1,720,973 research outputs found
Bundle generation for last-mile delivery with occasional drivers
Full text linkIn this paper, we present the vehicle routing problem (VRP) with occasional drivers (OD) and order bundles (OB). The problem VRP-OD-OB is an extension of the VRP-OD, where instead of assigning one customer per driver, drivers are assigned bundles of customers. To deal with the bundle-to-driver assignment, a bidding system is exploited, in which a company offers a set of bundles and the drivers raise their bids. These bids depend on features such as the drivers’ destination, flexibility in deviating from the shortest path, and willingness to offer service. To generate valuable bundles of customers, we propose two strategies: (i) an innovative approach based on the creation of corridors, and (ii) a traditional approach based on clustering. Through an experimental study, carried out on randomly generated instances and on a real road network, we show that the innovative corridor-based approach strongly outperforms the clustering-based approach. Given a set of bundles and a corresponding set of bids, we provide a mathematical formulation and valid inequalities to solve the VRP-OD-OB. To address larger instances, we design an efficient large neighborhood search-based matheuristic. The results of an extensive computational study show that this method provides near-optimal solutions within very short run times. An analysis of the impact of drivers’ flexibility and willingness levels on the percentage of customers assigned to ODs is presented. Moreover, the case in which ODs dynamically appear at regular time intervals is investigated. Also in this dynamic setting, considerable total cost reductions are shown. Moreover, we derive several important managerial insights, which include the observation that it is not necessary to provide a high number of bundles to achieve good quality solutions. Companies should rather focus on generating fewer but more attractive bundles
Vehicle scheduling for rental-with-driver services
No full textIn this paper, we introduce a new vehicle scheduling problem (VSP) with driver consistency faced by rental-with-driver companies. A weekly time-horizon is considered and a set of potential customers, each one associated with a list of required tasks, is assumed. The company can choose to accept or reject a customer, but if accepted, all required tasks must be performed by the same driver. A profit is associated with each customer. The goal is to maximize the company's total profit, by respecting a list of daily and the weekly drivers’ workload limitations imposed by drivers’ contracts. We propose a mathematical formulation of the problem and design an exact solution method based on the combinatorial Benders cuts approach. A computational study based on several sets of instances reveals that the proposed solution method strongly outperforms the straightforward MIP approach. A deep analysis of the impact of different parameters is presented. Finally, we provide a measure of the cost of consistency, expressed as the loss of profit necessary to guarantee driver consistency. Results indicate that, for instance, if task durations are long, consistency can be achieved for almost no cost. However, if task durations are short, the loss of profit is below 6%. This provides an important managerial insight for companies offering luxury services, where the perceived quality of a service is key to its success
Bundle generation for the vehicle routing problem with occasional drivers and time windows
Full text linkIn this paper, we address the vehicle routing problem (VRP) with occasional drivers (ODs) and time windows (TWs). The problem (VRP-OD-TW) is an extension of the VRP-OD, where ODs serve customers within given TWs. Differently from the basic version of VRP-OD-TW, we assume that ODs not only accept single requests, but they can also serve bundles of requests. To deal with the bundle-to-driver assignment problem, an auction-based system has been designed; a company offers a set of bundles to the ODs, who bid for all the bundles they consider attractive. There is no limit on the number of bids a driver can place, but at most one bid per OD can be assigned to avoid infeasible workloads. This system could yield a large cost reduction for the company, but its success is strongly related to the bundles offered. Hence, determining bundles which are attractive for ODs and profitable for the company, becomes a crucial issue. We propose two different bundling strategies, which make use of a spatial-temporal representation of customers in a three-dimensional (3D) space. The former is based on the generation of 3D corridors, while the latter relies on 3D clustering techniques. Through extensive computational results, we show that the former technique outperforms the latter in terms of both solution quality and computational times and that both the approaches strongly outperform bundle generation techniques that neglect the temporal dimension and rely only on spatial information
The on-demand warehousing problem
Full text linkWarehouses are key elements of supply chain networks, and great attention is paid to increase their efficiency. Highly volatile space requirements are enablers of innovative resource sharing concepts, where warehouse capacities are traded on online platforms. In this context, our paper introduces the on-demand warehousing problem from the perspective of platform providers. The objective prioritises demand–supply matching with maximisation of the number of transactions. If there is a tie, the secondary objective maximises the number of suppliers matched with at least one customer and the number of customers that have matches within a specific threshold with respect to the minimum achievable cost. Besides the mathematical integer programming formulation, a myopic list-based heuristic and an efficient matheuristic approach are presented and benchmarked against the performance of a commercial optimisation solver. The impact of several parameters on the platform's objective is analysed. A particularly relevant finding is that the pricing flexibility on the demand side does not necessarily imply higher payments to the supply side. All data instances are made available publicly to encourage more researchers to work on this timely and challenging topic
Locker box location planning under uncertainty in demand and capacity availability
Full text linkIn this paper, we address the location of locker boxes in the last-mile delivery context under uncertainty in demand and capacity. The problem is modeled as an extension of the capacitated facility location problem, in which a fixed number of facilities has to be opened, choosing among a set of potential locations. Facilities are characterized by a homogeneous capacity, but a capacity reduction may occur with a given probability. The uncertainty in demand and capacity is incorporated through a set of discrete scenarios. Each customer can be assigned only to compatible facilities, i.e., to facilities located within a given radius from the individual location. The goal is to first maximize the total number of customers assigned to locker boxes, while, in case of a tie on this primary objective, a secondary objective intervenes aiming at minimizing the average distance covered by customers to reach their assigned locker box. A stochastic mathematical model as well as three matheuristics are presented. We provide an extensive computational study in order to analyze the impact of different parameters on the complexity of the problem. The importance of considering uncertainty in input data is discussed through the usage of general stochastic indicators from the literature as well as of problem specific indicators. A real-world case related to the City of Turin in Italy is analyzed in detail. The benefit achievable by optimizing locker box locations is discussed and a comparison with the current configuration is provide
Solving the Online On-Demand Warehousing Problem
Full text linkIn On-Demand Warehousing, an online platform acts as a central mechanism to match unused storage space and related services offered by suppliers to customers. Storage requests can be for small capacities and very short commitment periods if compared to traditional leasing models. The objective of the On-Demand Warehousing Problem (ODWP) is to maximize the number of successful transactions among the collected offers and requests, considering the satisfaction of both the supply and demand side to preserve future participation to the platform. The Online ODWP can be modeled as a stochastic reservation and assignment problem, where dynamically arriving requests of customers must be rapidly assigned to suppliers. Firstly, an online stochastic combinatorial optimization framework is adapted to the Online ODWP. The key idea of this approach is to generate samples of future requests by evaluating possible allocations for the current request against these samples. In addition, expectation, consensus, and regret, and two greedy algorithms are implemented. All solution methods are compared on a dataset of realistic instances of different sizes and features, demonstrating their effectiveness compared to the oracle solutions, which are based on the assumption of perfect information about future request arrivals. A newly proposed approach of risk approximation is shown to outperform alternative algorithms on large instances. Managerial insights regarding acceptance and rejection strategies for the platform are derived. It is shown how requests with large demand, long time frame, not very long spanning time, and average compatibility degree, are very likely to be rejected in the optimal solution
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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