Designing a Collaborative Network for Competing Shippers and Carriers

Transcription

1 Designing a Collaborative Network for Competing Shippers and Carriers Soumia Ichoua, Computer Science & Engineering Department, Johnson C. Smith University, 100, Beatties Ford Road, Charlotte, NC, sichoua@jcssu.edu CIRRELT, Université Laval, Québec, Canada, G1K 7P4 Abstract. Globalizations as well as the increased interest in Just-in-time distribution systems have led to larger complex and competitive freight transportation markets. In such hostile environment, shippers and carriers are turning towards freight transportation networks to increase their performance by sharing their resources and collaborating to lower their operating costs. A major issue to address in designing these networks is achieving a good trade-off between collaboration and competition since the transportation market is competitive by nature. In this paper, we propose a framework for distributed transportation networks where each shareholder has his private goals and has the flexibility to make collaborative or competitive decisions. These decisions are made through an optimizer that takes into account the interaction between local and global optimization. The optimizer also addresses the dynamic aspects that are inherent to the transportation market environment. Page 1 of 15

2 1- Introduction Globalizations as well as the increased interest in Just-in-time distribution systems have led to larger complex and competitive freight transportation markets. In such hostile environment, shippers and carriers are turning towards freight transportation networks to increase their performance by sharing their resources and collaborating to lower their operating costs. A freight transportation network is a web/information platform generally created by independent trucking companies that intent to increase their efficiency by optimizing their operation costs and resource utilization and by minimizing empty mileage through collaboration. In the trucking industry, empty mileage is a major source of inefficiency that occurs when a truck travels empty because a return shipment could not be scheduled resulting in a waste of energy, useless operating costs and unnecessary use of highways. According to National Statistics, 30% of all trucks on the roads are empty (Carmel 2007). On the other hand, the U.S. Environmental Protection Agency indicates that for a typical long-haul truck up to 15,000 miles each year may be non-revenue empty miles, consuming over 2,400 gallons of diesel fuel and producing 24 metric tons of carbon dioxide, the most prevalent greenhouse gas. Collaboration allows gathering a larger demand (set of loads) and a larger capacity (available drivers and trucks) which increases the number of possibilities to match loads to empty vehicles in order to minimize total empty miles. The emergence of freight transportation networks was possible because of the spectacular growth in information technology (e.g. exchange of electronic data and availability of real-time information at affordable costs). In the past few years, different type of groupings of transportation companies have emerged to help carriers facing the new challenges of the market (Châteauvert [6], Boyles 2000, and Normandeau 2003). However most of the networks that are actually implemented have the following shortcomings: Page 2 of 15

3 The decision-making is centralized which compromises the independence of the shareholder companies, The decision of adhesion to the network is not easily reversible since shareholders invest in the optimization base and reveal strategic information to the platform manager. A significant percent of the transportation requests are typically pre-assigned by contracts and therefore, the relevance to take part in such a network is questionable. These factors discourage many companies from joining freight transportation networks which may prevent these networks from having a significant impact on carriers performance since gathering a significant capacity and demand is essential to the functioning of transportation networks. To help overcoming these problems, some authors have proposed new frameworks and designs for managing collaboration in the freight transportation sector (Fisher et al. (1995, 1996, 1999), Burckert et al. (98, 2000a, 200b), Hoen and La Poutre (2004) and Figliozzi (2006). In this paper, we present an on-going work which presents a framework for distributed transportation networks where each shareholder has his private goals and has the flexibility to make collaborative or competitive decisions. Therefore, the proposed framework accurately takes into account the particularities and challenges of the freight transportation industry: Trucking industry is competitive by nature (carriers do not want to give the decision power to other hands. Moreover, a significant percent of the transport contracts are often pre-negotiated in advance). Collaboration helps increasing the efficiency by minimising empty miles through load exchanges and consequently improving resource utilization and lowering operating costs. On the other hand, it allows access to larger market (share market). Page 3 of 15

4 The design of the proposed framework has two major parts: The first part is the development of the system architecture. We addressed this part in a previous work (Ichoua 2008). The second part is the elaboration of the optimization base that controls the decision making process in a dynamic setting while taking into account the interaction between local and global optimization. This paper is mainly aimed at addressing this part. The rest of the paper is organized as follows. The second section describes the context of collaborative freight transportation networks. The third section briefly describes the distributed system architecture proposed in Ichoua The fourth section describes the problem considered in each company agent, briefly reviews the literature of related work to this problem and describes the algorithms and strategies used in optimization base module. Finally, the fifth section concludes and discusses future and on-going work. 2- Describing the Context We consider a society of freight transportation companies operating in a long-haul truckload trucking (TL) framework. In this context, the problem consists in dynamically assigning vehicles to loads which unfold randomly over time. Each load is characterized by its origin, its destination and its time windows and each vehicle must return to its associated depot before a given time. Hence, a demand may be rejected if it cannot be serviced within a reasonable time. Furthermore, at any time, a vehicle can be either empty or carrying a single load. Therefore, minimizing deadheads or empty miles is critical to lowering operating costs. To survive in a highly competitive market that has low profit margins, companies also need to focus on respecting customers time windows to maintain a good quality of service. This may be hard to achieve in a dynamic setting which is prone to unexpected events like accidents, vehicle breakdowns, traffic jams, etc Page 4 of 15

5 Clearly, collaboration will help these companies minimizing their empty miles, lowering their operating costs, improving their resource utilization and facing unexpected events through load exchanges. However, the trucking industry is competitive by nature since 80 to 90% of the transport contracts are often pre-negotiated in advance. Moreover, the net profit margin in this industry is around 2% and 3 % (Carmel 2007). Hence, a collaborative framework that would attract enough companies to reach the required critical mass of demand and resources must take into account the particularities and challenges of this industry. The key is to achieve a good trade-off between collaboration and competition. In a previous work, we proposed a distributed design for a freight transportation network composed of small or medium sized competing truckload trucking companies that collaborate through load and resource exchanges to improve their performance (Ichoua 2008). This design is briefly described in the next section. 3- A Distributed System Architecture In the proposed design, each company is represented by an agent controlled by the company. This agent manages the company internal network composed of transport contracts that were previously pre-negotiated directly with customers (we recall that this kind of loads represents 80% of the market demand). This agent also manages loads obtained through the exchanges that take place in the network. A coordinator agent collects sporadic shipping requests from erratic customers (we recall that this kind of loads represents 20% of the market demand) as well as shipping requests sent by the network participants. A company may send to the coordinator agent information about one of its vehicles which are traveling empty or about one of its shipping requests which cannot be satisfied because of an unexpected event (i.e., an accident, a vehicle breakdown, etc ) or because it induces a large additional empty mileage. The coordinator agent Page 5 of 15

6 applies a Contract Net Protocol procedure (Smith 1980) to assign the received loads to network participants that have reported an idle vehicle. Communications between the coordinator agent and the company agents is achieved through an internet connection. In the proposed framework, each agent includes the following modules: Communication module This module includes sensors that provide information about the environment (e.g. GPS-system which gives the current position of vehicles at any time, notification about the arrival of new service requests, etc ). Moreover, interactions between the company agents and the coordinator agent is handled by a Contract Net Protocol which specifies broadcasting and bidding for loads. Knowledge base module This module contains information about the transportation requests that are handled by the agent (e.g. time windows and locations), its resources (e.g. vehicles and drivers) and in the case of a company agent, data about its internal network (e.g. a map). A company agent has the following two additional modules: Reactive module This module is responsible for updating different parameters as time goes by (e.g. vehicle positions, travel times, vehicle status (idle or in service), etc ). It is also responsible for implementing the plans elaborated by the optimization base module (e.g. next customer to be served by an idle vehicle). Furthermore, the reactive module identifies loads that can no longer be serviced because of an unexpected change and notifies the optimization base module accordingly. In addition, it decides for which loads to bid based on some fast and simple on-line Page 6 of 15

7 strategies. The selected items are then transmitted to the optimization base module for further refinements. Optimization base module This module is responsible for dynamically dispatching available vehicles to incoming transportation requests while minimizing empty miles and satisfying time window constraints. It is also responsible for using the bidding mechanism implemented in the network to minimize its empty miles and to maximize the utilization of its resources. These goals are achieved through the use of on-line strategies and optimization procedures that are adapted to cope with time pressure which is inherent to a dynamic setting. In the following section, the problem addressed in the optimization base module of each company agent is first described. Then, some related papers found in the literature are reviewed. Finally, the general methodology adopted as well as the strategies and algorithms developed are described. 4- Developing the Optimization Base Module 4.1 Problem Description The problem considered in each company agent is a dynamic truckload pick-up and delivery problem. The static version of this problem can be stated as follows. A fleet of vehicles is used to satisfy a set of known transportation requests. Each transportation request is characterized by its pick-up location, its earliest pick-up time, its delivery location and its latest delivery time. A vehicle can arrive before the earliest pick-up time or after the latest delivery time. If the vehicle arrives too early, it must wait for the load to be ready. On the other hand, if the vehicle is too late, a penalty for lateness is incurred in the objective function. At any given time, a truck is either empty or carrying a single load. Each truck starts its route from a central depot and must Page 7 of 15

8 return to this depot before a maximum time limit. This constraint allows taking into account some restrictions related to drivers (e.g. maximum driving time before coming back home). The objective is to minimize a weighted sum of total empty mileage and total penalty of lateness over all customers. In the dynamic version of the problem, a number of transportation requests are not known in advance, but are rather received in real-time. Given a new service request at instant t, the goal is to assign this request to a particular truck and include it into its tour at minimum cost. The decision to accept or to reject the transportation request needs to be made within a short amount of time t since this decision is in general communicated to the customer while he is waiting on the phone. t is typically in the order of one or two minutes. At any instant t, a truck v is characterized by its status s v and is assigned a list L v of pending requests that have been accepted but not serviced yet. The status s v is either idle, empty or loaded. If s v = idle,. Otherwise, v is moving towards the pick-up location of load 1 when s v =empty whereas it is moving towards the delivery point of load 1 when s v =loaded. 4.2 Literature Review Existing literature related to the problem considered in each company agent includes the work of Powell et al. (1988, 1996, 2000a, 2000b) and Topaloglu and Powell (2006). The problem addressed in these papers is motivated from long-haul truckload trucking applications where the goal is to dynamically assign drivers to loads which arise randomly over time. The authors address the issue of relocating vehicles in anticipation of future demands. Regan et al. (1995, 1996a, 1996b, 1998) propose different local rules for assigning vehicles to loads in a dynamic setting. The authors also investigate the benefit of diverting a vehicle from its planned destination to service a new request that appears in its vicinity. Simulations conducted Page 8 of 15

9 under simplified conditions show the effectiveness of these local operations. These local strategies were later generalized in Yang et al. (1998) and compared to an exact re-optimization procedure which is applied over currently known events each time a new service request occurs. Yang et al. (2004) introduce a new optimization-based policy and compare it to both the simple local rules proposed in Regan et al. (1998) and the re-optimization procedure proposed in Yang et al. (1998). The new re-optimization policy is based on a repeated re-optimization of different instances of the static problem which is modeled using a mixed integer programming formulation. This mathematical model is modified to account for future job requests using probabilistic knowledge about job pick-up and delivery locations. Simulations conducted under different scenarios show the effectiveness of the new re-optimization policy. 4.3 Methodology An adaptation of the savings based heuristic proposed in Gronalt et al. (2003) is first performed to construct an initial solution. Then, a rolling time horizon approach is adopted where a static problem, as defined over known events is solved each time a new transportation request occurs. Basically, a fast insertion procedure is first used to include an incoming request in the solution. This solution is then re-optimized using an adaptation of the mixed integer programming formulation proposed in Yang et al. (2004), until the occurrence of the next new request. The insertion procedure is based on the same cost-savings calculation rule used in the initialization phase. To construct an initial solution, each load is first inserted in a separate tour. Then, loads are sequentially joined into tours starting with the feasible load combination corresponding to the largest cost-savings and proceeding until no more savings are possible. It is worth noticing that if the latest delivery time of a given request is far away from the current time, this request may be postponed to allow servicing requests that are more urgent. This is achieved by adding the term Page 9 of 15

10 α(t arv h-b h ) to the cost savings corresponding to the insertion of load h, where t arv h is the arrival time to the delivery location of load h, b h is its latest delivery time (t arv h b h ) and α is a parameter (0 α 1). On the other hand, to assure the availability of a feasible planned schedule at any time, the execution of the re-optimization procedure is accelerated by taking into account only the first M loads in each truck list of pending loads assigned to truck v (M is a fixed parameter of a small value). A dynamic setting To maintain the consistency of the planned schedule with the current environment state, the search is interrupted whenever a new event occurs. Then, it is restarted after completing the appropriate updates. We consider three types of new events: (i) When a local customer request occurs, it is included in the solution as previously described. If it could not be added to the solution because of the time constraints or because it induces a large extra empty distance, it is sent to the coordinator agent who initiates a bidding process and sends the best offer within a fixed amount of time t. A previously accepted transportation request which cannot be serviced because of an unexpected event is also handled with the same bidding process. (ii) When a vehicle has arrived to the pick-up point of its current load or has finished servicing its current customer, the vehicle status and the list of pending requests assigned to it are updated and the planned schedule is used to determine the vehicle next destination. (iii) When a load is received from the coordinator, the company agent computes a bid based on its cost estimate of using an available vehicle to service this load. This cost is calculated using the proposed insertion procedure to incorporate the received load into the tour of the available vehicle. If the coordinator agent accepts the offer, the load is actually assigned to this vehicle. A re- Page 10 of 15

11 optimization procedure is then executed, until the occurrence of the next new event, to improve the solution quality. The solution approaches outlined above raise several issues: To evaluate the inclusion of a new request received at instant t in the solution, some amount of time t is required. Since the environment is dynamic, a decision based on the situation at instant t when the evaluation was started, may not reflect the state of the system at (t+ t) when the decision becomes available. When computing the bid for a load received from the network coordinator, if the load pickup and delivery points correspond to the current location of the vehicle moving empty and its destination, respectively, the company agent may offer the service at a very cheap price and still have an acceptable profit margin. However, when the locations of the load and the available vehicle do not match perfectly, opportunities to make an acceptable profit may still be possible but need to be considered with caution. Two key questions must then be tackled carefully: o How far can a vehicle moving empty be diverted from its planned route in order to pick up a partner s load? o How long the vehicle driver can wait at the pickup location for the load to be ready? In the bidding mechanism, a network participant who does not intent to actually engage in the negotiation process may be tempted to send a false offer in order to gather information about the network. Thus, the bidding mechanism should include some policies that prevent this behavior without compromising the participants privacy. These issues are currently addressed in an-on going work. Page 11 of 15

12 5- Conclusion In this paper, we presented a framework for distributed transportation networks where each shareholder has his private goals and has the flexibility to make collaborative or competitive decisions. These decisions are made through an optimizer that takes into account the interaction between local and global optimization. The optimizer also addresses the dynamic aspects that are inherent to the transportation market environment. We proposed optimization algorithms and online strategies needed to run this optimizer. In elaborating these approaches, some important issues related to the dynamic nature of the problem arise (e.g. accuracy of the decisions made that must reflect the actual state of the environment). We are currently addressing these issues and developing strategies that will discourage any participant from sending a false bid in order to gather information about the network. Future work will be aimed at assessing the effectiveness of the optimization base module under different scenarios, on simulated data that are inspired from real-world applications. References [1] M.D. Boyle, Business-to-business marketplaces for freight transportation, MS. Thesis, Engineering System Division, Massachusetts Institute of Technology, USA, [2] H.J. Bürckert, K. Fischer, and G. Vierke, Teletruck: A holonic fleet management system, in Proc. 14th European Meeting on Cybernetics and Systems Research, 1998, vol. 2, pp [3] H.J. Burckert, K. Fisher, and G. Vierke, Holonic transport scheduling with TELETRUCK, Appl. Artif. Intell., vol. 14, no. 7, [4] H.J. Bürckert, P. Funk, and G. Vierke, An Intercompany Dispatch Support System for Intermodal Transport Chains, in Proc. Hawaii Int. Conf. on System Sciences (HICSS-33), Page 12 of 15

13 [5] E.Carmel, Trucking Industry, Kogod School of Business, American University, Washington D.C, [6] J.S. Chateauvert, La collaboration dans le transport en chargements complets, MBA. Thesis, FSA, Univ. Laval, Quebec, [7] M.A. Figliozzi, Analysis and evaluation of incentive compatible dynamic mechanisms for carrier collaboration, Transportation Research Record, vol. 1966, pp , [8] K. Fisher, K., B. Chaib-draa, B, J.P. Muller, M. Pischel, and C. Gerber, "A Simulation Approach based on Negotiation and Cooperation between Agents: A case Study", IEEE Trans. on Systems, Man, and Cybernetics, vol. 29, no. 4, pp , [9] K. Fisher, J.P. Muller, and M. Pischel, Cooperative transportation scheduling: An application domain for distributed AI, Appl. Artif. Intell., vol. 10, no. 2, [10]. K. Fischer, J.P. Muller, M. Pischel, and D. Schier, A Model for Cooperative Transportation Scheduling, in Proc. 1st Int. Conf. Multi-Agent Systems, San Francisco, CA, 1995, pp [11] M. Gronalt, R. F. Hartl and M. Remann, New Savings Based Algorithms for Time Contrained Pickup and Delivery of Full Truckloads, Ejor 151, pp , [12] P. J. 't Hoen and J. A. La Poutre, A decommitment strategy in a competitive multi-agent transportation setting, in Agent-Mediated Electronic Commerce V (AMEC-V), P. Faratin, D. Parkes, J. Rodriquez-Aguilar,Eds. Berlin, Germany: Springer-Verlag, pp , [13] S. Ichoua, Design of a Collaborative-Distributed Framework for the Long-Haul Truckload Trucking Industry, submitted to IEEE, [14] N. Neagu, K. Dorer, D. Greenwood, and M. Calisti, LS/ATN: Reporting on a Successful Agent-Based Solution for Transport Logistics Optimization, in Proc. IEEE Page 13 of 15

14 Workshop on Distributed intelligent Systems: Collective intelligence and Its Applications (Dis'06), vol. 00, Washington, DC, [15] G. Normandeau, Les nouvelles technologies de l information et des communications et la concentration des entreprises de transport routier des marchandises, Univ. Paris Dauphine, France, Tech. Rep. 24, [16] Powell, W.B. "A Comparative Review of Alternative Algorithms for the Dynamic Vehicle Allocation Problem," Vehicle Routing: Methods and Studies (B. Golden and A. Assad, eds.), North Holland, pp , [17] Powell, W.B. "A Stochastic Formulation of the Dynamic Assignment Problem, with an Application to Truckload Motor Carriers," Transportation Science. Vol. 30, No. 3, pp , [18] Powell, W.B., W. Snow and R. K.-M. Cheung, "Adaptive Labeling Algorithms for the Dynamic Assignment Problem," Transportation Science, Vol. 34, No. 1, pp , 2000a. [19] Powell, W.B., M.T. Towns and A. Marar, "On the Value of Globally Optimal Solutions for Dynamic Routing and Scheduling Problems," Transportation Science, Vol. 34, No. 1, pp , 2000b. [20] Regan, A C, Mahmassani, H S, Jaillet, P, Improving Efficiency of Commercial Vehicle Operations Using Real-Time Information: Potential Uses and Assignment Strategies, Transportation Research Record 1493, , [21] Regan, A C, Mahmassani, H S, Jaillet, P, Dynamic decision making for commercial fleet operations using real-time information, Transportation research record 1537, 91-97, 1996a. Page 14 of 15

15 [22] Regan, A C, Mahmassani, H S, Jaillet, P, Dynamic Dispatching Strategies under Real- Time Information for Carrier Fleet Management, in Lesort, J.B. (ed), Transportation and Traffic Theory Pergamon, , 1996b. [23] Regan, A C, Mahmassani, H S, Jaillet, P, Evaluation of Dynamic Fleet Management Systems: Simulation Framework, Transportation Research Record 1645, , [24] R. G. Smith. The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver, IEEE Trans. On Computers, series C-29, no. 12, pp , [25] Topaloglu, H. and W.B. Powell, Dynamic Programming Approximations for Stochastic, Time-Staged Integer Multicommodity Flow Problems, Informs Journal on Computing, Vol. 18, No. 1, pp , [26] Yang, J., P. Jaillet, and H. Mahmassani, On-line Algorithms for Truck Fleet Assignment and Scheduling under Real-time Information, Transportation Research Record, 1667, pp , 998. [27] Yang J., P. Jaillet and H. Mahmassani. "Real-Time Multi-Vehicle Truckload Pick-Up and Delivery Problem". Transportation Science, 38, pp , Page 15 of 15