Evolutionary computation algorithms to coordinating order acceptance and batch delivery for an integrated supply chain scheduling

Journal Article · Tue May 15 04:00:00 EDT 2018 · Computational and Applied Mathematics

DOI:https://doi.org/10.1007/S40314-016-0415-8· OSTI ID:22769335

Noroozi, Amir; Noghondarian, Kazem ^[1]; Rasti-Barzoki, Morteza ^[2]; Heydari, Mehdi ^[1]

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1. Iran University of Science and Technology, Department of Industrial Engineering (Iran, Islamic Republic of)
2. Isfahan University of Technology, Department of Industrial and Systems Engineering (Iran, Islamic Republic of)

This paper proposes a novel approach of coordinating decisions in an integrated supply chain (ISC): coordinating order acceptance (OA) and batch delivery (BD) due to round trip transportation (RTT) and using third-party logistics (3PL) vehicles. The paper aims at trading off among accepted orders revenue, delivery costs as well as any penalties incurred in the ISC to maximize the total benefit. A novel mixed-integer programming is proposed for the problem. In addition, the paper provides a heuristic to form batches and develops a hybrid evolutionary computation algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) to solve the problem. An information sharing mechanism is improved and applied. To explore and to locate the proposed PSO in a better neighborhood, a local search is proposed. Taguchi experimental design is utilized to set the appropriate values of the algorithms’ parameters and random instances are generated to evaluate the performance of the algorithms. The paper investigates the profitability sensitivity of the problem to parameters and analyzes the effect of the changes in the parameters on the performance of our proposed algorithms. The attained results show the appropriate performance of our algorithms.

• OSTI ID: 22769335
• Journal Information: Computational and Applied Mathematics, Journal Name: Computational and Applied Mathematics Journal Issue: 2 Vol. 37; ISSN 0101-8205
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
CALCULATION METHODS
GENETIC ALGORITHMS
OPTIMIZATION
PERFORMANCE
RANDOMNESS