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Local Container Drayage Problem (LCDP) refers to the optimization of the process of planning and scheduling container trucks between a terminal and customers to offer door-to-door service to customers in a local area. The time required for (un)packing containers at customers’ sites are often relatively long and uncertain due to the current (un)packing work level and unexpected deviations in operational situations, which has a significant influence on the planning and scheduling of the container transportation process. This paper examines the LCDP with Separable tractors and trailers, and additionally with consideration of (un)packing time Uncertainties (LCDPSU). A proactive strategy is employed to tackle the uncertainty by proposing a “model robust” bi-objective optimization model to balance the tradeoff between operational cost, which includes traveling cost and tractor deployment setup cost, and robustness, which is represented as an exponential expression of the time buffer between two stages of each individual task. The deterministic version of our problem is proved to be NP hard, and an Ant Colony Optimization (ACO) scheme is therefore proposed to search for feasible solutions in which the Zoutendijk feasible direction algorithm is embedded in order to tackle the nonlinearity brought in by the robustness of the model. Numerical experiments are conducted to validate the efficiencies and effectivenesses of the proposed models and methods, and managerial implications are drawn from the numerical results.

The local container drayage problem (LCDP) plays an important role in the waterborne transportation for global trade. Truck platooning is an emerging and promising container transportation mode in which a platoon is formed by a leading truck followed by a set of trucks using semi-automated technologies. This paper focuses on the truck platooning operation mode (TPOM) in which only the leading truck is human-driven. We also focus on attempts to exploit the advantages of the new operation mode, such as the ability to use multiple trucks simultaneously with only one driver. Moreover, one customer can be serviced by two different drivers through coordination, and one container can be shared among customers without returning to the depot. All of these advantages improve the LCDP operational performance. A mathematical model is proposed to capture these advantages and related features pertaining to this new operation mode. The model is compared, theoretically and numerically, with three well-studied LCDP models reported in the literature, and we prove that our model is a generalization of the previous three models. For even small-scale problems, CPLEX has difficulty achieving satisfactory solutions due to the NP-hardness and complicated structure of the problem. Therefore, a heuristic method that incorporates the learning mechanism based on the ant colony algorithm is proposed to overcome the problem. Extensive numerical experiments were conducted to validate the model and proposed methods, and the experimental results show that the benefits of the new operation mode can be achieved by increasing the platoon length and/or well coordinating the sharing of containers among customers.

Shared parking is one of effective solutions to alleviate urban parking problems. This paper studies the problem of shared parking space allocation. An integer programming model based on global information is established. The model especially improves discrete time windows into continuous time windows, which is closer to practical situation and is more precise. The allocation scheme of shared parking is obtained by solving the model using commercial solver. The proposed model is verified to be better than the frequently used first-come-first-serve and first-book-first-serve strategies through numerical simulations.

Purpose This paper aims to study the pricing problems on the two-sided market for cases of monopoly and duopoly competition, specifically investigating the impact of platform service quality on the market. Theoretical analysis and computational studies are conducted to investigate the impact of different parameters on the system outcomes. Design/methodology/approach Mathematical formulations are proposed for cases of monopoly and duopoly competition. For monopolistic market, the optimal pricing and service quality strategies are obtained using mathematical programming method. For duopolistic market, the equilibrium outcomes are derived by game theory. Sensitivity analysis and numerical studies are also adopted to investigate the impact of different parameters. Findings For monopolistic market, the platform will provide a low service quality when the service cost parameter is large. However, when the cost parameter is small, the platform provides a higher service quality and higher registration prices. Furthermore, the sum of the optimal prices is proportional to the service quality and inversely proportional to the user price sensitivity. For duopolistic market, the competitive equilibrium prices exist under a certain condition. The determinants of equilibrium prices are the gap between the service qualities of two platforms and the cross-group externalities. Originality/value For monopolistic market, this paper specifies the role of platform service quality in determining the platform’s pricing strategy. For duopolistic market, this paper presents a market sharing mechanism between two platforms and explores the equilibrium pricing strategies for platforms with different service quality level.

The research on the ability of sci-tech innovation has always been a hot topic in the field of regional innovation. It is of great significance to promote the overall competitiveness of industry and the development of regional economy. Based on the comprehensive factors of science and technology support ability, innovation environment, innovation ability and its output, this paper studies the sci-tech innovation ability of Guangdong Province, constructs STICI for the first time, analyzes the actual data of 2018 of 21 regions in Guangdong Province, and measures the sci-tech innovation ability of each region. The difference of sci-tech innovation ability is also analyzed based on two levels of comprehensive ability and first level factor. It is concluded that the development of regional sci-tech innovation is unbalanced in different regions in Guangdong Province. It is suggested that the regional sci-tech innovation ability can be improved and the coordinated development of the whole province can be promoted through policy guidance, precise assistance, cooperation and co-construction, and strengthened dominant position of enterprises in sci-tech innovation in eastern and western regions of Guangdong Province.

This paper describes the world's largest carbon capture and storage project applied to a fluid catalytic cracking (FCC) process at a Sinopec oil refinery in He’Nan Province, China. FCC units emit a large amount of CO2 in the process of regenerating catalysts deactivated by carbon deposits. There are few carbon capture pilots around the world targeting FCC regeneration flue gas. This project started construction in 2014 and was commissioned in June 2015. The pollutant removal process includes four major units: NOx removal; SOx and particulate removal; treatment of waste water associated with SOx removal; CO2 capture and liquefaction. Since becoming operational more than one year ago, the project has generated significant economic and environmental benefits by reducing SOx, NOx, particulate and CO2 emissions. The project provides valuable experience for whole chain CCS operation in China.

Mobile technology has led to more and more app-based ride-sourcing platforms on the market. This paper analyzes the pricing strategies of ride-sourcing platforms by economic model and numerical experiment. We propose an overall model which includes demand, labor supply, and the matching function between demand and supply. The model explicitly considers the interaction between labor supply and driver income, while modeling the market as a system of nonlinear equations. We prove the existence of market equilibrium through the fixed point theorem, and propose an approximate iterative algorithm to obtain the equilibrium. Finally, we use numerical experiments to study the impact of pricing strategies on the ride-sourcing market.

Mobile technology development has led to the upsurge of app-based ride-sourcing platforms on the market. This paper analyzes the pricing strategies of ride-sourcing platforms. Economic modeling is used to propose an aggregate model, which includes the demand, labor supply, and the matching between demand and supply. The interactive relationship between labor supply and drivers' revenue is explicitly considered. The market is ultimately modeled as a system of nonlinear equations. We prove the existence and stability of market equilibrium and propose an approximate iterative algorithm to solve for the equilibrium. The algorithm is verified through a numerical experiment. A partial-derivative-based sensitivity analysis is conducted to quantitatively evaluate the impacts of platform pricing strategies on ride-sourcing market performance, based on which we examine desirable price perturbations from the perspectives of customers, drivers, and the platform, respectively. The triple-win Pareto improvement condition is also numerically analyzed.

Two players sequentially locate a fixed number of facilities, competing to capture market share. Facilities face disruption risks, and each customer patronizes the nearest operational facility, regardless of who operates it. The problem therefore combines competitive location and location with disruptions. This combination has been absent from the literature. We model the problem as a Stackelberg game in which the leader locates facilities first, followed by the follower, and formulate the leader’s decision problem as a bilevel optimization problem. A variable neighborhood decomposition search heuristic which includes variable fixing and cut generation is developed. Computational results suggest that high quality solutions can be found quickly. Interesting managerial insights are drawn.

This paper considers the impact of service quality on two-sided markets and studies the optimal pricing problems for a monopoly platform. Considering that improving service quality commonly brings a non-linear operation cost, the platform can adjust its registration prices and service quality to balance the cost and the earnings, so as to maximize its profit. This paper shows that the monopoly platform has three kinds of strategies: offering a high service quality and charging two high registration prices; offering a lowest service quality and charging two cheaper registration prices; exiting the two-sided market when there is no profit space. Furthermore, the sensitivity analysis on the service cost parameter, the user price sensitivity and the cross-group externalities is proposed to provide some important management insights for the platform.

This paper examines the joint optimization of storage location assignment and storage/retrieval scheduling in multi-shuttle automated storage/retrieval systems (AS/RSs) under shared storage, in which the reuse of empty location yielded by retrieval operation is allowed. From the view of analytical model, the advantage of operational mode under shared storage is verified. A variable neighborhood search (VNS) algorithm is developed to solve the large-sized problems. Various numerical experiments are conducted to evaluate the performance of the proposed algorithm and investigate the impact of different parameters on computational efficiency.

This paper examines the local container drayage problem under a special operation mode in which tractors and trailers can be separated; that is, tractors can be assigned to a new task at another location while trailers with containers are waiting for packing or unpacking. Meanwhile, the strategy of sharing empty containers between different customers is also considered to improve the efficiency and lower the operation cost. The problem is formulated as a vehicle routing and scheduling problem with temporal constraints. We adopt combinatorial benders’ cuts algorithm to solve this problem. Numerical experiments are performed on a group of randomly generated instances to test the performance of the proposed algorithm.

Closed-loop logistics (forward and reverse logistics) has re- ceived increased attention of late due to customer expectations, greater environmental concerns, and economic aspects. Unlike previous works, which consider single products or single periods in multi-objective func- tion problems, this paper considers a multi-product multi-period closed- loop logistics network with regard to facility expansion as a facility location-allocation problem, which is closer to real-world scenarios. A multi-objective mixed integer nonlinear programming formulation is developed to minimize the total cost, the product delivery time, and the used product collection time. The model is linearized by defining new variables and adding new constraints to the model. Then, to solve the model, a priority-based genetic algorithm is proposed that uses straight encoding and decoding methods. To assess the performance of the above algorithm, its final solutions and CPU times are compared to those generated by an initial priority-based genetic algorithm from the recent literature and the lower bound obtained by CPLEX. The numerical results show that the straight priority-based genetic algo- rithm outperforms the initial priority-based genetic algorithm at least in terms of obtaining a reasonable quality of final solutions for closed- loop logistics problems.

This paper extends the unlimited capacity location problem with fixed cost in a competitive environment where a firm tends to enter the market competing for exotic products. The demands will increase if a new facility sets into the market. And the demands will be served by both the nearest facilities of the two competitors. A revenue maximization model is presented and a greedy adding and substitution algorithm is proposed to solve it. Then the paper proposes an instance generator to randomly generate some different sizes of instances and make computational experiments to test the algorithm.

This paper explores the optimal storage rack design for a multi-deep compact Automated Storage and Retrieval System (AS/RS) considering the acceleration and deceleration of storage and retrieval (S/R) machine. The expected travel time under the single-command cycle and dual-command cycle for a random storage strategy is derived. Based on the travel time, the general models we propose calculate the optimal ratio between three dimensions that minimises the travel time under different speed profile scenarios. From the result, it is proven that the optimal storage rack design model in the constant speed situation can be treated as a special case of the model considering the acceleration and deceleration of S/R machine. Finally, this study investigates the effect of speed profile of S/R machine and fixing dimensions by various numerical experiments.

This paper examines the Local Container Drayage Problem (LCDP) under an operation mode in which a tractor can be detached from its companion trailer and assigned to a new task. We have incorporated a set of temporal constraints into the classical VRP to realize this operation by utilizing the idle time available to tractors and coordinating the empty containers moving between customers. A tabu search algorithm is proposed. Some numerical experiments are conducted to assess the performance of the proposed algorithm, quantify the benefit of the new operation mode, and identify the conditions under which the mode is effective.

In recent years, the Local Container Drayage Problem (LCDP) has received lots of studies in research as part of container transportation. This paper examines the LCDP under a special operation mode in which tractors and trailers can be separated, i.e., tractors can be assigned to a new task at another location while trailers are waiting for packing or unpacking. The problem is formulated as a vehicle routing and scheduling problem with temporal constraints. The resulting formulation can be used to solve small-scale problems. For large-scale problems, we develop a max–min Ant colony optimization algorithm to reduce the computation time. Numerical experiments with randomly generated instances are performed to demonstrate the performance of the algorithm.

This paper deals with the location assignment and storage/retrieval scheduling problem simultaneously in multi-shuttle automated storage/retrieval systems. Both the situations of performing a single operation cycle and multiple operation cycles are considered. Two related models, including an integer programming model and a dynamic programming model, are developed to formulate the problem. A two-phase tabu search algorithm is proposed to solve the integer programming model for the situation of performing a single operation cycle. Based on the search results, a construction heuristic is developed to improve the solution for the situation of performing multiple operation cycles. The computational results show that the two-phase tabu search algorithm can find better solutions than GA + MNN methods especially for the large-sized problems. In addition, it reveals that the construction heuristic can also significantly improve the quality of the solution for the situation of performing multiple operation cycles.

This paper examines the yard truck scheduling, the yard location assignment for discharging containers, and the quay crane scheduling in container terminals. Taking into account the practical situation, we paid special attention to the loading and discharging precedence relationships between containers in the quay crane operations. A Mixed Integer Program (MIP) model is constructed, and a two-stage heuristic algorithm is proposed. In the first stage an Ant Colony Optimization (ACO) algorithm is employed to generate the yard location assignment for discharging containers. In the second stage, the integration of the yard truck scheduling and the quay crane scheduling is a flexible job shop problem, and an efficient greedy algorithm and a local search algorithm are proposed. Extensive numerical experiments are conducted to test the performance of the proposed algorithms.

This paper discusses how to design an optimal storage rack for a multi-deep compact AS/RS considering the acceleration and deceleration of storage and retrieval (S/R) machine. We derive the expected travel time under the single-command cycle for a random storage strategy. On the basis of the derived travel time for the random storage strategy, we propose the optimal storage rack design model and calculate the optimal ratio between three dimensions that minimizes the travel time under different speed profile scenarios. Finally, we investigate the effect of speed profile of S/R machine by numerical experiments.