Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective

Mathematical Problems in Engineering, Jan 2018

This paper presents mixed integer programming for a transportation service procurement bid construction problem from a less than full truckload perspective, in which the bidders (carriers) generate their best bid (package) using a bundled price to maximize their utility and increase the chance of winning the business. The models are developed from both the carriers and shippers perspectives to establish a relationship between the quoted price and the likelihood of winning to assist the carriers in balancing the potential benefits and the possibility of winning the bid. An intelligent algorithm based on Particle Swarm Optimization is then designed to solve the proposed model and hypothetical data sets are used to test the effectiveness and efficiency of the proposed model and algorithm.

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Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective

Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective Fang Yan,1 Yanfang Ma,2 Manjing Xu,3 and Xianlong Ge1 1School of Economics and Management, Chongqing Jiaotong University, Chongqing 40074, China 2School of Economics and Management, Hebei University of Technology, Tianjin 300130, China 3College of Information and Business, Zhongyuan University of Technology, Zhengzhou 450007, China Correspondence should be addressed to Fang Yan; nc.ude.utjqc@gnafnay Received 13 March 2017; Revised 12 June 2017; Accepted 20 June 2017; Published 17 January 2018 Academic Editor: Anna Vila Copyright © 2018 Fang Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract This paper presents mixed integer programming for a transportation service procurement bid construction problem from a less than full truckload perspective, in which the bidders (carriers) generate their best bid (package) using a bundled price to maximize their utility and increase the chance of winning the business. The models are developed from both the carriers and shippers perspectives to establish a relationship between the quoted price and the likelihood of winning to assist the carriers in balancing the potential benefits and the possibility of winning the bid. An intelligent algorithm based on Particle Swarm Optimization is then designed to solve the proposed model and hypothetical data sets are used to test the effectiveness and efficiency of the proposed model and algorithm. 1. Introduction Combinatorial auctions (CA), which allow bidders to submit bids on a combination of products or services, can generate greater profits [1, 2]. Therefore, for transportation service procurement, CA has attracted increasing attention because of the synergies available on the transportation pathways [3–7] as it allows carriers to submit bids to the shipper for the transportation loads they prefer which are grouped into packages or bundles of loads of various products [8, 9]. Generally, there are three CA stages needed for transportation service procurement. First, the auctioneer (on behalf of the buyer) announces the multiple loads available for bidding (henceforth, bid loads) at the auction. Second, the bidders (here, the carriers) submit bids for sets of bid loads (or bundles), rather than bidding on each bid load individually. Third, the auctioneer determines the best set of bundles that collectively contain each bid load exactly once and awards contracts for these bundles (rather than awarding individual bid loads) to the corresponding bidders [10]. These three stages are known as a Shipper Lane Selection Problem (SLSP) [11, 12], bid generation problem (BGP) [7], and a Winner Determination Problem (WDP) [13, 14], in which the BGP is the main focus in this paper. BGP is a complex challenge for bidders as it is necessary to evaluate an exponential number of potential bundles that represent all possible subsets of the auctioned loads [8]. For example, if there are loads posted, each carrier can theoretically submit up to different combinations to the auctioneer; therefore, this is highlighted as a NP-hard problem [15, 16]. Most studies researched BGP from truckload perspective, while in fact if permitted, less than truckload procurement could bring more benefits for both shipper and carriers. Carriers could make full use of not only their existing network (as concerned from truckload perspective) but also their capacities, which means the empty load rate and the loading rate could be optimized simultaneously. However, to date, there has been little research that has specifically focused on this type of bid generation problem. Apart from the lanes combinations, the bidding price is another issue that must be considered by the carriers for higher price may bring more benefits but also increase the chance of losing business [8, 17]. Hence, the likelihood of winning business is taken into account in this paper. This paper considers bundled lane selections with the prices set from the carrier’s perspective for this transportation service procurement. The shipper has some new transportation loads that need to be serviced and the carrier has their own transportation networks and the ability to undertake additional loads. The problem focus is how to give the carrier the ability to choose loads on different lanes to maximize their utility revenue minus costs. It is obvious that while higher prices could generate higher revenue, this could also lead to the business failure and that while lower prices could increase the likelihood of winning business, this could lead to a loss of potential profits. Therefore, this paper seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which (...truncated)


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Fang Yan, Yanfang Ma, Manjing Xu, Xianlong Ge. Transportation Service Procurement Bid Construction Problem from Less Than Truckload Perspective, Mathematical Problems in Engineering, 2018, 2018, DOI: 10.1155/2018/1728512