Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/62007
Title: A hybrid approach for cost-optimized lateral transshipment in a supply chain environment
Authors: Nakandala, D
Lau, H
Ning, A
Keywords: Cost optimization
Hybrid genetic algorithm
Inventory cost
Lateral transshipment
Supplier selection
Suppliers
Supply chain management
Issue Date: 2016
Publisher: Emerald Group Publishing Limited
Source: Business process management journal, 2016, v. 22, no. 4, p. 860-878 How to cite?
Journal: Business process management journal 
Abstract: Purpose – When making sourcing decisions, both cost optimization and customer demand fulfillment are equally important for firm competitiveness. The purpose of this paper is to develop a stochastic search technique, hybrid genetic algorithm (HGA), for cost-optimized decision making in wholesaler inventory management in a supply chain network of wholesalers, retailers and suppliers. Design/methodology/approach – This study develops a HGA by using a mixture of greedy-based and randomly generated solutions in the initial population and a local search method (hill climbing) applied to individuals selected for performing crossover before crossover is implemented and to the best individual in the population at the end of HGA as well as gene slice and integration. Findings – The application of the proposed HGA is illustrated by considering multiple scenarios and comparing with the other commonly adopted methods of standard genetic algorithm, simulated annealing and tabu search. The simulation results demonstrate the capability of the proposed approach in producing more effective solutions. Practical implications – The pragmatic importance of this method is for the inventory management of wholesaler operations and this can be scalable to address real contexts with multiple wholesalers and multiple suppliers with variable lead times. Originality/value – The proposed stochastic-based search techniques have the capability in producing good-quality optimal or suboptimal solutions for large-scale problems within a reasonable time using ordinary computing resources available in firms.
URI: http://hdl.handle.net/10397/62007
ISSN: 1463-7154
DOI: 10.1108/BPMJ-08-2015-0122
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