This research investigates a two-stage no-wait hybrid flow-shop scheduling problem in which the first stage contains a single machine, and the second stage contains several identical parallel machines. The objective is to minimise the makespan. For this problem, existing literature emphasises on heuristics or optimal solutions for special cases, whereas this paper proposes a branch-and-bound algorithm. Several lower bounds for optimal and partial schedules are derived. Also, three dominance rules are deduced, and seven constructive heuristics are used to obtain initial upper bounds. Extensive computational tests on randomly generated problems are conducted. The results with comparisons indicate that the proposed bounds (especially partial lower bounds), dominance rules and the branch-and-bound algorithm are efficient.

This paper deals with an integrated optimisation model for production scheduling and preventive maintenance (PM) in a single machine with its time to failure subject to a Weibull probability distribution. The objective is to minimise the total expected weighted completion time of jobs. To solve this problem, we develop a branch and bound (B&B) algorithm (hereafter called the B&BA). Several lower bounds, dominance rules and upper bounds are developed to enhance the performance of the B&BA. Extensive computational experiments on randomly generated problems with different configurations are conducted and the results show that the proposed method can find optimal solutions for problems with up to 18 jobs in a reasonable amount of computation time.

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.

Infectious disease outbreaks have occurred many times in the past decades and are more likely to occur in the future. Recently, Büyüktahtakin et al. (2018) proposed a new epidemics-logistics model to control the 2014 Ebola outbreak in West Africa. Considering that different diseases have dissimilar diffusion dynamics and can cause different public health emergencies, we modify the proposed model by changing capacity constraint, and then apply it to control the 2009 H1N1 outbreak in China. We formulate the problem to be a mixed-integer non-linear programming model (MINLP) and simultaneously determine when to open the new isolated wards and when to close the unused isolated wards. The test results reveal that our model could provide effective suggestions for controlling the H1N1 outbreak, including the appropriate capacity setting and the minimum budget required with different intervention start times.