skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Abstract

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.

Authors:
; ;  [1];  [2];  [1]
  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)
Publication Date:
OSTI Identifier:
22769335
Resource Type:
Journal Article
Journal Name:
Computational and Applied Mathematics
Additional Journal Information:
Journal Volume: 37; Journal Issue: 2; Other Information: Copyright (c) 2018 SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0101-8205
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; CALCULATION METHODS; GENETIC ALGORITHMS; OPTIMIZATION; PERFORMANCE; RANDOMNESS

Citation Formats

Noroozi, Amir, Mahdavi Mazdeh, Mohammad, E-mail: mazdeh@iust.ac.ir, Noghondarian, Kazem, Rasti-Barzoki, Morteza, and Heydari, Mehdi. Evolutionary computation algorithms to coordinating order acceptance and batch delivery for an integrated supply chain scheduling. United States: N. p., 2018. Web. doi:10.1007/S40314-016-0415-8.
Noroozi, Amir, Mahdavi Mazdeh, Mohammad, E-mail: mazdeh@iust.ac.ir, Noghondarian, Kazem, Rasti-Barzoki, Morteza, & Heydari, Mehdi. Evolutionary computation algorithms to coordinating order acceptance and batch delivery for an integrated supply chain scheduling. United States. doi:10.1007/S40314-016-0415-8.
Noroozi, Amir, Mahdavi Mazdeh, Mohammad, E-mail: mazdeh@iust.ac.ir, Noghondarian, Kazem, Rasti-Barzoki, Morteza, and Heydari, Mehdi. Tue . "Evolutionary computation algorithms to coordinating order acceptance and batch delivery for an integrated supply chain scheduling". United States. doi:10.1007/S40314-016-0415-8.
@article{osti_22769335,
title = {Evolutionary computation algorithms to coordinating order acceptance and batch delivery for an integrated supply chain scheduling},
author = {Noroozi, Amir and Mahdavi Mazdeh, Mohammad, E-mail: mazdeh@iust.ac.ir and Noghondarian, Kazem and Rasti-Barzoki, Morteza and Heydari, Mehdi},
abstractNote = {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.},
doi = {10.1007/S40314-016-0415-8},
journal = {Computational and Applied Mathematics},
issn = {0101-8205},
number = 2,
volume = 37,
place = {United States},
year = {2018},
month = {5}
}