At present, home health care (HHC) has been accepted as an effective method for handling the healthcare problems of the elderly. The HHC scheduling and routing problem (HHCSRP) attracts wide concentration from academia and industrial communities. This work proposes an HHCSRP considering several care centers, where a group of customers (i.e., patients and the elderly) require being assigned to care centers. Then, various kinds of services are provided by caregivers for customers in different regions. By considering the skill matching, customers’ appointment time, and caregivers’ workload balancing, this article formulates an optimization model with multiple objectives to achieve minimal service cost and minimal delay cost. To handle it, we then introduce a brain storm optimization method with particular multi-objective search mechanisms (MOBSO) via combining with the features of the investigated HHCSRP. Moreover, we perform experiments to test the effectiveness of the designed method. Via comparing the MOBSO with two excellent optimizers, the results confirm that the developed method has significant superiority in addressing the considered HHCSRP.

Currently, manufacturing enterprises face increasingly fierce market competition due to the various demands of customers and the rapid development of economic globalization. Hence, they have to extend their production mode into distributed environments and establish multiple factories in various geographical locations. Nowadays, distributed manufacturing systems have been widely adopted in industrial production processes. In recent years, many studies have been done on the modeling and optimization of distributed scheduling problems. This work provides a literature review on distributed scheduling problems in intelligent manufacturing systems. By summarizing and evaluating existing studies on distributed scheduling problems, we analyze the achievements and current research status in this field and discuss ongoing studies. Insights regarding prior works are discussed to uncover future research directions, particularly swarm intelligence and evolutionary algorithms, which are used for managing distributed scheduling problems in manufacturing systems. This work focuses on journal papers discovered using Google Scholar. After reviewing the papers, in this work, we discuss the research trends of distributed scheduling problems and point out some directions for future studies.