Sequential and parallel large neighborhood search algorithms for the periodic location routing problem

We propose a large neighborhood search (LNS) algorithm to solve the periodic location routing problem (PLRP). The PLRP combines location and routing decisions over a planning horizon in which customers require visits according to a given frequency and the specific visit days can be chosen. We use parallelization strategies that can exploit the availability of multiple processors. The computational results show that the algorithms obtain better results than previous solution methods on a set of standard benchmark instances from the literature

Synchronizing vans and cargo bikes in a city distribution network

One of the significant side-effects of growing urbanization is the constantly increasing amount of freight transportation in cities. This is mainly performed by conventional vans and trucks and causes a variety of problems such as road congestion, noise nuisance and pollution. Yet delivering goods to residents is a necessity. Sustainable concepts of city distribution networks are one way of mitigating difficulties of freight services. In this paper we develop a two-echelon city distribution scheme with temporal and spatial synchronization between cargo bikes and vans. The resulting heuristic is based on a greedy randomized adaptive search procedure with path relinking. In our computational experiments we use artificial data as well as real-world data of the city of Vienna. Furthermore we compare three distribution policies. The results show the costs caused by temporal synchronization and can give companies decision-support in planning a sustainable city distribution concept

Integrated service selection, pricing and fullfillment planning for express parcel carriers - Enriching service network design with customer choice and endogenous delivery time restrictions

Express parcel carriers offer a wide range of guaranteed delivery times in order to separate customers who value quick delivery from those that are less time but more price sensitive. Such segmentation, however, adds a whole new layer of complexity to the task of optimizing the logistics operations. While many sophisticated models have been developed to assist network planners in minimizing costs, few approaches account for the interplay between service pricing, customer decisions and the associated restrictions in the distribution process. This paper attempts to fill this research gap by introducing a heuristic solution approach that simultaneously determines the ideal set of services, the associated pricing and the fulfillment plan in order to maximize profit. By integrating revenue management techniques into vehicle routing and eet planning, we derive a new type of formulation called service selection, pricing and fulfillment problem (SSPFP). It combines a multi-product pricing problem with a cycle-based service network design formulation. In order derive good-quality solutions for realistically-sized instances we use an asynchronous parallel genetic algorithm and follow the intuition that small changes to prices and customer assignments cause minor changes in the distribution process. We thus base every new solution on the most similar already evaluated fulfillment plan. This adapted initial solution is then iteratively improved by a newly-developed route-pattern exchange heuristic. The performance of the developed algorithm is demonstrated on a number of randomly created test instances and is compared to the solutions of a commercial MIP-solver.Series: Schriftenreihe des Instituts für Transportwirtschaft und Logistik - Supply Chain Managemen

Analytic Hierarchy Process for City Hub Location Selection - The Viennese Case

Growing urbanization and rising urban freight volumes contribute to increasing congestion, noise and pollution which negatively impact a city’s population. City hubs are one means of mitigating this problem by consolidating goods of different suppliers at the hub and cooperating in the last mile delivery. Because of the general shortage of urban space, a major challenge is finding an appropriate location for such a hub. This paper provides a decision support tool based on the analytic hierarchy process for the hub location selection problem, which considers quantitative and qualitative criteria. By involving three stakeholder groups – the municipality, logistics companies and citizens – the approach insures a comprehensive view. The application of the model is tested for the location selection of a midi-hub – a medium-sized city hub – in Vienna. Hence, our results show that a good compromise between different stakeholder views regarding a mid-hub location selection problem can be achieved by the application of our AHP-based decision support tool

A Periodic Location Routing Problem for Collaborative Recycling

Motivated by collaborative recycling efforts for non-profit agencies, we study a variant of the periodic location routing problem, in which one decides the set of open depots from the customer set, the capacity of open depots, and the visit frequency to nodes, in an effort to design networks for collaborative pickup activities. We formulate this problem, highlighting the challenges introduced by these decisions. We examine the relative dfficulty introduced with each decision through exact solutions and a heuristic approach which can incorporate extensions of model constraints and solve larger instances. The work is motivated by a project with a network of hunger relief agencies (e.g., food pantries, soup kitchens and shelters) focusing on collaborative approaches to address their cardboard recycling challenges collectively. We present a case study based on data from the network. In this novel setting, we evaluate collaboration in terms of participation levels and cost impact. These insights can be generalized to other networks of organizations that may consider pooling resources

Impact of travel time uncertainties on the solution cost of a two-echelon vehicle routing problem with synchronization

Two-echelon vehicle routing problems which contain synchronization between vehicles can be deeply impacted by time uncertainty, because one vehicle's delay can propagate to other vehicles. In this paper, we evaluate the deterministic solution of such a problem based on simulated travel time scenarios. The information obtained by simulation is incorporated in the optimization procedure iteratively. Computational results show that the degree of synchronization in an instance is directly correlated with the potential improvements by reoptimization. We present findings on the number of travel time scenarios required to obtain a representative picture of the stochastic solutions. In addition, we demonstrate that time dependent travel times can be aggregated on a city-wide level and linearized as a function of free flow times without major loss of reliability

Citylogistik und intermodaler Transport als Unterstützer grüner Logistik

Logistik ist ein wichtiger Eckpfeiler der Wirtschaft, ist aber mit sehr starken negativen Auswirkungen für die Umwelt verbunden. Innovative Ideen, die die negativen ökologischen Auswirkungen der Logistik reduzieren und gleichzeitig die wirtschaftlich notwendige Gestaltung der Logistik ermöglichen, sind essenziell. In diesem Artikel werden zwei Ansätze beschrieben, wie Logistik nachhaltiger gestaltet werden kann. Auf der einen Seite wird über Citylogistik berichtet, auf der anderen Seite werden die Besonderheiten des intermodalen Verkehrs analysiert. Anschließend werden Projekte, die in letzter Zeit zu diesem Thema abgeschlossen wurden, vorgestellt. Dabei wird ein besonderer Fokus auf die Rolle quantitativer Modelle gelegt

Models and Algorithms for the Integrated Planning of Bin Allocation and Vehicle Routing in Solid Waste Management

The efficient organization of waste collection systems based on bins located along the streets involves the solution of several tactical optimization problems. In particular, the bin configuration and sizing at each collection site as well as the service frequency over a given planning horizon have to be decided. In this context, a higher service frequency leads to higher routing costs, but at the same time less or smaller bins are required, which leads to lower bin allocation investment costs. The bins used have different types and different costs and there is a limit on the space at each collection site as well as a limit on the total number of bins of each type that can be used. In this paper we consider the problem of designing a collection system consisting of the combination of a vehicle routing and a bin allocation problem in which the trade-off between the associated costs has to be considered. The solution approach combines an effective variable neighborhood search metaheuristic for the routing part with a mixed integer linear programming-based exact method for the solution of the bin allocation part. We propose hierarchical solution procedures where the two decision problems are solved in sequence, as well as an integrated approach where the two problems are considered simultaneously. Extensive computational testing on synthetic and real-world instances with hundreds of collection sites shows the benefit of the integrated approaches with respect to the hierarchical ones

A heuristic solution method for node routing based solid waste collection problems

This paper considers a real world waste collection problem in which glass, metal, plastics, or paper is brought to certain waste collection points by the citizens of a certain region. The collection of this waste from the collection points is therefore a node routing problem. The waste is delivered to special sites, so called intermediate facilities (IF), that are typically not identical with the vehicle depot. Since most waste collection points need not be visited every day, a planning period of several days has to be considered. In this context three related planning problems are considered. First, the periodic vehicle routing problem with intermediate facilities (PVRP-IF) is considered and an exact problem formulation is proposed. A set of benchmark instances is developed and an efficient hybrid solution method based on variable neighborhood search and dynamic programming is presented. Second, in a real world application the PVRP-IF is modified by permitting the return of partly loaded vehicles to the depots and by considering capacity limits at the IF. An average improvement of 25% in the routing cost is obtained compared to the current solution. Finally, a different but related problem, the so called multi-depot vehicle routing problem with inter-depot routes (MDVRPI) is considered. In this problem class just a single day is considered and the depots can act as an intermediate facility only at the end of a tour. For this problem several instances and benchmark solutions are available. It is shown that the algorithm outperforms all previously published metaheuristics for this problem class and finds the best solutions for all available benchmark instances

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

We present a multi-objective two-echelon vehicle routing problem with vehicle synchronization and "grey zone" customers arising in the context of urban freight deliveries. Inner-city center deliveries are performed by small vehicles due to access restrictions, while deliveries outside this area are carried out by conventional vehicles for economic reasons. Goods are transferred from the first to the second echelon by synchronized meetings between vehicles of the respective echelons. We investigate the assignment of customers to vehicles, i.e., to the first or second echelon, within a so-called "grey Zone" on the border of the inner city and the area around it. While doing this, the economic objective as well as negative external effects of transport, such as emissions and disturbance (negative impact on citizens due to noise and congestion), are taken into account to include objectives of companies as well as of citizens and municipal authorities. Our metaheuristic - a large neighborhood search embedded in a heuristic rectangle/cuboid splitting - addresses this problem efficiently. We investigate the impact of the free assignment of part of the customers ("grey Zone") to echelons and of three different city layouts on the solution. Computational results show that the impact of a "grey Zone" and thus the assignment of these customers to echelons depend significantly on the layout of a city. Potentially pareto-optimal solutions for two and three objectives are illustrated to efficiently support decision makers in sustainable city logistics planning processes