Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Related Work
Multi-Objective Vehicle Routing Problems
Discussion
Multi-Objective And/Or Search
Our Multi-Objective Variant of And/Or Search
Multi-Objective And/Or Search Applied to Our Case
The LI & Lim Data Set
Real-World Test Cases
Results
Analysis of Results
Limitations of Our Approach
Conclusions and Future Work
References
All Topics
Mathematics
Applied Mathematics

Multi-objective vehicle routing with automated negotiation

Profile image of Dave de JongeDave de Jonge

Applied Intelligence

https://doi.org/10.1007/S10489-022-03329-2
visibility

…

description

24 pages

link

1 file

Abstract

This paper investigates a problem that lies at the intersection of three research areas, namely automated negotiation, vehicle routing, and multi-objective optimization. Specifically, it investigates the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads for one another, in order to improve efficiency and reduce the distance they drive. In order to do so, these companies need to find ways to exchange their truck loads such that each of them individually benefits. We present a new heuristic algorithm that, given one set of orders for each company, tries to find the set of all truck load exchanges that are Pareto-optimal and individually rational. Unlike existing approaches, it does this without relying on any kind of trusted central server, so the companies do not need to disclose their private cost models to anyone. The idea is that the companies can then use automated negotiation techniques to negotiate which of these truck load exchange...

Related papers

Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and ’grey zone’ customers arising in urban logistics
Theo Crainic

European Journal of Operational Research

Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and "grey Zone" customers arising in urban logistics Article (Published) (Refereed)

View PDFchevron_right
Multi-Objective Vehicle Routing Problem Applied to Large Scale Post Office Deliveries
Luis Meira

ArXiv, 2018

The number of optimization techniques in the combinatorial domain is large and diversified. Nevertheless, real-world based benchmarks for testing algorithms are few. This work creates an extensible real-world mail delivery benchmark to the Vehicle Routing Problem (VRP) in a planar graph embedded in the 2D Euclidean space. Such problem is multi-objective on a roadmap with up to 25 vehicles and 30,000 deliveries per day. Each instance models one generic day of mail delivery, allowing both comparison and validation of optimization algorithms for routing problems. The benchmark may be extended to model other scenarios.

View PDFchevron_right
Multi-objective vehicle routing and loading with time window constraints: a real-life application
Tim Pigden

Annals of Operations Research, 2019

Motivated by a real-life application, this research considers the multi-objective vehicle routing and loading problem with time window constraints which is a variant of the Capacitated Vehicle Routing Problem with Time Windows with one/two-dimensional loading constraints. The problem consists of routing a number of vehicles to serve a set of customers and determining the best way of loading the goods ordered by the customers onto the vehicles used for transportation. The three objectives pertaining to minimisation of total travel distance, number of routes to use and total number of mixed orders in the same pallet are, more often than not, conflicting. To achieve a solution with no preferential information known in advance from the decision maker, the problem is formulated as a Mixed Integer Linear Programming (MILP) model with one objective-minimising the total cost, where the three original objectives are incorporated as parts of the total cost function. A Generalised Variable Neighbourhood Search (GVNS) algorithm is designed as the search engine to relieve the computational burden inherent to the application of the MILP model. To evaluate the effectiveness of the GVNS algorithm, a real instance case study is generated and solved by both the GVNS algorithm and the software provided by our industrial partner. The results show that the suggested approach provides solutions with better overall values than those found by the software provided by our industrial partner.

View PDFchevron_right
A Solution for Multi-objective Commodity Vehicle Routing Problem by NSGA-II
Ali Asghar Rahmani Hosseinabadi

Vehicle routing is considered the basic issue in distribution management. In real-world problems, customer demand for some commodities increases on special situations. On the one hand, one of the factors that are very important for customers is the timely delivery of the demanded commodities. In this research, customers had several different kinds of demands. Therefore, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel cost and maximizing demand coverage. Moreover, two approaches were designed for the problem-solving model based on the NSGA-II algorithm with diversification of the mutation operator structure. The two criteria of spread and coverage of non-dominated solutions were used to compare algorithms. Study of some typical created problems indicated the validity of the model and the computational efficiency of the proposed algorithm. The proposed algorithm could increase the criterion of solution spread by about 10%, and increased the number of obtained solutions on the Pareto border compared to other algorithms, which indicated its high efficiency.

View PDFchevron_right
A multi-agent platform for auction-based allocation of loads in transportation logistics
han noot

Expert Systems with Applications, 2011

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i.e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. We exemplify how the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings.

View PDFchevron_right
A Method for Transportation Planning and Profit Sharing in Collaborative Multi-Carrier Vehicle Routing
Qiaomei Han

Mathematics, 2020

This paper addresses a collaborative multi-carrier vehicle routing problem (CMCVRP) where carriers tackle their orders collaboratively to reduce transportation costs. First, a hierarchical heuristics algorithm is proposed to solve the transportation planning problem. This algorithm makes order assignments based on two distance rules and solves the vehicle routing problem with a hybrid genetic algorithm. Second, the profit arising from the coalition is quantified, and an improved Shapley value method is proposed to distribute the profit fairly to individual players. Extensive experiment results showed the effectiveness of the proposed hierarchical heuristics algorithm and confirmed the stability and fairness of the improved Shapley value method.

View PDFchevron_right
Automated Negotiation for Package Delivery
Dave de Jonge

2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems Workshops, 2012

ABSTRACT Package delivery companies compete with each other and have costumers spread over wide areas. We propose a negotiation algorithm that allows companies and individual postmen to negotiate over who delivers what package. This way, package delivery can be made more efficient, yielding a higher profit and/or lower costs for all parties. Our system does not force competing companies to cooperate, but proposes solutions that allow all parties to increase their individual profit.

View PDFchevron_right
Collaborative vehicle routing: A survey
Richard F Hartl

European Journal of Operational Research

In horizontal collaborations, carriers form coalitions in order to perform parts of their logistics operations jointly. By exchanging transportation requests among each other, they can operate more efficiently and in a more sustainable way. Collaborative vehicle routing has been extensively discussed in the literature. We identify three major streams of research: (i) centralized collaborative planning, (ii) decentralized planning without auctions, and (ii) auction-based decentralized planning. For each of them we give a structured overview on the state of knowledge and discuss future research directions.

View PDFchevron_right
A multi-objective transportation routing problem
STEFANOS KATSAVOUNIS

Operational Research, 2015

This paper presents a variant of vehicle routing problem, incorporating factors of transportation costs in conjunction with adverse situations. The effects of hazardous materials (HazMat) transportation are examined on airpollution exposure levels, as well as the risk and damage in the case of accidents. In an urban network, the various weights that can be are assigned to each road of a route are associated to the following factors: time, cost, pollution exposure levels, and the risk of damage in HazMat transportation. These weights express the travel cost and the estimated provoked adverse consequences at each road of a vehicle transit route between two disparate points of the network concerning the pollution and risk of unforeseen accident during hazardous materials transportation. The sum of the weights assigned to every road of a route represents the route value according to the corresponding factors. We introduce an optimization criterion in order to select the optimal route among all vehicle transition routes joining any given pair of the network points that does not surpass a predefined threshold route value for each factor. The predefined weights for each factor are a percentage increment of its corresponding shortest route value. The optimal location of a central depot is determined where each vehicle serves a customer using one specific route with respect to the posed restrictions. Subsequently, a suitable partition into subnetworks is carried out in the case of large urban areas and a depot is assigned to each sub-network by applying a specific optimization criterion. A branch and

View PDFchevron_right
Comex: Combinatorial Auctions for the Intra-Enterprise Exchange of Logistics Services
Michael Schwind

Proceedings of the Ninth International Conference on Enterprise Information Systems, 2007

The exchange of cargo capacities is an approach that is well established in the practice of logistics. Few of these mostly web-based market places, however, are able to take synergies into consideration that can be generated by the appropriate combination of the transportation lanes of different carriers. One way to achieve this is to employ combinatorial auctions, that allow one to bid on bundles of lanes. This article describes a combinatorial auction for the intra-enterprise exchange of logistic services. In the real world case considered here, we implement and analyze such an exchange process in an enterprise that is related to the food sector and organized in a profit center structure. In the intra-enterprise exchange process, each profit center is able to release delivery contracts for outsourcing if the geographic location of a customer allows a reduced-cost delivery by another profit center in the neighborhood. The cost calculation is based on the results of an integrated routing system, and the in and outsourcing process is managed by using the auction mechanism ComEx. For the purpose of customer retention the delivery contracts are kept by the corresponding profit center, the incentive for exchanging the customers is achieved by a cost-savings distribution mechanism. After a description of the web-based logistics auction together with the route optimization system DynaRoute, the article describes the search for a cost optimizing strategy that bundles the appropriate delivery contracts.

View PDFchevron_right

References (44)

  1. Ferrell W, Ellis K, Kaminsky P, Rainwater C (2020) Horizontal collaboration: opportunities for improved logistics planning. Int J Prod Res 58(14):4267-4284. https://doi.org/10.1080/00207543. 2019.1651457
  2. de Jonge D, Bistaffa F, Levy J (2021) A heuristic algorithm for multi-agent vehicle routing with automated negotiation. In: Proceedings of the 20th international conference on autonomous agents and multiagent systems (AAMAS 2021). International Foundation for Autonomous Agents and Multiagent Systems
  3. Dantzig GB, Ramser JH (1959) The truck dispatching problem. Manag Sci 6(1):80-91
  4. Clarke G, Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12(4):568-581
  5. Desrochers M, Lenstra JK, Savelsbergh MWP (1990) A classifi- cation scheme for vehicle routing and scheduling problems. Eur J Oper Res 46(3):322-332
  6. Toth P, Vigo D (2002) The vehicle routing problem, SIAM monographs on discrete mathematics and applications, vol 9. SIAM. https://doi.org/10.1137/1.9780898718515
  7. Braekers K, Ramaekers K, Van Nieuwenhuyse I (2016) The vehicle routing problem: state of the art classification and review. Comput Ind Eng 99:300-313. https://doi.org/10.1016/j.cie.2015. 12.007
  8. Uchoa E, Pecin D, Pessoa A, Poggi M, Vidal T, Subramanian A (2017) New benchmark instances for the capacitated vehicle routing problem. Eur J Oper Res 257(3):845-858. https://doi.org/ 10.1016/j.ejor.2016.08.012, https://www.sciencedirect.com/ science/article/pii/S0377221716306270
  9. Savelsbergh MWP, Sol M (1995) The general pickup and delivery problem. Transp Sci 29(1):17-29
  10. Dumas Y, Desrosiers J, Soumis F (1991) The pickup and delivery problem with time windows. Eur J Oper Res 54(1):7-22
  11. Dixit A, Mishra A, Shukla A (2019) Vehicle routing problem with time windows using meta-heuristic algorithms: a survey. In: Yadav N, Yadav A, Bansal JC, Deep K, Kim JH (eds) harmony search and nature inspired optimization algorithms. Springer Singapore, Singapore, pp 539-546
  12. Gansterer M, Hartl RF (2018) Collaborative vehicle routing: a survey. Eur J Oper Res 268(1):1-12
  13. Wang X, Kopfer H (2014) Collaborative transportation planning of less-than-truckload freight. OR Spectrum 36(2):357-380
  14. Wang X, Kopfer H (2015) Rolling horizon planning for a dynamic collaborative routing problem with full-truckload pickup and delivery requests. Flex Serv Manuf J 27(4):509-533
  15. Wang X, Kopfer H, Gendreau M (2014) Operational transportation planning of freight forwarding companies in horizontal coalitions. Eur J Oper Res 237(3):1133-1141
  16. Dahl S, Derigs U (2011) Cooperative planning in express carrier networks -an empirical study on the effectiveness of a real-time decision support system. Decis Support Syst 51(3): 620-626. https://doi.org/10.1016/j.dss.2011.02.018, http://www. sciencedirect.com/science/article/pii/S0167923611000947
  17. Jozefowiez N, Semet F, Talbi E-G (2008) Multi-objective vehicle routing problems. Eur J Oper Res 189(2):293-309
  18. Ombuki BM, Ross B, Hanshar F (2006) Multi-objective genetic algorithms for vehicle routing problem with time windows. Appl Intell 24(1):17-30. https://doi.org/10.1007/s10489-006-6926-z
  19. van der Putten S, Robu V, La Poutré H, Jorritsma A, Gal M (2006) Automating supply chain negotiations using autonomous agents: A case study in transportation logistics. In: Proceedings of the fifth international joint conference on autonomous agents and multiagent systems, AAMAS '06. ACM, New York, pp 1506- 1513. https://doi.org/10.1145/1160633.1160926
  20. Robu V, Noot H, La Poutré H, van Schijndel W-J (April 2011) A multi-agent platform for auction-based allocation of loads in transportation logistics. Expert Syst Appl 38(4):3483-3491. https://doi.org/10.1016/j.eswa.2010.08.136
  21. de Jonge D, Sierra C (2012) Automated negotiation for package delivery. In: Self-Adaptive and Self-Organizing Systems Workshops (SASOW), 2012 IEEE sixth international conference on, pp 83-88
  22. de Jonge D, Sierra C (2015) NB3: a multilateral negoti- ation algorithm for large, non-linear agreement spaces with limited time. Auton Agent Multi-Agent Syst 29(5):896-942. https://doi.org/10.1007/s10458-014-9271-3
  23. Faratin P, Sierra C, Jennings NR (1998) Negotiation decision functions for autonomous agents. Robot Auton Syst 24(3-4):159- 182. https://doi.org/10.1016/S0921-8890(98)00029-3, http:// www.sciencedirect.com/science/article/pii/S0921889098000293, Multi-Agent Rationality
  24. Rosenschein JS, Zlotkin G (1994) Rules of encounter. The MIT Press, Cambridge
  25. Baarslag T, Hindriks KV, Jonker CM, Kraus S, Lin R (2012) The first automated negotiating agents competition (ANAC 2010). In: new trends in agent-based complex automated negotiations, studies in computational intelligence, vol 383. Springer, pp 113- 135. https://doi.org/10.1007/978-3-642-24696-8 7
  26. Fujita K, Aydogan R, Baarslag T, Ito T, Jonker CM (2014) The fifth automated negotiating agents competition (ANAC 2014). In: Recent advances in agent-based complex automated negotiation [revised and extended papers from the 7th international work- shop on Agent-based Complex Automated Negotiation, ACAN 2014, Paris, France, May 2014], studies in computational intel- ligence, vol 638. Springer, pp 211-224. https://doi.org/10.1007/ 978-3-319-30307-9 13
  27. Fujita K, Aydogan R, Baarslag T, Hindriks K, Ito T, Jonker C (2017) The sixth automated negotiating agents competition (anac 2015). In: Modern approaches to agent-based complex automated negotiation. Springer, pp 139-151
  28. Aydogan R, Baarslag T, Fujita K, Mell J, Gratch J, de Jonge D, Mohammad Y, Nakadai S, Morinaga S, Osawa H, Aranha C, Jonker CM (2020) Challenges and main results of the automated negotiating agents competition (anac) 2019. In: Bassiliades N, Chalkiadakis G, de Jonge D (eds) multi-agent systems and agreement technologies. Springer International Publishing, Cham, pp 366-381
  29. Mell J, Gratch J, Baarslag T, Aydogan R, Jonker CM (2018) Results of the first annual human-agent league of the automated negotiating agents competition. In: Proceedings of the 18th international conference on Intelligent Virtual Agents, IVA 2018, Sydney, NSW, Australia, November 05-08, 2018. ACM, pp 23- 28. https://doi.org/10.1145/3267851.3267907
  30. de Jonge D, Baarslag T, Aydogan R, Jonker C, Fujita K, Ito T (2019) The challenge of negotiation in the game of diplomacy. In: Lujak M (ed) agreement technologies, 6th international conference, AT 2018, Bergen, Norway, December 6-7, 2018, revised selected papers, lecture notes in computer science, vol 11327. Springer International Publishing, Cham, pp 100-114
  31. Baarslag T, Aydogan R, Hindriks KV, Fuijita K, Ito T, Jonker CM (2015) The automated negotiating agents competition, 2010- 2015. AI Mag 36(4):115-118. http://www.aaai.org/ojs/index.php/ aimagazine/article/view/2609
  32. de Jonge D, Zhang D (2020) Strategic negotiations for extensive-form games. Auton Agent Multi-Agent Syst 34(1). https://doi.org/10.1007/s10458-019-09424-y
  33. de Jonge D, Sierra C (2017) D-Brane: a diplomacy playing agent for automated negotiations research. Appl Intell 47(1):158-177. https://doi.org/10.1007/s10489-017-0919-y
  34. Ito T, Klein M, Hattori H (2008) A multi-issue negotiation protocol among agents with nonlinear utility functions. Multiagent Grid Syst 4:67-83. http://dl.acm.org/citation.cfm?id=1378675. 1378678
  35. de Jonge D, Sierra C (2016) GANGSTER: an automated negotiator applying genetic algorithms. In: Fukuta N, Ito T, Zhang M, Fujita K, Robu V (eds) recent advances in agent-based complex automated negotiation, studies in computational intelligence. Springer International Publishing, pp 225-234. http://www.iiia. csic.es/davedejonge/homepage/files/articles/Gangster.pdf
  36. Li H, Lim A (2003) A metaheuristic for the pickup and delivery problem with time windows. Int J Artif Intell Tools 12(02):173- 186
  37. Perron L, Furnon V (2019) Google or-tools v7.4. https:// developers.google.com/optimization/
  38. Marinescu R, Dechter R (2009) AND/OR branch-and-bound search for combinatorial optimization in graphical mod- els. Artif Intell 173(16-17):1457-1491. https://doi.org/10.1016/j. artint.2009.07.003
  39. Liu Q, Li X, Liu H, Guo Z (2020) Multi-objective meta- heuristics for discrete optimization problems: a review of the state-of-the-art. Appl Soft Comput 93:106382. https://doi.org/10. 1016/j.asoc.2020.106382, https://www.sciencedirect.com/ science/article/pii/S1568494620303227
  40. Lin R, Kraus S, Baarslag T, Tykhonov D, Hindriks K, Jonker CM (2014) Genius: An integrated environment for supporting the design of generic automated negotiators. Comput Intell 30(1):48- 70. https://doi.org/10.1111/j.1467-8640.2012.00463.x
  41. Aydogan R, Fujita K, Baarslag T, Jonker CM, Ito T (2019) ANAC 2018: Repeated multilateral negotiation league. In: Ohsawa Y, Yada K, Ito T, Takama Y, Sato-Shimokawara E, Abe A, Mori J, Matsumura N (eds) advances in artificial intelligence - selected papers from the annual conference of Japanese Society of Artificial Intelligence (JSAI 2019), Niigata, Japan, 4-7 June 2019, Advances in Intelligent Systems and Computing, vol 1128. Springer, pp 77-89. https://doi.org/10.1007/978-3-030-39878-1 8
  42. Palhazi Cuervo D, Vanovermeire C, Sörensen K (2016) Deter- mining collaborative profits in coalitions formed by two partners with varying characteristics. Transp Res Part C: Emerging Tech- nol 70:171-184. https://doi.org/10.1016/j.trc.2015.12.011, https:// www.sciencedirect.com/science/article/pii/S0968090X15004271
  43. Farinelli A, Bicego M, Bistaffa F, Ramchurn SD (2017) A hierarchical clustering approach to large-scale near-optimal coalition formation with quality guarantees. Eng Appl Artif Intell 59:170-185. https://doi.org/10.1016/j.engappai.2016.12.018
  44. Baarslag T, Hindriks K, Hendrikx M, Dirkzwager A, Jonker C (2014) Decoupling negotiating agents to explore the space of negotiation strategies. In: Marsa-Maestre I, Lopez-Carmona MA, Ito T, Zhang M, Bai Q, Fujita K (eds) Novel Insights in Agent- based Complex Automated Negotiation. Springer Japan, Tokyo, pp 61-83

Related papers

A Heuristic Algorithm for Multi-Agent Vehicle Routing with Automated Negotiation
Dave de Jonge, Jordi Levy

AAMAS, 2021

We investigate a problem that lies at the intersection of three research areas, namely Automated Negotiation, Vehicle Routing, and Multi-Objective Optimization. Specifically, we investigate the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads for one another, in order to improve efficiency and reduce the distance they drive. In order to do so, these companies need to find ways to exchange their truck loads such that each of them individually benefits. We present a new heuristic algorithm that, given one set of orders to deliver for each company, tries to find the set of all order-exchanges that are Pareto-optimal and individually rational. Furthermore, we present experiments based on real-world test data from two major logistics companies, which show that our algorithm is able to find hundreds of solutions in a matter of minutes.

View PDFchevron_right
Collaborative transportation planning of less-than-truckload freight : A route-based request exchange mechanism (Regular Article)
Herbert Kopfer

Operations Research Spektrum, 2014

Collaborative transportation planning (CTP) within a coalition of small and mid-sized freight carriers can be used as a powerful instrument to improve the operational efficiency of the coalition members. In such coalitions, transportation requests acquired from different freight carriers are exchanged among coalition members in order to reduce the total fulfillment costs. We consider the CTP for a set of independent freight carriers exchanging less-than-truckload (LTL) transportation requests and we include the realistic restriction that all collaborating partners have only limited capacities in their fleets. In order to remain their autonomy, participants in such coalitions keep their sensitive information including customer payments and cost structures unexposed during CTP. A route-based combinatorial auction inspired by column generation is proposed as request exchange mechanism for the CTP. It is tested on some newly generated instances with LTL pickup and delivery requests with time window restrictions. The CTP solutions are compared with those obtained by isolated planning without collaboration and by centralized planning. The results indicate that our mechanism is very efficient and effective in terms of realizing potential cost-savings by CTP, even when capacity limitations and restrictions on the exposure of information are explicitly considered.

View PDFchevron_right
From Single-Objective to Multi-Objective Vehicle Routing Problems: Motivations, Case Studies, and Methods
Frédéric Semet

Operations Research/Computer Science Interfaces, 2008

Multi-objective optimization knows a fast growing interest for both academic researches and real-life problems. An important domain is the one of vehicle routing problems. In this chapter, we present the possible motivations for applying multi-objective optimization on vehicle routing problems and the potential uses and benefits of doing so. To illustrate this fact, we also describe two problems, namely the vehicle routing problem with route balancing and the bi-objective covering tour problem. We also propose a two-phased approach based on the combination of a multi-objective evolutionary algorithm and single-objective techniques that respectively provide diversification and intensification for the search in the objective space. Examples of implementation of this method are provided on the two problems.

View PDFchevron_right
From Single-Objective to Multi-Objective Vehicle Routing: Problems: Motivations, Case Studies, and Method
Frédéric Semet

HAL (Le Centre pour la Communication Scientifique Directe), 2008

Multi-objective optimization knows a fast growing interest for both academic researches and real-life problems. An important domain is the one of vehicle routing problems. In this chapter, we present the possible motivations for applying multi-objective optimization on vehicle routing problems and the potential uses and benefits of doing so. To illustrate this fact, we also describe two problems, namely the vehicle routing problem with route balancing and the bi-objective covering tour problem. We also propose a two-phased approach based on the combination of a multi-objective evolutionary algorithm and single-objective techniques that respectively provide diversification and intensification for the search in the objective space. Examples of implementation of this method are provided on the two problems.

View PDFchevron_right
Collaborative transportation planning of less-than-truckload freight
Herbert Kopfer

OR Spectrum, 2014

Collaborative transportation planning (CTP) within a coalition of small and mid-sized freight carriers can be used as a powerful instrument to improve the operational efficiency of the coalition members. In such coalitions, transportation requests acquired from different freight carriers are exchanged among coalition members in order to reduce the total fulfillment costs. We consider the CTP for a set of independent freight carriers exchanging less-than-truckload (LTL) transportation requests and we include the realistic restriction that all collaborating partners have only limited capacities in their fleets. In order to remain their autonomy, participants in such coalitions keep their sensitive information including customer payments and cost structures unexposed during CTP. A route-based combinatorial auction inspired by column generation is proposed as request exchange mechanism for the CTP. It is tested on some newly generated instances with LTL pickup and delivery requests with time window restrictions. The CTP solutions are compared with those obtained by isolated planning without collaboration and by centralized planning. The results indicate that our mechanism is very efficient and effective in terms of realizing potential cost-savings by CTP, even when capacity limitations and restrictions on the exposure of information are explicitly considered.

View PDFchevron_right

Related topics

  • Computer Science
  • Vehicle Routing Problem
  • Applied artificial intelligence
  • Pareto principle

    • Cited by

    A new bargaining solution for finite offer spaces
    Dave de Jonge

    Applied Intelligence

    The bargaining problem deals with the question of how far a negotiating agent should concede to its opponent. Classical solutions to this problem, such as the Nash bargaining solution (NBS), are based on the assumption that the set of possible negotiation outcomes forms a continuous space. Recently, however, we proposed a new solution to this problem for scenarios with finite offer spaces de Jonge and Zhang (Auton Agents Multi-Agent Syst 34(1):1–41, 2020). Our idea was to model the bargaining problem as a normal-form game, which we called the concession game, and then pick one of its Nash equilibria as the solution. Unfortunately, however, this game in general has multiple Nash equilibria and it was not clear which of them should be picked. In this paper we fill this gap by defining a new solution to the general problem of how to choose between multiple Nash equilibria, for arbitrary 2-player normal-form games. This solution is based on the assumption that the agent will play either...

    View PDFchevron_right
    580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved