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We investigate a stochastic distributed appointment assignment and scheduling problem, which consists of assigning appointments to distributed service units and determining service sequences at each service unit. In particular, the service time duration and release time uncertainties are well-considered. The solution to this generic problem finds interesting applications in distributed production systems, healthcare systems, and post-disaster operations. We formulate the problem as a two-stage stochastic program to minimise the total transportation cost and expected makespan, idle time or overtime, and apply the sample average approximation method to make the problem tractable. We then develop a stochastic logic-based Benders decomposition method, decomposing the problem into a master problem and a subproblem. The master problem determines the appointment assignment variables, and the subproblem handles the sequence and service start time variables. Benders optimality cuts are generated from the subproblem's solution and added to the master problem. The developed stochastic logic-based method is advantageous since it can manage many scenarios in parallel. We further consider each appointment's due date, minimise the weighted earliness and tardiness, and adjust the developed method to solve this variant. Experiments on random instances demonstrate the excellent performance of the proposed model and methods. [ABSTRACT FROM AUTHOR]

Smart manufacturing has boosted the wide application of mobile robots in robotic cells for automated material delivery. However, the mismatching between machine production process and robot movement process causes extensive energy waste. Nevertheless, most existing robotic job-shop scheduling (RJSP) studies mainly focus on minimising makespan but overlook the low energy efficiency problem faced by robotic cells. Motivated by the importance of green smart manufacturing, in this study, we innovatively propose to achieve robotic cell energy saving through coordinating the machine production process and robot movement process. Specifically, both machines and the mobile robot can flexibly adjust operating speeds with a V-scale speed framework. Two novel energy-efficient RJSP approaches (i.e. the RJSP-E and the RJSP-EM) are thus proposed. The RJSP-E focuses on minimising energy consumption, while the RJSP-EM simultaneously considers makespan (i.e. productivity) and energy consumption. Through computational experiments, the RJSP-E demonstrates superior performances in reducing energy consumption (15% on average), at a loss of productivity (20% on average). On the other hand, the RJSP-EM can select the most suitable energy-saving operating speeds without much sacrifice in productivity. Notably, the RJSP-EM can reduce energy consumption by a mean of 10% even without increasing makespan. The RJSP-EM also demonstrates higher solution efficiency. [ABSTRACT FROM AUTHOR]

In the digital age, operations can be improved by a wise use of information and technological tools. During the COVID-19 pandemic, governments faced various choices of vaccines possessing different efficacy and availability levels at different time points. In this article, we consider a two-stage vaccine ordering problem of a government from a first and only supplier in the first stage, and either the same supplier or a new second supplier in the second stage. Between the two stages, potential demand information for the vaccine is collected to update the forecast. Using dynamic programming, we derive the government's optimal vaccine ordering policy. We find that the government should select its vaccine supplier based on the disease's infection rate in the society. When the infection rate is low, the government should order nothing at the first stage and order from the supplier with a higher efficacy level at the second stage. When the disease's infection rate is high, the government should order vaccines at the first stage and switch to the other supplier with a lower efficacy level at the second stage. We extend our model to examine (i) the value of blockchain adoption and (ii) the impact of vaccines' side effects. [ABSTRACT FROM AUTHOR]