule the material delivery tasks, which jointly take the energy consumption into consideration as well.Design/methodology/approachThis research proposes an optimal method for material handling in automobile assembly line. To solve the problem, several properties and definitions are proposed to solve the model more efficiently. Because of the non-deterministic polynomial-time-hard nature of the proposed problem, a Multi-objective Discrete Differential Evolution Algorithm with Variable Neighborhood Search (VNS-MDDE) is developed to solve the multi-objective problem.FindingsThe performances of VNS-MDDE are evaluated in simulation and the results indicate that the proposed algorithm is effective and efficient in solving BOGIHTS&FSDP problem.Originality/valueThis study is the first to take advantage of the robot's interactive functions for part supply in automobile assembly lines, which is both the challenge and trend of future intelligent logistics under the pressure of energy and resource. To solve the problem, a VNS-MDDE is developed to solve the multi-objective problem.

Integrated Production and Transportation Scheduling Method in Hybrid Flow Shop

The connection between production scheduling and transportation scheduling is getting closer in smart manufacturing system, and both of those problems are summarized as NP-hard problems. However, only a few studies have considered them simultaneously. This paper solves the integrated production and transportation scheduling problem (IPTSP) in hybrid flow shops, which is an extension of the hybrid flow shop scheduling problem (HFSP). In addition to the production scheduling on machines, the transportation scheduling process on automated guided vehicles (AGVs) is considered as another optimization process. In this problem, the transfer tasks of jobs are performed by a certain number of AGVs. To solve it, we make some preparation (including the establishment of task pool, the new solution representation and the new solution evaluation), which can ensure that satisfactory solutions can be found efficiently while appropriately reducing the scale of search space. Then, an effective genetic tabu search algorithm is used to minimize the makespan. Finally, two groups of instances are designed and three types of experiments are conducted to evaluate the performance of the proposed method. The results show that the proposed method is effective to solve the integrated production and transportation scheduling problem.

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.

Bi-objective coordinated production and transportation scheduling problem with sustainability: formulation and solution approaches

ABSTRACT 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%.

Task scheduling for transport and pick robots in logistics: a comparative study on constructive heuristics

We study the Transport and Pick Robots Task Scheduling (TPS) problem, in which two teams of specialized robots, transport robots and pick robots, collaborate to execute multi-station order fulfillment tasks in logistic environments. The objective is to plan a collective time-extended task schedule with the minimization of makespan. However, for this recently formulated problem, it is still unclear how to obtain satisfying results efficiently. In this research, we design several constructive heuristics to solve this problem based on the introduced sequence models. Theoretically, we give time complexity analysis or feasibility guarantees of these heuristics; empirically, we evaluate the makespan performance criteria and computation time on designed dataset. Computational results demonstrate that coupled append heuristic works better for the most cases within reasonable computation time. Coupled heuristics work better than decoupled heuristics prominently on instances with relative few pick robot numbers and large work zones. The law of diminishing marginal utility is also observed concerning the overall system performance and different transport-pick robot numbers.

An integrated production and transportation scheduling problem with order acceptance and resource allocation decisions

In the global market competition, coordinating decisions and integrating different aspects of supply chains would help managers promote the overall performance of systems, and reduce the overall costs. The integration of production and transportation is one of the essential considerations of manufacturers to achieve more efficiency levels. In this paper, an integrated production and transportation scheduling problem is addressed. The objective is to maximize the revenue of accepted orders and to minimize the sum of delivery cost, weighted tardiness cost, and fixed cost of vehicles, by considering resource allocation and resource-dependent processing times. The problem is regarded as strong NP-Hard. A Mixed Integer Linear Programming (MILP) is proposed. Also, two meta-heuristic solution approaches, based on Adaptive Genetic Algorithm (AGA) and Tabu Search (TS) are proposed to solve this problem. The computational experiments based on small-scale and large-scale analyses are provided to assess the effects of different parameters of the problems on both optimality and solving time criteria. It is deduced that the proposed AGA has a better performance and higher efficiency, rather than the proposed TS, in terms of optimality. The results indicate the capability of the proposed algorithms in solving the real-life problems in an efficient manner.

Integrated production and transportation scheduling with order-dependent inventory holding costs

In this study, we consider an integrated production and transportation scheduling problem faced by make-to-order manufacturers that adopt a commit-to-delivery business mode and cooperate with third-party logistics providers to deliver processed products to customers. The third-party logistics providers typically offer multiple shipping modes chosen by the manufacturers to deliver products, each shipping mode with a shipping time guarantee and a shipping cost function that is non-increasing in shipping time and sub-additive, non-decreasing in shipping quantity. The problem involves inventory holding costs which not only depend on the time that the products spend in temporary storage but also depend on customer types. The problem is to determine an integrated production and shipping schedule that satisfies the committed delivery due date limitations for the customers, such that the total cost of shipping and inventory holding is minimized. We investigate two cases with and without split delivery. For both cases, we first show that both of them are ordinarily NP-hard, prove that there exist no polynomial-time approximation algorithms with constant worst-case ratios, propose exact algorithms to solve them, and finally design column generation-based heuristic algorithms to find feasible solutions. The computational results demonstrate that the heuristic algorithms are capable of generating near-optimal solutions efficiently. We also consider two interesting practical variants of the problem.

A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

This paper presents a set covering model based on route representation to solve the green ship routing and scheduling problem (GSRSP) with berth time-window constraints for multiple bulk ports. A bi-objective set covering model is constructed with features based on the minimization of the total CO2 equivalent emissions and the total travel time subject to a limited number of berths in each port, berthing time windows, and the time window for each job. The solutions are obtained using the ε-constraint method, after which a Pareto frontier is plotted. This problem is motivated by the operations of feeder barges and terminals, where the logistics control tower is used to coordinate the routing and berthing time of its barges. We show that the proposed method outperforms the weighted sum method in terms of the number of Pareto solutions and the value of the hypervolume indicator.