ICORES 2023 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: 7
Title:

A Comparison of Several Speed Computation Methods for the Safe Shortest Path Problem
Authors:

Aurélien Mombelli, Alain Quilliot and Mourad Baiou
Abstract: This paper is based on (Mombelli et al., 2022). In their article, they dealt with a fleet of autonomous vehicles which is required to perform internal logistics tasks in some protected areas. This fleet is supposed to be ruled by a hierarchical supervision architecture which, at the top level, distributes and schedules Pick up and Delivery tasks, and, at the lowest level, ensures safety at the crossroads and controls the trajectories. They presented the problem of finding a shortest path under risk constraints and proposed a way to compute speed functions along the path. In this paper, we present some theoretical results and focus on the fixed path problem. We propose several new ways of computing speed functions including a couple with reinforcement learning.
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Paper Nr: 10
Title:

Workforce Modelling with Experience Accumulation
Authors:

Timo Lahteenmaa-Swerdlyk, François-Alex Bourque and Slawomir Wesolkowski
Abstract: The purpose of this work is to investigate the population dynamics of on-the-job training in a military application, a scenario where mentees enter the system to receive training under the supervision of mentors, before becoming mentors themselves. This work builds on the two-level model which only considers the total mentee (level 1) and mentor (level 2) populations. Accumulation of experience is used to denote training progression, improving the tracking of mentees through their training. Two new models are considered: (1) A multi-stage mentee training model, which sub-divides the training into stages the mentees must progress through, and (2) A transport model, a limit case as the number of stages is increased to infinity, resulting in a continuous training medium mentees progress through. These two models are investigated by solving their respective analytical models. To verify system behaviours, analytical solutions are compared to discrete-event simulations.
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Paper Nr: 16
Title:

Counterfactual Explanations for Workforce Scheduling and Routing Problems
Authors:

Mathieu Lerouge, Céline Gicquel, Vincent Mousseau and Wassila Ouerdane
Abstract: End-users of system solving combinatorial optimization problems such as the Workforce Scheduling and Routing Problem usually do not have the necessary background for understanding the reasons which has lead the system to output specific solutions and may have questions about them, especially questions about nonexpected facts observed in the solutions. Developing techniques to automatically generate explanations in response to end-users questions would then help enhancing their understanding of the results and preserve their trust in the system. In this paper, we propose a new mathematical programming-based approach to compute counterfactual explanations in response to end-users’ questions. Such explanations emphasize the few changes that may be operated on the instance data for obtaining solutions corresponding to the end-users’ expectations.
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Paper Nr: 43
Title:

Automatic Placer for Analog Circuits Using Integer Linear Programming Warm Started by Graph Drawing
Authors:

Josef Grus, Zdeněk Hanzálek, Dalibor Barri and Patrik Vacula
Abstract: Due to its diversity, the physical design of the Analog and Mixed-Signal Integrated Circuits is not as automated as the physical design of digital Integrated Circuits. The placement process is one of the critical steps of the physical design, and automating it would significantly shorten the design time. We formulate the placement process using an Integer Linear Programming approach, with features to support a specific semiconductor technology. We include an enumeration of possible variants of the circuit’s topological structures, which are afterward considered during optimization. We use the Gurobi solver to minimize both the approximate wire length and the placement area. The results were evaluated by layout design experts and compared with manual designs. We also utilize a graph drawing-based method to generate an initial feasible solution to warm start the Integer Linear Programming solver, which noticeably improves the performance and shortens the computation time (5x to 15x), and makes the approach applicable even for larger problem instances containing 100 independent elements. Experiments performed on real-life industrial problem instances show that our graph drawing-enhanced approach can produce high-quality placement in a much shorter time than the designers need.
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Short Papers
Paper Nr: 1
Title:

Integrated Production and Energy Supply Planning on an Industrial Site by Mixed-Integer Linear Programming
Authors:

Ruiwen Liao and Céline Gicquel
Abstract: Industrial production sites are nowadays faced with two major concerns: the need to reduce the environmental impact of their processes and the economic difficulties caused by the rising energy prices. Both challenges may be partially tackled by powering industrial processes with electricity generated on-site from renewable sources. However, the volatility of the electricity prices and the intermittence of the locally generated renewable energy sources result in the need to solve an integrated industrial production and energy supply planning problem. This work investigates a single-machine multi-product lot-sizing problem for an industrial process powered by both grid electricity and on-site renewable energy. We propose a new extension of the Proportional Lot-sizing and Scheduling Problem relying on a two-level structure for time discretization. The first level is related to the product demand satisfaction, the second one is used for both production and energy supply planning. The proposed extension is compared to a previously published extension of the General Lot-sizing and Scheduling Problem dealing with a similar problem. Our preliminary numerical results show that in most cases, our model provides a production plan of the same cost, but with a significantly reduced computational effort.
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Paper Nr: 3
Title:

Synchronizing Vehicle Routing and Photo-Voltaic Production
Authors:

Alejandro O. Gonzalez, Alain Quilliot and Hélène Toussaint
Abstract: We deal here with the routing and scheduling of electric vehicles in charge of performing internal logistic tasks inside some protected area. Some vehicles are provided in energy by a local photo-voltaic facility with limited production/storage capacities and time dependent production rates. In order to avoid importing energy from outside (notion of self consumption) one must synchronize energy consumption and production while minimizing both production and routing costs. Because of the complexity of resulting bi-level optimization model, we handle it while shortcutting the production scheduling level with the help of surrogate estimators, whose values are computed through a pricing mechanism and machine learning devices. According to this purpose we design, implement and test several algorithms and get an evaluation of the potentiality of such surrogate component based approaches. This work was carried on in partnership with national power company EDF, in the context of the PGMO program.
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Paper Nr: 9
Title:

Hybrid Methods to Solve the Two-Stage Robust Flexible Job-Shop Scheduling Problem with Budgeted Uncertainty
Authors:

Carla Juvin, Laurent Houssin and Pierre Lopez
Abstract: This paper addresses the robust flexible job-shop scheduling problem considering uncertain operation processing times associated with an uncertainty budget. Exact solution methods based on mixed integer linear programming and constraint programming are proposed to solve the problem. Such solutions are hybridized in the framework of a two-stage robust optimization, and a column and constraint generation algorithm is used to solve representative instances. The experimental results show the advantages of a two-stage approach where constraint programming and integer programming are mixed to solve a master problem and a subproblem, respectively.
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Paper Nr: 13
Title:

Welcome to the Jungle: A Conceptual Comparison of Reinforcement Learning Algorithms
Authors:

Kenneth Schröder, Alexander Kastius and Rainer Schlosser
Abstract: Reinforcement Learning (RL) has continuously risen in popularity in recent years. Consequently, multiple RL algorithms and extensions have been developed for various use cases. This makes RL applicable to a wide range of problems today. When searching for suitable RL algorithms to specific problems, the options are overwhelming. Identifying the advantages and disadvantages of methods is difficult, as sources use conflicting terminology, imply improvements to alternative algorithms without mathematical or empirical proof, or provide incomplete information. As a result, there is the chance for engineers and researchers to miss alternatives or perfect-fit algorithms for their specific problems. In this paper, we identify and explain essential RL properties. Our discussion of different RL concepts allows to select, optimize, and compare RL algorithms and their extensions, as well as reason about their performance.
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Paper Nr: 19
Title:

Performance Metrics Based on Data Envelopment Analysis for Evaluating Multi-Objective Linear Programming Solution Methods
Authors:

Javier E. Gómez-Lagos, Marcela C. González-Araya and Luis G. Acosta Espejo
Abstract: Three performance metrics based on data envelopment models are proposed for evaluating MOLP solution methods. Every proposed metric is associated to a one category, being these categories the cardinality, accuracy, and diversity. In addition, the proposed metrics are classified as unary or binary. The cardinality and accuracy metrics are estimated using a DEA model based on the slack based measure model, while the diversity metric is calculated using the super-efficiency DEA model. The proposed metrics were applied to compare two sets of solutions obtained by two strategies of a MO-GRASP algorithm for solving a MOLP tactical harvest planning model. The results show that the metrics allow discrimination between the algorithms and, moreover, to select a suitable algorithm for a MOLP model.
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Paper Nr: 24
Title:

Simulation Study for the Comparison of Power Flow Models for a Line Distribution Network with Stochastic Load Demands
Authors:

Mark Christianen, Maria Vlasiou and Bert Zwart
Abstract: We use simulation to compare different power flow models in the process of charging electric vehicles (EVs) by considering their random arrivals, their stochastic demand for energy at charging stations, and the characteristics of the electricity distribution network. We assume the distribution network is a line with charging stations located on it. We consider the Distflow and the Linearized Distflow power flow models and we assume that EVs arrive at the network with an exponential rate, have an exponential charging requirement, and that voltage drops on the distribution network stay under control. We provide extensive numerical results investigating the effect of using different power flow models on the performance of the network.
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Paper Nr: 28
Title:

The Single Depot Multiple Set Orienteering Problem
Authors:

Ravi Kant, Abhishek Mishra and Siddharth Sharma
Abstract: In this article, we present the single Depot multiple Set Orienteering Problem (sDmSOP), a new variant of the classical Set Orienteering Problem (SOP). A significant feature of sDmSOP is the presence of many travelers who set off from the same depot and return there at the end of their journey. The objective of the problem is to maximize the profit while remaining within the budget; hence the challenge at hand involves searching multiple paths among the mutually clustered sets for travelers. A set’s profit can be collected with a single node visit only. Supply-chain management, the bus delivery problem, etc., are just a few examples where the sDmSOP has proven useful. By simulating the instances of the Generalized Traveling Salesman Problem (GTSP) using GAMS 37.1.0, we determine the optimal profit for GTSP instances for some small and medium instances which follow the triangular and symmetric properties. We find that the use of multiple travelers is beneficial for both service providers and customers, as it allows service providers to offer their services to customers at a lower cost because the service provider gets a significant amount of profit using multiple travelers.
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Paper Nr: 33
Title:

A Robust Optimization for a Single Operating Room Scheduling Problem with Uncertain Durations
Authors:

Yoshito Namba, Mari Ito and Ryuta Takashima
Abstract: In order to improve the quality of patient care, efficient surgical management is significant for overall hospital management. This study proposes a robust optimization model that minimizes delay in surgery by taking the surgical sequence into account. We verified an influence of the risk-averse tendency on the schedule. In the numerical analysis, the schedule created by the robust optimization model was compared with that of the stochastic programming model. The results suggest that robust optimization models tend to avoid long delays.
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Paper Nr: 34
Title:

Joint Multi-Item Production and Condition-Based Maintenance Control of a System with Setup Times and Stochastic Demand
Authors:

Alp Darendeliler, Dieter Claeys and El-Houssaine Aghezzaf
Abstract: In this paper, we address the joint production and Condition-based maintenance (CBM) planning problem for a deteriorating single-machine multi-product manufacturing system with uncertain product demands. The objective is to find an integrated production and maintenance policy such that the sum of expected setup, holding, lost sales, preventive and corrective maintenance costs is minimized. We formulate the problem as a semi-Markov decision process and propose a Q-learning algorithm for the problem. A numerical example is provided to illustrate the solution method.
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Paper Nr: 40
Title:

Optimization of Circular Conveyor Belt Systems with Multi-Commodity Network Flows
Authors:

Antonin Novak, Matous Pikous and Zdenek Hanzalek
Abstract: Modern industrial production with alternative process plans and the use of complex machine equipment increases requirements for its intralogistics operations in terms of efficiency, resilience, and flexibility. One of the most common solutions for transporting workpieces between the manufacturing stations is a system of conveyor belts where each conveyor rotates in a fixed direction at a constant speed. The movement of the individual workpieces can be controlled only indirectly via a set of gates connecting different carousels. In this paper, we aim to increase the flexibility of conveyor belt systems by carefully scheduling the gates to route the workpieces efficiently along the production line according to their process plans. The key component of our solution is the discretization of both the time and positions on the belts to represent the system by a directed graph with circular components. To find the routing of workpieces that minimizes the total flow time, we have reduced the problem to the integer multi-commodity flow on the time-expanded network with an extension for the vertex precedences. Despite the simplicity of the formulation, the results suggest that off-the-shelf solvers can find optimized routing for instances with tens of workpieces and more than hundreds of belt positions within a few minutes.
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Paper Nr: 50
Title:

Optimization of Direct Transportation Flows for the Removal of Construction Waste Bins with both Resource and Task Availability Interval Constraints
Authors:

Safae Abderebbi and Wahiba Ramdane Cherif-Khettaf
Abstract: This study focuses on a new real-word problem encountered in the construction sector, which concerns the optimization of the removal of construction waste bins from construction sites to a massification platform, where a limited heterogeneous fleet of tipper trucks (vehicles) must perform direct trips from the platform to the construction sites to collect the waste bins. Each vehicle has a capacity of one bin, it leaves the platform with an empty bin, travels to the construction site, drops off the empty bin in the construction site, collects the full bin and returns to the platform to unload the full bin. The issue is that the vehicles and the construction sites have one or more periods of availability, and thus are not available any time. This problem is modeled as a parallel machine scheduling problem of bin removal tasks on non-identical machines (vehicles), with new constraints that concern the presence of multiple availability intervals for both vehicles and tasks. Two mixed-integer programming (MIP) models are presented and evaluated on 18 new instances derived from real industrial case study.
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Paper Nr: 23
Title:

Opportunistic Maintenance of Multi-Component Systems Under Structure and Economic Dependencies: A Healthcare System Case Study
Authors:

Abdelhamid Boujarif, David W. Coit, Oualid Jouini, Zhiguo Zeng and Robert Heidsieck
Abstract: This paper presents an opportunistic maintenance model for a multi-component system. We develop a model that considers the ages and residual values of the non-failed components and component failure time distributions. We also consider the structural and economical dependency between the items by favoring grouped over individual replacement to reduce operation costs. We use a genetic algorithm to derive the optimal opportunistic maintenance plan by minimizing the long-run operational cost considering both maintenance cost and potential penalty costs due to failure in the future. The model recommends additional preventive operations in cases where the reliability does not satisfy the quality condition and to reduce the long-run operational cost. A sensitivity analysis shows that the optimal decision is mainly affected by the logistic cost, the interest rate, and the planning horizon. The model’s performance has been evaluated using several real case problems, demonstrating that the proposed method is very efficient.
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Paper Nr: 49
Title:

Modeling and Optimization of Virtual Networks in Multi-AS Environment
Authors:

Yong Xue, Alexander Brodsky and Daniel Menasce
Abstract: An evolutionary approach to the Internet Ossification problem is to adopt a pluralistic architectural paradigm by leveraging the existing Internet infrastructure and developing multiple virtual service networks on top of it to satisfy diverse and ever-demanding new service and application requirements from end users. However, optimal provisioning or mapping of virtual network requests (VNR) to shared underlay network resources across multiple autonomous network systems (i.e., Multi-AS) is still an open problem. This paper investigates issues related to the multi-domain virtual network embedding (MD-VNE) problems and proposes a novel Multi-AS Virtual Network Synthesis (VNS) paradigm that closely mimics real-world network settings. Proposed in this paper is a model-based approach to optimally solving the VNS problem through formal Multi-AS network modeling and optimization using Integer Linear Programming (ILP) with defined optimal objectives for deriving exact optimal solution. Besides, the paper also proposes a simple greedy-heuristic (GH) approximation algorithm to the optimization solution to address implementation complexity concerns. The experiment and evaluation results of the exact optimization solution and the approximation solution are presented and compared based on a number of defined evaluation metrics.
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Area 2 - Applications

Full Papers
Paper Nr: 11
Title:

A Simulation Tool for Exploring Ammunition Stockpile Dynamics
Authors:

Jean-Denis Caron, Robert M. Bryce and Chad Young
Abstract: The Canadian Armed Forces (CAF) uses ammunition of various types for both training and operations. We introduce a Discrete-Event Simulation (DES) model, named Future Event Ammunition Stockpile Tool (FEAST), to explore issues regarding anticipated stockpile drawdown and resupply. Schedules for military training and operations and associated ammunition demands are stochastically defined as inputs to FEAST, as are budgets, various reorder policies and other inventory management parameters. Outputs of FEAST enable statistical characterization of various performance attributes, including anticipated fulfillment of demands and costs over time. FEAST is designed to be flexibly used by practitioners to answer “what if” type questions. This paper provides an overview of FEAST and illustrates a simplified scenario where a single nature of ammunition is required to simultaneously fulfill demands from several training types and military missions, for which several resupply options are to be explored.
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Paper Nr: 21
Title:

The Green Tourist Trip Design Problem with Time Windows: A Model Application on a Urban Scale
Authors:

Annarita De Maio, Roberto Musmanno and Aurora Skrame
Abstract: We model a variant of the Tourist Trip Design Problem with time windows. The novelty of the proposed model is that we also take care of the needs of vulnerable users and of the sustainability of the tour in terms of reduction of the level of CO2 emissions generated by the vehicles associated with the different transportation modes that can be selected to plan the tour. The mathematical formulation of the proposed model consists of three objective functions, with the attempt to generate a green tour which maximizes the number of point of interests (POIs) to visit and, at the same time, the total scores of the visited POIs. The correctness of the proposed model has been tested on a real case, at a urban level. The preliminary results allow to better understand the impact of the sustainability constraints on the tour and to address the direction for further research developments.
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Paper Nr: 25
Title:

Distributionally Robust Optimization of Adaptive Cruise Control Under Uncertainty
Authors:

Shangyuan Zhang, Makhlouf Hadji and Abdel Lisser
Abstract: Due to the recent advances in intelligent and connected vehicles, Adaptive Cruise Control (ACC) has become a key functionality of advanced driver-assistant systems (ADAS) to enhance comfort and safety. The evaluation of ACC’s efficiency and safety is also crucial for the industry to prove the reliability of its products. In our paper, we propose a distributional robust optimization-based ACC reference generation model to produce the optimal commands facing the uncertainty of sensors. By taking into account the uncertainty set with knowledge of the first and second moments, the original optimization problem with chance constraints can be simplified and solved more efficiently. Numerical experiments in a driving simulator illustrate that the robustness of the results is largely increased by minimizing the risks of violation of safety constraints.
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Paper Nr: 35
Title:

Estimating Workforce Attrition Rate Parameters: A Controlled Comparison
Authors:

Robert M. Bryce and Jillian A. Henderson
Abstract: Here we consider workforce attrition rate parameter estimators (three continuous, three discrete), demonstrating that they originate in the Ordinary Differential Equation (ODE) describing a memoryless survival time population. This demonstration reveals how to craft estimators and reason about them. Setting up controlled numerical experiments we test this family of estimators to determine their relative performance, both in steady state and under dynamic change of population level. Additionally, we find the analytic expressions for the residual error of the estimators which provides insight into their bias properties. Our results suggest that, out of common simple estimators, the two-point average population and half-intake estimators are robust choices.
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Paper Nr: 41
Title:

Storage Assignment Using Nested Annealing and Hamming Distances
Authors:

Johan Oxenstierna, Louis J. van Rensburg, Peter J. Stuckey and Volker Krueger
Abstract: The assignment of products to storage locations significantly impacts the efficiency of warehouse operations. We propose a multi-phase optimizer for a Storage Location Assignment Problem (SLAP) where solution quality is based on a distance estimate of future-forecasted order picking. Candidate assignments are first sampled using a Markov Chain accept/reject method. Future-forecasted pick-rounds are then modified according to the candidate assignments and solved as Traveling Salesman Problems (TSP). The model is graph-based and generalizes to any obstacle layout in 2D. Due to the intractability of the SLAP, methods are proposed to speed up search for strong solution candidates. These include usage of fast function approximation to find potentially strong samples, as well as restarts from local minima. Results show that these methods improve performance and that total travel distance can be reduced by as much as 30% within 8 hours of CPU-time. We share a public repository with SLAP instances and corresponding benchmark results on the generalizable TSPLIB format.
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Short Papers
Paper Nr: 39
Title:

Multi-Objective Task Assignment Solution for Parked Vehicular Computing
Authors:

Jia H. Sun, Salimur Choudhury and Kai Salomaa
Abstract: With significant advances in recent technology, computational power must meet new demands. As a result, Multi-access Edge Computing (MEC) is a new networking paradigm that has received a surge in interest from both academia and industry. MEC aims to push powerful computing and storage capabilities from remote cloud servers to up-close edge servers. Vehicular Edge Computing (VEC), a subfield of MEC, has been introduced to specifically increase the computing capacity of vehicular networks, an essential component for the development of Intelligent Transportation Systems (ITS). A problem in the current development of VEC is the high cost of installing enough edge servers to compute all offloaded tasks at peak hours. However, we have observed that parked vehicles (PVs) are a rich reserve of underutilized computing resources, and their incorporation into the VEC network could lead to a solution to the aforementioned problem. This paper proposes a task offloading system with an assumed parking time estimation mechanism. Then, a novel formulation of the task offloading problem is presented that minimizes both task delay and wireless channel load. Finally, a matching based heuristic is proposed and evaluated at various configurations of the VEC environment.
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Paper Nr: 51
Title:

Managing Inventory Level and Bullwhip Effect in Multi Stage Supply Chains with Perishable Goods: A New Distributed Model Predictive Control Approach
Authors:

Beatrice Ietto and Valentina Orsini
Abstract: We consider the inventory control problem for multi stage Supply Chains (SC) whose dynamics is characterized by uncertainties on the perishability factor of stocked goods and on the customer forecast information. The control problem is to define a replenishment policy keeping the inventory level as close as possible to a desired value and mitigating the Bullwhip Effect (BE). The solution we propose is based on Distributed Robust Model Predictive Control (DRMPC) approach. This implies solving a constrained min-max optimization problem. To drastically reduce the numerical complexity of this problem, the control signal is parametrized using B-spline functions.
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Paper Nr: 52
Title:

A Blockchain-Based System for the Last-Mile Delivery
Authors:

Francesca Guerriero, Edoardo Scalzo, Rodolfo P. Calabrò and Giuseppe Scarfò
Abstract: This paper describes the application of the blockchain technology in the last-mile delivery. The attention is focused on the Vehicle Routing Problem with Occasional Drivers and Time Windows constraints. The main aim is to develop a strategy, which uses the blockchain as a support to achieve a partially decentralized delivery system. An analysis of the advantages obtained by using such a technology, in terms of economic sustainability, is carried out on small-size test instances. The proposed system is implemented and tested by using three different EVM-compatible blockchains. The collected experimental results have shown that the implemented Polygon and VeChain blockchain systems allow to achieve good performance in terms of sustainability and seems to have good prospects in terms of scalability.
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