Service processes in modern logistic systems tend to be highly specialized and intellectualized. However, some casual and unexpected behavior may occur, causing specific dynamic interactions among their many constituents. As such, the optimization and modeling of complex problems have become increasingly tough. Besides, technicians typically need to possess appropriate skills that match assigned tasks. Faced with real service scenarios, however, employees inevitably suffer from an increasing level of fatigue attributed to continuous work, resulting in a gradual decrease in the efficiency of workers over time. In this situation, the service time for a given task can no longer be treated as a constant, but instead, it should be treated as dynamic. Moreover, highly skilled technicians are usually paid higher than the junior ones with basic or lower skill level, which introduces new challenges in the optimization of service task schedules' problem. In this paper, we first present the skill vehicle routing problem considering dynamic service times and time-skill-dependent costs, in which the efficiencies of the workers are dynamically affected by their fatigue levels, and the costs, i.e., salaries paid to the employees, are related to skill levels and continuous work time. Furthermore, we develop a comprehensive and general mixed-integer linear programming dynamicbased model to formulate the proposed problem, which is directly solvable by MIP solvers for small-sized problems. We also initiate an iteratively dynamic neighborhood search (IDNS) algorithm that combines iterative partial optimization with dynamic neighborhood search to efficiently solve large-sized problems with near-optimal solutions. The comprehensive computational experiments were performed on the problems of different sizes to test the effectiveness and efficiency of the proposed model and solution approach. Some useful managerial insights were obtained from the computational results that can help decision-makers to determine cost-effective service routes and schedules in complex transportation-related issues. INDEX TERMS Skill vehicle routing problem, dynamic service times, time-skill-dependent costs, dynamic-based modeling, iteratively dynamic neighborhood search algorithm.

In an actual industrial or military operations environment, a multi-state system (MSS) consisting of multi-state components often needs to perform multiple missions in succession. To improve the probability of the system successfully completing the next mission, all the maintenance activities need to be performed during maintenance breaks between any two consecutive missions under limited maintenance resources. In such case, selective maintenance is a widely used maintenance policy. As a typical discrete mathematics problem, selective maintenance has received widespread attention. In this work, a selective maintenance model considering human reliability for multi-component systems is investigated. Each maintenance worker can be in one of multiple discrete working levels due to their human error probability (HEP). The state of components after maintenance is assumed to be random and follow an identified probability distribution. To solve the problem, this paper proposes a human reliability model and a method to determine the state distribution of components after maintenance. The objective of selective maintenance scheduling is to find the maintenance action with the optimal reliability for each component in a maintenance break subject to constraints of time and cost. In place of an enumerative method, a genetic algorithm (GA) is employed to solve the complicated optimization problem taking human reliability into account. The results show the importance of considering human reliability in selective maintenance scheduling for an MSS.

This paper studies the selective maintenance problem for a multi-state system (MSS) performing consecutive production missions with scheduled intermission breaks. To improve the reliability of the system successfully performing the next mission, all maintenance actions need to be carried out during maintenance breaks. However, it may not be feasible to repair all components due to the limited maintenance resources (such as time, costs, and manpower). Hence, a selective maintenance model was established to identify a subset of maintenance actions to perform on the repairable components. We extend the original model in several ways. First, we consider the role of degradation interaction in determining the state transition probability of each component. Back-propagation (BP) neural network is employed to predict the transition matrix since it is not practicable to analyze the degradation processes of all components using the traditional probability model. Second, a selective maintenance optimization model for an MSS is established based on the prediction results of the BP neural network and solved by a genetic algorithm (GA). Finally, an example is illustrated to verify the effectiveness and superiority of the proposed method.

No abstract

No abstract