Ship Routing 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.

Ship routing in inland waterway liner transportation with foldable and standard empty containers repositioning

This paper addresses the container ship routing problem in inland waterway liner transportation. Given estimated weekly full container shipping demands between port pairs and the potential empty container demands at each port, our goal is to design the container ship route and determine the amount of foldable and standard empty containers to be shipped between each port pair in the context of navigational restrictions, so as to maximize the profit of the shipping services. To save possible leasing or storage costs of empty containers at various ports and meet navigational restrictions, the release of foldable containers and the repositioning of empty containers were taken into account in the decision. Two mixed integer programming models are established to optimize ship route and shipping strategies under splittable and unsplittable demands. The models are further improved by projecting arc variables for simulating the transportation of empty containers, and the improved model has fewer decision variables and higher computational efficiency. Numerical experiments based on the Yangtze River are conducted to test the applicability of methods. Finally, some reference recommendations are presented.

Multi-objective optimization for multimodal transportation routing problem with stochastic transportation time based on data-driven approaches

We study a multi-objective optimization model of a stochastic multimodal transportation network considering key impact factors such as transit cost, time, and transport mode schedule while minimizing total transportation cost and transportation time. In this study, we apply the Monte Carlo simulation to deal with the stochastic transportation time in the network and propose a data-driven approach that combines historical data and the dataset generated by the data mining algorithm to accelerate the search for the nondominated solution in the simulation. To validate the effectiveness of the proposed Data-Driven Multi-Objective Simulation Ant Colony (DD-MSAC) algorithm, we compare the optimum-seeking performance and the running time consumption of the Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Simulation Ant Colony (MSAC) algorithm. Then, the MSAC algorithm is adopted as the benchmark for the comparison study on the solving performance of the proposed DD-MSAC algorithm. We conducted 30 times simulation run under different network scales in our numerical examples to show that the DD-MSAC algorithm can be equally effective as the non-data-driven MSAC algorithm in finding a nondominated solution as the average error does not exceed 5%. Meanwhile, we analyze the impact of different data-driven approaches, including data pool and support vector machine, on the solution quality and the running time. Finally, we use an example of China’s Belt Road Initiative to verify the effectiveness of the proposed algorithm.

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.

Stochastic tramp ship routing with speed optimization: analyzing the impact of the Northern Sea Route on $$\mathrm {CO_2}$$ emissions

AbstractTo address the decarbonization challenge, we consider a tramp ship routing problem with speed optimization where the availability of future cargoes is uncertain. We propose a two-stage stochastic programming model to solve it. The first stage of the model decides on the deterministic cargoes with the known availability, while the second stage decides on the detailed routing and scheduling plans with the available spot cargoes revealed. Since sailing speed heavily influences on the fuel consumption, and hence costs and emissions, we include the ships’ speeds along the different sailing legs as decision variables. The opening of the Northern Sea Route (NSR) provides a shorter alternative in connecting Asia and Europe, which may benefit the shipping industry in reducing $$\mathrm {CO_2}$$ CO 2 emissions. We use our model to evaluate the impact of NSR on $$\mathrm {CO_2}$$ CO 2 emissions in tramp shipping considering speed optimization and possible future carbon tax schemes, such as fixed and progressive carbon taxes. The computational results indicate that using the NSR improves the gross margins and reduces the $$\mathrm {CO_2}$$ CO 2 emissions for tramp operators.

Mathematical models for a ship routing problem with a small number of ports on a route

This paper presents an industrial ship routing problem with split loads faced by a steel company that exports its steel products to neighboring countries. The problem considers two types of ships: ships with contracts of affreightment and spot ships. It also considers other practical constraints, such as cargo–cargo and cargo–ship compatibility and the minimum loading amount for using a ship. Due to high surcharges for multiple port visits, each ship is allowed to visit at most two loading and two unloading ports. Herein, arc flow-based and path flow-based mathematical models are proposed to solve the problem. Computational experiments show that the path flow model is more efficient than the arc flow model. The path flow model can reduce the computational time by 67% on average and find optimal solutions to real-world ship routing problems in acceptable computational time in most cases.

Multi-Objective Optimization of Customer-Centered Intermodal Freight Routing Problem Based on the Combination of DRSA and NSGA-III

The satisfaction of requirements and preferences of shippers is critical to enable the practicability of solutions that are derived from intermodal transportation routing problems. This study aims to propose a decision process to help shippers participate better in routing decisions. First, we considered shippers’ requests on transportation cost, timeliness, reliability, and flexibility to construct a multi-objective optimization model. Then, to solve the interactive optimization method that was proposed, NSGA-III was applied to obtain the Pareto front and dominance-based rough set approach to model the preference information. Finally, a case study was conducted and an expert was invited as decision-maker to demonstrate the applicability of the proposed model and the effectiveness of the interactive method for shippers. The results are expected to provide shippers with more rational transportation schemes and insights for the sustainable development of intermodal transportation.

A variable neighborhood search algorithm with constraint relaxation for the two-echelon vehicle routing problem with simultaneous delivery and pickup demands

This paper considers a special vehicle routing problem, the two-echelon vehicle routing problem with simultaneous delivery and pickup demands (2E-VRPSDP). The 2EVRPSDP differs from classic transportation and vehicle routing problems in two ways. First, freight delivery from the depot to the customers is managed by shipping the freight through intermediate satellites. Second, each customer in the 2E-VRPSDP may have simultaneous delivery and pickup demands. The 2E-VRPSDP is an extension of the two-echelon vehicle routing problem (2E-VRP) and the vehicle routing problem with simultaneous delivery and pickup (VRPSDP). A variable neighborhood search algorithm is designed to solve the 2E-VRPSDP in which both feasible and infeasible solutions can be explored. Numerical results show that the proposed algorithm is effective and that the algorithm can provide reasonable solutions within an acceptable computational time.

Multi-Objective Optimization for Multimodal Transportation Routing Problem with Stochastic Transportation Time Based on Data-Driven Approaches

Multi-Objective Optimization for Multimodal Transportation Routing Problem with Stochastic Transportation Time Based on Data-Driven Approaches

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.

Integrated production inventory routing planning for vendor-buyers coordination supply chain with probabilistic demand, unstable lead time, setup cost reduction, service level consideration

This paper introduces a single-vendor multi-buyer system in a divergent supply chain associated with the inventory, production, and transportation or vehicle routing problem (VRP). The demand is distributed normally. The vendor is concerned with lowering setup costs through investments, while buyers are concerned with reducing lead time. Buyers' lead time, except transportation time, can be shortened by adding crashing expense. The vendor produces goods and transports them to buyers in various locations using a fleet of vehicles with similar capability. The related VRP's goal is to reduce the overall distance travelled by all vehicles. The aim of this study is to develop an integrated system considering production, inventory, and transportation; and to minimise the total expected cost by determining the optimal solution of lead time, order quantity, safety factor, routing decision and shipment frequency, which has been demonstrated numerically. The optimum output, inventory, and distribution for the vendor and buyers can be obtained by designing an integrated-inventory algorithm and employing the Clark-Wright algorithm.

The Robust Bulk Ship Routing Problem with Batched Cargo Selection

Maritime transportation forms the backbone of the world merchandise trade. In this paper, we consider a problem that combines three interconnected subproblems in tramp shipping: the fleet adjustment problem, the cargo selection problem, and the ship routing problem. For cargo selection, we consider the decision behaviors under the setting of Contract of Affreightment (COA), in which cargoes should be rejected or accepted as a batch. In view of the uncertainties observed in maritime transportation, we formulate the problem in a robust way so that the solutions can protect the profitability of shipping companies against variations in voyage costs. We first provide compact mixed integer linear programming formulations for the problem and then convert them into a strengthened set covering model. A tailored branch-and-price-and-cut algorithm is developed to solve the set covering model. The algorithm is enhanced by a multi-cut generation technique aimed at tightening the lower bounds and a primal heuristic aimed at finding high-quality upper bounds. Extensive computational results show that our algorithm yields optimal or near-optimal solutions to realistic instances within short computing times and that the enhancement techniques significantly improve the efficiency of the algorithm.

Research on Port Logistics Distribution Route Planning Based on Artificial Fish Swarm Algorithm

Ouyang, F., 2020. Research on port logistics distribution route planning based on artificial fish swarm algorithm. In: Bai, X. and Zhou, H. (eds.), Advances in Water Resources, Environmental Protection, and Sustainable Development. Journal of Coastal Research, Special Issue No. 115, pp. 78-80. Coconut Creek (Florida), ISSN 0749-0208.As a sub-problem of the inventory routing problem, the transportation routing problem is one of the main problems to be solved in studying how to reduce the total cost of logistics. Through the establishment of a reasonable distribution path, it plays a very important role in the cost, speed and efficiency of the whole logistics transportation. Based on the concept of artificial fish swarm algorithm, combined with the characteristics of port logistics distribution process, this paper constructs an optimization model and uses artificial fish swarm algorithm to solve specific cases. Finally, Matlab 6.5 is used to program and the artificial fish swarm algorithm is verified, which proves the feasibility and effectiveness of the algorithm. The results show that the mathematical model and artificial fish swarm algorithm have certain theoretical guidance and reference value for solving the port logistics distribution path planning problem.

Optimization of transportation routing problem for fresh food in time-varying road network: Considering both food safety reliability and temperature control.

Study on fresh food safety reliability and temperature control has being a research focus in the fresh food cold distribution optimization study field. On this basis, optimization of transportation routing problem with time windows for fresh food in time-varying road network is studied by considering both economic cost and fresh food safety loss. A calculation method for path division strategy is designed. A food safety value loss measurement function, a metric function of energy and heat conversion a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, cost of temperature control, the loss of food safety reliability and carbon emissions from transportation and temperature control. The fresh food cold chain distribution vehicle routing problem model with time windows in time-varying road network is formulated based on the objective of the distribution cost and food safety value loss minimization. According to the characteristics of the model, an adaptive improved ant colony algorithm is designed. Finally, the experimental data show that the model can effectively avoid the congestion period, reasonably control the refrigeration temperature, reduce the distribution cost, and improve food safety.

Intelligent decision support systems for determining tour bus route with time windows: A metaheuristic approach

The transportation routing problem is a very popular field in operations research and management science area. Many previous researches are still continuing to develop model and algorithm to produce the best transportation route optimization in various research objects. Technology has enabled the development of the intelligent decision support system (I-DSS) to optimize transportation route. Although technology has evolved, there are still few researches that build I-DSS software to minimize transportation distance and fuel consumption, especially using the metaheuristic approach. Thus, this paper aims to develop I-DSS software to minimize fuel consumption and travel distance by considering time-windows constraint using metaheuristic approach. The software can receive input dynamically to evaluate and select the best transportation route. The results of this software have been tested using real historical data from one of tour and travel company in Indonesia. The company provides travel services using a bus to visit several tourist attractions based on the opening hours of the destinations. I-DSS software output shows that the travel distance is 25.64% more efficient than the current real conditions. In addition, the fuel consumption produced by I-DSS is better than the actual conditions, which is 17.35% more efficient.

Collaborative transportation and urban routing problems: state of classification and literature

Carriers' collaboration is achieved when two or more carriers share their requests and resources in order to reduce their routing costs and increase their total profit. In this article we present a...

Collaborative transportation and urban routing problems: state of classification and literature

Collaborative transportation and urban routing problems: state of classification and literature

Maritime Routing Problem with Soft Time Windows in the One Belt, One Road Initiative

Zhang, M. ; Wu, Z., and Liu, C., 2019. Maritime routing problem with soft time windows in the One Belt, One Road Initiative.In: Li, L.; Wan, X.; and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 350–355. Coconut Creek (Florida), ISSN 0749-0208.To establish the bilateral and multilateral mechanisms between China and relevant countries, the One Belt, One Road Initiative calls for more enterprises to enter into the economic partnership with other initiative-involved countries under the existing and effective regional cooperation platforms. Based on these requirements, the Maritime Silk Road also brings forth new challenges to transnational maritime transportation enterprises in these countries. Specifically, the differences in time zones and working hours between countries increase the difficulty of maritime transportation. If ships arrive earlier or later than their time windows, penalty fees will be charged. Therefore, companies are required to arrange transportation routes to save the costs as much as possible, provided that the customer's demands are met. This study investigates the maritime transportation routing problem with soft time windows and proposes an improved ant colony algorithm. A real example highlights the effectiveness of the algorithm. The results can assist enterprises in making decisions.

Industrial and tramp ship routing problems: Closing the gap for real-scale instances

Recent studies in maritime logistics have introduced a general ship routing problem and a benchmark suite based on real shipping segments, considering pickups and deliveries, cargo selection, ship-dependent starting locations, travel times and costs, time windows, and incompatibility constraints, among other features. Together, these characteristics pose considerable challenges for exact and heuristic methods, and some cases with as few as 18 cargoes remain unsolved. To face this challenge, we propose an exact branch-and-price (B&P) algorithm and a hybrid metaheuristic. Our exact method generates elementary routes, but exploits decremental state-space relaxation to speed up column generation, heuristic strong branching, as well as advanced preprocessing and route enumeration techniques. Our metaheuristic is a sophisticated extension of the unified hybrid genetic search. It exploits a set-partitioning phase and uses problem-tailored variation operators to efficiently handle all the problem characteristics. As shown in our experimental analyses, the B&P optimally solves 239/240 existing instances within one hour. Scalability experiments on even larger problems demonstrate that it can optimally solve problems with around 60 ships and 200 cargoes (i.e., 400 pickup and delivery services) and find optimality gaps below 1.04% on the largest cases with up to 260 cargoes. The hybrid metaheuristic outperforms all previous heuristics and produces near-optimal solutions within minutes. These results are noteworthy, since these instances are comparable in size with the largest problems routinely solved by shipping companies.