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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 ...

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.

Large e-commerce retailers usually establish their own logistics systems. Such systems make use of their own dedicated fleets but will also use a crowdsourced delivery mode by hiring occasional fleets. These mixed logistics systems with both dedicated and occasional fleets serve both retailers’ internal delivery tasks and external tasks requested by local businesses. This paper studies the problem of scheduling heterogeneous (internal and external) delivery tasks on a mixed logistics platform with multiple depots and two types of vehicles (dedicated and occasional). A delivery task is executed by either a dedicated vehicle or an occasional vehicle. The dedicated vehicles depart from and return to the platform's depots; the occasional vehicles depart from their original location and pick up goods from depots or external pickup locations, fulfill the delivery tasks, and finish their route at the final delivery location. We propose mixed integer programming models and column generation-based solution methods to solve the problem. A computational study is conducted based on a series of randomly generated instances and real-world instances involving 15 depots, 120 internal customers, 15 external delivery tasks, and 38 dedicated and occasional vehicles. The results obtained demonstrate the efficiency of the column generation-based solution methods. Moreover, the effectiveness of the proposed models is validated by a significant cost saving in comparison to intuitive decision rules. A sensitivity analysis is also conducted to derive a number of managerial implications.

Rehabilitation is an important branch of the modern healthcare system. Every day, rehabilitation patients move in hospital campus to receive treatments from therapists. The long timespan of these treatment routes leads to several patients’ complaints and results in negative effects. However, scheduling the treatment routes for patients is a complex task for hospital managers. This study investigates a rehabilitation patient scheduling and routing problem, which focuses on reducing the timespan of patients’ treatment routes. This real-life motivated problem can be described as a combination of several interrelated traveling salesman problems with time windows (TSPTWs) and is difficult to solve. We formulate the problem as an integer linear program (ILP) and we develop a greedy heuristic called “route-first, schedule-second”. Then a column generation solution method is proposed on a set partitioning-based reformulation of the original model. Specifically, a tailored genetic algorithm and several effective accelerating strategies are developed within the column generation method. Numerical experiments are conducted on 30 instances devised from real data to validate the efficiency of the proposed solution approaches. Experimental results show that our methodology can generate high-quality solutions efficiently and is therefore suitable to be applied in practice.

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...

The Emission Control Areas (ECAs) established by the International Maritime Organization are beneficial to reduce the sulphur emissions in maritime transportation but bring a significant increase in operating cost for shipping liners. Low sulphur emissions are required when ships berth or sail within ECAs. It is an irreversible trend that green technologies such as scrubbers and shore power will be implemented in maritime shipping industry. However, the literature lacks a quantitative decision methodology on green technology adoption for fleet deployment in a shipping network in the context of ECAs. Given a shipping network with multiple routes connected by transshipment hubs, this study proposes a nonlinear mixed integer programming model to optimally determine fleet deployment along routes (including green technology adoption), sailing speeds on all legs, timetables, cargo allocation among routes for each origin-destination pair, and berth allocation considering the availability of shore power at different berths in order to minimize total five types of cost. A three-phase heuristic is also developed to solve this problem. Numerical experiments with real-world data are conducted to validate the effectiveness of the proposed model and the efficiency of the three-phase heuristic. Some managerial implications are also outlined on the basis of the numerical experiments.

This study investigates an integrated model for the continuous berth allocation and quay crane scheduling problem by considering quay crane coverage range, ship fuel consumption, and transshipment costs. A nonlinear mixed-integer programming model is proposed. Some nonlinear parts in this model are linearized by approximation approaches. While the objective function aims to minimize waiting costs, it also seeks to minimize fuel consumption costs from the current port to the next port and housekeeping costs generated by transshipment between vessels. A local branching-based solution algorithm is designed to solve the proposed model. Computational experiments are conducted to validate the effectiveness of the proposed scientific programming model and efficiency of the 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 ...

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 models and solves a fleet deployment and demand fulfillment problem for container shipping liners with consideration of the potential overload risk of containers. Given the stochastic weights of transported containers, chance constraints are embedded in the model at the strategic level. Several realistic limiting factors such as the fleet size and the available berth and yard resources at the ports are also considered. A non-linear mixed integer programming (MIP) model is suggested to optimally determine the transportation demand fulfillment scale for each origin-destination pair, as well as the ship deployment plan along each route, with an objective incorporating revenue, fixed operation cost, fuel consumption cost, holding cost for transhipped containers, and extra berth and yard costs. Two efficient algorithms are then developed to solve the non-linear MIP model for different instance sizes. Numerical experiments based on real-world data are conducted to validate the effectiveness of the model and the algorithms. The results indicate the proposed methodology yields solutions with an optimality gap less than about 0.5%, and can solve realistic instances with 19 ports and four routes within about one hour.

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.

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.

In this work, we present and evaluate heuristic techniques for a real-world territory design problem of a major dairy company which produces and distributes perishable products. The problem calls for grouping customers into geographic districts, with the objective of minimising the total operational cost, computed as a function of the fixed costs of the districts and the routing costs. Two inter-connected decision levels have to be tackled: partitioning customers into districts and routing vehicles according to complex operational constraints. To solve the problem, a hybrid multi-population genetic algorithm is designed, enhanced with several evolution and search techniques. The proposed design is extensively tested on instances derived from the literature and on real-world large-scale instances, involving more than 1000 customers. The results show the effectiveness of the different components of the algorithm and the feedback from the company’s planners confirms that it produces high-quality, operational solutions. Additionally, we explore some managerial findings with respect to the adoption of alternative objectives and service requirements.

Gas and particulate emissions from ship transportation have been increasing in recent years. In order to mitigate ship emissions near coastal areas, voluntary vessel speed reduction incentive programs (VSRIPs) were put in place by a number of ports. This paper studies a schedule design problem faced by liner shipping companies under VSRIPs. It proposes a mixed-integer nonlinear mathematical model for the minimization of the total cost, consisting of fuel cost, as well as operating cost, minus dockage refunds. The model balances three determinants, that is, the compliance of VSRIPs, the speed limit (the maximum physical speed of ships and the upper speed limit imposed by VSRIPs), and the limited number of ships. An enumerative algorithm and a piecewise-linear approximation algorithm are developed, based on some properties of the nonlinear model. The efficiency of the proposed algorithms is validated through extensive computational experiments.

The cruise industry has maintained a steady growth in the past 20 years. Due to the large number of cruise passengers and regulations on sea environment protection, determining at which ports to dispose of the waste generated onboard a cruise ship is a key decision to reduce the cost for a cruise company. We address four versions of the problem: the cruise itinerary is either static or dynamic and the amount of waste generated on each voyage leg is either deterministic or stochastic. We propose a polynomial-time solution algorithm for the static deterministic model, and the idea of the algorithm can also be used to solve the static stochastic model and the dynamic deterministic model. Second, we identify the structure of the optimal policy to the dynamic stochastic problem, based on which an efficient dynamic programming algorithm is developed. Extensive numerical experiments derived from problems of real-case scales demonstrate the efficiency of the proposed algorithms.

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.

The berth allocation problem (BAP) has been extensively studied in the literature, which seeks to determine berths and times for upcoming ships. The previous studies do not distinguish berths and ships among different liner carriers. By taking into account the increasing use of dedicated berths in practice, this paper proposes a special BAP where berths and ships could be distinguished among different liner carriers. For our BAP under a cooperative environment, we propose a mixed-integer programming model. Based on an assignment model with binary variables, our BAP can be reduced to the liner carrier clustering problem. To generate stable liner carrier clusters used in our assignment model, queueing theory and core theory are applied. A three-stage optimization method is proposed to solve our BAP. Namely, stage I proposes a queueing theory approach for various liner carrier clusters, and based on core theory stage II determines the stability of different liner carrier clusters. Based on the results obtained in stages I and II, stage III solves the liner carrier clustering problem. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.

Recent trends in pursuing green ports have had positive effects on terminal operators constructing more eco-friendly decision schemes. A yard template in a container port not only determines the scheduling of the terminal handling equipment by container allocation, but also affects the carbon emissions from yard trucks caused by loading and unloading operations, due to the differing degree of yard road congestion. Moreover, the uncertainty regarding the number of transporting containers makes it challenging to construct a robust yard template for a container terminal. Motivated by research pointing to the need to alleviate the exhaust pollution generated by port handling equipment, this paper presents a two-stage stochastic programming model for yard template planning, with the aim of minimizing the maintenance and manpower costs of activating yard cranes for container handling and of reducing the carbon dioxide emitted from yard trucks. More specifically, the paper considers the impact of road congestion on carbon dioxide emissions from the perspective of the increase in transportation time caused by yard truck interruptions. The uncertainty regarding the number of transport containers is reflected by taking into account numerous scenarios in which different numbers of containers are loaded onto (discharged from) vessels that visit the port periodically. In addition, an improved Benders decomposition-based solution method is designed to solve the proposed model with large-scale problem instances. Several experiments are performed to validate the effectiveness of the model and the efficiency of the method. The numerical results demonstrate that yard template planning that considers road congestion can effectively reduce the carbon emissions of yard trucks in loading and unloading operations.

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.

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.

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 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.

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.

The tactical berth allocation problem (BAP) concerns the allocation of favorite berthing positions to ships that periodically call at the terminals. This paper investigates the tactical-level berth allocation scheduling models. First a deterministic model for tactical BAP is formulated with considering the periodicity of schedule. However, in reality, the number of containers that need to be handled (discharging & loading) for each ship is uncertain in the ship's future periods. Thus for the tactical BAP, there is significant uncertainty with respect to the operation time (dwell time) of ships, which further complicates the traditional berth allocation decisions. From stochastic perspective, this paper proposes both a stochastic programming formulation that can cope with arbitrary probability distributions of ships’ operation time deviation, and a robust formulation that is applicable to situations in which limited information about probability distributions is available. The relationship between the two models is also investigated in an analytic way. Some meta-heuristic algorithms are suggested for solving the models. Numerical experiments are performed to validate the effectiveness of the proposed models and the efficiency of the proposed solution algorithms. The experiments also compare the above stochastic programming formulation and the robust formulation models, as well as evaluate their potential benefits in practice. This study finds that the robust method can derive a near optimal solution to the stochastic model in a fast way, and also has the benefit of limiting the worst-case outcome of the tactical BAP decisions.

© 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.

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.

This paper investigates a stowage planning problem, in which a liner ship will visit a sequence of ports, the number of available quay cranes in ports and the numbers of loading/unloading containers in ports are uncertain. This stowage planning problem is about how to assign the loaded containers to the bays of the ship considering uncertain information in the future, so as to minimise the sum of the expected quay crane handling time at the ports. Based on stochastic programming, a two-stage decision model is proposed for this problem. A particle swarm optimisation based solution method is developed to solve the model for large-scale problem instances. Numerical experiments are conducted to validate the effectiveness of the proposed model and the efficiency of the proposed solution method.

With the rapid development of container transportation and the fierce competition among ports, container terminal operators pay more attention to meet the individual needs of customers. This study develops a system to estimate container liner shipping arrival time and the probability distribution for each time set by considering transit process and the periodicity of liner shipping. This system can give the most possible arrival time of cargoes to customers in advance, and then improve the level of port services. Also, a recursive algorithm based solution method and a parallel computing based solution method are proposed for solving the problem. Computational test is conducted on different scales and the results show the effectiveness of the proposed solution approach.

New Product Development (NPD) is one of the most important activities for companies. The NPD activities get to depend on knowledge more and more. Therefore, Knowledge Management (KM) has become a key issue and a hot topic in the fields of NPD researches. This paper proposes a new design of an effective NPD-support KM tool for facilitating the knowledge sharing and acquisition among collaborative NPD team members. The KM tool mainly contains two channels for knowledge acquisition: knowledge query and knowledge recommendation. The former one is the basic function for all types of KM tools, and also acts as the basis for the latter one. This paper proposes two types of knowledge recommendation ways: one is a vector-based way; the other is a phrase-based way. Key techniques for implementing the KM tool are elaborated. A demo example is also illustrated to show the functions of the proposed KM tool.

Service-Oriented Manufacturing (SOM) strategy is a new manufacturing mode by integrating servitization with the traditional manufacturing industry. The advantages and importance of the SOM strategy are gradually recognized by more and more enterprises. However, the enterprises' specific cost structures and cost configurations for providing customized services have influences on the decisions about whether the SOM strategy should be adopted, how to determine the suitable scope of SOM, and how to set the suitable pricing scheme of their offerings. This paper performs an analytical study on the above objectives and proposes an optimal decision model for designing SOM strategy. Our analysis on the SOM strategy under competition offers some insights into: when enterprises should perform SOM; what the suitable scope of SOM is, and how to design pricing strategies for SOM. From this analytical study, we draw some managerial implications that: for an enterprise in a duopoly market, if the fixed part in the unit cost of service is lower than the one third of the equilibrium price, the enterprise should adopt the SOM strategy; in addition, one half of the enterprise's customers should be covered by the SOM strategy at most.