Berth scheduling by customer service differentiation: A multi-objective approach

In this paper the discrete and dynamic berth allocation problem is formulated as a multi-objective combinatorial optimization problem where vessel service is differentiated upon based on priority agreements. A genetic algorithms based heuristic is developed to solve the resulting problem. A number of numerical experiments showed that the heuristic performed well in solving large, real life instances. The heuristic provided a complete set of solutions that enable terminal operators to evaluate various berth scheduling policies and select the schedule that improves operations and customer satisfaction. The proposed algorithm outperformed a state of the art metaheuristic and provided improved results when compared to the weighted approach.     

The simultaneous berth and quay crane allocation problem

This paper addresses efficient berth and crane allocation scheduling at a multi-user container terminal. First, we introduce a formulation for the simultaneous berth and crane allocation problem. Next, by employing genetic algorithm we develop a heuristic to find an approximate solution for the problem. The fitness value of a chromosome is obtained by crane transfer scheduling across berths, which is determined by a maximum flow problem-based algorithm based on a berth allocation problem solution defined by the chromosome. The results of numerical experiments show that the proposed heuristic is applicable to solve this difficult but essential terminal operation problem.     

Evaluating the solution performance of IP and CP for berth allocation with time-varying water depth

This paper considers the berth allocation problem (BAP) with time-varying water depth at a tidal river port. Both integer programming (IP) and constraint programming (CP) models are developed. Numerical experiments find that CP tends to be superior to IP when the feasible domain is small (e.g. dynamic vessel arrivals), when the restriction of the objective towards decision variables is loose (e.g. makespan, departure delay), or when the size of IP models is too large due to fine time resolution. Meanwhile, CP’s incapability of proving optimality can be compensated by post-optimization with IP, by using a simple CP/IP hybrid procedure.     

Joint planning of berth and yard allocation in transshipment terminals using multi-cluster stacking strategy

In this work, a joint planning problem for berth and yard allocation in transshipment terminals is addressed. Multi-cluster stacking strategy is proposed to split each transshipment flow into a number of container clusters and then stack each cluster in different yard blocks. A mixed integer quadratic programming model is formulated to minimize the total distance of exchanging containers between mother vessels and feeders, and the workload imbalance among yard blocks. A novel three-stage heuristic solution approach is developed and extensive numerical experiments are conducted to show the effectiveness of the proposed approach and the benefit of the multi-cluster strategy.     

Exact and heuristic methods to solve the berth allocation problem in bulk ports

In this paper, we study the dynamic hybrid berth allocation problem in bulk ports with the objective to minimize the total service times of the vessels. We propose two exact methods based on mixed integer programming and generalized set partitioning, and a heuristic method based on squeaky wheel optimization, explicitly considering the cargo type on the vessel. The formulations are compared through extensive numerical experiments based on instances inspired from real bulk port data. The results indicate that the set partitioning method and the heuristic method can be used to obtain near-optimal solutions for even larger problem size.     

Tactical berth and yard template design at container transshipment terminals: A column generation based approach

Quay-side berthing congestion is an emerging challenging issue that arises in busy container transshipment terminals and calls for effective management of terminal operations. This paper tackles the berthing congestion problem by introducing a proactive management strategy from the terminal’s perspective that adjusts the calling schedule of feeder vessels in such a way that the quay-side workload distribution in the temporal dimension can be balanced. Such a schedule template design problem is considered simultaneously with another two tactical level decision problems, berth template design (i.e., determining preferred berthing positions for vessels) and yard template design (i.e., allocating storage yard space to transshipment flows). This highly integrated problem is formulated as a set covering model. Heuristic methods based on column generation are developed to obtain near-optimal solutions in an efficient way. Computational experiments on real-world sized test instances demonstrate the efficiency and effectiveness of the proposed approach.     

A robust optimization approach to the integrated berth allocation and quay crane assignment problem

This paper investigates the integrated berth allocation and quay crane assignment problem in container terminals. A deterministic model is formulated by considering the setup time of quay cranes. However, data uncertainties widely exist, and it may cause the deterministic solution to be infeasible. To handle the uncertainties, a robust optimization model is established. Furthermore, to control the level of conservativeness, another robust optimization model with the price constraints is proposed. A genetic algorithm and an insertion heuristic algorithm are suggested to obtain near optimal solutions. Computational experiments indicate that the presented models and algorithms are effective to solve the problems.     

Berth allocation considering fuel consumption and vessel emissions

We propose a more elaborate model on berth allocation considering fuel consumption than before, and overcome the nonlinear complexity by casting it as a mixed integer second order cone programming model. Furthermore, we conduct the vessel emission (in sailing periods) calculation with the widely-used emission factors. Besides, vessel emissions in mooring periods are also analyzed through a post-optimization phase on waiting time. Experimental results demonstrate that the new berth allocation strategy, reflected by the proposed model, is competent to significantly reduce fuel consumption and vessel emissions, while simultaneously retaining the service level of the terminal.     

Heuristics for quay crane scheduling at indented berth

This paper discusses the quay crane scheduling problem at indented berth, an extension to the current quay crane scheduling problem in the field of container terminal operation. A mixed integer programming model by considering the unique features of the quay crane scheduling problem at indented berth is formulated. For solution, decomposition heuristic framework is developed and enhanced by Tabu search. To evaluate the performance of the proposed heuristic framework, a comprehensive numerical test is carried out and its results show the good quality of the proposed heuristic framework.     

Heuristics for the integration of crane productivity in the berth allocation problem

In this paper, the combined problem of berth allocation and crane assignment in container terminals is investigated. The proposed problem formulation includes important real world aspects such as the decrease of marginal productivity of quay cranes assigned to a vessel and the increase in handling time if vessels are not berthed at their desired position at the quay. To solve the problem a construction heuristic, local refinement procedures, and two meta-heuristics are presented. These methods perform well on a set of real world like instances. The results emphasize the important role of quay crane productivity in berth planning.     

Routing and refueling plans to minimize travel time in alternative-fuel vehicles

A driver who drives an alternative-fuel vehicle (AFV) from an origin point to a destination point needs to consider how to get there (i.e., the routing problem), when to stop, and how and when to refuel (i.e., the refueling plan). In this study, models and algorithms are proposed that optimize a one-way-trip path such that the total travel time from the origin to the destination is minimized. The travel time consists of the setup time, the refueling time and the driving time. The setup time includes waiting for the AFV to be served at a refueling station and the preparation time of charging the machine. We categorized the problems into two types: (1) the refueling plan problem when the routing decision is given and (2) an integrated problem of routing and refueling. Another axis of categorization is when (1) setup time and refueling times are site-independent and (2) parameters are site-dependent. We propose optimal algorithms for site-independent problems and the integrated problem of routing and refueling planning with site-independent parameters. We also conduct experiments and sensitivity analyses for the site-dependent integrated problems of routing and refueling.     

铁路客运站配套停车场泊位数量计算模型

铁路客运站配套停车场主要用于停靠接、送旅客的非营运车辆和出租车与其他社会车辆。在考虑接旅客车辆在停车场停留时间、旅客列车到达时刻、乘坐接旅客车辆人数等因素的基础上,构建接旅客车辆所需泊位数计算模型,采用系数法构建送旅客车辆和其他社会车辆所需泊位数计算模型,形成停车场泊位数量计算模型。以计算石家庄客运站停车场所需泊位数量为例,通过模型应用验证,分析模型的有效性。     

A novel methodology for designing a household waste collection system for insular zones

This paper addresses the problem of designing a household waste collection system for rural insular areas using a barge for transportation, based on a novel mixed integer programming model that simultaneously integrates decisions of waste collection sites selection within the islands to be served, visit schedule for each selected collection site, and multi-period vehicle routing. An application to a real-world instance consisting of small rural islands located in the south of Chile shows the effectiveness and complexity of the model, along with the advantages of using a waste compactor instead of transporting the waste using bins onboard a barge.     

The liner shipping berth scheduling problem with transit times

In this paper speed optimization of an existing liner shipping network is solved by adjusting the port berth times. The objective is to minimize fuel consumption while retaining the customer transit times including the transhipment times. To avoid too many changes to the time table, changes of port berth times are only accepted if they lead to savings above a threshold value. Since the fuel consumption of a vessel is a non-linear convex function of the speed, it is approximated by a piecewise linear function. The developed model is solved using exact methods in less than two minutes for large instances. Computational experiments on real-size liner shipping networks are presented showing that fuels savings in the magnitude 2–10% can be obtained. The work has been carried out in collaboration with Maersk Line and the tests instances are confirmed to be representative of real-life networks.     

侦查信息化存在的问题及对策

随着社会的发展，信息技术给整个社会带来巨大变革，同时，犯罪行为也日趋隐蔽化、智能化、职业化，这给公安刑侦工作带来了重大挑战。面对严峻的犯罪新形势，如何将信息处理技术、网络工程技术和计算机技术等高科技领域的成果注入刑事侦查工作中，向高科技要警力，向高科技要效益，如何更好地推进侦查信息化进程，成为侦查机关和侦查人员面临的一大现实问题。     

The generalized lock scheduling problem: An exact approach

The present paper introduces an integrated approach to solving the generalized lock scheduling problem. Three interrelated sub problems can be discerned: ship placement, chamber assignment and lockage operation scheduling. In their turn, these are closely related to the 2D bin packing problem, the assignment problem and the (parallel) machine scheduling problem respectively. In previous research, the three sub problems mentioned were considered separately, often using (heuristic) interaction between them to obtain better solutions. A mixed integer linear programming model is presented and applied to instances from both inland locks and locks in a tide independent port. The experiments show that small instances incorporating a wide range of real-life constraints can be solved to optimality.     

A reactive tabu search algorithm for the multi-depot container truck transportation problem

A container truck transportation problem that involves multiple depots with time windows at both origins and destinations, including the reposition of empty containers, is formulated as a multi-traveling salesman problem with time windows (m-TSPTW) with multiple depots. Since the problem is NP-hard, a cluster method and a reactive tabu search (RTS) algorithm are developed to solve the problem. The two methods are compared with the mixed integer program which can be used to find optimum solutions for small size problems. The computational results show that the developed methods, particularly the RTS algorithm, can be efficiently used to solve the problem.     

The fixed charge facility location problem with coverage restrictions

This paper develops a fixed charge facility location model with coverage restrictions, minimizing cost while maintaining an appropriate level of service, in identifying facility locations. Further, it discusses the insights that can be gained using the model. Two Lagrangian relaxation based heuristics are presented and tested. Both heuristics use a greedy adding algorithm to calculate upper bounds and subgradient optimization to calculate lower bounds. While both procedures are capable of generating good solutions, one is computationally superior.     

Combining service frequency and vehicle routing for managing supplier shipments

This paper proposes a new approach to designing inbound material collection routes that considers pick-up frequency and spatial design as joint decisions to minimize total logistics (transportation plus inventory) cost. The clustering-based optimization uses an approximation to the actual cost of a routing solution without actual route construction. We show that the problem is analogous to a single-source fixed-charge facility location problem, and near-optimal solutions can be found using an efficient heuristic algorithm. Tests show the effectiveness of how this model is formulated and a case study demonstrates that substantial total cost savings can be achieved in realistic applications.     

用蚁群算法求解类TSP问题的研究

铁路运输调度问题能否很好解决对于铁路运输公司至关重要,旅行商问题(简称TSP)经常被用来研究运输调度问题。根据某化工集团铁路运输公司的生产实际,提出了“类TSP”问题,但由于“类TSP”和TSP有很大区别,以前求解TSP的优化算法不能直接用于“类TSP”的求解。利用蚁群算法是可以较好解决TSP的一类新型模拟进化算法,适应“类TSP”的要求,并通过“蚁后规则”和变异机制的引入,提出了一种改进的人工蚁群算法。计算机仿真结果表明该算法求解“类TSP”是行之有效的。