Abstract
Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.
Highlights
As is well known, reducing energy consumption and its associated environmental impacts is one of the most important challenges for manufacturing industries
There has been an increasing number of studies on production scheduling integrated with energy efficiency in the literature [4,5,6]
Generate a set of new offspring individuals N(t) according to the probabilistic model Create a new population X(t + 1) by replacing some individuals of X(t) by N(t) according to the updating mechanism Else While (T >Te) do Generate the temporary individuals N(t) according to the neighborhood structure Evaluate the improvement of the fitness value Update annealing rate function End while End if T=t+1 End while Report best results End
Summary
As is well known, reducing energy consumption and its associated environmental impacts is one of the most important challenges for manufacturing industries. In China, the energy efficiency of traditional manufacturing enterprises is low and the related pollution emissions are very high. One of the most effective ways to develop energy-savings mechanisms and methods is to optimize the energy efficiency of the production process for manufacturing enterprises [2,3]. Researchers have come to realize that workshop scheduling could play an important role in reducing energy consumption during manufacturing processes. There has been an increasing number of studies on production scheduling integrated with energy efficiency in the literature [4,5,6]. Energy-related production scheduling can mainly be divided into three aspects: energy-aware single-machine scheduling, energy-aware flow-shop scheduling, and energy-aware job-shop scheduling
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