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This paper introduces a container transportation scheduling problem in a large logistic company (Yunfeng) in Shanghai City, PR China. This problem considers two types of container transportation orders (i.e., door-to-door orders and warehouse orders) between port yards and the hinterland. Unlike the pickup and delivery problem with time windows, this problem needs to consider not only container pickups and deliveries, but also the loading or unloading of cargo inside containers. Moreover, this problem takes account of some constraints on container transportation regulations in China. We formulate this problem as a mixed-integer programming model and design an improved backtracking search optimization algorithm. Based on the proposed model and algorithm, a vehicle scheduling decision support system is also developed to integrate data collection, preprocessing, Amap (a digital map, navigation, and location service provider in China) interfacing, and optimization. The improvement of the operational efficiency is validated by the usage of the proposed system in practice.

In semiconductor manufacturing, an extremely stringent quality control is enforced for wafer fabrication processes. Consequently, cleaning operations that clear residual chemical gases and heat in ...

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 ...

Maritime transport is the backbone of international trade. The amount of total international maritime trade in million tonnes loaded was 8408 in 2012 and had increased to 11.076 by 2019, for an average annual increase of 3.12%. In early 2020, the world fleet contained 98.140 ships of 100 gross tonnes and above with 2.06 million dead weight tonnage of capacity. The greenhouse gas (GHG) emissions from shipping activities are not negligible. According to the fourth GHG study commissioned by the International Maritime Organization (IMO), in 2018, global shipping emitted a total of 1056 million tonnes of carbon dioxide (CO2), accounting for around 2.89% of global anthropogenic CO2 emissions. Due to the international nature of shipping, efforts to control CO2 emissions from ships are absent from the Kyoto Protocol and the Paris Agreement. In an attempt to phase out carbon emissions from shipping entirely, the IMO formulated a strategy to cut the total annual GHG emissions from shipping by at least 50% from their 2008 levels by 2050; however, no mandatory rules have been promulgated since the release of this strategy.Given the insufficient progress made by the IMO, the European Union (EU) decided to take a leading role in promoting the reduction of CO2 emissions from maritime transport. In 2015, the EU issued regulations on the monitoring, reporting, and verification (MRV) of CO2 emissions from ships with a gross tonnage above 5000 arriving at, within, or departing from ports under the jurisdiction of an EU member state, to come into force at the beginning of 2018. It should be noted that, under the MRV regime, even if only one port on a voyage is within the European Economic Area (EEA) and the other is not (e.g., a voyage from Rotterdam directly to Singapore), the ship must still report the total CO2 emissions of the whole voyage, rather than just the emissions of the part of the voyage within EU waters.The MRV regime has been in operation for over two years, and the CO2 emissions data for the 2018 and 2019 reporting periods have already been published. Based on the data collected, on 16 September 2020, the European Parliament took the bold step of voting for the inclusion of maritime transport in the EU Emissions Trading System (ETS). This is a market-based system that uses economic tools such as a levy on bunker fuels and an emission trading system to provide monetary incentives for polluters to reduce emissions. The European Commission is conducting an impact assessment of the ETS, the results of which are expected in 2021. At this time, it is unclear how the inclusion of shipping into the EU ETS will work. There are two possibilities. The first is that only intra-EU voyages will be included; that is, only voyages from one EEA port to another EEA port will have to pay CO2 emission costs. The second is that both intra-EU voyages and voyages between an EEA port and a non-EEA port will have to pay CO2 emission costs, with the cost of a voyage between an EEA port and a non-EEA port being based on the CO2 emissions over the whole voyage, rather than the part of the voyage within EU waters. As the second possibility also covers the first possibility, we examine the implications of both possibilities but focus more on the second.

Context Ulcerative colitis (UC) is a recrudescent and chronic inflammatory disease. Artesunate (ART) has shown its anti-inflammatory and antioxidative properties in severe diseases, including UC. Objective The present study investigates the molecular mechanisms for effects of ART on UC, and the role of miR-155 in this process. Materials and methods The in vitro UC model was established by using lipopolysaccharide (LPS)-induced RAW264.7 cells. For BALB/c mice model, different concentrations/doses of ART were treated once a day for 7 days. The apoptosis and viability were measured by CCK-8 and flow cytometry assay, respectively. The expressions and concentrations of inflammatory factors were detected by qRT-PCR and ELISA, respectively. Colon tissues of mice were used for detecting the activity of MPO, and the histological changes were observed by H&E staining. Results The IC50 of ART for RAW264.7 cells was 107.3 μg/mL. In LPS-induced cells, ART treatment inhibited the cell apoptosis and promoted cell viability compared with the model group. Besides, ART treatment also reduced the expressions of pro-inflammatory factors and miR-155. However, overexpression of miR-155 showed opposite effects and attenuated the effects of ART. Meanwhile, inhibiting miR-155 expression also improved the inflammatory response induced by LPS. In UC mice model, ART treatment also alleviated the mice’s survival and alleviated the inflammatory response. In addition, the expression of p-NF-κB was suppressed by ART. Conclusion ART reduced the inflammatory response by inhibiting the expression of miR-155 in UC to inhibit the NF-κB pathway. This research showed ART might have potential in UC treatment.

Introduction Immuno checkpoint inhibitors (ICIs) including anti-PD-L1 antibody have shown certain therapeutic effects on various cancer types. Here, we reported a case of the patient with resectable non-small cell lung cancer (NSCLC) showing a complete response to nivolumab combined with chemotherapy. Patient information A 66-year-old male was diagnosed with stage IIIA large cell lung cancer, cT2N2M0, who was considered impossible to have a tumor resection due to his right hilar node enlargement. The diameter of the neoplasms was 22mm, and the patient wanted to get the chance of surgery. Light of all, surgery was the only radical treatment option and therefore planned. Interventions After administering 2 cycles of nivolumab combined with paclitaxel and cisplatin, the second chest computed tomography (CT) after the first scanning revealed the tumor apparent shrinking and vascular compression was disappeared than before. We performed a right upper lobectomy and mediastinal lymph node dissection. Pathologically, we confirmed no large cell lung cancer cells in the resected lung specimen. Outcomes The follow-up showing that patient remains alive without recurrence to these days. Conclusion This case report may provide a clue to the future development of induction therapy using nivolumab and surgery. The combined treatment of nivolumab and chemotherapy is likely to be considered as an optional management of resectable NSCLC.

The consistent vehicle routing problem (ConVRP) aims to design synchronized routes on multiple days to serve a group of customers while minimizing the total travel cost. It stipulates that customers should be visited at roughly the same time (time consistency) by several familiar drivers (driver consistency). This paper generalizes the ConVRP for any level of driver consistency and additionally addresses route consistency, which means that each driver can traverse at most a certain proportion of different arcs of routes on planning days, which guarantees route familiarity. To solve this problem, we develop two set partitioning-based formulations, one based on routes and the other based on schedules. We investigate valid lower bounds on the linear relaxations of both of the formulations that are used to derive a subset of columns (routes and schedules); within the subset are columns of an optimal solution for each formulation. We then solve the reduced problem of either one of the formulations to achieve an optimal solution. Numerical results show that our exact method can effectively solve most of the medium-sized ConVRP instances in the literature and can also solve some newly generated instances involving up to 50 customers. Our exact solutions explore some managerial findings with respect to the adoption of consistency measures in practice. First, maintaining reasonably high levels of consistency requirements does not necessarily always lead to a substantial increase in cost. Second, a high level of time consistency can potentially be guaranteed by adopting a high level of driver consistency. Third, maintaining high levels of time consistency and driver consistency may lead to lower levels of route consistency.

Both interference alignment (IA) and interference neutralization (IN) are exploited for coordinated multi-point (CoMP) communications. With the cooperation of the base station (BS), a transmit precoder and a receive filter are jointly designed, and concurrent transmissions of multiple data streams are then enabled. In the design of a precoder, the IN is applied to the interferences carrying the same data so as to align the interfering signals in the opposite direction in a subspace. On the other hand, for interferences carrying different information, IA is employed to align them in the same direction in a subspace, thus reducing the interference signal observed at the receiver side. Based on different precoding schemes at the transmitter’s side, receivers adopt zero forcing (ZF) so as to recover the desired data. The proposed IA- and IN-based CoMP (IAN-CoMP) mechanism can achieve effective interference cancellation and suppression by exploiting limited and flexible collaboration at the BS side. It can also make a flexible tradeoff between the cooperation overhead and the system’s achievable degrees of freedom (DoFs). We extend the mechanism to general cases where the antenna configurations at both the transmitter and receiver sides, the number of transmitters participating in CoMP, and that of simultaneously served users are variable. Moreover, we discuss both single-location and multi-location-based realizations of the IAN-CoMP. Finally, by defining the average transmit and receive cooperation order, we analyze the upper bound for IAN-CoMP. Our in-depth simulation shows that the IAN-CoMP can significantly improve the spectral efficiency (SE) for cell-edge users.

This study develops a general method for modeling changes in response to items relating to students perceptions of personal finance and financial products. The new method is illustrated to analyze data from a sample of 1,250 students aged 16–18 who participated in a financial capability education study in the UK. We demonstrate how a quantitative indicator of the changes in students' responses can be applied in various educational research projects, particularly as a measure of program effectiveness. Predictions are based on prior survey responses, which are taken as relevant historical information for a cohort of students. We find significant changes in the responses of students toward reported career choice following the Financial Literacy Education course at national colleges in the UK.

Global ports and maritime shipping networks are important carriers for global supply chain networks, but they are also the main sources of energy consumption and pollution. To limit ship emissions in ports and offshore areas, the International Maritime Organization, as well as some countries, has issued a series of policies. This study highlights the importance and necessity of investigating emergent research problems in the operation management of green ports and maritime shipping networks. Considerable literature related to this topic is reviewed and discussed. Moreover, a comprehensive research framework on green port and shipping operation management is proposed for future research opportunities. The framework mainly comprises four research areas related to emission control and grading policies. This review may provide new ideas to the academia and industry practitioners for improving the performance and efficiency of the operation management of green ports and maritime shipping networks.

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 ...

Objective Liver is uniquely vulnerable during sepsis. MicroRNA-155 (miR-155) is confirmed to play crucial roles in septic liver injury. The present study aims to investigate the mechanisms of miR-155 in septic liver injury. Methods The sepsis model was established by intraperitoneal injection of lipopolysaccharide (LPS) in mice. Mice were divided into four groups: Vehicle, miR-155 antagomir, LPS, LPS+ miR-155 antagomir. The survival rate and body weight were monitored. Liver injury was assessed by H&E staining. The levels of serum ALT and inflammatory cytokines were determined by ELISA kits. Oxidative stress was detected by MDA and SOD detection kits. The miR-155, Nrf-2, and markers related to oxidative stress, endoplasmic reticulum (ER) stress, mitochondrial injury and apoptosis were detected by western blotting and qPCR. Apoptosis in liver tissues was detected by TUNELstaining. Results MiR-155 antagomir alleviated liver injury as evidenced by enhancing survival rate and body weight, inhibiting inflammatory cell infiltration, liver cells necrosis and decreasing ALT level. The productions of TNF-α, IL-6 were suppressed, while anti-inflammatory cytokine IL-10 was promoted by miR-155 antagomir. Oxidative stress was inhibited by miR-155 antagomir via enhancing nuclear factor, erythroid 2-like 2 (Nrf-2) expression. ER stress and Cytochrome C (Cyto-C) release were restrained by miR-155 antagomir. Sepsis-induced apoptosis was repressed by miR-155 antagomir as manifested by the decreased levels of Bax, cleaved caspase-12, 9 and 3, and increased levels of Bcl-2 and uncleaved PARP. Conclusion MiR-155 antagomir relieved septic liver injury through inhibiting oxidative stress-mediated ER stress, mitochondrial dysfunction and apoptosis via targeting Nrf-2, suggesting miR-155 as a therapeutic target for septic liver injury.

As a green port and shipping-related policy, the vessel speed reduction incentive program (VSRIP) involves using a subsidy to induce ships to reduce their speed in a port area so that the emissions can be reduced at the port. However, this program may attract new ships to visit the port because of the subsidy; in this case, the port's profit will grow due to more ship visits, but its total emissions may also increase, which is counter to the original intention of the subsidy. The government could then intervene by providing part of the subsidy for the VSRIP or by collecting air emission taxes for the increased emission at the port. This paper studies how to design suitable subsidies for ships participating in a VSRIP. Two bilevel subsidy design models are formulated based on a Stackelberg game to maximize the port's profit (related to the profits from original and new ships, the subsidy provided by the port, and air emission taxes) and to minimize the government's cost (related to the damage cost of air emissions, the subsidy provided by the government, and air emission taxes). We determine which policy (including a sharing subsidy policy, no government intervention, and an air emission tax policy) should be implemented by the government in different cases and how much subsidy should be provided by the port under each government policy. We find that these decisions are affected by several practical factors, such as the damage cost of air emissions per ton of fuel and the subsidy sensitivities of original and new ships. We also outline several meaningful insights based on the analysis of these practical factors.

Objective. Systematically evaluate the efficacy of physical ablation combined with TKI in the treatment of advanced non-small cell lung cancer (NSCLC). Methods. We performed a comprehensive search of databases including OVID, PubMed, EMBASE, the Cochrane Library, and three Chinese databases (China National Knowledge Infrastructure, Wanfang Database, and Chongqing Weipu Database). The aim was to identify randomized controlled trials (RCT) investigating physical ablation as the treatment for advanced NSCLC. We also evaluated the methodological quality of the included studies and summarized the data extracted for meta-analysis with Review Manager 5.3. Results. A total of 9 studies, including 752 patients, were evaluable. The meta-analysis results show that the complete response rate (CRR) (RR: 2.23, 95% CI: 1. 46 to 3.40, P 0.01), partial response rate (PRR) (RR: −2.25, 95% CI: 1.41 to 3.59, P 0.01), and disease control rate (DCR) (RR: −2.80, 95% CI: 1.64 to 4.80, P < 0.01) of patients with advanced NSCLC who received physical ablation combined with TKI therapy were higher than those who did not receive physical ablation therapy. The control groups from seven of the studies had a total of 606 patients with targeted therapies and chemotherapy. The complete response rate was (CRR) (RR: 2.48, 2.4895% CI: 1.55 to 2.47, P 0.01), partial response rate (PRR) (RR: −1.66, 95% CI: 1.20 to 2.31, P < 0.01), and disease control rate (DCR) (RR: −2.68, 95% CI: 1.41 to 5.06, P < 0.01) for patients with advanced NSCLC who had received physical ablation combined with targeted therapies and chemotherapy, compared to patients who had not received physical ablation therapy. This difference was statistically significant. Above all, these results showed that the clinical efficacy of physical ablation combined EGFR-TKIs therapy (regardless of whether it was combined with chemotherapy) was better than that of EGFR-TKIs therapy alone. Conclusion. Physical ablation combined with TKI treatment in patients with advanced NSCLC can improve efficacy.

China has emerged as the second largest economy in the world during the globalization in the last forty years. However, in the last decade, Chinese manufacturing has also demonstrated its dark side causing wide range of concerns globally and directly jeopardize people’s health because of serious pollutions. How could the world keep its industrialization yet without damages to the natural environment? The paper proposes a new framework entitled ‘IE3’ by integrating three domains of knowledge—Industrial Entrepreneurship, Industrial Engineering and Industrial Ecology. The IE3 model provides a potential answer to the future development pathway for industrialization, changing from pursuit of quantity to quality via considering resources efficiency and ecology efficiency. The novelty of the research lies in incorporating three originally separated theories into a comprehensive system.

The traditional neural network-based education platform has the problem of slow access to resources, which affects teaching efficiency. To this end, artificial intelligence technology is introduced for the design of distance multimedia sports teaching platforms. Through system hardware and software design, enhance the integration capability of the teaching platform, improve the controllability of the teaching platform, help the physical education platform to obtain more educational resources, accelerate the processing and integration analysis of educational information, and better assist teachers in developing physical education jobs. In order to verify the effectiveness of the method in this paper, the method in this paper is compared with the teaching platform based on neural network,the experimental results show that the platform can obtain resources quickly and has broad application prospects.

Some studies have shown that basketball can promote the development of young children’s nervous system, exercise their physical coordination ability, allow young children to have a team awareness, increase young children’s social time, and establish social relationships. However, there is a problem of high system resource consumption level in the traditional visualized teaching system for infant basketball courses. Therefore, a data-driven visualized teaching system design for infant basketball courses is proposed. The hardware part adopts the intelligent display terminal DMT80480507OSW to realize human-computer interaction, which allows children to increase learning interest through human-computer interaction. The software part uses data compression algorithms to compress basketball course resource data. After testing, the feasibility of the design system was proved to provide richer teaching resources for children’s basketball courses.

With the rapid development of wireless technologies, interference has become a main impediment to network performance, making interference management (IM) a critical issue that warrants a thorough investigation. In this paper, we propose a novel IM technique, called signal dodging and interference guiding (SDIG). With this method, we first intellectually move the data transmission away from the interference, hence can partially avoid the influence of disturbance to the desired signal. Then, we employ existing IM method, including zero-forcing (ZF) reception, interference alignment (IA), interference steering (IS), and etc., to further eliminate the interference. The SDIG consists of two phases, the first one is modifying the desired signal while the second is mitigating the interference. This is similar to the idea of Taichi in that both involve the actions of passive defense and active attack. Therefore, we also use the term Taichi as an equivalent for SDIG. Our in-depth simulation has shown that with the proposed scheme, both the interfered user's and the system's spectral efficiency can be significantly improved.

Maritime transport is the backbone of international trade and globalization. Maritime transport research can be roughly divided into two categories, namely the shipping side and the port side. Most of the classic approaches adopted to address practical problems in these research topics are based on long-term observations and expert knowledge, while few of them are based on historical data accumulated from practice. In recent years, emerging approaches, which we refer to as machine learning and deep learning techniques in this essay, have been receiving a wider attention to solve practical problems. As a relatively conservative industry, there are some initial trials of applying the emerging approaches to solve practical problems in the maritime sector. The objective of this essay is to review the application of emerging approaches to maritime transport research. The main research topics in maritime transport and classic methods developed to solve them are first presented. The introduction of emerging approaches and their suitability to be applied in maritime transport research is then discussed. Related existing studies are then reviewed according to problem settings, main data sources, and emerging approaches adopted. Challenges and solutions in the process are also discussed from the perspectives of data, model, users, and targets. Finally, promising future research directions are identified. This essay is the first to give a comprehensive review of existing studies on developing machine learning and deep learning models together with popular data sources used to address practical problems in maritime transport.

This paper considers promoting off-site construction using government subsidy. A three-stage Stackelberg game model is formulated for the optimal subsidy design for the government, accounting for the decisions of government, suppliers (owners of off-site construction yards), and customers (entities that buy precast concrete from the suppliers). The government has two objectives: one is to maximize the use of precast concrete produced by regional yards and the other is to reduce green-house gas emissions from the transport of precast concrete. A number of properties of the three-stage Stackelberg game framework have been analyzed. We find that an inappropriately designed subsidy scheme may decrease the use of precast concrete produced by regional yards and increase green-house gas emissions from the transport of precast concrete, compared with a no-subsidy benchmark.

This paper considers the transportation of prefabricated products in the construction industry. Prefabricated products can be transported by road transport or intermodal transport, the latter of which incurs lower carbon emissions. A construction contractor always selects the transport mode with a lower cost rather than one with a lower carbon emission. A bi-level programming model was formulated to decide the optimal subsidy for intermodal transport to achieve the lowest carbon emissions while accounting for the cost minimization decisions faced by construction contractors who use prefabricated products. A numerical example was conducted to demonstrate the effectiveness of the proposed model. This research is important with regard understanding carbon emissions and how they relate to transportation options for industry.

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.

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.

Firstly, this article introduces a two-dimensional fuzzy entropy threshold segmentation method, taking characteristics of underwater sonar image of moving target into account, the concept of time in the traditional frame difference motion detection method is used. To determine the grayscale image segmentation threshold, a two-dimensional spatio-temporal fuzzy entropy algorithm is established. The algorithm is used in forward-looking sonar image threshold segmentation to verify the applicability and good performance. Compared to other threshold algorithms, the algorithm of this article can obtain better segmentation threshold value and have more advantages in the processing of sonar image which contains more noise.

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.

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.

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.

The vigorous development of O2O e-commerce promote the appearance of many small orders, increasing the stresses on logistics operators to carry out city distribution. However, a crowdsourcing joined to release these stresses is a new try and become more popular. This paper focuses on matching of the crowds and tasks from crowdsourcing platform for city distribution. To address exploring the impact of time, space and efficiency on the task matching in crowdsourcing platform, a bi-objective matching and differentiated pricing model creates to achieve the highest efficiency and the lowest total cost in urban distribution system. For solving the model, a two-dimensional and multi-stage roulette algorithm has been designed, with combining modeling and simulation method. The proposed method takes full use of economic development of the region where the task is located and the space-time efficient distance and space-time reachable distance. To illustrate the effectiveness and validity of the proposed method, a sample test is conducted with the actual operating data of a company in the PRD region, and the results show that 719 tasks out of 746 matching pairs are executed, the task matching rate is 89.4%, the completion rate is 86.1% and the total task price 39140 RMBs. Compared with the original matching situation, the total price increases at 3.90%, while the task completion rate is improved at 37.7%, which greatly enhance the efficiency of crowdsourcing platform member matching. The matching of the participants and tasks of the city distribution crowdsourcing platform, which combines with the measurement of differential pricing and crowds’ credibility, can be applied in this problem successfully.

Interference management (IM) is essential to wireless communication networks, but interference suppression, a key component of IM, is known to degrade users' achievable spectral efficiency (SE). It is thus important to select an appropriate IM method with optimal operating parameters according to diverse network deployments, transmit power differences of various communication equipments, and dynamically changing channel conditions so as to balance the benefits brought by and the cost of IM. Interference neutralization (IN) has recently been receiving considerable attention, with which a duplicate of interference of the same strength and opposite phase w.r.t. the original interfering signal is generated to neutralize the disturbance at the intended receiver. However, to the best of our knowledge, all existing IN schemes assume that interference is completely neutralized without accounting for their power consumption. To remedy this deficiency, we propose a novel scheme, called dynamic interference neutralization (DIN). By intelligently determining the appropriate portion of interference to be neutralized, we balance the transmitter's power used for IN and the desired signal's transmission. We then present a new way to adaptively select one of DIN and other IM methods by taking into account the cost of multiple IM methods and their benefits. Our analysis has shown that DIN can include complete IN and non-interference management (non-IM) as special cases. The proposed strategy is shown via simulation to be able to make better use of the transmit power than existing IM methods, hence enhancing users' SE.

© 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.

A yard template determines the assignment of spaces in a yard for arriving vessels. Fluctuation of demand for freight transportation brings new challenges for making a robust yard template when facing uncertain maritime market. A model is proposed for yard template planning considering random numbers of containers that will be loaded onto vessels that visit the port periodically. Traffic congestions and multiple schedule cycle times for vessel arrival patterns are also considered. Moreover, a meta-heuristic method is developed for solving the model in large-scale cases. Numerical experiments are conducted to validate effectiveness and efficiency of the model.

This paper studies an integrated optimization problem on outsourcing and production decisions in the context of the global supply chain and China’s export-oriented tax policies. A three-stage decision model is proposed for this integrated optimization problem. A cross-entropy-based solution method is also developed to solve the three-stage decision model. Numerical experiments are performed to investigate the effectiveness of the proposed model and the efficiency of the proposed method. Additionally, some managerial insights are drawn from these experiments.

The assignment of tasks to teams is a challenging combinatorial optimisation problem. The uncertainty in the tasks’ execution processes further complicates the assignment decisions. This study investigates a variant of the typical assignment problem, in which each task can be divided into two parts, one is deterministic and the other is uncertain with respect to their workloads. From the stochastic perspective, this paper proposes both a stochastic programming model that can cope with arbitrary probability distributions of tasks’ random workload requirements, and a robust optimisation model that is applicable to situations in which limited information about probability distributions is available. An example of its application in the software project management is given. Some numerical experiments are also performed to validate the effectiveness of the proposed models and the relationships between the two models.

In engineering design, the decision to select an optimal material for a particular product is a problem requiring multi-criteria decision analysis that involves both qualitative and quantitative factors. The evaluation of alternative materials may be based on imprecise information or uncertain data. Furthermore, there can be significant dependence and feedbacks between the different criteria for material selection. However, most existing decision approaches cannot capture these complex interrelationships. In response, this paper proposes a general framework for evaluating and selecting the best material for a given application. A novel hybrid multiple criteria decision making (MCDM) model combining DEMATEL-based ANP (DANP) and modified VIKOR is used to solve the material selection problems of multiple dimensions and criteria that are interdependent. Moreover, target-based criteria as well as cost and benefit criteria can be addressed simultaneously in the proposed model. Finally, an empirical case concerning the bush material selection for a split journal bearing is presented to illustrate the potential of the new model. The results show that the proposed method for material selection is effective and provides meaningful implications for designers and engineers to refer.

This paper studies ambulance deployment and relocation problems, which are two of the core decisions faced by emergency medical service control centers in metropolis. The challenge in the problems is to estimate the operational performance of a deployment plan under stochastic environments. More specifically, the stochastic and dynamic nature of request arrivals, fulfillment processes, and complex traffic conditions as well as the time-dependent spatial patterns of some parameters complicate the decisions in the problems. This paper proposes a simulation optimization method that enables evaluating the operational performance of deployment plans through a detailed simulation model. For guiding the search process in the simulation optimization method, the genetic algorithm is employed in this study. On the basis of the deployment decisions, a mathematical model on ambulance relocation is also proposed for adapting to the dynamic changing environments along the time. To illustrate the proposed method’s usage in practice, a demo example about its application in Shanghai is given. Some numerical experiments are also performed to validate the effectiveness and the efficiency of the proposed methods.