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As an essential carrier of cross-border trade, the maritime supply chain has always been plagued by long service cycles, complex structures, and heterogeneous information from multiple sources. In ...

Air emissions from ships have become an important issue in sustainable shipping because of the low quality of the marine fuel consumed by ships. To reduce sulfur emissions from shipping, the International Maritime Organization has established emission control areas (ECAs) where ships must use low-sulfur fuel with at most 0.1% sulfur or take equivalent emission-reduction measures. The use of low-sulfur fuel increases the costs for liner shipping companies and affects their operations management. This study addresses a holistic liner shipping service planning problem that integrates fleet deployment, schedule design, and sailing path and speed optimization, considering the effect of ECAs. We propose a nesting algorithmic framework to address this new and challenging problem. Semianalytical solutions are derived for the sailing path and speed optimization problem, which are used in the schedule design. A tailored algorithm is applied to solve schedule design problems, and the solutions are used in fleet deployment. The fleet deployment problem is then addressed by a dynamic programming-based pseudo-polynomial time algorithm. Numerical experiments demonstrate that considering the effect of ECAs in liner shipping operations management can reduce over 2% of the costs, which is significant considering that the annual operating cost of a shipping company’s network can be as high as several billion dollars.

In the era of sharing economy and the mobility-as-service, the last mile delivery system is undergoing revolutionary changes with emerging a lot of new operation modes and policies. The crowdsourced delivery mode has become a novel and popular practice in the last mile delivery industry. This mode brings a novel way for facilitating the efficiency of last mile delivery on the basis of the shared mobility. This paper studies an open vehicle routing problem (OVRP) for the shared mobility based last mile delivery mode, in which parcel delivery orders are disseminated within a circle to attract occasional vehicles (or couriers) to fulfill these orders. Different from the traditional OVRP, this study further considers the decision of the above mentioned circle’s radius, which should be balanced between attracting enough vehicles and reducing additional cost (reward) for covering vehicles’ empty trips to the pickup location. This paper proposes a nonlinear mixed-integer programming model for the problem. A column generation based solution method is implemented to solve the model. Some numerical experiments are also conducted to validate the efficiency of the solution method and draw out some managerial implications for the practitioners. The proposed methodology in this paper may be potentially benefit not only for reducing the last mile delivery cost but also for promoting the modern transportation paradigm revolution to ridesharing and mobility-as-service.

This article investigates a low carbon-oriented berth allocation and quay crane assignment problem considering vessels’ uncertain arrival time and loading/unloading workload for vessels. A two-stag...

This paper extends a traditional supply chain network design problem through two perspectives, i.e., the multiperiod decision environment and the environmental concerns. A planning horizon contains multiple periods. Different periods have different demands of products and hence should have different supply chain decisions, which mainly include facilities’ locations, the environment protection level in facilities, as well as the transportation flows of products in the supply chain. For each facility in two consecutive periods, the changes of the location and environment protection level related decisions may incur adjustment cost. A bi-objective mixed-integer programming model is proposed to minimize both the total cost and the CO2 emissions of the supply chain during the planning horizon with multiple periods. A solution approach is proposed to solve the model. Numerical experiments are conducted to validate the effectiveness of the proposed model and algorithm.

Unmanned aerial vehicles (UAVs) are widely used to perform monitoring tasks both in the military and civilian areas, and the planning of their routes is critical. This study investigates a routing problem in which UAVs monitor a set of areas with different accuracy requirements. This problem is a variant of the classical vehicle routing problem (VRP), where one must determine not only the order in which to visit a set of nodes located in the plane, but also the height at which to visit them, which impacts the accuracy level and the service time. An integer programming model is formulated to optimize flight routes and minimize the total time needed to complete the monitoring tasks. A tabu search metaheuristic is developed for the problem. Extensive numerical experiments are conducted to assess the efficiency of the heuristic.

To improve customer service in the reverse logistics, this article defines a new variant of the vehicle routing problem (VRP) by combining the consistent VRP (ConVRP) and the VRP with simultaneous ...

Coal mine safety is the foundation of coal mine enterprise development, and inspections performed by safety personnel are an indispensable part of ensuring the production of coal mines. The complex network of tunnels in underground mines restricts the efficiency of safety inspection routes; therefore, planning these routes is critical. This study investigates a routing problem in which safety personnel start from multiple sites to inspect a set of sites. A mixed integer linear programming (MILP) model is formulated to optimize the inspection routes. Three methods are used to address the problem: a local branching (LB)-based solution method, a tabu search-based solution method and a back propagation (BP) neural network method. Numerical experiments are conducted to assess the efficiency of the methods on different scales.

With the development of green logistics, logistics companies gradually are paying attention to the application of hybrid electric vehicles (HEVs). HEVs have the advantages of low energy consumption and pollution, while their disadvantage mainly lies in their limited continuous driving range. Therefore, it is necessary to optimize the use of fuel during the distribution process. We study the mode selection system in HEVs based on the background of green logistics and the above characteristics of HEVs. The mode selection system can adjust the driving mode of the HEV according to different road conditions to obtain the optimal use of fuel. In this paper, we propose a new study of a hybrid electric vehicle routing problem with mode selection. This problem is formulated as a mixed integer linear programming model. An improved particle swarm optimization algorithm (IPSO) is developed to solve this problem. Extensive numerical experiments are conducted to validate the effectiveness of the proposed model and the ...

This paper solves a strategic-level decision problem on determining the optimal location of (re)manufacturers and logistics centres with the consideration of facility scales in an integrated closed...

This paper investigates crane scheduling problems for a new type of automated container terminal system, which is based on multi-storey frame bridges. For the new design concept, this paper studies how to schedule two types of cranes, i.e., quay cranes and bridge cranes that transfer containers between different storeys. The schedules of these two types of cranes impact the operation efficiency of the terminal system. Mathematical models are proposed for the two scheduling problems. Meta-heuristics are developed to solve them. Numerical experiments are conducted to validate the effectiveness of the proposed models and the efficiency of the proposed solution method.

Automatic guided vehicles are widely used in various types of warehouses including the automated container terminals. This paper provides a decision framework for port managers to design and schedule automatic guided vehicle routing plans under time-varying traffic conditions. A large number of computational experiments on a grid graph are conducted to validate the efficiency of the proposed decision framework. We also proposed one efficient queueing rule in automatic guided vehicle routing scheduling. Although the complexity of the problem is high, computational results show that our proposed decision framework can provide high quality solutions within a relatively short computation time.

This study optimizes the design of a closed-loop supply chain network, which contains forward and reverse directions and is subject to uncertainty in demands for new & returned products. To address uncertainty in decision-making, we formulate a two-stage stochastic mixed-integer non-linear programming model to determine the distribution center locations and their corresponding capacity, and new & returned product flows in the supply chain network to minimize total design and expected operating costs. We convert our model to a conic quadratic programming model given the complexity of our problem. Then, the conic model is added with certain valid inequalities, such as polymatroid inequalities, and extended with respect to its cover cuts so as to improve computational efficiency. Furthermore, a tabu search algorithm is developed for large-scale problem instances. We also study the impact of inventory weight, transportation weight, and marginal value of time of returned products by the sensitivity analysis. Several computational experiments are conducted to validate the effectiveness of the proposed model and valid inequalities.

For more accurate multiple-period real-time monitoring of road traffic, this paper investigates the unmanned aerial vehicle scheduling problem with uncertain demands. A mixed integer programming model is designed for this problem by combining the capacitated arc routing problem with the inventory routing problem. A local branching based solution method is developed to solve the model. A case study which applies this model to the road traffic in Shanghai is performed. In addition, numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

This paper studies an integrated decision problem on assigning and sequencing tasks to service providers. By considering the uncertain available time of the service providers, a stochastic programming model is proposed on the basis of a finite set of scenarios. Some realistic factors are also taken into account and formulated as some non-linear cost functions, which are linearised in this study. Moreover, a heuristic solution method is designed for solving some extremely large-scale problem cases within a reasonable period of time. Numerical experiments are performed to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

This paper studies an operational-level berth allocation and quay crane assignment problem (daily berth planning) considering tides and channel flow control constraints. An integer programming model is proposed for this problem. Then a column generation solution approach is developed on a set partitioning based reformulation of the original model. Computational study is conducted on 30 test cases constructed from real-world data to validate efficiency of the proposed solution approach. Results show that this simple but practical solution approach can optimally solve the daily berthing planning problem instances with up to 80 vessels, 40 berths, and 120 quay cranes within one hour, which is reasonable and acceptable for the real-world applications. The proposed decision model and the solution approach could be potentially useful for some tidal ports with (or without) navigation channels.

This paper studies a strategic-level decision problem on assigning production stages to geographically distributed subsidiaries in a large corporation so as to minimize the fixed cost and the expected value of the production/transportation costs under stochastic demands of customers. The influence of the economies and diseconomies of scale on unit production cost is considered. A nonlinear mixed integer programming model is designed for this problem. A local branching based solution method and a particle swarm optimization based solution method are developed for solving the model. Computational tests on real world data of Shanghai Volkswagen Auto Corp. are conducted. The results show that the proposed model can save about 2.5% of its revenue by comparing with the current decisions.

This paper addresses a problem of ship type decision considering empty container repositioning and foldable containers, which determines the capacity of ships deployed in a given shipping route at a tactical level and empty container repositioning between ports at an operational level. It considers the use of foldable containers and aims to find under what conditions, a shipping liner needs to use the foldable containers. To solve the problem, we formulate a network flow model with a revised network simplex algorithm, based on which an exact solution approach is designed to determine the optimal ship type.

In supply chain networks of large companies, how to determine the location for establishing distribution centers is an important strategic-level decision problem. This paper proposes a stochastic programming model to determine distribution centers’ locations as well as their scales. The objective of the model is to minimize the expected total transportation cost under uncertain demands of customers. An improved particle swarm optimization algorithm and a Lagrangean relaxation-based solution approach are proposed to solve the model. This paper also performs a case study on applying this model to the largest retailer in China. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods.

As containers need several weeks to be delivered to their destinations and the future container shipment demand is uncertain, a myopic strategy for container routing that maximizes the immediate profit may lead to insufficient remaining capacity for transporting more profitable containers in the future. This study conducts a theoretical analysis to compare three container routing strategies, i.e., (i) a widely used myopic strategy, (ii) a non-myopic heuristic routing strategy that incorporates information about the future demand distribution, and (iii) a strategy that assumes future demand is known. Then a practical non-myopic heuristic container routing strategy is developed for practical purposes to increase the profit of liner shipping companies. Extensive numerical experiments show that the proposed non-myopic heuristic container routing strategy brings in an extra of 2%–3% profit over the myopic strategy.

The fast-growing practice of e-commerce implies a strong increase in the urban parcel delivery, which in turn creates significant pressure on last-mile city logistics. Because the crowdsourced delivery offers greater flexibility and requires less capital investment than traditional delivery methods, it has been playing a more crucial role when faced with the growing demand for the urban parcel delivery. With the increasing maturity of the crowdsourced delivery and the fierce competition among platforms, the evaluation of different crowdsourcing modes for the urban parcel delivery becomes important. This study proposes six mathematical models to evaluate different operation modes of the crowdsourced delivery in a quantitative way. Several realistic factors, such as the latest service time for each task, task cancellation rate and range distribution of tasks, are also analyzed in this paper. Numerical experiments are conducted to validate the effectiveness of the proposed models and to analyze the impact of different modes. Some managerial implications are also outlined on the basis of the numerical experiments and sensitivity analysis to help crowdsourced companies to make scientific decisions.

This study investigates an optimal control policy for a liner ship to decide at which ports and how much fuel the liner ship should be refueled under stochastic fuel consumption in each leg and stochastic fuel price at each port. Based on some properties proved in this study, a dynamic programming algorithm is then designed to obtain some important threshold values, which are used in the optimal control policy for ship refueling decision. Extensive experiments show that the proposed method can obtain the optimal decision within a reasonable time (about 170 s) for various scales of problem instances (up to 30 ports) as well as various settings of probability distributions. In addition, some comparative experiments also show that the proposed optimal decision policy can save at least 8% fuel consumption cost by comparing with some relatively simple rules and save about 1% cost on average by comparing with some brilliantly-designed rules.

Refrigerator cars are widely used for fresh product distribution. The energy consumption of these vehicles is sensitive to the environment temperature, and changes continuously due to fluctuations of the environment temperature. As a result, the total refrigeration cost is influenced by the car’s departure time. To reduce operation costs of third-party transportation providers, the refrigerator car scheduling problem is addressed in this research. A time-dependent mixed-integer programming model is established to reduce total operation costs, including routing, time penalty, cargo damage and refrigeration costs. An adaptive heuristic method is proposed by combining the variable neighbourhood search and particle swarm optimisation. To improve the algorithm quality, a two-stage decomposition method is developed. The problem is divided into two echelon sub-problems. One is the shortest path problem, and the other is the departure time scheduling problem. A feedback strategy is utilised to avoid local optimal...

© 2016 Elsevier Ltd This paper addresses a decision problem on planning cruise services for a cruise ship so as to maximize the total profit during a planning horizon. The service is a sequence of ports (harbor cities) that the cruise ship visits. In this decision problem, the constraint about the availability of berths at each port is taken into account. In reality, if a cruise service is executed by the ship repeatedly for several times, the profit earned by the cruise service in each time decreases gradually. This effect of decreasing marginal profit is also considered in this study. We propose a nonlinear integer programming model to cater to the concavity of the function for the profit of operating a cruise service repeatedly. To solve the nonlinear model, two linearization methods are developed, one of which takes advantage of the concavity for a tailored linearization. Some properties of the problem are also investigated and proved by using the dynamic programming (DP) and two commonly used heuristics. In particular, we prove that if there is only one candidate cruise service, a greedy algorithm can derive the optimal solution. Numerical experiments are conducted to validate the effectiveness of the proposed models and the efficiency of the proposed linearization methods. In case some parameters needed by the model are estimated inexactly, the proposed decision model demonstrates its robustness and can still obtain a near-optimal plan, which is verified by experiments based on extensive real cases.

This study extends a problem on locating distribution centers under uncertainty.The decision on facility size is considered.This study is oriented to multiple period decision contexts.A mixed-integer programming model as well as lower, upper bounds are developed. This study extends a traditional problem on locating distribution centers (DC) under uncertain demands, and proposes a stochastic programming model on locating DCs, determining their suitable scales, as well as adjusting DCs so as to adapt to the dynamically changing demands at the sites of retailers. Some lower and upper bounds are also designed to analyze the values of the perfect information, stochastic solution, and the DC adjustments. Numerical experiments are conducted to validate the effectiveness of the proposed model.

This paper is about yard management in container ports. As a tactical level decision-making tool in a port, a yard template determines the assignment of spaces (subblocks) in a yard for arriving vessels, which visit the port periodically. The objective of yard template planning is to minimize the transportation cost of moving containers around the yard. To handle yard template planning, a mixed integer programming model is proposed that also takes into account traffic congestion in the yard. A further complication is that the cycle time of the vessels' periodicities is not uniform and varies among them, perhaps being one week, ten days, or two weeks, etc. However, this multiple cycle time of the periodicities of vessel arrival patterns, which complicates the yard template decision, is also considered in the model. Moreover, a local branching based solution method and a Particle Swarm Optimization based solution method are developed for solving the model. Numerical experiments are also conducted to validate the effectiveness of the proposed model, which can save around 24% of the transportation costs of yard trucks when compared with the commonly used First-Come-First-Served decision rule. Moreover, the proposed solution methods can not only solve the proposed model within a reasonable time, but also obtain near-optimal results with about 0.1–2% relative gap.

This paper studies an integrated optimization problem on quay crane and yard truck scheduling in container terminals. A mixed-integer programming model is formulated. For the model, we show the integrated scheduling problem is strongly NP-hard and investigate some properties that can considerably reduce the computational complexity. For solving the proposed model within a reasonable time, a particle swarm optimization based solution method is developed. Numerical experiments are conducted to compare the proposed method with the CPLEX solver and the genetic algorithm. The results validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

This article investigates the method of allocating arriving vessels to the terminals in transshipment hubs. The terminal allocation decision faced by a shipping alliance has the influence on the scheduled arrival time of vessels and further affects the bunker consumption cost for the vessels. A model is formulated to minimize the bunker consumption cost as well as the transportation cost of inter-terminal transshipment flows/movements. The capacity limitation of the port resources such as quay cranes (QCs) and berths is taken into account. Besides the terminal allocation, the QC assignment decision is also incorporated in the proposed model. A local branching based method and a particle swarm optimization based method are developed to solve the model in large-scale problem instances. Numerical experiments are also conducted to validate the effectiveness of the proposed model, which can save around 14% of the cost when compared with the “First Come First Served” decision rule. Moreover, the proposed solution methods not only solve the proposed model within a reasonable computation time, but also obtain near-optimal results with about 0.1∼0.7% relative gap. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 529–548, 2016

This paper proposes a blockchain-based framework to improve the efficiency of ship traffic in port. In the framework, ship agents, terminals, tug company, pilot station, and government share information and the information is stored in a blockchain. Based on the shared information, we discuss three categories of data-driven models that can improve the operations management of the above five parties. The first category is decisions made by a single party. The second category involves decisions of at least two ship agents. The third category relates to multi-party decision-making under uncertainty. This study hopes to stimulate maritime practitioners to embrace blockchain technology and data-driven approaches to enhance the competitiveness of the industry.

Optimization of the green logistics location-routing problem with eco-packages involves solving a two-echelon location-routing problem and the pickup and delivery problem with time windows. The first echelon consists of large eco-package transport, which is modeled by a time-discretized transport-concentrated network flow programming in the resource sharing state–space–time (SST) network. The second echelon focuses on small eco-package pickups and deliveries, established by the cost-minimized synchronization-oriented location routing model that minimizes the total generalized cost, which includes internal transportation cost, value of eco-packages, short-term benefits and environmental externalities. In addition, the Gaussian mixture clustering algorithm is utilized to assign customers to their respective service providers in the pickup and delivery process, and a Clarke–Wright saving method-based non-dominated sorting genetic algorithm II is designed to optimize pickup and delivery routes, and improve their cost-effectiveness and degree of synchronization. Different strategy testing results are used in the service phase as input data to calculate the cost of the transport phase, which is solved through a Lagrangian relaxation approach. The 3D SST network representation innovatively captures the eco-package route sequence and state transition constraints over the shortest path in the pickup and delivery at any given moment of the transport phase. A large-scale logistics network in Chengdu, China, is used to demonstrate the proposed model and algorithm, and undertake sensitivity analysis considering the life cycle of green eco-packages.

For large multinational companies, the complex production process of their finished goods usually contains plenty of stages, which constitute a production flow network. Each production stage in the production flow network can be undertaken by one or more suppliers. This study proposes a stochastic programming model for the production flow network oriented supply chain network design problem, which optimizes the decision of allocating stages to suppliers with the objective of minimizing the total expected costs of production and transportation among suppliers under uncertain demands of customers. A local branching based solution method is developed to solve the model. A case study on applying this model to a large automobile company is performed. In addition, some numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

The influences of triethanolamine (TEA) on the portlandite in hardened cement pastes (HCPs) were systematically investigated. Results show that the addition of TEA in cement pastes leads to a visible reduction of Ca(OH)2 (CH) content and considerably alters the morphology of CH crystals from large and parallel-stacked lamellar shape to smaller and distorted actinomorphic one. For the first time, the CH micro-crystals and even non-crystalline CH in HCPs were observed in the presence of TEA. Due to integration of CH micro-crystals in C–S–H phase, remarkable higher Ca/Si ratio of C–S–H phase was found. The formation of TEA-Ca2 + complex via the interaction between Ca2 + and the oxygen atoms in TEA molecule was evidenced by the results of NMR and UV. It is believed that TEA can be introduced into the crystallization process of portlandite and thus significantly alters the morphology of CH crystals and even the content of the crystalline CH phase.

As a tactical-level plan, a yard template determines the assignment of spaces in a container port yard for arriving vessels. This paper investigates the concept of yard congestion quantitatively in the context of yard truck interruptions, and develops a combination of probabilistic and physics-based models for truck interruptions. The above work enables us to exactly evaluate the expected link travel time, which then acts as the basis for proposing a mixed-integer programming model that minimizes the total expected travel time of moving containers around the yard. A Squeaky Wheel Optimization based meta-heuristic is developed to solve the model. Experiments are also conducted to validate the effectiveness of the model and the solution method.

This study investigates a quay crane scheduling problem with considering the impact of tides in a port and fuel consumption of ships. A mixed-integer nonlinear programming model is proposed. Some nonlinear parts in the model are linearized by approximation approaches. For solving the proposed model in large-scale problem instances, both a local branching based solution method and a particle swarm optimization based solution method are developed. Numerical experiments with some real-world like cases are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution methods.

This paper studies a decision problem for scheduling ships׳ starting operation time in a transshipment hub with considering the bunker cost and the transshipment cost of containers. Based on the information about the handing capacity of the container port in each period, the port operator makes a proper decision and informs the shipping companies about the start operation days of each liner route. The optimized decision may help shipping companies significantly reduce the cost. This study proposes a mixed integer programming model. The NP-hardness of the problem is proved. A local branching based solution method is also developed for solving the model. Extensive numerical experiments demonstrate the optimality of the solution by the proposed method is less than 3% for large-scale problem instances.

This study investigates a joint optimisation of routing and truck deployment for a small-package shipping company. Bus tour-based services are provided to collect express packages at customer bases. The objective of the optimisation problem is to minimise the average waiting time for packages stored at the customer bases. We first propose a mixed-integer nonlinear programming model. The proposed formulation is linearised and made solvable by an off-the-shelf mixed-integer linear programming solver. For solving larger instances of the problem, we propose two optimisation approaches that can obtain near-optimal solutions – a local branching-based method and a particle swarm optimisation-based method. We conduct numerical experiments to validate the effectiveness and efficiency of the proposed solution methods. The results indicate that both methods can obtain optimal solutions quickly for most of the small-scale instances. For medium-size instances, the local branching-based method performs best, and the PSO-based method outperforms the others for large-size instances.

© 2017 Elsevier B.V. In a hinterland barge transport system, barges are usually not self-propelled and need to be towed or pushed by tugs. This study investigates a tug-scheduling problem at a seaport that is located at a river mouth and that connects the hinterland ports along the river with the global maritime transportation network. A mixed-integer programming model is proposed to optimize the assignment of barges to tugs as well as the time when the tugs depart from the seaport and go to the hinterland ports. Some properties of the model are also investigated. Moreover, an exact solution method based on a branch-and-price approach is developed to solve the proposed model. Numerical experiments are also conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

© 2017 "IISE". The Cruise Itinerary Schedule Design (CISD) problem determines the optimal sequence of a given set of ports of call (a port of call is an intermediate stop in a cruise itinerary) and the arrival and departure times at each port of call in order to maximize the monetary value of the utility at ports of call minus the fuel cost. To solve this problem, in view of the practical observations that most cruise itineraries do not have many ports of call, we first enumerate all sequences of ports of call and then optimize the arrival and departure times at each port of call by developing a dynamic programming approach. To improve the computational efficiency,we propose effective bounds on the monetary value of each sequence of ports of call, eliminating non-optimal sequences without invoking the dynamic programming algorithm. Extensive computational experiments are conducted and the results showthat, first, using the bounds on the profit of each sequence of ports of call considerably improves the computational efficiency; second, the total profit of the cruise itinerary is sensitive to the fuel price and hence an accurate estimation of the fuel price is highly desirable; third, the optimal sequence of ports of call is not necessarily the sequencewith the shortest voyage distance, especially when the ports do not have a natural geographic sequence.