Abstract
Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.
Highlights
Most scheduling problems usually involve multiple objectives like cost, tardiness, and earliness due to demand of practical production and these objectives are often conflicting with each other, which is a challenging task for solving the optimal solution [1]
In this work we mainly study on a single machine scheduling with controllable processing and setup times for minimizing production costs and energy consumption
The three objective functions for the optimization of total E/T, cost, and total energy consumption in this scheduling model can be defined as follows
Summary
Most scheduling problems usually involve multiple objectives like cost, tardiness, and earliness due to demand of practical production and these objectives are often conflicting with each other, which is a challenging task for solving the optimal solution [1]. To the best of the authors’ knowledge, the single machine scheduling problem with controllable processing and setup times, including energy consumption conception, still has not been studied in the previous researches reported This addressed scheduling is a multi-objective optimization problem (MOP) in nature and a NP-hard problem. For a single-machine scheduling environment, Mouzon et al [15] proposed a multi-objective mathematical programming model and several algorithms for a single Computer numerical control (CNC) machine scheduling problem with the goals of reducing both energy consumption and total completion time. In this work we mainly study on a single machine scheduling with controllable processing and setup times for minimizing production costs (i.e., cost and total E/T) and energy consumption These objectives are important Key Performance Indicators (KPIs) for enterprises.
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