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Diagram of discrete berths and berthing order. (a) Description of the discretized berths. (b) Description of the berthing sequence of berth k.
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The continuous berth allocation and quay crane assignment problem considers the size of berths and ships, the number of quay cranes, the dynamic ships and non-crossing constraints of quay cranes. In this work, a mixed-integer linear programming model of this problem is established, aiming at minimizing the total stay time and delay penalty of ships...
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... Although some exact algorithms have been developed to efficiently solve midscale instances of two-dimensional bin packing problems, such as branch-and-bound (Clautiaux et al., 2007;Côté et al., 2014) and branch-and-cut (Souza Queiroz and Andretta, 2022), it is difficult to handle large-scale instances. Thus, heuristic packing algorithms have been widely used in the scheduling problems with two-dimensional bin packing constraints, such as VRP with loading constraints (Pollaris et al., 2015;Wei et al., 2018), lock scheduling problem (Ji et al., 2019), and berth allocation problem (Tang et al., 2022). The experiments of the above research show that high-quality solutions to two-dimensional bin packing problems can be achieved by heuristics efficiently and stably. ...
Addressed in this study is a vehicle routing problem with two‐dimensional loading constraints and time windows (2L‐CVRPTW), aiming to minimize the transportation cost while satisfying the two‐dimensional loading and routing constraints with time windows. To solve this problem, for the first time a mixed‐integer linear programming model is formulated with considering practical last‐in‐first‐out loading constraints, and a branch‐and‐price‐based (BP‐based) heuristic is proposed based on a set partitioning formulation. In the heuristic, a modified labeling algorithm is proposed for the complex pricing problem, which is a relaxation of the elementary shortest path problem with resource constraints and two‐dimensional loading constraints. Therein, an effective Tabu‐maximum open space packing heuristic is proposed to verify the feasibility of the two‐dimensional packing problem of each route generated by the labeling algorithm. In addition, effective accelerating and branching strategies are introduced to improve the solving efficiency of the heuristic. To evaluate the effectiveness and the advantages of the proposed heuristic, extensive computational experiments are performed based on the generated instances. The computational results show that the proposed BP‐based heuristic can effectively solve the 2L‐CVRPTW, in which the optimal solutions can be achieved much faster than CPLEX in small‐scale problems. Relationships between the transportation cost and the characteristics of the instances are analyzed. The stability of the algorithm and the effectiveness of the accelerating strategies are verified and discussed.
... While the vehicle routing problem (VRP) [44] has been extensively researched due to its relevance across various domains, including berth allocation [45] and machine scheduling [46], this section primarily offers a comprehensive review of diverse strategies employed in drone-based last-mile delivery, particularly highlighting the most recent solutions for VRP-D. Murray and Chu was the first to formally define the problem of drone-truck collaboration for parcel delivery [9]. ...
Drone-based last-mile delivery is an emerging technology that uses drones loaded onto a truck to deliver parcels to customers. In this paper, we introduce a fully automated system for drone-based last-mile delivery through incorporation of autonomous vehicles (AVs). A novel problem called the autonomous vehicle routing problem with drones (A-VRPD) is defined. A-VRPD is to select AVs from a pool of available AVs based on crowd sourcing, assign selected AVs to customer groups, and schedule routes for selected AVs to optimize the total operational cost. We formulate A-VRPD as a Mixed Integer Linear Program (MILP) and propose an optimization framework to solve the problem. A greedy algorithm is also developed to significantly improve the running time for large-scale delivery scenarios. Extensive simulations were conducted taking into account real-world operational costs for different types of AVs, traveled distances calculated considering the real-time traffic conditions using Google Map API, and varying load capacities of AVs. We evaluated the performance in comparison with two different state-of-the-art solutions: an algorithm designed to address the traditional vehicle routing problem with drones (VRP-D), which involves human-operated trucks working in tandem with drones to deliver parcels, and an algorithm for the two echelon vehicle routing problem (2E-VRP), wherein parcels are first transported to satellite locations and subsequently delivered from those satellites to the customers. The results indicate a substantial increase in profits for both the delivery company and vehicle owners compared with the state-of-the-art algorithms.
... An approach to using models and algorithms inspired by the research of commercial ports can help marina managers efficiently allocate berthing places. Tang et al. [15] studied the allocation of berths using a discretization strategy and proposed an algorithm to solve the problem, considering the time spent in port. This approach can also be extended to other practical applications, such as marina scheduling for optimal allocation of space and minimization of crowding. ...
The aim of this paper is to highlight the state of development of tourist ports in the Romanian Black Sea coastal area and their implications for the sustainable provision of quality recreational transport. As indicated by the collected data, both locals and tourists are showing a growing interest in nautical sports and maritime recreational activities, and there are plans to upgrade existing marinas and build new ones. Although the boating activity in the Romanian Black Sea coastal area is not as developed and popular as that in other areas of the Balkan Peninsula, it has particular advantages due to its geographical position close to the Danube and its delta, as well as its historical and cultural heritage. Between 2014 and 2019, the south marinas of Romania’s Black Sea coast experienced a 65% increase in the number of visiting boats. Despite some decreases in traffic during the pandemic, the general trend continues to be upward. An evaluation of the operational capacities and policies implemented by tourist ports and relevant stakeholders identifies the strengths and weaknesses of the current system and provides insights into the current activity and implemented policies of the four most known and developed marinas along the Romanian Black Sea coast. The study’s main objective is to assess sustainable practices in relation to the environmental, social, and economic systems, with a focus on environmental protection, the use of renewable energy sources, and the implementation of quality management standards. The study uses a mix of qualitative and quantitative analyses to achieve this. Interviews with representatives of the four coastal marinas helped gather the data. The size of boat traffic was evaluated by taking into consideration the data from the local harbor master. The research identified gaps and highlighted areas that require improvement, subsequently providing recommendations to enhance sustainability. The findings can guide policymakers and stakeholders in developing practices that can promote the growth of recreational nautical transport in Romania while ensuring the sustainable development of the sector.
... The basic method is based on the approximation algorithms introduced by Lee et al. [16] and Liu et al. [17]. The mixed-integer linear programming (MILP) standard solver [18][19][20][21][22][23] is an extensively applied method. Since the BAP is a multi-objective optimization problem, some Pareto algorithms, such as the genetic algorithm [24][25][26][27][28][29][30][31][32] and unidirectional-search-based algorithm [33,34], are also appropriate. ...
Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficient in maintaining operational efficiency, especially under complicated conditions such as parallel scheduling with different cargo setups. In addition, in the face of rising demand, data-driven strategies are necessary. To tackle this issue, this paper proposes a mixed-integer model for allocating quay cranes, terminals, and berths. It considers not only cargo types, but also the time required for a quay crane setup. The proposed model features a greedy-insert-based offline algorithm that optimizes berth allocation when vessel information is available. In situations where vessel information is uncertain, the model utilizes an online optimization strategy based on a reinforcement-learning algorithm that is capable of learning from feedback and of adapting quickly in real time. The results of the numerical experiments demonstrate that both the offline and online algorithms can significantly enhance cargo handling efficiency and overall harbor operation. Furthermore, they have the potential to be extended to other complex settings.
... They addressed the research trends and future study direction together with the limitation of published papers. In the study of Tang et al. [21], a solution method incorporating the discretization strategy and a MILP was presented to deal with the continuous berth allocation and QCs assignment problem. Cho et al. [22] developed an integrated method for berth allocation and QCs assignment problems, of which the reassignment of the vessel to other terminals was allowed. ...
At container terminals, quay cranes, yard trucks, and yard cranes are mainly used to transfer containers. Driven by the demand for a green and low-carbon economy, an integrated scheduling problem considering three types of handling equipment of container handling systems is studied. As the task of transferring each container is completed by the three handling equipment sequentially, the optimal solution may not be found by only studying one type of equipment separately from a green operations perspective. The inter-dependency of different equipment should be considered to guarantee the overall performance of container handling systems with low-carbon operations so as to reduce energy consumption. In this paper, this integrated problem is formulated as a mixed integer linear programming (MILP). Since the MILP cannot be applied to solve large-sized practical problems, a genetic algorithm (GA) is developed. In the proposed GA, a three-dimension chromosome representation is proposed, which integrates the coordination of three handling equipment. A new mechanism including three pairs of crossover and mutation is used in parallel in GA with the aim of enhancing the efficiency of searching for good solutions. Each pair of crossover and mutation is specific to one dimension of a solution. Moreover, a novel heuristic mutation is developed to diversify solutions. The computational results indicate that the developed solution method for the integrated scheduling problem is promising and the heuristic mutation can highly improve the solution quality.
... Ji et al. [18] solved the Berth Allocation and Quay Crane Assignment Problem (BACAP) with stochastic vessel arrival times using a scenario generation method and an MILP model to minimize the expected total vessel stay time. Tang et al. [19] proposed a mixed-integer linear programming model and a large neighborhood search algorithm to solve the continuous berth allocation and quay crane assignment problem, aiming to minimize the total stay time and delay penalty of ships. Most research has focused on the integrated berth and quay crane scheduling problem. ...
As container ports become increasingly important to the global supply chain, a growing number of ports are improving their competencies by consolidating multiple terminal resources internally. In addition, in the context of energy conservation and emission reduction, ports measure competitiveness not only in terms of terminal size, throughput and service level, but also in terms of low energy consumption and low pollution. Therefore, a nonlinear mixed-integer programming model considering the cost of carbon is developed for the multi-terminal berth and quay crane joint robust scheduling problem under uncertain environments to minimize the sum of expectation and variance of total cost under all randomly generated samples. The model considers the water depth and interference of quay cranes, etc. The expected vessel arrival time and the average operational efficiency plus relaxation are used as their actual values when scheduling. Finally, an improved adaptive genetic algorithm is developed by combining the simulated annealing mechanism, and numerical experiments are designed. The results show that the joint berth and quay crane scheduling with uncertainties and a multi-terminal coordination mechanism can effectively reduce the operating cost, including carbon costs and the vessel departure delay rate, and can improve resource utilization. Meanwhile, the scheduling with the multi-terminal coordination mechanism can obtain more significant improvement effects than the scheduling with uncertainties.
... The ALNS combines multiple removal and repair operators, which will be adaptively selected according to historical performances and will enable the ALNS to perform well for problems with diverse characteristics. It has been widely used in solving complex combinatorial optimization problems and shown powerful capacity in applications such as vehicle routing problems (Ropke and Pisinger 2006), berth allocation problems and quay crane problems (Tang et al. 2022). Hence, we employ the framework of adaptive large neighborhood search and propose a new tabu-based ALNS for solving the UPBPMSP. ...
In this study, we investigate an unrelated parallel batch processing machines scheduling problem (UPBPMSP). A set of jobs with non-identical sizes and arbitrary ready times are scheduled on unrelated parallel batch processing machines with different capacities to minimize the makespan (i.e., the completion time of the last batch). Existing studies have either decomposed the scheduling problem into two sub-problems and employed population-based heuristic interaction for searching the best solutions or used neighborhood search algorithms to search the current solution’s neighborhood to find a better solution. However, the performances of these methods are not quite satisfactory due to the complicated interactions or oversimplified neighborhood search strategies, especially for large-scale UPBPMSPs. In this study, we propose a novel tabu-based adaptive large neighborhood search (TALNS) algorithm to obtain high-quality solutions for the UPBPMSP. To avoid complex interactions, we propose a new solution structure, and the proposed TALNS is applied to obtain the optimal solution structure of the UPBPMSP. Extensive experiments are conducted to evaluate the performance of the proposed algorithm on a total of 360 instances from the literature and 30 new instances. Numerical results demonstrate that the proposed TALNS outperforms the neighborhood search methods, which outperforms the population-based methods. With the proposed TALNS, 55 out of 360 instances’ best-known solutions are updated from this study.
... This mechanism helps to prevent the algorithm from getting stuck in local optima and encourages the exploration of the entire search space. Tabu search also uses aspiration criteria to enable the algorithm to revisit previously explored solutions if they provide a significant improvement to the current solution [111,112]. ...
Vehicular ad hoc networks (VANETs) are wireless networks of automotive nodes. Among the strategies used in VANETs to increase network connectivity are broadcast scheduling, data aggregation, and vehicular node clustering. In the context of extremely high node mobility and ambiguous vehicle distribution (on the road), VANETs degrade in flexibility and quick topology, facing significant issues such as network physical layout construction and unstable connections. These challenges make it difficult for vehicle communication to be robust, reliable, and scalable, especially in urban traffic networks. Numerous research investigations have revealed a nearly optimal solution to various VANET difficulties through the application of techniques derived from nature and evolution. On the other hand, as key productivity sectors continue to demand more energy, sustainable and efficient ways of using non-renewable resources continue to be developed. With the help of information and communication technologies (ICT), parameter tuning approaches can reduce accident rates, improve mobility, and mitigate environmental impacts. In this article, we explore evolutionary algorithms to mobile ad hoc networks (MANETs), as well as vehicular ad hoc networks (VANETs). A discussion of three major categories of optimization is provided throughout the paper. There are several significant research works presented regarding parameter tuning in cluster formation, routing, and scheduling of broadcasts. Toward the end of the review, key challenges in VANET and MANET research are identified.
... This study is limited to the food industry; therefore, the proposed method may be useful for various industry MCDM problems. Future research can adopt other methods, such as (Fuzzy)VIKOR [21], fuzzy ARAS and MSGP methods [27], QFD in RLs service process design [44,45], the mixedinteger linear programming model [46], and the fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) [47], to estimate the criteria for provider selection. In addition, it is possible to explore the comparison between forward logistics (FLs) and RLs [48] using a. ...
... This study is limited to the food industry; therefore, the proposed method may be useful for various industry MCDM problems. Future research can adopt other methods, such as (Fuzzy)VIKOR [21], fuzzy ARAS and MSGP methods [27], QFD in RLs service process design [44,45], the mixed-integer linear programming model [46], and the fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) [47], to estimate the criteria for provider selection. In addition, it is possible to explore the comparison between forward logistics (FLs) and RLs [48] using a. ...
As consumers become ever more conscious of environmental issues, socially responsible corporate practices, and government regulations, companies are increasingly motivated to incorporate reverse logistics (RLs) into their operations, thus raising the question of provider selection. In previous studies, the food industry generally lacked a systematic reference method for RLs provider selection, especially during the post-COVID-19 pandemic. This study aims to develop a comprehensive approach that combines a technique for order preference by similarity to ideal solution (TOPSIS) and multi-segment goal programming (MSGP) models to select optimal RLs providers. Furthermore, this method will enable decision makers (DMs) to evaluate and select the best RLs provider considering the limited resources of the business. This approach allows DMs to consider both qualitative and quantitative criteria, set multiple target segmentation expectations, and achieve optimal RLs provider selection. This study also provides case studies of applications by food manufacturers. The main finding is that considering multiple criteria in making a decision produces better results than using a single criterion.
... As such, this study presents a hybrid large neighborhood search (HLNS), a heuristic method, to solve the UPBPMIJF. The LNS framework has shown excellent results in discrete/combinatorial optimization problems [31,32], which shows powerful global searching ability due to a large neighborhood reconstruction mechanism. This study adopts the LNS framework and improves its performance by incorporating tabu search and local search strategies according to the characteristics of the UPBPMIJF. ...
This paper studies a scheduling problem with non-identical job sizes, arbitrary job ready times, and incompatible family constraints for unrelated parallel batch processing machines, where the batches are limited to the jobs from the same family. The scheduling objective is to minimize the maximum completion time (makespan). The problem is important and has wide applications in the semiconductor manufacturing industries. This study proposes a mixed integer programming (MIP) model, which can be efficiently and optimally solved by commercial solvers for small-scale instances. Since the problem is known to be NP-hard, a hybrid large neighborhood search (HLNS) combined with tabu strategy and local search is proposed to solve large-scale problems, and a lower bound is proposed to evaluate the effectiveness of the proposed algorithm. The proposed algorithm is evaluated on numerous compatible benchmark instances and newly generated incompatible instances. The results of computational experiments indicate that the HLNS outperforms the commercial solver and the lower bound for incompatible problems, while for compatible problems, the HLNS outperforms the existing algorithm. Meanwhile, the comparison results indicate the effectiveness of the tabu and local search strategies.
