ICORES 2025 Abstracts

Area 1 - Methodologies and Technologies
Full Papers Short Papers
Area 2 - Applications
Full Papers Short Papers

Area 1 - Methodologies and Technologies

Full Papers
Paper Nr: 21
Title:

Exploration of a Generalized Benders Decomposition Method for Solving Project Scheduling Problems with Resource Constraints
Authors:

Alfredo S. Ramos, Pablo A. Miranda-Gonzalez and Elias Olivares-Benitez
Abstract: This research introduces a new Generalized Benders Decomposition-based Algorithm (GBDA) to solve the Multi-Mode Resource-Constrained Project Scheduling Problem (MRCPSP). The MRCPSP is a scheduling problem that besides precedence constraints, includes renewable and non-renewable resource constraints, as well as the selection of execution modes for the project activities. This mode selection determines the resource usage and duration of each activity. The GBDA splits the problem into a Master Problem (MP) and a Sub-Problem (SP) with a relaxation. Both problems are solved alternately, each one incorporating information from the other at each iteration, until a stopping criterion is met. Additionally, at each iteration, a non-relaxed SP is solved to obtain a solution for the original problem, and the best solution from all iterations is reported. The GBDA was tested, with three different stopping criteria, on benchmark instances from a public library and compared against solving the traditional formulation of the problem with an exact Mixed Integer Linear Programming (MILP) method. The GBDA found solutions of good quality in less than half the computing time than the exact method, with one of the stopping criteria. The analysis of the results provides valuable insights for future research.
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Paper Nr: 23
Title:

Online Joint Optimization of Sponsored Search Ad Bid Amounts and Product Prices on e-Commerce
Authors:

Shoichiro Koguchi, Kazuhide Nakata, Ken Kobayashi, Kosuke Kawakami, Takenori Nakajima and Kevin Kratzer
Abstract: With the rapid development of the e-commerce market, sellers are increasingly required to devise effective strategies to maximize sales and profits within limited resources. This paper insists that demand on e-commerce platforms can be partially controlled through advertising bid amounts. We examine the simultaneous control of demand and product pricing via advertising bids. Specifically, this study proposes a method for the joint optimization of advertising bid amounts and product prices to maximize sellers’ sales and profits. Previous research has often focused on either advertising bid amounts or product prices, with little consideration of their simultaneous optimization. In contrast, our study develops an optimization method that accounts for the interdependencies between advertising bid amounts, advertising budgets, product prices, and inventory control. This comprehensive approach enables sellers to optimize advertising bid amounts and product prices simultaneously by considering these interrelated factors. Moreover, the proposed method demonstrates high scalability and is well-suited to real-world e-commerce markets, allowing for adaptation to various market conditions. Simulation results indicate that the proposed method significantly enhances sales and profits compared to approaches that do not incorporate price variability.
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Paper Nr: 25
Title:

A Ship Routing Algorithm Generating Precise and Diverse Paths
Authors:

Alexandre Coppé and Nicolas Prcovic
Abstract: We present an algorithm that accurately determines the optimal trajectories of a ship in a multi-objective and dynamic context, where factors such as travel time and fuel consumption must be considered under varying weather conditions during the journey. Our approach combines two recent algorithms, NAMOA*-TD and WRM, allowing us to obtain a range of precise and diverse trajectories (a subset of the Pareto front) from which a user can choose. Initial experiments conducted using real meteorological data demonstrate the effectiveness of this approach.
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Paper Nr: 32
Title:

A Parallel Implementation of the Clarke-Wright Algorithm on GPUs
Authors:

Francesca Guerriero and Francesco Paolo Saccomanno
Abstract: The Clark & Wright (CW) algorithm is a greedy approach, aimed at finding good-quality solutions for the capacitated vehicle routing problem (CVRP). It is the most widely applied heuristic algorithm to solve CVRP due to its simple implementation and effectiveness. In this work, we propose a parallel implementation of the CW algorithm well suited to be executed on GPUs. In order to evaluate the performance of the developed approach, an extensive computational phase has been carried out, by considering a large set of test problems. The results are very encouraging, showing a significant reduction in computational time compared to the sequential version, especially for large-scale networks.
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Paper Nr: 47
Title:

A Stochastic Location-Routing Problem for the Optimal Placement of Lockers
Authors:

Guido Barbieri, Annarita De Maio, Roberto Musmanno and Sara Stoia
Abstract: This paper presents a Stochastic Location-Routing Problem aimed at optimizing the placement of parcel lockers in last-mile delivery. The model integrates locker location decisions with vehicle routing, taking into account customer preferences for home delivery or locker collection. It considers multiple scenarios of service requests to address the uncertainty in customer behavior. The problem is formulated as a two-stage stochastic program, where the first stage determines which lockers to activate, and the second stage optimizes vehicle routes based on the service preferences for each scenario. Computational experiments are based on a test problem used to validate the model’s effectiveness. Proposed future extensions include integrating multi-period planning, introducing capacity constraints for both vehicles and lockers, enabling dynamic activation of lockers, and optimizing the algorithm for multi-core architectures to enhance computational efficiency. These advancements aim to enhance the model’s applicability and scalability in tackling complex logistics challenges under uncertainty.
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Paper Nr: 53
Title:

Models and Algorithms for the Optimization of Multi-Period Fiber Wholesale Investments Strategies
Authors:

Youssouf Hadhbi, Aurélien Bechler and Matthieu Chardy
Abstract: This paper focuses on optimizing multi-period investment strategies for Fiber deployment. The main objective is to provide guidelines to improve the cost-effectiveness of Fiber investment strategies employed by telecommunication operators. To achieve this objective, an optimization framework is developed, providing a systematic approach to multi-period investment planning for Fiber deployment. It combines mathematical modeling and data analysis. For this, we introduce two mixed-integer linear programs to formulate the problem, taking into account demands, budget constraints and market conditions. Additionally, we propose several valid inequalities for the associated polytopes to enhance the linear relaxation and achieve tighter bounds. Relying on this modeling framework, we devise an exact optimization approach based on a Branch-and-Cut algorithm to solve the problem. Furthermore, we present a computational study that considers various instances and scenarios to assess the performance of the proposed models and algorithms.
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Paper Nr: 62
Title:

Optimized Scheduling for Electric Vehicle Charging: A Multi-Objective Approach to Grid Stability and User Satisfaction
Authors:

Aimen Khiar, Mohamed-el-Amine Brahmia, Ammar Oulamara and Lhassane Idoumghar
Abstract: The transition to electric mobility offers substantial environmental benefits but also introduces significant challenges, particularly in managing the high demand for electric vehicle (EV) charging. This demand creates the need for intelligent scheduling to optimize charging station resources and maintain grid stability. In order to address this purpose, we propose a multi-objective scheduling model designed to both minimize peak energy consumption and maximize user satisfaction by reducing waiting times at charging stations. Our model accurately represents real-world scenarios, including sequential charger usage, vehicle-to-charger compatibility, and limited availability of various charger types, each providing a constant power output. Given the complexity of the problem, we adapt and evaluate two metaheuristic algorithms: the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Cuckoo Search (MOCS), to approximate optimal solutions. The results show that the proposed MOCS adaptation surpasses that of NSGA-II in terms of dominance and achieving a well-distributed Pareto front approximation in a reasonable time frame. The proposed approach thus provides a powerful framework for efficient EV charging management, balancing user needs with grid stability and highlighting its strong potential for adoption in large-scale charging infrastructures.
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Paper Nr: 81
Title:

The Value of Perfect Forecasting in Optimizing the Management of Energy Communities
Authors:

Patrizia Beraldi, Luigi Gallo and Alessandra Rende
Abstract: The rise of Renewable Energy Communities (REC) is transforming energy systems by promoting decentralized renewable energy production, but their operational efficiency is hindered by the inherent uncertainty of production sources like photovoltaic systems. Accurate day-ahead forecasting is pivotal for optimizing REC energy management strategies, balancing production, consumption, and grid reliance. This study evaluates five machine learning (ML) models—Support Vector Regression, Random Forest, Adaptive Boosting, Gradient Boost Regression Tree, and Stacking Generalization—against standard accuracy metrics and introduces the Value of Perfect Forecast, a novel metric that quantifies the economic impact of forecast inaccuracies on REC optimization. Results indicate that, while some models perform better in standard accuracy metrics, others are more effective in reducing the economic impact of forecast errors, emphasizing the necessity of aligning forecasting approaches with optimization goals to achieve meaningful operational improvements.
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Paper Nr: 86
Title:

Upper Bound Computation for the Multiple Close-Enough Traveling Salesman Problem
Authors:

Francesco Carrabs, Raffaele Cerulli, Ciriaco D’Ambrosio and Gabriele Murano
Abstract: This paper addresses the Multiple Close-Enough Traveling Salesman Problem, a variant of the Close-Enough Traveling Salesman Problem, where multiple vehicles are used to visit a given number of points. A vehicle visits a point if it passes through the neighborhood set of that point. The goal of the problem is to minimize the longest of the defined routes. We face the problem by using a discretization schema that reduces it to the Multiple Generalized Traveling Salesman Problem for which we propose a Mixed Integer Linear Programming formulation. The use of discretization schema introduces a discretization error that makes the optimal solution value of our model an upper bound of the optimal solution value of the Multiple Close-Enough Traveling Salesman Problem. Moreover, we apply a graph reduction algorithm to remove arcs and nodes without changing the optimal solution of the problem. We provide proof of the correctness of this algorithm, and we show that it significantly reduces the size of the instances tested. We verified the performance of our model on the benchmark instances of Close-Enough Traveling Salesman Problem.
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Short Papers
Paper Nr: 20
Title:

Indoor Navigation: Navmesh Applied to Indoor Graph Creation
Authors:

Maxime Callico, Rodolphe Giroudeau, Benoît Darties and Jean Carrière
Abstract: This paper explores the adaptation of the Navmesh algorithm, widely used in video games for pathfinding, to real-world indoor navigation. By applying polygon decomposition techniques, we generate routing graphs for complex indoor environments. Our results demonstrate that the algorithm is particularly efficient with orthogonal polygons, commonly found in real-life buildings, due to their structured geometry. We compare the performance across various polygon types and discuss optimization strategies for path naturalness and computational efficiency. This work opens pathways for practical applications in indoor navigation.
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Paper Nr: 41
Title:

Fuzzy Rewards on the Multi-Armed Bandits Model
Authors:

Ciria R. Briones-García, Raúl Montes-de-Oca, Víctor H. Vázquez-Guevara and Hugo Cruz-Suárez
Abstract: In this paper an extension of the Armed Bandits problem is considered under the possibility that reward functions take trapezoidal fuzzy values as the results of a fuzzy affine transformation (which is susceptible of being interpreted as receiving “approximately” a reward located in some interval instead of such reward itself). The main objective is to find an optimal selection strategy that maximizes the fuzzy total expected discounted reward with respect to the partial order based on α-cuts and the one provided by the average ranking. For this, it is obtained that Gittins strategy (that is optimal in the crisp setting) is still optimal at the fuzzy paradigm. In addition, it is found that optimal stopping time associated with crisp Gittins index is the same for its fuzzy counterpart by finding a link between the fuzzy and crisp versions of Gittins index which leads us to demonstrate that fuzzy value function is connected to its crisp analog via some fuzzy affine transformation, with this in mind, it is possible to ensure that value function is approximately in certain interval related to the fuzzy transformation.
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Paper Nr: 45
Title:

Integrating Machine Learning and Optimisation to Solve the Capacitated Vehicle Routing Problem
Authors:

Daniel Antunes Pedrozo, Prateek Gupta, Jorge Augusto Meira and Fabiano Silva
Abstract: The Capacitated Vehicle Routing Problem (CVRP) is a fundamental combinatorial optimisation challenge in logistics. It aims to optimise routes so a fleet of vehicles can supply customer’s demands while minimising costs - that can be seem as total distance travelled or time spent. Traditional techniques - exact algorithms, heuristics and metaheuristics - have been thoroughly studied, but this methods often face limitations in scalability and use of computational resources when confronted with larger and more complex instances. Recently, Graph Neural Networks (GNNs) and Graph Attention Networks (GATs) have been used to tackle these more complex instances by capturing the relational structures inherent in graph-based information. Existing methods often rely on the REINFORCE approach with baselines like the Greedy Rollout, which uses a double-actor architecture that introduces computational overhead that could hinder scalability. This paper addresses this problem by introducing a novel approach that uses a GAT network trained using reinforcement learning with the DiCE estimator. By using DiCE, our method eliminates the need for a double-actor structure, which contributes to lower the computational training cost without sacrificing solution quality. Our experiments indicate that our model achieves solutions close to the optimal, with a notable decrease in training time and resource utilisation when compared with other techniques. This work provides a more efficient machine learning framework for the CVRP.
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Paper Nr: 46
Title:

Solving Monge Problem by Hilbert Space Embeddings of Probability Measures
Authors:

Takafumi Saito and Yumiharu Nakano
Abstract: We propose deep learning methods for classical Monge’s optimal mass transportation problems, where where the distribution constraint is treated as a penalty term defined by the maximum mean discrepancy in the theory of Hilbert space embeddings of probability measures. We prove that the transport maps given by the proposed methods converge to optimal transport maps in the problem with L2 cost. Several numerical experiments validate our methods. In particular, we show that our methods are applicable to large-scale Monge problems.
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Paper Nr: 49
Title:

Cost Optimization Analysis of Retrial Machine Repair Problem with Warm Standby Components and Imperfect Coverage
Authors:

Tseng-Chang Yen, Wei-Ping Lai, Kuo-Hsiung Wang and Chia-Huang Wu
Abstract: In this paper, we examine the cost optimization analysis of retrial machine repair problem with warm standby components and imperfect coverage. This research suggests that failure times and repair times of the primary and warm standby components are exponentially distributed and that the coverage factor is the same for a primary component failure and a standby component failure. When the server is either busy with other tasks or is repairing a failed component, the failed component will be sent to the retrial orbit. The steady-state probabilities of the number of failed components in an orbit is developed by using the matrix-analytic method. The particle swarm optimization (PSO) algorithm is implemented to simultaneously determine the joint optimal values of the number of warm standbys, the repair rate, and the retrial rate at minimum cost. Under optimal operating conditions, numerical experiments were presented to illustrate results. Sensitivity analysis for system parameters is performed additionally.
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Paper Nr: 67
Title:

Bottleneck Identification in Resource-Constrained Project Scheduling via Constraint Relaxation
Authors:

Lukáš Nedbálek and Antonín Novák
Abstract: In realistic production scenarios, Advanced Planning and Scheduling (APS) tools often require manual intervention by production planners, as the system works with incomplete information, resulting in suboptimal schedules. Often, the preferable solution is not found just because of the too-restrictive constraints specifying the optimization problem, representing bottlenecks in the schedule. To provide computer-assisted support for decision-making, we aim to automatically identify bottlenecks in the given schedule while linking them to the particular constraints to be relaxed. In this work, we address the problem of reducing the tardiness of a particular project in an obtained schedule in the resource-constrained project scheduling problem by relaxing constraints related to identified bottlenecks. We develop two methods for this purpose. The first method adapts existing approaches from the job shop literature and utilizes them for so-called untargeted relaxations. The second method identifies potential improvements in relaxed versions of the problem and proposes targeted relaxations. Surprisingly, the untargeted relaxations result in improvements comparable to the targeted relaxations.
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Paper Nr: 7
Title:

Enhance Equity in Agricultural Economic Interest Groups
Authors:

Pascal Francois Faye, Daba Dieng, Jeanne Ana Awa Faye and Mariane Senghor
Abstract: In agriculture, several structures are set up as cooperative or economic interest group (EIG). However, these structures have a set of limits such as the access to finance, to national and international markets, etc. In addition, they do not care about gender balance or farmers’ vulnerability (climate, education, disability, age, fitness, assets, communication channels, socio-cultural norms, prejudice, ethnicity, etc.). This work provides a Citizen Support and Solidarity (CSS) mechanism in a context of self-interested farmers (agents) in unstable and uncertain context (interests, availabilities, interdependencies, etc.) where we consider each EIG or each cooperative as a coalition. CSS proposes a core-stable, auto-stabilizing coalition formation mechanism which maximizes social welfare, and converges gradually to near optimal results. CSS combines game theory methods and the laws of probability. Our experiments and their analysis demonstrate the efficiency of CSS.
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Paper Nr: 10
Title:

A Discrete Event Simulation Tool for Conducting a Fleet Mix Study
Authors:

Mikayla Holmes and Lise Arseneau
Abstract: A fleet mix study is currently being undertaken by the Royal Canadian Navy (RCN) to determine the optimal composition of its future fleet to meet operational requirements. We introduce a Discrete Event Simulation (DES) model developed within the Operational Research Integrated Graphical Analysis and Modelling Environment (ORIGAME), called the ORIGAME Fleet Capacity Evaluation Tool (OFCET) that will be used to examine how well a proposed future fleet (number and types of naval platforms) meets the desired operational requirements to fulfill the Navy’s mandate. This paper describes the OFCET in terms of inputs, outputs and assumptions and presents a case study with notional data to demonstrate how the tool can be used as part of a fleet mix analysis to answer “what if” type questions. Furthermore, extensions of OFCET and other problems that can be solved using this model will be provided.
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Paper Nr: 17
Title:

Data Clustering Using Mother Tree Optimization
Authors:

Wael Korani and Malek Mouhoub
Abstract: Clustering is the process of dividing data objects into different groups called clusters, without prior knowledge. Traditional clustering techniques might suffer from stagnation, where the solution is stuck in a local optimum. In the last decade, many metaheuristics, including swarm intelligence, have been applied to address the problem of clustering stagnation in a reasonable time. We propose a new clustering framework that is based on metaheuristics and, more precisely, swarm intelligence optimization algorithms that include particle swarm optimization (PSO) (Kennedy and Eberhart, 1995), whale optimization algorithm (WOA) (Mirjalili and Lewis, 2016), bacterial foraging optimization algorithm (BFOA) (Das et al., 2009) and mother tree optimization (MTO). To evaluate the performance of our framework and the new metaheuristic based on MTO called CMTO, we conducted a set of experiments on eight different datasets and using four different metrics: rand coefficient, Jaccard coefficient, distance matrix and running time. The results show that MTOC outperforms BF and WOA in terms of random coefficient (accuracy) in five of the eight instances.
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Paper Nr: 33
Title:

Renewable Energy-Based Micro-Grid for Clean Electricity and Green Hydrogen Production
Authors:

Issa Zaiter, Ahmad Mayyas and Raed Jaradat
Abstract: The expected rise in hydrogen use offers a chance to speed up the decarbonization of the power generation sector. In this study, a linear programming optimization model is developed to determine the optimal technology capacity for a power and hydrogen production system driven by 100% renewable energy serving 25,000 capita with a total annual power demand of 532 GWh and an annual hydrogen demand of 5255 tons. The model aims to identify the optimal capacities of renewable energy sources and energy storage technologies to minimize system costs while meeting the demand for electricity and hydrogen. The results show the optimal system includes 59 MW of wind turbines, 630 MW of solar PV panels, 368 MW of polymer electrolyte membrane electrolyzer, 126 MW of proton exchange membrane fuel cells, 163 MW of lithium-ion batteries, and 111,000 m3 of hydrogen storage. The total annualized system cost is $182 million, with electricity priced at $0.29 per kWh and green hydrogen at $5 per kg. By integrating hydrogen production with renewable energy-based power generation, It is concluded that a 100% renewable energy-driven system can meet the power and hydrogen demand for a sustainable community with the environmental benefit of zero carbon emissions, albeit with a higher price for a unit of power.
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Paper Nr: 38
Title:

Modeling for Assessment of Risks in Smart City Mobility Operations
Authors:

Reem Al Sharif and Shaligram Pokharel
Abstract: Smart city operations face risks due to project complexity and the involvement of multiple stakeholders. Such risks include cybersecurity, data security and privacy, system interoperability, maintenance of smart technology, shortage of trained experts, complicated governance, and stakeholder engagement challenges. Assessing these risks is vital to ensure the availability and efficiency of smart city services, support reputation, and sustain revenue. Existing assessment tools evaluate smart cities' operational smartness, sustainability, and management but often lack comprehensiveness in risk assessment. This paper contributes by proposing a risk assessment model using the Dempster-Shafer theory that can consider a full spectrum of risks in smart city operations. The model is evaluated on preliminary operational data from a smart transportation system in Qatar, and key operational phase risks in smart mobility are assessed.
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Paper Nr: 39
Title:

Predicting Postpartum Depression in Maternal Health Using Machine Learning
Authors:

Maria Alejandra Terreros-Lozano, Diana Lopez-Soto, Samuel Nucamendi-Guillén and María Alejandra López-Ceballos
Abstract: Postpartum depression (PPD) is a severe mental health condition affecting mothers after childbirth, characterized by prolonged sadness, anxiety, and fatigue. Unlike the transient "baby blues," PPD's symptoms can last months, impacting a mother's ability to care for herself and her baby. In the U.S., PPD affects about 1 in 7 women, with a significant rise in prevalence from 13.8% to 19.8% in recent years. This condition leads to adverse effects on maternal and infant health. Early diagnosis and treatment of PPD can help prevent longterm depression and minimize the emotional and financial burden associated with the condition. This research aims to evaluate machine learning models to predict PPD risk. Critical factors were identified, and an accuracy of 96.57% and a precision of 99.88% were obtained. This predictive model enables early, personalized interventions, aiming to improve maternal health outcomes and reduce the societal burden of PPD.
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Area 2 - Applications

Full Papers
Paper Nr: 12
Title:

Effective Inventory Control Under Very Large Unknown Deterioration Rate and Volatile, Almost Unpredictable Customer Demand
Authors:

Beatrice Ietto and Valentina Orsini
Abstract: We consider a periodically reviewed perishable Supply Chain (SC) whose dynamics shows the following elements of complexity: the goods are affected by a very large, uncertain deterioration factor (DF), the customer demand is highly unpredictable and volatile. The problem we face is to define an effective Inventory Replenishment Policy (IRP) conciliating the conflicting requirements of maximizing the satisfied customer demand and containing the Bullwhip Effect (BE). The method we propose is situated in the general framework of min-max Model Predictive Control (MPC) applied to SC management. We exploit the flexibility and generality of min-max MPC to define a specifically tailored method to address the peculiarity of the current, extremely complex issue. Especially, we demonstrate the advantages of using a short prediction horizon and point-wise constraints on the IRP.
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Paper Nr: 13
Title:

A Mixed-Integer Programming Approach for an Extended Fixed Route Hybrid Electric Aircraft Charging Problem
Authors:

Anthony Deschênes, Raphaël Boudreault, Jonathan Gaudreault and Claude-Guy Quimper
Abstract: Air mobility is rapidly transitioning towards hybrid electric aircraft. In the context of multi-flight missions, aircraft operators will need to consider numerous infrastructure and operational constraints in their planning, where predicting energy usage is critical. This problem is introduced in previous work as the Fixed Route Hybrid Electric Aircraft Charging Problem (FRHACP) and a Dynamic Programming (DP) approach is proposed. It consists of deciding a cost-optimal charging, refueling, and hybridization strategy for a given aircraft route. In this paper, we introduce an extended version of this problem with additional constraints and soft schedule requirements. We then propose a Mixed-Integer Programming (MIP) approach to solve it and compare its performance against an updated DP approach on a benchmark of 10 realistic instances. Results demonstrate that MIP consistently produces superior solutions than DP, both with and without gradient descent post-treatment, achieving average cost reductions of 145$ (1.7%) and 377$ (4.3%), respectively. However, it increases on average the solving time. We finally discuss the benefits and drawbacks of both approaches, with a particular emphasis on the scalability of DP through additional experiments.
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Paper Nr: 18
Title:

Long-Term Planning of Preventive Maintenance Using Constraint Programming: A Naval Case Study
Authors:

Raphaël Boudreault and Vanessa Simard
Abstract: Maintenance planning is an essential element in the life-cycle management of an asset. Unplanned maintenance work can cause significant productivity and financial loss, while manually assessing compliance is complex and prone to errors. In the naval domain, ensuring mission readiness and operational availability is critical. Thus, periodic preventive maintenance tasks must be carefully allocated over a long-term horizon considering the ship availability, business rules, and workload limitations. This distribution over fixed short work periods can result in tasks being excessively advanced or deferred instead of executed when due. We propose a Constraint Programming approach to produce feasible tactical plans of preventive maintenance for ships minimizing advancements and deferrals of tasks. We validate our methodology on an industrial naval use case and demonstrate its relevance compared to a currently used planning method, greatly reducing over-maintenance with occurrences decreased by up to 25% and advancements by up to 93%. The method is integrated into Maintenance Optimizer™, an interactive planning solution that supports decision-making in this context.
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Paper Nr: 19
Title:

A Constraint Satisfaction Problems Based Scalable Framework to Address Large-Scale Realistic Scheduling and Routing Problems
Authors:

Liwen Zhang, Sara Maqrot, Florent Mouysset and Christophe Bortolaso
Abstract: Scheduling and routing solutions for organizational staff , along with decision-making support for timetabling, have become increasingly complex. This paper addresses the challenges associated with realistic large-scale generic routing and scheduling problems with a multi-day horizon. We introduce a 2-level scalable framework featuring a scalable use-case adapter and a scalable optimizer. In optimizer, a Scheduling and Routing Problem (SRP) model and a configurable constraint system are implemented using OptaPlanner. In the experimentation section, we present two real-life use cases in Spain and France, involving up to 481 activities to be performed by 18 staff members over 4 weeks. These scenarios are submitted to our model under different meta-heuristic configurations. The results demonstrate the achievement of high-quality optimized solutions within a short computing time of just 8 minutes. Additionally, a detailed investigation is conducted to interpret the scores of optimized solutions in an understandable manner.
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Paper Nr: 26
Title:

Enhancing Circularity in Medical Device Supply Chains by Optimizing EoL Decisions Through Reinforcement Learning: A Multi-Objective Approach
Authors:

Soufiane El Bechari, Oualid Jouini, Zied Jemai, Fourat Trabelsi and Robert Heidsieck
Abstract: Circular supply chains are becoming essential in the pursuit of sustainability, as they promote the responsible disposal, recycling, and reuse of products at the end of their life cycles. This research, developed in collaboration with GE HealthCare, presents a multi-objective optimization framework that incorporates environmental, economic, and circularity performance in end-of-life (EoL) decision-making. The proposed model leverages historical data on reuse and recycling success rates to capture the operational realities of circular supply chains. By employing Q-learning, this paper aims to develop a decision-support mechanism that optimizes EoL actions for components, thereby enhancing the circularity, reducing carbon footprint, and minimizing economic costs within the circular supply chain.
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Paper Nr: 37
Title:

A Real-World Multi-Depot, Multi-Period, and Multi-Trip Vehicle Routing Problem with Time Windows
Authors:

Mirko Cavecchia, Thiago Alves de Queiroz, Riccardo Lancellotti, Giorgio Zucchi and Manuel Iori
Abstract: Rich vehicle routing problems frequently arise in real-world applications. The related literature is trying to effectively address many of them, considering all their constraints and the need to solve large practical instances. In this paper, we handle a rich vehicle routing problem that emerges from an Italian service provider company operating in the delivery of pharmaceutical products. This problem is characterized by constraints that include multiple depots, periods, and trips, as well as specific time windows, customer-vehicle incompatibilities, and restrictions on vehicle working hours. To solve this problem, we propose a two-phase decomposition approach. In the first phase, we generate a set of trips that satisfy all problem constraints for a single period. Then, in the second phase, three algorithms based, respectively, on a greedy heuristic, a variable neighbourhood search metaheuristic, and a mixed integer linear programming model are tested to combine the generated trips into routes for the overall set of periods. Computational results on real-world instances show that the model hits the one-hour time limit for cases with more than 150 trips. The metaheuristic algorithm performs better in terms of objective function value. The greedy algorithm is faster but is outperformed by the other methods in terms of solution quality.
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Paper Nr: 65
Title:

Modelling Defence Planning as a Sequential Decision Problem
Authors:

Carolyn Chen, Mark Rempel and Kendall Wheaton
Abstract: Defence planning in a nation’s defence organization is a complex process that requires considering hundreds of projects and billions of taxpayer dollars. While a variety of methods, such as integer programming and genetic algorithms, are used in practice to help decision makers select in which projects to invest, their application tends to not account for the fact that these decisions are made sequentially and under uncertainty. In this paper, we present the first steps towards developing a sequential decision model of the Canadian Department of National Defence’s multi-gate Project Approval Process that addressed both issues. Our contributions are twofold. First, using the universal modelling framework for sequential decisions we present a mathematical model that accounts for the sequential nature of project selection, arrival of new projects over time, and uncertainty in future budgets. In addition, we extend this model to account for the uncertainty in how a project’s cost changes over time when its selection is delayed. Second, we demonstrate how these models may be used to compare the effectiveness of three project selection decision policies, namely a ranked list approach, a knapsack approach, and a knapsack approach that reserves a contingency fund for future projects.
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Paper Nr: 80
Title:

Hierarchical Decomposition Framework for Steiner Tree Packing Problem
Authors:

Hanbum Ko, Minu Kim, Han-Seul Jeong, Sunghoon Hong, Deunsol Yoon, Youngjoon Park, Woohyung Lim, Honglak Lee, Moontae Lee, Kanghoon Lee, Sungbin Lim and Sungryull Sohn
Abstract: In this paper, we address the complex combinatorial optimization (CO) challenge of efficiently connecting objects at minimal cost, specifically within the context of the Steiner Tree Packing Problem (STPP). Traditional methods often involve subdividing the problem into multiple Steiner Tree Problems (STP) and solving them in parallel. However, this approach can fail to provide feasible solutions. To overcome this limitation, we introduce a novel hierarchical combinatorial optimizer (HCO) that applies an iterative process of dividing and solving sub-problems. HCO reduces the search space and boosts the chances of getting feasible solutions. This paper proposes for the first time a learning-based approaches to address STPP, introducing an iterative decomposition method, HCO. Our experiments demonstrate that HCO outperforms existing learning-based methods in terms of feasibility and the quality of solutions, and showing better training efficiency and generalization performance than previous learning-based methods.
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Short Papers
Paper Nr: 24
Title:

An Innovative Urban Delivery System Based on Customer-Selected Addresses and Cost-Effective Driver Rates
Authors:

Oualid Benbrik, Rachid Benmansour and Raca Todosijević
Abstract: Last-mile delivery is undergoing rapid transformation due to the surge in home delivery services and the increasing demand for convenience, driven by the digitalization of businesses. In response to this evolving landscape, businesses are exploring innovative delivery methods, including the use of parcel lockers and drone delivery. This study investigates a novel approach to last-mile delivery, utilizing non-professional delivery personnel (crowd-shippers) to fulfill customer orders at designated addresses. By leveraging crowd-shippers and considering service locations alongside a comprehensive set of drivers’ preferences—including familiarity with the delivery area, traffic conditions, and ease of access— our aim is to minimize unsuccessful delivery attempts, reduce costs, and align with environmental and societal sustainability goals. The main objective is to minimize the total cost of delivery, accounting for both the total distance traveled and the drivers’ preferences regarding delivery points. We develop and solve a mixed-integer programming model that represents this scenario, providing insights into the advantages of integrating drivers’ preferences into last-mile delivery optimization strategies.
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Paper Nr: 40
Title:

An Airline Profit Management Model with Overbooking and No-Shows
Authors:

Elias Olivares-Benitez, Ana Paula Orozco Esparza, Juan Orejel and Catya Zuniga
Abstract: This research presents a model for airline profit optimization considering information such as demand forecasts, seat inventory, operational costs, overbooking penalties, expected no-shows, and time-dependent fare classes. The main decisions in the model are the selection of the aircraft, the number of seats sold per fare, including overbooking, and the number of denied seats. The model incorporates probabilistic information, like the expected demand and the expected proportion of no-shows. The model is constructed as a deterministic mixed-integer program. Some data was estimated using information acquired from different industry sources, and some data was set with reasonable estimations. A factorial experiment was designed to understand the importance of different parameters. The input variables were the overbooking compensation penalty, the no-show probabilities per fare and time block, and the seat demand. Using a statistical analysis, it was determined that the no-show estimation has the most significant impact on the total revenue, and the demand forecast after that. These results highlight the importance of precise estimations to increase the airline’s profit.
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Paper Nr: 42
Title:

A Green Transportation Problem for e-Commerce Deliveries
Authors:

Théo Le Brun, Marie-José Huguet, Sandra Ulrich Ngueveu and Romulus Grigoras
Abstract: To get involved in the fight against climate change, e-commerce actors should reduce the environmental impact of their activities. For retailers, a key challenge to is identify the stock sources for fulfilling online orders. In this paper, our goal is to orchestrate orders while minimizing the associated environmental impact. We propose a model of Green Transportation Problem for E-commerce Deliveries (GTP-ED) which can be seen as a general case of Fixed Charge Transportation Problem. We detail how we obtain the environmental objective function and how we generate instances based on real world and realistic data and that good quality solutions can be obtained quickly. Then, we show the relevance of our environmental objective function by comparing the results with an orchestration based on minimizing the distance traveled by the parcels, which leads to a 30% increase of environmental cost. Finally, we compare the GTP-ED with an economic approach and outline a significant tension between our environmental and economic objectives in that context.
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Paper Nr: 50
Title:

Optimal Covering and Trajectory Planning for Air-Ground Integrated Networks in Post-Disaster Scenarios
Authors:

Khouloud Kessentini, Raouia Taktak and Lamia Chaari
Abstract: In this paper, we examine a post-disaster scenario in which Access Points (APs) in an affected area are deactivated, resulting in the disconnection of End Devices (EDs). This disruption weakens situational awareness and impacts the overall effectiveness of rescue operations. Thus, quick network recovery becomes essential for emergency efforts. Toward this end, Air-Ground Integrated Networks (AGIN) present new opportunities, which this study explores by using cruising Unmanned Aerial Vehicles (UAVs) to reconnect deactivated APs effectively. In this study, we solve this problem through a two-step process. First, we identify the best disconnected APs for reactivation. This subproblem is formulated as an Unsplittable Capacitated Facility Location Problem (UCFLP). Second, we plan the UAVs paths to reactivate these selected APs. This subproblem is formulated as a Capacitated Vehicle Routing Problem (CVRP). We present an Integer Linear Programming (ILP) formulation for the UCFLP and a Mixed-Integer Linear Programming (MILP) formulation for the CVRP, then we propose a Variable Neighborhood Search (VNS) algorithm to solve the CVRP in a reasonable amount of time. Computational results show the efficiency of the proposed method.
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Paper Nr: 51
Title:

Minimizing Energy Cost in a Job-Shop Scheduling Problem Under ToU Pricing: A New Method Based on a Period-Indexed MILP
Authors:

Marouane Felloussi, Xavier Delorme and Paolo Gianessi
Abstract: This work addresses the job-shop scheduling problem under energy considerations, specifically focusing on minimizing total energy costs within a Time-of-Use pricing framework, denoted as Jm||TEC. We propose a period-indexed Mixed-Integer Linear Programming formulation, which proves advantageous due to its smaller model size compared to traditional time-indexed approaches. Initial studies highlight that while our model can rapidly find feasible solutions, it struggles with weak linear relaxations. Different families of valid inequalities are thus considered to improve the obtained lower bounds. In order to evaluate and compare the impact of the proposed valid inequalities, computational experiments are presented and numerical results are discussed and analyzed.
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Paper Nr: 60
Title:

Synchronized Drone and Truck Routing Problem: A Multi-Stakeholder Perspective
Authors:

Maria Elena Bruni, Sara Khodaparasti, Giuseppe Muratore and Vincenzo Gentile
Abstract: This paper introduces a synchronized truck and drone routing problem consisting a fleet of capacitated trucks and drones dispatching last-mile deliveries. Each truck, while traveling on its route to serve customers, could stop and launch drones which perform multiple back and forth trips between the truck and one or more delivery destination, serving one customer at a time. The synchronization between trucks and drones moves is mandatory since the drone is launched and retrieved by the truck, that should wait for delivery completion and drone retrieval. Following a multi-stakeholder perspective, different customer- and business-oriented objectives are included to account for the presence of different (public an private) actors and end-users with conflicting interests and preferences. We formulate the problem as a mixed-integer program to determine the optimal truck routes and sequence of drone trips under the synchronization assumption. The proposed model is then solved by a meta-heuristic solution approach.
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