Consolidation terminal location-allocation and consolidated routing problems

Journal of Business Logistics, 1996 by Min, Hokey

Due to the increased competition resulting from deregulation and globalization, today's logistics industry has become extremely vulnerable to any increase in distribution costs. To cope with these tremendous cost pressures, the logistics industry must drastically improve productivity (efficiency). As a Council of Logistics Management (CLM) survey1 reveals, one of the most promising ways of improving logistics productivity is to use freight consolidation.

In general terms, freight consolidation refers to a transportation option that combines a number of frequent, small shipments destined for a similar geographical region into a single large shipment in an effort to reduce per-unit shipping cost. It aims to capitalize on various freight-rate discount programs. Consolidation can be especially effective when large freight-rate differentials exist between small (less-than-truckload or LTL) and large (truckload or TL) shipments. This effectiveness depends to some degree upon the strategy employed. Since these strategies are varied and complex,2 the logistics manager should carefully choose a proper form of consolidation strategies relevant to given situation. Brennan's classification3 gives three basic consolidation strategies: (I) spatial, (2) product, and (3) temporal.

Spatial consolidation is primarily concerned with determining which customers and vehicle routes are to be aggregated to combine small shipments into a single large shipment. Product consolidation is concerned with combining different types of products in one shipment in order to increase the quantity sent to each customer per delivery. Temporal consolidation aggregates small orders across time to balance both good customer services and higher inventory costs against lowering shipping costs. Combinations of these strategies may maximize the benefit of consolidation. Unfortunately, problem complexity limits the ability to simultaneously consider all of these strategies. Throughout this paper, we focus our attention on spatial consolidation. Focus of the study on a single class of consolidation strategies is not uncommon in the logistics literature.4

Regardless of the specific setting, spatial consolidation should consider three specific components: (I) where consolidation terminals should be located, (2) which customers should be aggregated and then allocated to which consolidation terminals, and (3) in what sequence should customers to be served by consolidated vehicles. Simultaneous treatment of these components, however, will undoubtedly increase problem complexity, since both location-allocation and routing subproblems are "NP-hard" (inherently intractable) and therefore cannot be solved in reasonable time by any algorithm.5 On the other hand, separate treatment of them will undoubtedly lead to suboptimality. To compromise this dilemma, prior location/routing studies6 often used a sequential solution method that iteratively solves the respective location and routing subproblems and relates those subproblems. Similarly, we propose a multiphase decomposition heuristic procedure comprised of three phases: (1) aggregation of closely-located customers into "capacitated clusters," (2) location of capacitated consolidation terminals and allocation of capacitated supply sources and customers to their nearest open terminal, and (3) individual route configuration. This procedure allowed us to solve a practical-sized problem with 8 potential terminal sites, 10 supply sources, and 92 customers.

RELEVANT LITERATURE

For an excellent review of consolidation literature prior to 1980, interested readers should refer to Jackson? For the more recent literature review after 1980, the readers should refer to Min and Cooper.8 This paper will briefly review some selected past analytical studies that can be a basis of this study.

Such studies may include the pioneering work done by Powell and Sheffi,9 who initiated spatial consolidation research by mathematically solving the load planning problem of LTL motor carriers. The load planning problem, however, does not consider links between terminals and individual customers because it is primarily concerned with the links among terminals. After Hall10 laid a conceptual foundation for analyzing the consolidation tradeoffs between the benefit of lower transportation charges and the penalties of increased inventory costs, longer vehicle routes, and added terminal operating and ownership costs, Hall11 developed simple optimization procedures to find the minimum-cost network flow from many origins (supply sources) to many destinations (customers) rather than simply considering terminal-to-terminal links. Daganzo12 developed a matching algorithm that provided guidance for: (1) determining which items from each origin should be combined together to form each consolidated load, (2) the routing of each of these consolidated shipments, and (3) the composition of shipments from the terminal to the destination.

The aforementioned studies did not consider tours connecting supply sources and/or customers because their main concern was with straight-line links between origins and destinations. In contrast, Pooley13 utilized the modified savings algorithm for analyzing carrier selection between less-than-truckload (LTL) and multiple-stop truckload (TL) carriers. In addition, the algorithm provided a conceptual basis for evaluating the option of consolidated versus non-consolidated routings. Despite these merits, the pure routing studies reviewed above assume that the location of consolidation terminals is known and fixed, thereby ignoring the cost of locating these terminals.