#6652. A JIT part supply scheduling strategy with electric transport device between central receiving store and supermarkets in the automobile industry considering energy

December 2026publication date
Proposal available till 30-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Automotive Engineering;
Electrical and Electronic Engineering;
Energy Engineering and Power Technology;
Transportation;
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Abstract:
Sustainable scheduling has been attracting arousing attention of modern manufacturers, and energy efficiency becomes a critical issue related to sustainability. This paper aims at providing an effective solution method for a real-world energy-efficient part supply scheduling problem (EPSSP) between the central receiving store and supermarkets with electric transport device on purpose of coordinating production and transportation in the automobile industry. A bi-objective mixed integer linear programming (MILP) model is formulated to jointly optimize the total energy consumption and a just-in-time (JIT) metric. An epsilon–constraint approach is presented to obtain the optimal solutions in small-scale problems. Owing to EPSSPs NP-hard nature, an indicator-based bi-objective teaching-learning-based optimization (I-BTLBO) algorithm is proposed by combining the binary quality indicator and teaching-learning-based optimization (TLBO) metaheuristic. I-BTLBO adopts a novel solution codification embedded with an adjustment technique to accommodate EPSSPs characteristic. For infeasible solutions, penalty function is applied to deal with the solution that violate vehicle capacities. A variable neighborhood search-based local optimizer is introduced to enhance I-BTLBOs convergence ability. In addition, an external population (EP) is employed to preserve non-dominated solutions diversity and to guide the evolution process based on the Pareto dominance and preference indicators.
Keywords:
Bi-objective optimization; Electric transport device; Energy-efficiency; Part supply; Scheduling; Teaching-learning-based optimization

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