An integrated assignment, routing, and speed model for roadway mobility and transportation with environmental, efficiency, and service goals

Managing all the mobility and transportation services with autonomous vehicles for users of a smart city requires determining the assignment of the vehicles to the users and their routing in conjunction with their speed. Such decisions must ensure low emission, efficiency, and high service quality by also considering the impact on traffic congestion caused by other vehicles in the transportation network. In this paper, we first propose an abstract trilevel multi-objective formulation architecture to model all vehicle routing problems with assignment, routing, and speed decision variables and conflicting objective functions. Such an architecture guides the development of subproblems, relaxations, and solution methods. We also propose a way of integrating the various urban transportation services by introducing a constraint on the speed variables that takes into account the traffic volume caused across the different services. Based on the formulation architecture, we introduce a (bilevel) problem where assignment and routing are at the upper level and speed is at the lower level. To address the challenge of dealing with routing problems on urban road networks, we develop an algorithm that alternates between the assignment-routing problem on an auxiliary complete graph and the speed optimization problem on the original non-complete graph. The computational experiments show the effectiveness of the proposed approach in determining approximate Pareto fronts among the conflicting objectives

OPTIMIZATION OF RAILWAY TRANSPORTATION HAZMATS AND REGULAR COMMODITIES

Transportation of dangerous goods has been receiving more attention in the realm of academic and scientific research during the last few decades as countries have been increasingly becoming industrialized throughout the world, thereby making Hazmats an integral part of our life style. However, the number of scholarly articles in this field is not as many as those of other areas in SCM. Considering the low-probability-and-high-consequence (LPHC) essence of transportation of Hazmats, on the one hand, and immense volume of shipments accounting for more than hundred tons in North America and Europe, on the other, we can safely state that the number of scholarly articles and dissertations have not been proportional to the significance of the subject of interest. On this ground, we conducted our research to contribute towards further developing the domain of Hazmats transportation, and sustainable supply chain management (SSCM), in general terms. Transportation of Hazmats, from logistical standpoint, may include all modes of transport via air, marine, road and rail, as well as intermodal transportation systems. Although road shipment is predominant in most of the literature, railway transportation of Hazmats has proven to be a potentially significant means of transporting dangerous goods with respect to both economies of scale and risk of transportation; these factors, have not just given rise to more thoroughly investigation of intermodal transportation of Hazmats using road and rail networks, but has encouraged the competition between rail and road companies which may indeed have some inherent advantages compared to the other medium due to their infrastructural and technological backgrounds. Truck shipment has ostensibly proven to be providing more flexibility; trains, per contra, provide more reliability in terms of transport risk for conveying Hazmats in bulks. In this thesis, in consonance with the aforementioned motivation, we provide an introduction into the hazardous commodities shipment through rail network in the first chapter of the thesis. Providing relevant statistics on the volume of Hazmat goods, number of accidents, rate of incidents, and rate of fatalities and injuries due to the incidents involving Hazmats, will shed light onto the significance of the topic under study. As well, we review the most pertinent articles while putting more emphasis on the state-of-the-art papers, in chapter two. Following the discussion in chapter 3 and looking at the problem from carrier company’s perspective, a mixed integer quadratically constraint problem (MIQCP) is developed which seeks for the minimization of transportation cost under a set of constraints including those associating with Hazmats. Due to the complexity of the problem, the risk function has been piecewise linearized using a set of auxiliary variables, thereby resulting in an MIP problem. Further, considering the interests of both carrier companies and regulatory agencies, which are minimization of cost and risk, respectively, a multiobjective MINLP model is developed, which has been reduced to an MILP through piecewise linearization of the risk term in the objective function. For both single-objective and multiobjective formulations, model variants with bifurcated and nonbifurcated flows have been presented. Then, in chapter 4, we carry out experiments considering two main cases where the first case presents smaller instances of the problem and the second case focuses on a larger instance of the problem. Eventually, in chapter five, we conclude the dissertation with a summary of the overall discussion as well as presenting some comments on avenues of future work

Clustering and routing in waste management: A two-stage optimisation approach

This paper proposes a two-stage model to tackle a problem arising in Waste Management. The decision-maker (a regional authority) is interested in locating sorting facilities in a regional area and defining the corresponding capacities. The decision-maker is aware that waste will be collected and brought to the installed facilities by independent private companies. Therefore, the authority wants to foresee the behaviour of these companies in order to avoid shortsighted decisions. In the first stage, the regional authority divides the clients into clusters, further assigning facilities to these clusters. In the second stage, an effective route is defined to serve client pickup demand. The main idea behind the model is that the authority aims to find the best location–allocation solution by clustering clients and assigning facilities to these clusters without generating overlaps. In doing so, the authority tries to (i) assign the demand of clients to the facilities by considering a safety stock within their capacities to avoid shortages during the operational phase, (ii) minimise Greenhouse Gases emissions, (iii) be as compliant as possible with the solution found by the second stage problem, the latter aiming at optimising vehicle tour lengths. After properly modelling the problem, we propose a matheuristic solution algorithm and conduct extensive computational analysis on a real-case scenario of an Italian region. Validation of the approach is achieved with promising results

Exact solution of the evasive flow capturing problem

The Evasive Flow Capturing Problem is defined as the problem of locating a set of law enforcement facilities on the arcs of a road network to intercept unlawful vehicle flows traveling between origin-destination pairs, who in turn deviate from their route to avoid any encounter with such facilities. Such deviations are bounded by a given tolerance. We first propose a bilevel program that, in contrast to previous studies, does not require a priori route generation. We then transform this bilevel model into a single-stage equivalent model using duality theory to yield a compact formulation. We finally reformulate the problem by describing the extreme rays of the polyhedral cone of the compact formulation and by projecting out the auxiliary variables, which leads to facet-defining inequalities and a cut formulation with an exponential number of constraints. We develop a branch-and-cut algorithm for the resulting model, as well as two separation algorithms to solve the cut formulation. Through extensive experiments on real and randomly generated networks, we demonstrate that our best model and algorithm accelerate the solution process by at least two orders of magnitude compared with the best published algorithm. Furthermore, our best model significantly increases the size of the instances that can be solved optimally

Solving a green logistics bi-level bi-objective problem.

The situation here addressed is modelled as a bi-level programming problem with multiple objectives in the upper level and a single objective in the lower level. In this problem, a company (hereafter the leader) distribute a commodity over a selected subset of customers; while a manufacturer (hereafter the follower) will fabricate the commodities demanded by the selected customers. The leader has two objectives: the maximization of the profit gained by the distribution process and the minimization of CO2 emissions. The latter is important due to the regulations imposed by the government. It is clear that exists a compromise between both objectives, since the maximization of profit will attempt to include as much customers for being served as possible. Then, largest routes will be needed causing more CO2 emissions. For analyzing the problem, the single-commodity case is studied first. Under this assumption, the problem can be reduced into a single-level one. Hence, a tabu search algorithm for solving the aforementioned case is proposed. The tabu search is designed for solving two single-level simplifications of the problem: a monoobjective problem and the bi-objective one. After that, the multi-commodity bi-level case is studied and the respective adaptation of the tabu search is made. Then, a co-evolutionary algorithm is designed for obtaining good quality bi-level feasible solutions. The co-evolutionary approach is related with having two separated populations, one for each leader’s objective. Then, the solutions will evolve in each population and an interchange of information is made through the process. In other words, a swap between the best solutions from both populations in each generation is conducted. By doing this, the algorithm intends to find efficient solutions. The evolution performed in each population is done through a Biased Random Keys Genetic Algorithm( BRKGA). Furthermore, a path relinking algorithm is adapted in order to find the Pareto frontier for the bi-level bi-objective multi-commodity problem, in which the no dominated solutions of the tabu search and the co-evolutionary algorithms are used to initialize this procedure. Numerical experimentation showed the efficiency of the proposed methods for finding good quality solutions (for the mono-objective case) and for reaching a good approximation of the Pareto front (for the bi-objective cases) in reasonable computational time

Multilevel decision-making: A survey

© 2016 Elsevier Inc. All rights reserved. Multilevel decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a multiple level hierarchy. Significant efforts have been devoted to understanding the fundamental concepts and developing diverse solution algorithms associated with multilevel decision-making by researchers in areas of both mathematics/computer science and business areas. Researchers have emphasized the importance of developing a range of multilevel decision-making techniques to handle a wide variety of management and optimization problems in real-world applications, and have successfully gained experience in this area. It is thus vital that a high quality, instructive review of current trends should be conducted, not only of the theoretical research results but also the practical developments in multilevel decision-making in business. This paper systematically reviews up-to-date multilevel decision-making techniques and clusters related technique developments into four main categories: bi-level decision-making (including multi-objective and multi-follower situations), tri-level decision-making, fuzzy multilevel decision-making, and the applications of these techniques in different domains. By providing state-of-the-art knowledge, this survey will directly support researchers and practical professionals in their understanding of developments in theoretical research results and applications in relation to multilevel decision-making techniques