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Pre-auction lane selection in an integrated production–distribution planning problem

Triki, Chefi, Piya, Sujan, Fu, Liang-Liang (2021) Pre-auction lane selection in an integrated production–distribution planning problem. Engineering Optimization, 53 (11). pp. 1855-1870. ISSN 0305-215X. E-ISSN 1029-0273. (doi:10.1080/0305215X.2020.1833875) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:91493)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1080/0305215X.2020.1833875

Abstract

Integrating production scheduling with transportation decisions is an important problem that is receiving increasing interest from the logistics industry. As an order is received, the manufacturer starts planning its production while considering the appropriate decisions for delivery. The company can adopt an auction paradigm to involve external occasional drivers, besides using its own fleet for the delivery. Ahead of an auctioning process, the company should decide which deliveries should be served by its fleet and which can be assigned to the occasional drivers. This is known as the shipper lane selection problem (SLSP). This article integrates the SLSP with production scheduling and proposes an integer formulation and heuristic methods to solve the resulting integrated problem (I-SLSPS). Experiments show the validity of the proposed methods and their capability in achieving minimum cost for the integrated problem. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Item Type: Article
DOI/Identification number: 10.1080/0305215X.2020.1833875
Uncontrolled keywords: Manufacture; Production control; Scheduling, Distribution planning; Integrated production; Logistics industry; Minimum cost; Production Scheduling; Selection problems, Heuristic methods
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Chefi Triki
Date Deposited: 18 Nov 2021 09:28 UTC
Last Modified: 19 Nov 2021 09:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/91493 (The current URI for this page, for reference purposes)
Triki, Chefi: https://orcid.org/0000-0002-8750-2470
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