Sustainable engineering is very important for logistics systems. Nowadays, sustainable warehouse management is a key factor in market success. Workforce fluctuation and inverting the number of customers’ demands make a lot of problems in distribution warehouses. This study addresses a sustainable approach for the workforce scheduling problem recognized in a real distribution warehouse. The problem arises from the high variability of demand for workers over one workday, which causes workforce surplus in some periods of the workday and shortages in others. Engineering managers of the distribution warehouse already use different full-time and part-time shifts, and schedule workers on different activities, but they still have significant workforce surpluses or shortages in some periods. This study proposes the scheduling of activities’ execution together with workers to face that variability and decrease the cost of the workforce. This idea comes from the fact that some activities in a distribution warehouse can be done in a specific time period after the need for them occurs. In this way, the variability of demand for workers can be decreased, and a lower workforce cost may be ensured. Based on this idea, the entire problem is modeled as integer linear programming. The real example of the problem is solved, and the proposed model is tested on randomly generated instances of the problem in Python by means of the PuLP linear programming package. The results indicate different positive effects in the manner of sustainable warehouse management: lower workforce costs, time savings, better utilization of all types of resources and equipment, increased employee satisfaction, and so on. For even 61% of instances of the introduced problem, the obtained cost of the workforce is lower by more than 20% if activities’ executions are scheduled together with employees.

One of the key challenges to model condition monitoring is; how to suitable match the level of detail of the model description of the diagnostic requirements. To do so, a counter-current heat exchanger of the tire factory was investigated. Moreover, a two-stage grey-box modelling was deployed. The modelling is deployed in a discrete state space, relying on a combination of prior knowledge, along with the recorded data. The nonlinear model structure of heat exchanger is derived considering the use of the prior knowledge; thereby, it is converted into linearized form. The discretization of the linearized model is applied. The optimization procedure based on recorded data is adopted to estimate the unknown physical parameters in state space system matrices. The well-known N4SID subspace identification method is used to compare the obtained results.

Improvements in battery technology make electric vehicles more and more suitable for the use as electricity storages. Many benefits could be achieved by using electric vehicles for storing electricity in their batteries. This paper talks about the idea of electric vehicles as electricity storages in electric power systems. The idea has a great number of supporters, but also a significant part of the professional community believes that is unfeasible. This paper is not classified in either side and strives to give a realistic picture of this idea. For this purpose, findings from papers published in scientific journals are mainly used. There is also some information from websites, mainly for some technical issues. Partly, the opinions of the authors are present. Specificities of EVs and EPSs that enabled the birth of this idea are explained along with proposed concepts through which the idea can be implemented. Keeping with the vehicle to grid concept, issues about the implementation of the idea are considered. Achievements in the practical realization of the idea are also presented.