A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs
Introduction
Today's competitive business environment has resulted in increasing cooperation among individual companies as members of a supply chain. The success of a company will depend on its ability to achieve effective integration of worldwide organizational relationships within a supply chain [1].
Beyond the current interest in supply chain management, recent attention has been given to extending the traditional forward supply chain to incorporate a reverse logistic element owing to liberalized return policies, environmental concern, and a growing emphasis on customer service and parts reuse. Implementation of reverse logistics especially in product returns would allow not only for savings in inventory carrying cost, transportation cost, and waste disposal cost due to returned products, but also for the improvement of customer loyalty and futures sales [2], [3], [4], [5]. Business processes in most companies are efficiently designed for forward flows only, the reason for this is that reverse logistics has been, often mistakenly, recognized as an unprofitable activity [6].
More specifically, one of the main difficulties associated with implementing reverse logistics activities is the degree of uncertainty in terms of the timing and quantity of products. Thus, managing return flow usually requires a specialized infrastructure and relatively high handling cost and time. For that reason, demand for reverse logistics services from third party logistics providers (3PLs) is increasing [7]. The market for 3PLs was estimated at more than $45 billion in 1999 and is growing by nearly 18 percent annually [8]. In addition, 74% of Fortune 500 companies used 3PLs’ services during 2000. These services involved transportation management, freight payment, warehouse management, shipment tracking, and reverse logistics. Virtually, all of the companies reported positive cost reduction results due to the avoidance of insurance and employee costs and material handling equipment and technology purchases [8].
To account for the integrated aspect of a supply chain, 3PLs such as UPS, FeDex, Genco, etc. thus are playing an increasing role in logistics elements. The main advantage of outsourcing services to 3PLs is that these 3PLs allow companies to get into a new business, a new market, or a reverse logistics program without interrupting forward flows; in addition, logistics costs can be greatly reduced. Some 3PLs offer complete supply chain solutions on warehousing, order fulfillment, and especially value-added services such as repackaging, re-labeling, assembly, light manufacturing, and repair. In addition, 3PLs have also become important players in reverse logistics since the implementation of return operations requires a specialized infrastructure needing special information systems for tracking/capturing data, dedicated equipment for the processing of returns, and specialist trained nonstandard manufacturing processes.
As such, this paper deals with warehousing and transportation operations since these are the key operations in a 3PL market. In such operations, clients expect 3PLs to improve lead times, fill rates, back-orders, inventory levels, and return processes, leading to reduced logistics costs in a global market. A prerequisite for meeting these requirements is that the 3PLs have a properly integrated logistics system for both forward and reverse flows, and must operate supply chains for a number of different clients. The requirements for individual clients as well as clients’ markets change over time. As a result of the dynamic environment in which these supply chains must operate, 3PLs must make a sequence of inter-related decisions over time. In order to make these decisions successfully, 3PLs are faced with several complicating factors. For example, a 3PL cannot forecast with much certainty who its clients will be, and hence the location of the clients’ manufacturing facilities or the clients’ markets, the volume of the products to be handled, or even the products themselves. A second complicating factor is the fact that trade-offs may have to be made among the quality measures related to service for the various clients. For example, improving service for one client may result in degradation of service for other clients. In fact, it is extremely difficult to take into account those factors simultaneously in a mathematical model.
To handle these problems, we thus employ a dynamic modeling approach. In this approach, a decision maker decides on an appropriate time interval such as monthly, quarterly, or yearly. In each time interval, the parameters are assumed to be deterministic. Accordingly, an appropriate time interval could depend on the particular industry. For this integrated network, instead of dealing with separate warehouse or collection centers, we also considered a type of a hybrid warehouse-repair facility. For example, United Parcel Service Logistic Group processes return activities through one of its warehouses in Louisville, Kentucky. An advantage of installing a hybrid facility might include savings as a result of sharing material handling equipment, infrastructure, and so on [9]. Fleischmann et al. [10] showed that network configurations involving both forward and reverse flows were different with respect to a sequential solution approach and an integrated solution approach. They found that an integrated approach, optimizing the forward and return network simultaneously, could provide a significant cost benefit against a sequential approach. Thus, an approach for the network design problem for 3PLs should be based on an integrated point of view.
With consideration of the factors noted above, this paper thus will present a mixed integer nonlinear programming model for the design of a dynamic integrated distribution network for 3PLs. In fact, this type of network design problem belongs to the class of NP-hard problems [11], so that a genetic algorithm-based heuristic will be presented in order to handle a realistically sized problem. Finally, we will apply the proposed model to an example problem and show the numerical results.
Section snippets
Literature review
Facility location decisions represent an important aspect of strategic planning for supply chain management. These decisions are instrumental in the construction of a distribution network and involve the determination of the sets of locations for facilities (e.g., warehouse, consolidation facilities, repair centers etc.), the capacities of the facilities, and the types of facilities. 3PLs’ logistics networks typically differ from the logistics networks owned by single company. The primary
Modeling a network for 3PLs
In this paper, the model for dynamic supply chain management by 3PLs belongs to a class of the multi-period, two-echelon, multi-commodity, capacitated location models. The main differences of this model as compared to existing location models lie in handing forward and reverse flows simultaneously. The network structure of this model is illustrated in Fig. 1. The network consists of the client's facilities, warehouses, repair facilities, and market places. In forward flow, manufacturers produce
Solution methodology
The decisions to be made in dynamic location problems involve the timing of facility installations on the network while considering various performance measures. However, obtaining optimal solutions for dynamic facility location problems in polynomial time is not possible since location problems belong to the class of NP-hard problems [11]. Furthermore, the proposed mathematical model in this paper deals with both forward and reverse flows simultaneously. Also, it includes nonlinear components
A base-line case
The GA described in the previous chapter has been applied to a base-line model for a 3PL. There were two clients, ten possible warehouses and repair centers, and a three-period planning horizon. The potential locations for market clusters, warehouses, repair centers, and plants of clients were generated from a uniform distribution with minimum and maximum distances of 0 and 150, respectively on the and coordinate system. Also, demands of the customer zones were assumed to be known and then
An equivalent linear model with experimentation
In order to assess the computational effectiveness of the GA, the original mathematical model was converted into a linear model through the use of dummy variables and additional constraints owing to the nonlinear components in the objective function. There are three nonlinear terms to be considered, dealing with the costs of opening warehouses, the costs of opening repair centers, and the costs of savings over the planning horizon. The transformed objective function is as follows:
Conclusions and future works
A growing number of companies have begun to realize the importance of implementing integrated supply chain management since they are under pressure for filling customers’ orders on time as well as for efficiently taking returned products back from customers after selling products. In terms of product flows, there are both forward flows and reverse flows in an integrated supply chain. 3PLs are playing an increasing role in supporting such integrated supply chain management using sophisticated
Acknowledgements
We acknowledge the helpful comments and suggestions of the editor and two anonymous referees.
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