Abstract
Industry consumes approximately half of the total worldwide energy usage. With the increasingly rising energy costs in recent years, it is critically important to consider one of the most widely used energies, electricity, during the production planning process. We propose a new mathematical model that can determine efficient scheduling to minimize the makespan and electricity consumption cost (ECC) for the flexible job shop scheduling problem (FJSSP) under a time-of-use (TOU) policy. In addition to the traditional two subtasks in FJSSP, a new subtask called speed selection, which represents the selection of variable operating speeds, is added. Then, a modified biogeography-based optimization (MBBO) algorithm combined with variable neighborhood search (VNS) is proposed to solve the biobjective problem. Experiments are performed to verify the effectiveness of the proposed MBBO algorithm for obtaining an improved scheduling solution compared to the basic biogeography-based optimization (BBO) algorithm, genetic algorithm (GA), and harmony search (HS).
Highlights
Under the pressure of sustainable development, manufacturers today must consider production efficiency and energy consumption
A modified biogeography-based optimization (BBO) algorithm integrated with variable neighborhood search (VNS) was proposed for solving flexible job shop scheduling problem (FJSSP)
(1) A mathematical model for the biobjective problem, to minimize makespan and electricity consumption cost (ECC) simultaneously, was proposed in an extended FJSSP, which considered a new subtask concerning the selection of adjustable operating speeds
Summary
Under the pressure of sustainable development, manufacturers today must consider production efficiency and energy consumption. Global energy consumption was 524 quadrillion Btu in 2010 and is expected to increase by 56.5%, to 820 quadrillion Btu, by 2040 [1]. As one of the most widely used industrial energies, electricity is an important sector that cannot be neglected. The rising cost of energy sources to generate electricity such as coal, natural gas, and nuclear energy leads to an increasing electricity consumption cost (ECC). Increasingly heavy investment has been made to support backup infrastructures, with the variable demand of consumers it remains difficult to achieve a trade-off between demand and supply. To address this issue, electricity suppliers have implemented demand response technology
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