Less-Than-Truckload carrier collaboration problem: modeling framework and solution approach
Post on 15-Dec-2016
<ul><li><p>J HeuristicsDOI 10.1007/s10732-013-9229-7</p><p>Less-Than-Truckload carrier collaboration problem:modeling framework and solution approach</p><p>Selvaprabu Nadarajah James H. Bookbinder</p><p>Received: 17 September 2012 / Revised: 13 June 2013 / Accepted: 18 June 2013 Springer Science+Business Media New York 2013</p><p>Abstract Less-Than-Truckload (LTL) carriers generally serve geographical regionsthat are more localized than the inter-city line-hauls served by truckload carriers. Thatlocalization can lead to urban freight transportation routes that overlap. If trucks aretraveling with less than full loads, there typically exist opportunities for carriers tocollaborate over such routes. We introduce a two stage framework for LTL carriercollaboration. Our first stage involves collaboration between multiple carriers at theentrance to the city and can be formulated as a vehicle routing problem with timewindows (VRPTW). We employ guided local search for solving this VRPTW. Thesecond stage involves collaboration between carriers at transshipment facilities whileexecuting their routes identified in phase one. For solving the second stage problem, wedevelop novel local search heuristics, one of which leverages integer programming toefficiently explore the union of neighborhoods defined by new problem-specific moveoperators. Our computational results indicate that integrating integer programmingwith local search results in at least an order of magnitude speed up in the second stageproblem. We also perform sensitivity analysis to assess the benefits from collaboration.Our results indicate that distance savings of 715 % can be achieved by collaboratingat the entrance to the city. Carriers involved in intra-city collaboration can further save315 % in total distance traveled, and also reduce their overall route times.</p><p>S. Nadarajah (B)Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh,PA 15213-3890, USAe-mail: firstname.lastname@example.org</p><p>J. H. BookbinderDepartment of Management Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canadae-mail: email@example.com</p><p>123</p></li><li><p>S. Nadarajah, J. H. Bookbinder</p><p>Keywords LTL collaboration Vehicle routing Constraint programming Integer programming Integrated methods</p><p>1 Introduction</p><p>Competitive pressures, economic volatility and increased service expectations haveforced companies to look outside their own operations for efficiency gains (Lynch2001). This has involved collaboration with potential competitors, and optimizingjoint operations to eliminate costs that cannot be individually controlled.</p><p>Collaboration is a strong relationship between multiple parties, with the goal ofa mutually beneficial outcome for all collaborating members. This paper focuseson operational collaboration (Sutherland 2006) between Less-Than-Truckload (LTL)transportation carriers. LTL carriers are concerned with delivery of small shipments(between 500 and 15,000 lbs on average) by truck to multiple consignees, ultimatelyover a limited geographical region. Figure 1 is a schematic showing the inter-cityline haul to the distribution network of the destination city and the subsequent localdeliveries. The distribution network includes distribution centers and vehicle depots,and is typically located away from the city center. Goods are received at this locationfrom their line haul journey and prepared for their local routes.</p><p>Shippers can also enter into operational collaboration, where they bundle lanesbefore their submission to a carrier. A lane is a contiguous portion of highway or road,considered by the carrier as a single link for routing purposes.</p><p>Carriers prefer bundled lanes, as they may lead to what are termed continuousmoves. Continuous Move Routes are ones in which we would ideally have zero dead-head miles and no asset-repositioning costs. The latter costs are incurred when atruck travels empty between two stops. This reduction in cost allows carriers to offermore competitive rates to the shipper, thereby providing an incentive for shippers tocollaborate.</p><p>The present paper deals with the specific problem of collaboration between LTLcarriers, whose loads are small in size and unpredictable. We consider the typical casewhere LTL carriers deliver loads within urban regions. In this setting, time windowsfor deliveries are usually tight. This leads to routes that are highly inefficient in termsof distance traveled, which our method aims to improve through collaboration.</p><p>We term the collaborative solution to the above stated problem as LTL col-laboration. LTL collaboration aims at designing efficient routes which minimize</p><p>Fig. 1 Line haul and localdelivery</p><p>123</p></li><li><p>Less-Than-Truckload carrier collaboration problem</p><p>asset-repositioning cost, total distance traveled, and maximizes truck asset utiliza-tion. Reduction in asset repositioning costs can lead to large savings for carriers, sincetrucks in the USA travel empty 20 % of the time on average (Wilson 2007). Localintra-city trucking costs were a staggering annual 435 billion United States dollars in2006 (Wilson 2007). The 2008 Canadian state of logistics report showed that truckingrepresented over 14 billion Canadian dollars, which was about 8 % of the Canadiangross domestic product (Industry Canada, 2008). These high costs also mean that smallimprovements through LTL collaboration will translate to large savings in real costsin both countries.</p><p>The main contributions of this paper are:</p><p>1. A two-stage framework for collaboration between LTL carriers. The first stageinvolves exchange of (partial) loads between carriers at the entry to the city, whiletrucks make such exchanges at transshipment points during local delivery in thesecond stage.</p><p>2. Novel heuristics that solve the mathematically complicated problem that resultsfrom the second stage of our collaborative framework. We develop a planningheuristic based on quadtree search to assist the decision maker in choosing trans-shipment points. For constructing collaborative routes using these transshipmentfacilities, we develop a local search heuristic based on new move operators andleverage integer programming to efficiently explore the union of the neighborhoodsdefined by these moves. Our computational tests indicate that our integration ofinteger programming with local search results in at least an order of magnitudespeed up when constructing collaborative routes.</p><p>3. Computational sensitivity analysis on a rectangular region with parameters cal-ibrated to the city of Toronto to assess the benefits to carriers from engaging incollaboration at the city entrance and in collaboration at transshipment points whileexecuting local routes. Our results indicate that collaboration at the city entranceresults in distance savings between 715 % over all carriers, and increases vehicleutilization by 45 %. Intra-city collaboration leads to route distance savings of315 % over collaborating carriers, and also reduces route times.</p><p>The remainder of this paper is organized as follows. We review the relevant liter-ature in Sect. 2. Definition of the carrier collaboration problem, and the hierarchicalframework for its study, are presented in Sect. 3. A high-level description of our solu-tion approach is given in Sect. 4; details of each heuristic are discussed in Sects.57. The evaluation procedure is described in Sect. 8, and computational results arepresented and discussed in Sect. 9. Concluding remarks and suggestions for futureresearch appear in Sect. 10.</p><p>2 Literature review</p><p>Carrier collaboration has received increased attention in recent years. A major part ofthis literature focuses on truckload collaboration; see Ergun et al. (2007), Liu et al.(2010), zener et al. (2011) and references therein. There are also several referencesthat concern the allocation of profits from carrier collaboration among partners using</p><p>123</p></li><li><p>S. Nadarajah, J. H. Bookbinder</p><p>game theoretic methods. Examples include Houghtalen et al. (2011) and zener et al.(2011).</p><p>Our emphasis in this article is rather on LTL collaboration, and Cruijssen andSalomon (2004), Krajewska and Kopfer (2006, 2009) are recent articles on this topic.Cruijssen and Salomon (2004) consider collaboration between multiple carriers, wherecollaboration involves the sharing of orders between transportation companies. Theyassess the benefit from collaboration by solving a capacitated vehicle routing problemfor each individual carrier (non-collaborative case) and compare this solution withthe solution from solving a single capacitated vehicle routing problem that combinesorders from all carriers (collaborative case). Krajewska and Kopfer (2006, 2009) alsostudy the problem of carriers sharing orders but they solve a pick up and deliveryproblem with time windows to determine when collaboration is beneficial. In additionto assessing the cost savings from collaboration, they use game theoretic mechanisms,namely core and Shapley value, to divide the savings among the coalition of carriers.A distinguishing feature of our work is the second stage of collaboration, collaborativerouting, which is absent in Cruijssen and Salomon (2004) and in Krajewska and Kopfer(2006, 2009). Thus we extend this literature in a significant manner, and our extensionprovides additional opportunities for LTL carriers to reduce travel distance and time.</p><p>The optimization problems that arise in our LTL collaboration framework are chal-lenging. Our first stage of collaboration at the entrance to the city involves solving avehicle routing problem with time windows (VRPTW). There is an extant literature onsolving VRPTW; see Brysy and Gendreau (2005a,b) for a review of exact methodsand metaheuristics for its solution. We employ guided local search (GLS) (Voudourisand Tsang 1998, 2002) for solving our VRPTW in a manner similar to De Backer et al.(2000), and do not attempt to find a new approach here. Rather, our methodologicalcontributions are for the second stage of collaboration, which involves identifyingtransshipment opportunities between local routes of LTL carriers.</p><p>Instead of assuming that we have a set of predetermined transshipment points, weprovide a planning module that uses a quadtree search heuristic (Finkel and Bentley1974) to identify good transshipment points based on a flexible objective functionbecause we expect this decision to involve considerable human input. For identify-ing transshipment opportunities using these locations, we develop an integrated localsearch heuristic that leverages integer programming. Previous applications of inte-grated methods to solve challenging problems include Focacci et al. (1999), Jain andGrossmann (2001) and van Hoeve (2003). A more comprehensive list can be found inYunes (2012).</p><p>3 Problem description</p><p>Truck transportation of goods involves a line-haul leg and local routing (Fig. 1). Theline-haul journey of a truck is between cities, often a single origin and destination,while local delivery entails a multi-drop route. Inbound loads transported by severalcarriers arrive at the boundary of the city after a line haul from the respective origins.</p><p>The local routes that each carrier had originally intended to operate within thecity are referred to as non-collaborative routes. However, the carriers will be able</p><p>123</p></li><li><p>Less-Than-Truckload carrier collaboration problem</p><p>(a) (b)Fig. 2 Entry-point collaboration example between two carriers at the NW entrance. (a) and (b) showroutes before and after entry-point collaboration, respectively. Customers A1 and A2 are transferred fromone carrier to the other</p><p>to improve them by collaboration: This is made possible by the existence of logis-tics platforms. A real world example is the Sigoris urban logistics zone in Marseille,France. These facilities are located away from the city center due to several advantagesthat include availability of land and less congestion from receiving and handling activ-ities. We assume logistics platforms to be located at each entrance to the city. In stageone of our two-stage framework, carriers can thus exchange goods after the inboundleg, before planning their intra-city routes (Fig. 2). Below, we call this entry-pointcollaboration.</p><p>The decision to exchange goods will depend on the distance savings attainable bybuilding routes over the expanded set of customer loads available at the entry point. Thesavings are determined by solving a VRPTW at each corner involving the customersof all collaborating carriers. In addition to distance reduction, any trucks saved can beused for a return line-haul trip or a line haul to another city.</p><p>Once entry-point collaboration is complete, each truck would start its local deliveryroute. Those routes overlap, due to the clustered nature of customers in an urban region.Hence an additional fine-tuning may be beneficial. Strategically chosen transshipmentfacilities can be used to further exchange loads between trucks originating at differententrances to the city (if appropriate). We refer to this as collaborative routing (Fig. 3),which is the second stage of collaboration. Local delivery thus includes the pre-plannedand coordinated pickups and drop-offs at transshipment facilities.</p><p>(a) (b)Fig. 3 Collaborative routing example between two carriers entering from the NW and SW corners.(a) and (b) show routes before and after collaborative routing, respectively. Customers A1, A2, B1 andB2 are transferred between carriers</p><p>123</p></li><li><p>S. Nadarajah, J. H. Bookbinder</p><p>Pre-planning is required since there is no real-time transfer of information betweentrucks. Coordination is crucial to avoid congestion or excessive truck wait times attransshipment points. To this end, we add the following restrictions (R1 and R2)to make transfers operationally feasible. Assume that vehicle V 1 transfers goods tovehicle V 2 at a transshipment point T .</p><p>R1 When V 2 arrives before V 1 at T, V 2 will wait a maximum of tw minutes. IfV 2 must wait longer than tw before V 1 arrives at T , the transfer is consideredoperationally infeasible.</p><p>R2 Suppose V 1 arrives at T earlier than V 2. Immediately upon arrival, V 1 will unloadthe goods to be transferred in a temporary storage area, and then depart. V 2 cancollect these goods when it arrives at T . To avoid congestion, we add a constraintthat goods cannot be stored at T for more than t s minutes.</p><p>Desired improvements in supply chain velocity, especially in urban areas, haveled to narrower time windows. A carriers total route distance then increases and itstrucks are more likely to incur longer waits. Collaborative routing between multiplecarriers provides additional opportunity to build efficient routes, and so may be highlybeneficial in reducing overall route distance and time.</p><p>4 Solution methodology</p><p>LTL collaboration, as proposed in this paper, requires solution of three sub-problems(P1P3). P1 concerns entry-point collaboration, while P2 and P3 relate to collaborativerouting:</p><p>P1 determines which loads are to be exchanged between trucks at an entry point tothe city, such that th...</p></li></ul>
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