A Pareto-metaheuristic for a bi-objective winner determination problem in a combinatorial reverse auction

The bi-objective winner determination problem (2WDP-SC) of a combinatorial procurement auction for transport contracts comes up to a multi-criteria set covering problem. We are given a set B of bundle bids. A bundle bid b in B consists of a bidding carrier c_b, a bid price p_b, and a set tau_b of transport contracts which is a subset of the set T of tendered transport contracts. Additionally, the transport quality q_t,c_b is given which is expected to be realized when a transport contract t is executed by a carrier c_b. The task of the auctioneer is to find a set X of winning bids (X is subset of B), such that each transport contract is part of at least one winning bid, the total procurement costs are minimized, and the total transport quality is maximized. This article presents a metaheuristic approach for the 2WDP-SC which integrates the greedy randomized adaptive search procedure, large neighborhood search, and self-adaptive parameter setting in order to find a competitive set of non-dominated solutions. The procedure outperforms existing heuristics. Computational experiments performed on a set of benchmark instances show that, for small instances, the presented procedure is the sole approach that succeeds to find all Pareto-optimal solutions. For each of the large benchmark instances, according to common multi-criteria quality indicators of the literature, it attains new best-known solution sets.Pareto optimization; multi-criteria winner determination; combinatorial auction; GRASP; LNS

RULES FOR THE IDENTIFICATION OF PORTFOLIO-INCOMPATIBLE REQUESTS IN DYNAMIC VEHICLE ROUTING

In this article, we propose and evaluate simple rules for selecting transport requests that do not fit into a request portfolio because their temporal or spatial requirements are incompatible with the requirements of other requests so that the compilation of profitable routes is compromised. We integrate these rules into an adaptive online vehicle operations planning system and analyze in numerical simulation experiments how their application has impacts on the flexibility, the stability and the profitability of the controlled transportation system and the integration of consecutively arriving requests

Distributed Decision Making in Combined Vehicle Routing and Break Scheduling

The problem of combined vehicle routing and break scheduling comprises three subproblems: clustering of customer requests, routing of vehicles, and break scheduling. In practice, these subproblems are usually solved in the interaction between planners and drivers. We consider the case that the planner performs the clustering and the drivers perform the routing and break scheduling. To analyze this problem, we embed it into the framework of distributed decision making proposed by Schneeweiss (2003). We investigate two different degrees of anticipation of the drivers’ planning behaviour using computational experiments. The results indicate that in this application a more precise anticipation function results in better objective values for both the planner and the drivers

Vehicle Routing under Consideration of Transhipment in Horizontal Coalitions of Freight Carriers

AbstractIn order to reduce operational costs related to transportation activities in road haulage, small and medium-sized freight carriers can establish horizontal coalitions and share their resources. Through exchange of customer requests with other members within the coalition, carriers can improve the operational efficiency of their transportation processes. In this paper, transhipment is integrated into the conventional pickup and delivery problem in the collaborative context. Specifically, vehicles involved in transferring the same request are synchronized at the transhipment points. A mixed-integer programming model is proposed for this problem. Based on this model the benefits of transhipment are analysed. Computational results show considerable cost-savings enabled by transhipment in the operational planning of carrier coalitions

Dynamic programming algorithm for the vehicle routing problem with time windows and EC social legislation

In practice, apart from the problem of vehicle routing, schedulers also face the problem of nding feasible driver schedules complying with complex restrictions on drivers' driving and working hours. To address this complex interdependent problem of vehicle routing and break scheduling, we propose a dynamic programming approach for the vehicle routing problem with time windows including the EC social legislation on drivers' driving and working hours. Our algorithm includes all optional rules in these legislations, which are generally ignored in the literature. To include the legislation in the dynamic programming algorithm we propose a break scheduling method that does not increase the time-complexity of the algorithm. This is a remarkable eect that generally does not hold for local search methods, which have proved to be very successful in solving less restricted vehicle routing problems. Computational results show that our method finds solutions to benchmark instances with 18% less vehicles and 5% less travel distance than state of the art approaches. Furthermore, they show that including all optional rules of the legislation leads to an additional reduction of 4% in the number of vehicles and of 1.5%\ud regarding the travel distance. Therefore, the optional rules should be exploited in practice

combinatorial reverse auction

for a bi-objective winner determination problem in a combinatorial reverse auctio