Carlos L. Quintero-Araujo

How to anticipate the level of activity of a sustainable collaborative network: The case of urban freight delivery through logistics platforms

In this paper, we elaborate a methodology to study a particular case of collaborative network: city logistics. We identify that many solutions for urban logistics are, most of time, badly evaluated. Indeed, the theory often predicts a positive effect but the reality is most of time counterbalanced. We tried to fill this gap by making use of innovative methods. To do so, we mobilize several domains of knowledge: operational research, game theory and transportation studies on real cases. We suggest a solution to anticipate the level of activity of an Urban Consolidation Center and determine the condition under which it generates benefit for a carrier using or not, the collaborative network. We present the result obtained by application of our method on the real case of the city of Saint-Etienne.

A biased-randomized metaheuristic for the capacitated location routing problem

The location routing problem (LRP) involves the three key decision levels in supply chain design, that is, strategic, tactical, and operational levels. It deals with the simultaneous decisions of (a) locating facilities (e.g., depots or warehouses), (b) assigning customers to facilities, and (c) defining routes of vehicles departing from and finishing at each facility to serve the associated customers’ demands. In this paper, a two-phase metaheuristic procedure is proposed to deal with the capacitated version of the LRP (CLRP). Here, decisions must be made taking into account limited capacities of both facilities and vehicles. In the first phase (selection of promising solutions), we determine the depots to be opened, perform a fast allocation of customers to open depots, and generate a complete CLRP solution using a fast routing heuristic. This phase is executed several times in order to keep the most promising solutions. In the second phase (solution refinement), for each of the selected solutions we apply a perturbation procedure to the customer allocation followed by a more intensive routing heuristic. Computational experiments are carried out using well-known instances from the literature. Results show that our approach is quite competitive since it offers average gaps below 0.4% with respect to the best-known solutions (BKSs) for all tested sets in short computational times.

Quantifying Potential Benefits of Horizontal Cooperation in Urban Transportation Under Uncertainty: A Simheuristic Approach

Horizontal Cooperation (HC) in transportation activities has the potential to decrease supply chain costs and the environmental impact of delivery vehicles related to greenhouse gas emissions and noise. Especially in urban areas the sharing of information and facilities among members of the same supply chain level promises to be an innovative transportation concept. This paper discusses the potential benefits of HC in supply chains with stochastic demands by applying a simheuristic approach. For this, we integrate Monte Carlo Simulation into a metaheuristic process based on Iterated Local Search and Biased Randomization. A non-cooperative scenario is compared to its cooperative counterpart which is formulated as multi-depot Vehicle Routing Problem with stochastic demands (MDVRPSD).

Using simheuristics to promote horizontal collaboration in stochastic city logistics

This paper analyzes the role of horizontal collaboration (HC) concepts in urban freight transportation under uncertainty scenarios. The paper employs different stochastic variants of the well-known vehicle routing problem (VRP) in order to contrast a non-collaborative scenario with a collaborative one. This comparison allows us to illustrate the benefits of using HC strategies in realistic urban environments characterized by uncertainty in factors such as customers’ demands or traveling times. In order to deal with these stochastic variants of the VRP, a simheuristic algorithm is proposed. Our approach integrates Monte Carlo simulation inside a metaheuristic framework. Some computational experiments contribute to quantify the potential gains that can be obtained by the use of HC practices in modern city logistics.

Using horizontal cooperation concepts in integrated routing and facility-location decisions

In a global and competitive economy, efficient supply networks are essential for modern enterprises. Horizontal cooperation (HC) concepts represent a promising strategy to increase the performance of supply chains. HC is based on sharing resources and making joint decisions among different agents at the same level of the supply chain. This paper analyzes different cooperation scenarios concerning integrated routing and facility-location decisions in road transportation: (a) a noncooperative scenario in which all decisions are individually taken (each enterprise addresses its own vehicle routing problem [VRP]); (b) a semicooperative scenario in which route-planning decisions are jointly taken (facilities and fleets are shared and enterprises face a joint multidepot VRP); and (c) a fully cooperative scenario in which route-planning and facility-location decisions are jointly taken (also customers are shared, and thus enterprises face a general location routing problem). Our analysis explores how this increasing level of HC leads to a higher flexibility and, therefore, to a lower total distribution cost. A hybrid metaheuristic algorithm, combining biased randomization with a variable neighborhood search framework, is proposed to solve each scenario. This allows us to quantify the differences among these scenarios, both in terms of monetary and environmental costs. Our solving approach is tested on a range of benchmark instances, outperforming previously reported results.

Short- and mid-term evaluation of the use of electric vehicles in urban freight transport collaborative networks: a case study

ABSTRACT Despite its negative impacts, freight transportation is a primary component of all supply chains. Decision makers have considered diverse strategies, such as Horizontal Collaboration (HC) and the usage of alternative types of vehicles, to reduce overall cost and the related environmental and social impacts. This paper assesses the implementation of an electric fleet of vehicles in urban goods distribution under HC strategy between carriers. A biased randomisation based algorithm is used to solve the problem with a multi-objective function to explore the relationships between both delivery and environmental costs. Real data from the city of Bogotá, Colombia are used to validate this approach. Experiments with different costs and demands projections are performed to analyse short- and medium-term impacts related to the usage of electric vehicles in collaborative networks. Results show that the optimal selection of vehicle types depends considerably on the time horizon evaluation and demand variation.

A simheuristic algorithm for horizontal cooperation in urban distribution: application to a case study in Colombia

The challenge in last-mile supply chains deliveries is to maintain and improve the operational cost-effectiveness by the implementation of efficient procedures while facing increased levels of congestion in cities. One competitive alternative is Horizontal Cooperation (HC). City distribution problems under HC conditions can be modeled as multi-depot vehicle routing problems, which are NP-hard problems meaning that exact methods provide optimal solutions only for small datasets. This complexity increases when considering stochastic demand. Therefore, real-life situations must be solved using heuristic algorithms. This paper proposes the implementation of a simheuristic (i.e., an algorithm combining heuristics with simulation). Experiments are carried out using realistic data from the city of Bogotá, Colombia, regarding the distribution of goods to the whole network of the three major chains of convenience stores currently operating in the city. Results show the power of the proposed simheuristic in comparison with traditional solution approaches based on mathematical programming.

Planning Freight Delivery Routes in Mountainous Regions

The planning of delivery routes in mountainous areas should pay attention to the fact that certain types of vehicles (such as large trucks) may be unable to reach some customers. The use of heterogeneous fleet is then a must. Moreover, the costs of a given route may be very different depending on the sense taken. The site-dependent capacitated vehicle routing problem with heterogeneous fleet and asymmetric costs is solved with the successive approximations method. The solution methodology proposed is tested on a set of benchmark instances. Preliminary tests carried out show the benefits, in terms of total costs, when using a heterogeneous fleet. In both cases, with and without site dependency, the increase in distance-based costs is mitigated by the use of heterogeneous fleet.

A New Randomized Procedure to Solve the Location Routing Problem

The Location Routing Problem (LRP) is one of the most important challenging problems in supply chain design since it includes all decision levels in operations management. Due to its complexity, heuristics approaches seem to be the right choice to solve it. In this paper we introduce a simple but powerful approach based on biased randomization techniques to tackle the capacitated version of the LRP. Preliminary tests show that near-optimal or near-BKS can be found in a very short time.