Determination of Bus Station Locations under Emission and Social Cost Constraints

This study proposes a two-stage stochastic programming model to determine an optimal set of bus stations that minimizes operational, environmental, and social costs under uncertain weather conditions and customer perceptions on sustainability. The first stage of the proposed model focuses on the derivation of a set of bus stations under uncertain demand and weather conditions. Then, the second stage determines an optimal vehicle capacity (i.e., bus size) to minimize the impact of vehicle shortages. In the proposed model, different customer perceptions on sustainability are conceptualized through a range of dissatisfaction levels. Weather conditions are considered as causing higher dissatisfaction for vehicle shortages in certain seasons. The proposed model is applied to a numerical case study for a bus transit network in a college town. This study also analyzes the effect of human behavior on system costs by comparing the proposed model with a traditional approach. The results provide managerial insights on the fact that bus transit network design problems should allow for tradeoffs between different types of costs

A simheuristic for routing electric vehicles with limited driving ranges and stochastic travel times

Green transportation is becoming relevant in the context of smart cities, where the use of electric vehicles represents a promising strategy to support sustainability policies. However the use of electric vehicles shows some drawbacks as well, such as their limited driving-range capacity. This paper analyses a realistic vehicle routing problem in which both driving-range constraints and stochastic travel times are considered. Thus, the main goal is to minimize the expected time-based cost required to complete the freight distribution plan. In order to design reliable Routing plans, a simheuristic algorithm is proposed. It combines Monte Carlo simulation with a multi-start metaheuristic, which also employs biased-randomization techniques. By including simulation, simheuristics extend the capabilities of metaheuristics to deal with stochastic problems. A series of computational experiments are performed to test our solving approach as well as to analyse the effect of uncertainty on the routing plans.Peer Reviewe

A matheuristic approach for the Pollution-Routing Problem

This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by [Bektas and Laporte (2011), Transport. Res. B-Meth. 45 (8), 1232-1250]. The objective is to minimize operational and environmental costs while respecting capacity constraints and service time windows. Costs are based on driver wages and fuel consumption, which depends on many factors, such as travel distance and vehicle load. The vehicle speeds are considered as decision variables. They complement routing decisions, impacting the total cost, the travel time between locations, and thus the set of feasible routes. We propose a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm. This hybridization enables to integrate more tightly route and speed decisions. Moreover, two other "green" VRP variants, the Fuel Consumption VRP (FCVRP) and the Energy Minimizing VRP (EMVRP), are addressed. The proposed method compares very favorably with previous algorithms from the literature and many new improved solutions are reported.Comment: Working Paper -- UFPB, 26 page

Lean and green in the transport and logistics sector – a case study of simultaneous deployment

© 2016 Informa UK Limited, trading as Taylor & Francis Group. The transport and logistics sector is of vital importance for the stimulation of trade and hence the economic development of nations. However, over the last few years, this sector has taken central stage in the green agenda due to the negative environmental effects derived from its operations. Several disciplines including operations research and sub-areas of supply chain management such as green supply chains, green logistics and reverse logistics have tried to address this problem. However, despite the work undertaken through these disciplines, theoretical or empirical research into the sequential or simultaneous deployment of the lean and green paradigms, particularly, in the road transport and logistics sector is limited. This paper presents a case study where both paradigms have been combined to improve the transport operations of a world leader logistics organisation in the metropolitan area of Monterrey, Mexico. To do this, a systematic methodology and a novel tool called Sustainable Transportation Value Stream Map (STVSM) were proposed. The results obtained from the case study indicate that the concurrent deployment of the green and lean paradigms through such methodology and the STVSM tool is an effective approach to improve both operational efficiency and environmental performance of road transport operations. The paper can be used as a guiding reference for transport and logistics organisations to undertake improvement projects similar to the one presented in this paper. Additionally, this research also intends to stimulate scholarly research into the application of lean and green paradigms in the transport and logistics sector to expand the limited research pursued in this area

Service level, cost and environmental optimization of collaborative transportation

Less than truckload is an important type of road-based transportation. Based on real data and on a collaboration with industry, we show that a collaborative approach between companies offers important benefits. We propose to develop partnerships between shipping companies and to synchronize their shipments. Four operational collaborative schemes with different objectives are developed. The first one focuses on minimizing shipping costs for shippers. The second and third ones minimize the carrier’s costs and the environmental cost, respectively. The fourth one is a combination of all three. The results of our computational experiments demonstrate that collaboration lead to significant cost reductions

Leveraging Contextual Information for Robustness in Vehicle Routing Problems

We investigate the benefit of using contextual information in data-driven demand predictions to solve the robust capacitated vehicle routing problem with time windows. Instead of estimating the demand distribution or its mean, we introduce contextual machine learning models that predict demand quantiles even when the number of historical observations for some or all customers is limited. We investigate the use of such predicted quantiles to make routing decisions, comparing deterministic with robust optimization models. Furthermore, we evaluate the efficiency and robustness of the decisions obtained, both using exact or heuristic methods to solve the optimization models. Our extensive computational experiments show that using a robust optimization model and predicting multiple quantiles is promising when substantial historical data is available. In scenarios with a limited demand history, using a deterministic model with just a single quantile exhibits greater potential. Interestingly, our results also indicate that the use of appropriate quantile demand values within a deterministic model results in solutions with robustness levels comparable to those of robust models. This is important because, in most applications, practitioners use deterministic models as the industry standard, even in an uncertain environment. Furthermore, as they present fewer computational challenges and require only a single demand value prediction, deterministic models paired with an appropriate machine learning model hold the potential for robust decision-making

Rich vehicle routing: A data-driven heuristic application for a logistics company

Changing online shopping behaviors have resulted in the emergence of different product and services that aim high customer satisfaction. In this thesis, we develop an alternative approach to solve problem of a logistics company, which operates solely for e-commerce transactions, using an Adaptive Large Neighborhood Search (ALNS) heuristic. To understand the nature of the distribution system and for the development of the solution procedure, we create, preprocess and analyze a dataset constructed from company’s database that is used for daily operations. The proposed solution provides a prioritization mechanism for the deliveries based on certain specifications related to deliveries. To evaluate the performance of the proposed ALNS, we perform computational experiments using scenarios with real-life instances extracted from the dataset. Our results show that, the proposed ALNS can produce solutions with high quality regarding customer satisfactio

A bi-objective turning restriction design problem in urban road networks

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Fuel emissions optimization in vehicle routing problems with time-varying speeds

The problem considered in this paper is to produce routes and schedules for a fleet of delivery vehicles that minimize the fuel emissions in a road network where speeds depend on time. In the model, the route for each vehicle must be determined, and also the speeds of the vehicles along each road in their paths are treated as decision variables. The vehicle routes are limited by the capacities of the vehicles and time constraints on the total length of each route. The objective is to minimize the total emissions in terms of the amount of Greenhouse Gas (GHG) produced, measured by the equivalent weight of CO2 (CO2e). A column generation based tabu search algorithm is adapted and presented to solve the problem. The method is tested with real traffic data from a London road network. The results are analyzed to show the potential saving from the speed adjustment process. The analysis shows that most of the fuel emissions reduction is able to be attained in practice by ordering the customers to be visited on the route using a distance-based criterion, determining a suitable path between customers for each vehicle and travelling as fast as is allowed by the traffic conditions up to a preferred speed