Theory of Constraints and Human Resource Management Applications

This purpose of this article is to assess constraints and suggest a theory that can improve Human Resource Management Systems (HRMS's). It investigates the relationship between Theory of Constraints (TOC) and operation management, which is based on the Critical Chain PMs book entitled The Goal. In 1984, the author, Goldratt, introduced an entire management philosophy about the TOC. The mental process and improvement theory tools discussed in his book mainly focus on manufacturing environments. However, the practice and examples in the book helped many organizations succeed, even in the private sectors. This study offers definitions of throughput, operating expense, and inventory measurements, and uses the principle of TOC to identify bottlenecks and constraints in every business process. By using these analyses, the study remodels the system to increase performance measurements of HRMS.

Artificial Neural Network in Operation Management Regarding Communication Issue

Communication issue in operation management is important concern in the age of 21st century. In operation, communication can be described based on major three wings- Travelling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and Transportation Problem (TP). Artificial Neural Network (ANN) is an important tool to handle these systems. In this chapter, different ANN based models are discussed in a comprehensive way. This chapter deals with how various approaches of ANN help to design the optimal communication network. This comprehensive study is important to the decision makers for the analytical consideration. Although there is a lot of development in this particular domain from a long time ago; but only the revolutionary contributed models are taken into account. Another motivation of this chapter is understanding the importance of ANN in the operation management area.

Optimal Load and Energy Management of Aircraft Microgrids Using Multi-Objective Model Predictive Control

Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the feasibility of the model. In addition, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.