To address the increasing demand for efficient and sustainable port operations amid the complex dynamics of global shipping, this study investigates the coordinated scheduling problem of berth allocation and shipping speed within port groups under the transshipment rationalization strategies (BSCS-TRS). A multi-objective mixed-integer linear programming (MILP) model is proposed for the BSCS-TRS. A multi-objective discrete combinatorial optimization algorithm based on decomposition and adaptive large neighborhood search (MODA/D-ALNS) is proposed to solve the problem, where a shortest path-based ship speeds optimization method (SPSOM) is developed to optimize the shipping speed. The performance of the proposed model and MODA/D-ALNS is systematically through comparative numerical instances and case studies. The results indicate that the proposed model can optimally solve small-scale instances of BSCS-TRS using Gurobi, while MODA/D-ALNS exhibits good performance in benchmark knapsack problems and BSCS-TRS. Sensitivity analysis results indicate that the proposed method can reduce ship waiting time by 79.06% and fuel consumption by 2.25% within port groups. These findings provide a scalable decision-making tool for port operators to balance operational efficiency with environmental sustainability.

This paper studies the multi-port berth allocation and time-invariant quay crane assignment problem with speed optimization (MBACAP). In the MBACAP, each vessel visits multiple ports, while the port operators allocate berths and quay cranes (QCs) to the arriving vessels. The objective of the MBACAP is to minimize the sum of vessel fuel consumption cost, waiting cost, delay cost and QC handling cost while meeting the constraints related to vessel sailing, berth allocation and QC assignment. A mixed integer liner programming (MILP) model for the MBACAP is formulated for the first time, and an improved adaptive large neighborhood search (IALNS) algorithm is developed to solve it. In the IALNS, an initial solution generation strategy is proposed, while modified removal and insertion operators combined with the MBACAP features are devised. Moreover, several speed decision operators are proposed to optimize vessel speed during insertion operations. The proposed MILP and IALNS are tested on the MBACAP instances based on real data. The numerical experimental results show that the MILP can be solved by CPLEX optimally for some small scaled instances, while the IALNS can efficiently solve instances of all scales. The importance of considering QC assignment in MBACAP and the influence of vessel fuel consumption in ports are analyzed by comparing the numerical results of different models. In addition, the impact of different fuel prices on the MBACAP is investigated through sensitivity analysis with management insights provided.

Collaboration vehicle routing has garnered increasing attention recently because it can help enterprises reduce costs by sharing resources. This paper addresses a novel two-echelon capacitated vehicle routing problem with sharing satellite resources (2E-CVRPSSR), which arises with the development of e-commerce and the Sharing economy in city logistics. In this problem, goods are delivered within a two-echelon routing network, where satellites are used to consolidate and transfer goods between first-and second-echelon routing. Moreover, the second-echelon vehicles can depart from and return to different satellites. A mixed integer linear programming model is first presented, and an adaptive large neighborhood search algorithm with several new search operators and strategies is proposed to solve this problem. The numerical results show that the proposed algorithm can effectively solve the 2E-CVRPSSR. Moreover, an average transportation cost savings of 6.29% can be obtained by sharing satellite resources. In addition, geographic analysis indicates that the layout of the depot, satellites, and customers largely impacts the economic advantages of sharing satellite resources. Specifically, the highest economic benefits are achieved when the satellites are distributed from nearest to farthest from the depot, with customers located between nearby and distant satellites and with satellites of large enough capacity close to the depot.

The commodity storage assignment problem (CSAP), which assigns stock-keeping units (SKUs) to a suitable location for matching the customer demand patterns, is crucial for improving the order picking efficiency. In this study, we jointly consider the SKUs classification and correlation, and propose a new scattered storage policy named scattered-correlation storage policy based on the commodity classification (SCSPCC) for mitigating CSAP in the robotic mobile fulfillment systems (RMFS). We call the new problem CSAP-SCSPCC. To address this problem, we construct a mixed-integer programming model, and propose a novel variable neighborhood search with self-adaption and simulated annealing acceptance mechanisms (SA-VNSSA). Besides, a heuristic algorithm is proposed to select the minimum number of shelves to evaluate the optimization effect of SA-VNSSA and SCSPCC in terms of the number of shelf transports. Extensive numerical experiments are conducted on small-, medium-, and large-scale instances, respectively. The results reveal that the proposed model and algorithms are reasonable and effective in solving CSAP-SCSPCC compared with the state-of-the-art methods. Specifically, SA-VNSSA outperforms the three state-of-the-art comparison algorithms [i.e., SA-1 (Muppani and Adil, 2008), SA-Pop (Assadi and Bagheri, 2016), and SA-2 (Zhang et al., 2019)] by more than 4.19% and 3.23% on average in medium- and larger-scale instances, respectively. The comparisons between SCSPCC and CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019) show that the order picking efficiency is improved by our SCSPCC more than 6.31%. It is can be concluded that SCSPCC is efficient and robust to match the SKU storage pattern and customer demand patterns in e-commerce retail. Note to Practitioners—Robotic mobile fulfillment systems (RMFS) have been widely used in the warehouses of Amazon, Jingdong, Cainiao, and so on. Considering practical situations and requirements in commodity storage assignment problems (CSAP) is necessary for improving RMFS order picking efficiency. We proposed a new problem named CSAP-SCSPCC for RMFS. Particularly, SCSPCC is a novel scattered storage policy that can assign best-selling SKUs and general-selling SKUs to a suitable location based on the SKU correlation, respectively. Computational results with small-, medium-, and large-scale instances show that our proposed SA-VNSSA and SCSPCC are effective, robust, and practically applicable compared with two state-of-the-art approaches [i.e., CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019)]. Compared with CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019), SCSPCC can improve order picking efficiency by more than 6.31%. In summary, the methods proposed in our work can match the SKU storage mode and the customer demand patterns in a giant e-commerce retail warehouse. This research work can contribute to the improvement of RMFS picking efficiency. In the future, it is necessary to study multiple problems in RMFS jointly, including CSAP-SCSPCC, shelves storage assignment problems, and order batching, etc.

The berth allocation and quay crane assignment problem (BACAP) is an important port operation planning problem. To obtain an effective and reliable schedule of berth and quay crane (QC), this study addresses the BACAP with stochastic arrival times of vessels. An efficient method combining scenario generation is presented to simulate the stochastic arrival times. After then, a mixed integer linear programming (MILP) model is established, aiming to minimize the expectation of the vessels’ total stay time in port. A multi-objective constraint-handling (MOCH) strategy is adopted to reformulate the developed model, which converts constraint violations into an objective, thus transforming the single-objective optimization model with complex constraints into a dual-objective optimization model with only easy-handling constraints. Then an enhanced non-dominated sorting genetic algorithm II (ENSGA-II) is proposed to solve the dual-objective model, in which a neighborhood search algorithm and a search bias mechanism are incorporated to strengthen the local exploitation capability. Furthermore, a repair method (RM), penalty function (PF) and the superiority of feasible solutions (SF) strategy for constraint handling are designed respectively and incorporated with genetic algorithm to solve the original single-objective optimization model. Finally, numerical experiments on instances in the literature are conducted to validate the effectiveness of the MOCH and the proposed ENSGA-II. The results show that the average total stay time of vessels is reduced when stochastic arrival times are considered. Comparison results with another two multi-objective methods and three single-objective methods combined with different constraint-handling strategies corroborate the superiority of the proposed ENSGA-II and MOCH.

The collaboration of drones and trucks for last-mile delivery has attracted much attention. In this paper, we address a collaborative routing problem of the truck-drone system, in which a truck collaborates with multiple drones to perform parcel deliveries and each customer can be served earlier and later than the required time with a given tolerance. To meet the practical demands of logistics companies, we build a multi-objective optimization model that minimizes total distribution cost and maximizes overall customer satisfaction simultaneously. We propose a hybrid multi-objective genetic optimization approach incorporated with a Pareto local search algorithm to solve the problem. Particularly, we develop a greedy-based heuristic method to create initial solutions and introduce a problem-specific solution representation, genetic operations, as well as six heuristic neighborhood strategies for the hybrid algorithm. Besides, an adaptive strategy is adopted to further balance the convergence and the diversity of the hybrid algorithm. The performance of the proposed algorithm is evaluated by using a set of benchmark instances. The experimental results show that the proposed algorithm outperforms three competitors. Furthermore, we investigate the sensitivity of the proposed model and hybrid algorithm based on a real-world case in Changsha city, China.

In this paper, we attempt to solve the dual-objective short-term hydro-thermal-wind co-scheduling (HTWCS) problem by proposing a novel multi-objective constraint handling (MOCH) based search framework. The two objectives of this problem are minimizing the cost and emissions associated with electric power generation, while satisfying various hydraulic and electric constraints. The violations of constraints of the HTWCS problem is innovatively converted to another objective and forming a triple-objective HTWCS problem, which is further solved by a Borg algorithm, consisting of i) ε-dominance based archive updating process, ii) ε -progress based restart strategy and iii) auto-adaptive multi-operator recombination mechanism. The sophisticatedly designed MOCH based Borg evolution framework guarantees the convergence capability and diversity and can avoid the blindness in selecting the recombination operator and complex constraint repairing strategies, due to the lack of priori knowledge of the HTWCS problem. Comparison among different approaches demonstrates the approach proposed in this paper outperforms its conventional counterparts and the MOCH technique performs better than the conventional constraint repairing strategies when applied to solve the HTWCS problem.

This paper proposed an anti-disturbance trajectory tracking controller for the flexible cable towed aerial drogue even under the towing aircraft's trailing vortex and continuous gust disturbance, by effectively integrating robust integral of the sign of the error (RISE) control with the model-assisted linear extended state observer (LESO). Inspired by the established modeling of hose-drogue aerial refueling systems, the towed flexible cable-drogue's multi-body dynamics with finite variable length links and joints are formulated, together with the towed drogue's 3 degrees of freedom (DOF) motion model. Then, the unmeasurable tensions acting on the drogue by the cable are viewed as a kind of disturbances of the dynamics and are estimated with the model-assisted LESO. With the compensation of these estimated disturbances, the RISE based feed-back trajectory controller is constructed for the aerial towed target under complex airflow disturbances. This combined method takes advantage of both the disturbance observer-based control and disturbance suppression-based RISE control. The simulation results show the proposed method's effectiveness for the aerial towed drogue's trajectory control from both control accuracy and anti-disturbance property perspectives, even under the trailing vortex and continuous gust.

The generalized lock scheduling problem (GLSP) is a mixed integer optimization problem which consists of a ship placement (SP) and a lockage operation scheduling (LOS) sub-problem. In previous research, the GLSP is solved by different exact and heuristic methods, which are confirmed inferior with respect to computation time and solution quality. Consequently, none of those methods is efficient for handling practical large-scale GLSP. For the first time, we show that high-quality solutions of GLSP can be efficiently obtained by using an innovative approach proposed in this paper. Specifically, an ingenious solution structure of GLSP is designed, by which the GLSP is converted to a combinatorial optimization problem. Furthermore, an adaptive large neighborhood search (ALNS) heuristic based on the principle of destruction and reconstruction of solutions is proposed for solving the GLSP. Test results using a large number of instances reported in the literature are compared with those obtained by two exact methods, the mixed integer linear programming (MILP) and combinatorial Benders' decomposition (CBD) method. The results show that our ALNS achieves optimal solutions within less time in terms of most of the small-scale instances. Much better solutions are obtained by the ALNS within a few minutes for those large-scale instances that cannot be solved to optimality by exact methods within 2 h. Especially, the advantage of the proposed method is more remarkable when there is no specific chronological rules forced, which indicates that the proposed method is capable of handling the GLSP in a broader scope of situations.

A complex and typical scheduling problem in waterway transportation: co-scheduling of cascaded locks with multiple chambers (CCLM) is studied. Based on in-depth analysis of the problem properties, the CCLM is handled by separating it into three interconnected subproblems, each with a simpler structure and higher flexibility to be handled. The outer layer and inner layer concerns the sum of lockage number and ship placement, respectively. The interlayer as a connection bridge between the other two refers to lockage direction combination and timetable optimization which is a high-dimensional mixed integer optimization problem. To solve the CCLM problem, a hybrid approach based on iteration which mainly combines quantum inspired binary gravitational search algorithm and modified moth-flame optimization algorithm is proposed. In addition, two different scheduling rules which are usually concerned in practice, the area utilization maximization and first-come-first-served (FCFS) rule, are also tested in the CCLM problem. Experiments are conducted on instances that are extracted from real world data. The scheduling and comparison results verify that the CCLM problem can be well handled by the proposed method.

The lock and transshipment co-scheduling problem (LTCP) is studied so that the delay time of ships at a dam as well as the extra cost of transshipment is minimized. A bi-objective optimization model of LTCP is proposed, which is decomposed to a main 0-1 optimization problem and two sub-problems, the lock scheduling and continuous berth allocation problem. Meanwhile, a binary Borg (B-Borg) multi-objective evolutionary algorithm is proposed to combine with an adaptive large neighborhood search and a multi-order best fit method to solve the LTCP. In order to evaluate the feasibility of the proposed LTCP model and the B-Borg-based heuristic, a large number of instances are generated by extracting the record from historical traffic at the Three Gorges Dam. The results show that the average delay of ships at the dam is reduced because of transshipment, among which the ships with lower extra transshipment costs tend to be more likely to be transshipped. Meanwhile, it indicates the number of ships that choose to be transshipped is mainly dependent on the level of traffic congestion as well as the available quay for transshipment. Furthermore, the performance of the B-Borg is confirmed superiority by comparing with non-dominated sorting genetic algorithm and decomposition-based multi-objective evolutionary algorithm in terms of solving LTCP.

Continuous berth allocation problem (BAPC) is a major optimization problem in transportation engineering. It mainly aims at minimizing the port stay time of ships by optimally scheduling ships to the berthing areas along quays while satisfying several practical constraints. Most of the previous literatures handle the BAPC by heuristics with different constraint handling strategies as it is proved NP-hard. In this paper, we transform the constrained single-objective BAPC (SBAPC) model into unconstrained multiobjective BAPC (MBAPC) model by converting the constraint violation as another objective, which is known as the multiobjective optimization (MOO) constraint handling technique. Then a bias selection modified non-dominated sorting genetic algorithm II (MNSGA-II) is proposed to optimize the MBAPC, in which an archive is designed as an efficient complementary mechanism to provide search bias toward the feasible solution. Finally, the proposed MBAPC model and the MNSGA-II approach are tested on instances from literature and generation. We compared the results obtained by MNSGA-II with other MOO algorithms under the MBAPC model and the results obtained by single-objective oriented methods under the SBAPC model. The comparison shows the feasibility of the MBAPC model and the advantages of the MNSGA-II algorithm.

Lockage co-scheduling of Three Gorges Dam and Gezhou Dam (LCSTD) is a mixed-integer nonlinear optimization (MINO) problem. This paper establishes a multi-objective MINO model for the LCSTD problem, in which two criteria reflecting the interests of shippers on the one side and owners of the lock on the other side are considered as two parallel optimization objectives. The LCSTD problem is separated into three sub-problems. The first sub-problem concerns the lockage number and lockage direction determination, while the second and third sub-problems refer to lockage time optimization and ship placement, respectively. An orthogonal design-based non-dominated sorting genetic algorithm III (ONSGA-III) is presented, and then, we combine the ONSGA-III with a time-area series assignment method to optimize the LCSTD problem. Meanwhile, a heuristic adjustment strategy is proposed according to the structure of the lockage time optimization sub-problem to enhance the exploration ability of ONSGA-III. Furthermore, a compromise solution is chosen from the achieved non-dominated Pareto solutions by utilizing a membership function. Finally, the method is tested on the instances, which are extracted from historical data at Three Gorges Dam and Gezhouba Dam. The Pareto solutions achieved by the proposed method are compared with those of other methods. The simulation results demonstrate that the proposed method is efficient for solving the LCSTD problem.