Low-Dimensional Halide Perovskites and Their Advanced Optoelectronic Applications

Nano-Micro Letters - A NiFe2O4/expanded graphite (NiFe2O4/EG) nanocomposite was prepared via a simple and inexpensive synthesis method. Its lithium storage properties were studied with the goal of...     

Due-window assignment and scheduling with general position-dependent processing times involving a deteriorating and compressible maintenance activity

In this paper, we consider common due-window assignment and scheduling problems with general position-dependent processing times involving deteriorating and compressible maintenance activity on a single machine. Two models associated with maintenance activity are examined in this article, in which the maintenance length is assumed to be either time-dependent and compressible or position-dependent and compressible. The objective is to find jointly the location and size of due-window, position of maintenance as well as resource amount allocated to it, and job sequence to minimise a total cost function based on earliness, tardiness, window location, window size and resource cost. We show that the problem considered in each of the two models’ setting can be optimally solved with polynomial time algorithm by reducing to assignment problem. Finally, two examples are provided to illustrate the solution procedures.     

An improved multi-objective genetic algorithm for heterogeneous coverage RFID network planning

Recent research has demonstrated the potential benefits of radio frequency identification (RFID) technology in the supply chain and production management via its item-level visibility. However, the RFID coverage performance is largely impacted by the surrounding environment and potential collisions between the RFID devices. Thus, through RFID network planning (RNP) to achieve the desired coverage within the budget becomes a key factor for success. In this study, we establish a novel and generic multi-objective RNP model by simultaneously optimising two conflicted objectives with satisfying the heterogeneous coverage requirements. Then, we design an improved multi-objective genetic algorithm (IMOGA) integrating a divide-and-conquer greedy heuristic algorithm to solve the model. We further construct a number of computational cases abstracted from an automobile mixed-model assembly line to illustrate how the proposed model and algorithm are applied in a real RNP application. The results show that the proposed IMOGA achieves highly competitive solutions compared with Pareto optimal solutions and the solutions given by four recently developed well-known multi-objective evolutionary and swarm-based optimisers (SPEA2, NSGA-II, MOPSO and MOPS²O) in terms of solution quality and computational robustness.