基于工作负荷均衡的车辆拯救设施选址

交通事故车辆的拯救设施选址是车辆拯救服务的重要问题,选址不当易造成车辆拯救行业服务效率低下、市场行业的不规范、利益不均等一系列问题。针对这类问题,分析了俱乐部和政府引导两种服务模式下的拯救设施选址特点,并提出了针对车辆拯救的工作负荷概念。在不确定性救援路径行程时间条件下,建立以负荷均衡为目标的车辆拯救设施选址模型,以提高设施站点的运营效率和实现车辆拯救企业间的利益平衡。采用三种染色体形式的混合编码和随机模拟方法,设计基于遗传优化的求解算法。最后,通过仿真实例验证了算法的有效性。     

Yard planning for vessel services with a cyclical calling pattern

In a land-scarce container terminal, congestions in the terminal yard due to highly concentrated workload often lead to unsatisfactory terminal productivity. Currently, yard planners use their experience to design a yard template for determining the storage locations of export containers to be loaded onto vessels deployed to services with a cyclical calling pattern. This article studies the problem of designing a yard template that balances the workload in an export yard. The template design problem is formulated as an integer program. It is found that the computational time required to optimally solve realistic sizes of the template design problem is unacceptably long. This article proposes a simpler integer program as an approximate model. On the basis of the analysis on the approximate model, a heuristic is developed to solve the template design problem. Results of computational experiments show that the heuristic can find effective solutions for the template design problem.     

Development and simulation analysis of real-time, dual-load yard truck control systems for seaport container transshipment terminals

We show how a seaport container terminal’s long-run average quay crane rate depends on the system that automatically assigns yard trucks to container transportation jobs in the terminal in real time. Several real-time, dual-load yard truck control systems are proposed and evaluated by a fully-integrated, discrete event simulation model of a vessel-to-vessel transshipment terminal. The model is designed to reproduce the microscopic, stochastic, real-time environment at a multiple-berth facility. Overall, the literature still lacks a comprehensive analysis that (1) considers different methods for controlling dual-load vehicles in real time within a fully-integrated, stochastic container terminal environment and (2) compares them in terms of an absolute global performance measure such as average quay crane rate. This paper provides such an analysis.     

Development and simulation analysis of real-time yard crane control systems for seaport container transshipment terminals

As more and more container terminals open up all over the world, terminal operators are discovering that they must increase quay crane work rates to remain competitive. In this paper, we develop a real-time yard crane control system and show that a terminal’s long-run average quay crane rate depends on the portion of this system that dispatches yard cranes in the storage area in real time. Several real-time yard crane dispatching systems are evaluated by a fully-integrated, discrete event simulation model of a pure transshipment terminal that is designed to reproduce the multi-objective, stochastic, real-time environment at an RTGC-based, multiple-berth facility. Results indicate that yard cranes should prioritize the retrieval of containers from the stacks, rather than the storage of containers into stacks. Also, the yard crane dispatching system should not only consider the trucks already waiting for service in the yard, but also the trucks that are heading towards the yard. The experiments provide the first direct connection in the literature between real-time yard crane control systems and long-run performance at a seaport container terminal. We also make a qualitative comparison between rule-based and look-ahead yard crane dispatching schemes, and discuss deadlocking issues in detail.     

A two-stage stochastic programming for single yard crane scheduling with uncertain release times of retrieval tasks

Nowadays maritime transportation has become the mainstream of the global logistics, and the operational efficiency of container terminals plays a critical role in maritime transportation. As one of the most important terminal operational issues, yard crane scheduling that handles both storage and retrieval tasks has caught a lot of interest. However, the uncertainty on the release times of retrieval tasks, as one common phenomenon in daily operations, has been ignored in the literature. This paper investigates single yard crane scheduling to minimise the expected total tardiness of tasks, and focus on the case with uncertain release times of retrieval tasks. A two-stage stochastic programming model is proposed, and the sample average approximation (SAA) approach is applied to solve small instances of the problem. For large-scale instances, a genetic algorithm (GA) and a rule-based heuristic are developed. To evaluate the performances of the solution methods, numerical experiments with 300 instances are implemented. Computational results show that the rule-based heuristic outperforms both GA and SAA in terms of solution quality and running time.     

A new decomposition algorithm for a liquefied natural gas inventory routing problem

We consider an inventory routing problem (IRP) in the liquefied natural gas (LNG) supply chain, called the LNG-IRP. Here, an actor is responsible for the LNG production and inventory management at the liquefaction plants, the routing and scheduling of a heterogeneous fleet of LNG ships, as well as the inventories and sales at the regasification terminals. Furthermore, all ports have a limited number of berths available for loading and unloading. The LNG-IRP is more complicated than many other maritime inventory routing problems because a constant rate of the cargo evaporates in the tanks each day and is used as fuel during transportation. In addition, a variable number of tanks are unloaded at the regasification terminals. We introduce a new path flow formulation for this problem arising from a novel decomposition scheme based on parts of a ship schedule, called duties. A ship schedule for the entire planning horizon can be divided into duties consisting of a visit to a liquefaction plant, then one or two visits to a regasification terminal before ending in a liquefaction plant. The solution method suggested is based on a priori generation of duties, and the formulation is strengthened by valid inequalities. The same problem was previously solved by a branch-price-and-cut algorithm for a schedule-based formulation. Computational results show that the new formulation provides tighter bounds than the previous schedule-based formulation. Furthermore, on a set of 27 benchmark instances, the proposed algorithm clearly outperforms the previous branch-price-and-cut algorithm both with regard to computational time and the number of problems solved within a 10-h time limit.     

Cargo scheduling decision support for offshore oil and gas production: a case study

Woodside Energy Ltd (Woodside), Australia’s largest independent oil and gas company, operates multiple oil and gas facilities off the coast of Western Australia. These facilities require regular cargo shipments from supply vessels based in Karratha, Western Australia. In this paper, we describe a decision support model for scheduling the cargo shipments to minimize travel cost and trip duration, subject to various operational restrictions including vessel capacities, cargo demands at the facilities, time windows at the facilities, and base opening times. The model is a type of non-standard vehicle routing problem involving multiple supply vessels—a primary supply vessel that visits every facility during a round trip taking at most 1 week, and other supply vessels that are used on an ad hoc basis when the primary vessel cannot meet all cargo demands. We validate the model via test simulations using real data provided by Woodside.     

A capacitated facility location problem with constrained backlogging probabilities

One of the assumptions of the capacitated facility location problem (CFLP) is that demand is known and fixed. Most often, this is not the case when managers take some strategic decisions such as locating facilities and assigning demand points to those facilities. In this paper we consider demand as stochastic and we model each of the facilities as an independent queue. Stochastic models of manufacturing systems and deterministic location models are put together in order to obtain a formula for the backlogging probability at a potential facility location. Several solution techniques have been proposed to solve the CFLP. One of the most recently proposed heuristics, a reactive greedy adaptive search procedure, is implemented in order to solve the model formulated. We present some computational experiments in order to evaluate the heuristics’ performance and to illustrate the use of this new formulation for the CFLP. The paper finishes with a simple simulation exercise.     

A GRASP algorithm for flexible job-shop scheduling problem with limited resource constraints

A greedy randomised adaptive search procedure (GRASP) is an iterative multi-start metaheuristic for difficult combinatorial optimisation. The GRASP iteration consists of two phases: a construction phase, in which a feasible solution is found and a local search phase, in which a local optimum in the neighbourhood of the constructed solution is sought. In this paper, a GRASP algorithm is presented to solve the flexible job-shop scheduling problem (FJSSP) with limited resource constraints. The main constraint of this scheduling problem is that each operation of a job must follow an appointed process order and each operation must be processed on an appointed machine. These constraints are used to balance between the resource limitation and machine flexibility. The model objectives are the minimisation of makespan, maximum workload and total workload. Representative benchmark problems are solved in order to test the effectiveness and efficiency of the GRASP algorithm. The computational result shows that the proposed algorithm produced better results than other authors’ algorithms.