Robust Solution Approach for the Dynamic and Stochastic Vehicle Routing Problem

The dynamic and stochastic vehicle routing problem (DSVRP) can be modelled as a stochastic program (SP). In a two-stage SP with recourse model, the first stage minimizes the a priori routing plan cost and the second stage minimizes the cost of corrective actions, performed to deal with changes in the inputs. To deal with the problem, approaches based either on stochastic modelling or on sampling can be applied. Sampling-based methods incorporate stochastic knowledge by generating scenarios set on realizations drawn from distributions. In this paper we proposed a robust solution approach for the capacitated DSVRP based on sampling strategies. We formulated the problem as a two-stage stochastic program model with recourse. In the first stage the a priori routing plan cost is minimized, whereas in the second stage the average of higher moments for the recourse cost calculated via a set of scenarios is minimized. The idea is to include higher moments in the second stage aiming to compute a robust a priori routing plan that minimizes transportation costs while permitting small changes in the demands without changing solution structure. Additionally, the approach allows managers to choose between optimality and robustness, that is, transportation costs and reconfiguration. The computational results on a generic dynamic benchmark dataset show that the robust routing plan can cover unmet demand while incurring little extra costs as compared to the preplanning. We observed that the plan of routes is more robust; that is, not only the expected real cost, but also the increment within the planned cost is lower

Dynamic demand management and online tour planning for same-day delivery

For providers to stay competitive in a context of continued growth in e-retail sales and increasing customer expectations, same-day delivery options have become very important. Typically, with same-day delivery, customers purchase online and expect to receive their ordered goods within a narrow delivery time span. Providers thus experience substantial operational challenges to run profitable tours and generate sufficiently high contribution margins to cover overhead costs. We address these challenges by combining a demand-management approach with an online tour-planning approach for same-day delivery. More precisely, in order to reserve capacity for high-value customer orders and to guide customer choices toward efficient delivery operations, we propose a demand-management approach that explicitly optimizes the combination of delivery spans and prices which are presented to each incoming customer request. The approach includes an anticipatory sample-scenario based value approximation, which incorporates a direct online tour-planning heuristic. It does not require extensive offline learning and is scalable to realistically sized instances with multiple vehicles. In a comprehensive computational study, we show that our anticipatory approach can improve the contribution margin by up to 50% compared to a myopic benchmark approach. We also show that solving an explicit pricing optimization problem is a beneficial component of our approach. More precisely, it outperforms both a pure availability control and a simple pricing rule based on opportunity costs. The latter idea is one used in other approaches for related dynamic pricing problems dealt with in the literature

Modelos logísticos estocásticos: una revisión de la literatura

The present article is done with the aim of to establish the state of the art of location, inventory and routing models with stochastic parameters. The literature reviewed was classified with an operations research approach. A systematic review of the literature in the databases ScienceDirect, ScholarGoogle, SpringerLink, Scopus, SemanticScholar, Scielo, and ResearchGate was done. Through guiding questions, 99 articles were selected, of which 74% is recent literature between 2015 and 2019. Stochastic logistic individual models were classified, a taxonomy with an operations research approach was proposed, from their characteristics, parameters, restrictions, the objective functions and solution methods used. Also, the trends and the future lines of research were identified. As a conclusion new strategies and operating policies that permit improve supply chain performance are identified, also the absence of efficient solution methods has been evident to large instances, according to real-life.El presente artículo se realiza con el objetivo de establecer el estado del arte de los modelos de localización, inventario y ruteo con parámetros estocásticos. Se realizó una revisión sistemática de la literatura en las bases de datos ScienceDirect, ScholarGoogle, SpringerLink, Scopus, SemanticScholar, Scielo y ResearchGate. A través de preguntas orientadoras, se seleccionaron 99 artículos, de los cuales el 74% es literatura reciente entre 2015 y 2019.  Se clasificaron los modelos individuales logísticos estocásticos, se propuso una taxonomía con un enfoque de investigación de operaciones, a partir de sus características, parámetros, restricciones, funciones objetivo y métodos de solución utilizados. Asimismo, se identificaron las tendencias y las futuras líneas de investigación. Como conclusión se identifican nuevas estrategias y políticas operativas que permiten mejorar el desempeño de la cadena de suministro, igualmente la ausencia de métodos de solución eficientes ha sido evidente en grandes instancias, según la vida real