In a dynamic production environment, not only the product portfolio and demands are varying throughout a multi-period horizon, but also the economic aspects of the environment, such as energy pricing, change with time. The thesis of this work states that energy price fluctuation has a considerable optimizable effect on manufacturing system structural and operational decisions. This work progressively presents three novel linear mathematical models to optimize that effect. In the first step, a novel basic linear mixed integer mathematical model is proposed to maximize the sustainability of changeable manufacturing systems (MSCM) on the operational level. The model focuses on three factors, which are the change pattern in energy prices throughout the day, the transportation cost of jobs between machines, and the setup cost of each machine, which is dependent on the job sequence. The model output is a system configuration plan, indicating arrangement of machines in the system, and the sequence of jobs, which need to be produced on one day. It is solved by CPLEX solver in GAMS software for nine different problem sizes. The new LMI model finds the optimum configuration plan and job sequence in a reasonable time, which illustrates the efficiency and practicality of the proposed model. In the second step, a new linear mathematical model is presented to maximize the sustainability of changeable manufacturing systems on the structural level (MSSCM) by selecting the layout reconfiguration and material handling system in each period. It is solved by CPLEX solver in GAMS software to analyze influence of energy pricing and demand fluctuation on system convertibility and scalability, which can affect layout configuration selection. In the last step, a novel mixed integer linear mathematical model (MILTEC) is presented to maximize the sustainability of RMS on both the structural and operational levels. The system configuration planning in each period of time consists of machines layout and task scheduling which are the most interrelated decisions on the system level. The novel aspect of the presented model is the consideration of energy sustainability concurrently with system configuration and task scheduling decisions in a changing manufacturing environment. The model objective is to minimize total costs of energy consumption, system reconfiguration throughout the planning horizon, and part transportation between machines, which all depend on fluctuations in energy pricing and demand during different periods of time. Several case studies are solved by GAMS Software using the branch-and-bound technique to illustrate the performance of the presented model and analyze its sensitivity to the volatility of energy pricing and demand and their effect on system changeability. An efficient genetic algorithm (GA) has been developed to solve the proposed model in larger scale due to its NP-hardness (non-deterministic polynomial-time hardness). The results are compared to GAMS to validate the developed GA. It shows that the proposed GA finds near-optimal solutions in 70% shorter time than GAMS on average. Different examples are also solved resulting in negligible differences between solutions in several runs of each example to verify the efficiency of the proposed GA.