Industrial projects are plagued by uncertainties, often resulting in both time and cost overruns. This research introduces an innovative approach, employing Reinforcement Learning (RL), to address three distinct project management challenges within a setting of uncertain activity durations. The primary objective is to identify stable baseline schedules. The first challenge encompasses the multimode lean project management problem, wherein the goal is to maximize a project’s value function while adhering to both due date and budget chance constraints. The second challenge involves the chance-constrained critical chain buffer management problem in a multimode context. Here, the aim is to minimize the project delivery date while considering resource constraints and duration-chance constraints. The third challenge revolves around striking a balance between the project value and its net present value (NPV) within a resource-constrained multimode environment. To tackle these three challenges, we devised mathematical programming models, some of which were solved optimally. Additionally, we developed competitive RL-based algorithms and verified their performance against established benchmarks. Our RL algorithms consistently generated schedules that compared favorably with the benchmarks, leading to higher project values and NPVs and shorter schedules while staying within the stakeholders’ risk thresholds. The potential beneficiaries of this research are project managers and decision-makers who can use this approach to generate an efficient frontier of optimal project plans.

Nas duas últimas décadas, o mundo industrial assistiu a uma crescente fragmentação e segmentação dos seus mercados, especialmente para o setor de bens de consumo duráveis. O resultado consiste na crescente dificuldade para a exploração das economias de escala 'tradicionais' por meio de equipamento altamente especializado e a produção de bens padronizados. Daí a recente ênfase no conceito de economias de escopo. Entretanto, várias conclusões imprecisas são encontradas na literatura referentes à transição de economias de escala para economias de escopo. O propósito deste artigo é identificar algumas dessas conclusões e introduzir argumentos no sentido e organizar a discussão sobre o assunto

Two important goals in project management are the maximization of the net present value (NPV) and project value, a more recent target. The former is a well-documented objective in project scheduling, and both are project evaluation tools used by decision makers. The literature has focused on the maximization of the project NPV problem and on project value as separate research tracks, but consideration of the tradeoff between both goals offers decision makers a more thorough evaluation of a project when weighing project alternatives. This paper introduces a novel formulation of the maximization problem that includes both a robust formulation of NPV and project value, develops algorithms to solve it, and illustrates the tradeoff between both objectives. The proposed mixed integer program (MIP) features a multimode setting, where the selection of an activity mode will impact cost, duration, resource usage and project value, and stochastic activity durations. To solve the problem, this study offers an innovative reinforcement learning (RL) based algorithm. The solution can be used to plot the efficient frontier between the robust NPV and the project value. Computational experiments revealed that the algorithm performs well compared to tabu search and an MIP solution using a commercial solver, and that the RL actions can be leveraged for coping with positive and negative cashflows. The utility of our work lies in its ability to respond to decision makers’ information needs, providing a framework for tradeoff analysis to select the most adequate project plan that satisfies stakeholders’ requirements.

As greenhouse gas emissions increasingly become a central issue in global sustainability, it is imperative to develop solutions that effectively reduce avoidable emissions. One significant contributor to these emissions is the high percentage of empty trucks on the road at any given time. This paper aims to investigate a full truckload marketplace, specifically designed to utilize the empty cargo space of trucks, thereby minimizing empty backhauls by facilitating container transportation between two terminals. The study examines two levels of planning: operational and tactical. At the operational level, we present a lower bound for the marketplace's costs and propose an online just-in-time policy. This policy involves the marketplace's decision to accept or reject a truck offer, aiming to synchronize the incoming rate of truck offers with that of shipment requests, ultimately maximizing profitability. Moving to the tactical planning problem, our objective is to identify optimal shipping prices and truck payments that maximize the objective function of the marketplace. Two scenarios determine this objective function: the marketplace can operate as a profit-maximizing firm or as a social planner. In the latter scenario, the goal is to maximize the volume of transportation services provided, consequently minimizing the additional non-productive miles driven by empty trucks. To address the tactical planning problem, we propose algorithms that leverage the problem structure. Furthermore, we conduct simulation experiments for both planning levels. The results reveal several key findings, including a pooling effect triggered by higher rates of drivers logging into the marketplace. Additionally, we observe an increase in optimal truck costs and shipping prices in response to an escalation in the delay penalty. Furthermore, we demonstrate the greater social value generated by the social planner approach. Overall, the experimental results offer valuable insights and practical guidelines for decision-makers regarding market design and pricing decisions.