ICORES 2020 Abstracts


Area 1 - Methodologies and Technologies

Full Papers
Paper Nr: 12
Title:

On Idle Energy Consumption Minimization in Production: Industrial Example and Mathematical Model

Authors:

Ondřej Benedikt, Přemysl Šůcha and Zdeněk Hanzálek

Abstract: This paper, inspired by a real production process of steel hardening, investigates a scheduling problem to minimize the idle energy consumption of machines. The energy minimization is achieved by switching a machine to some power-saving mode when it is idle. For the steel hardening process, the mode of the machine (i.e., furnace) can be associated with its inner temperature. Contrary to the recent methods, which consider only a small number of machine modes, the temperature in the furnace can be changed continuously, and so an infinite number of the power-saving modes must be considered to achieve the highest possible savings. To model the machine modes efficiently, we use the concept of the energy function, which was originally introduced in the domain of embedded systems but has yet to take roots in the domain of production research. The energy function is illustrated with several application examples from the literature. Afterward, it is integrated into a mathematical model of a scheduling problem with parallel identical machines and jobs characterized by release times, deadlines, and processing times. Numerical experiments show that the proposed model outperforms a reference model adapted from the literature.

Paper Nr: 15
Title:

Predicting Location Probabilities of Drivers to Improve Dispatch Decisions of Transportation Network Companies based on Trajectory Data

Authors:

Keven Richly, Janos Brauer and Rainer Schlosser

Abstract: The demand for peer-to-peer ridesharing services increased over the last years rapidly. To cost-efficiently dispatch orders and communicate accurate pick-up times is challenging as the current location of each available driver is not exactly known since observed locations can be outdated for several seconds. The developed trajectory visualization tool enables transportation network companies to analyze dispatch processes and determine the causes of unexpected delays. As dispatching algorithms are based on the accuracy of arrival time predictions, we account for factors like noise, sample rate, technical and economic limitations as well as the duration of the entire process as they have an impact on the accuracy of spatio-temporal data. To improve dispatching strategies, we propose a prediction approach that provides a probability distribution for a driver’s future locations based on patterns observed in past trajectories. We demonstrate the capabilities of our prediction results to (i) avoid critical delays, (ii) to estimate waiting times with higher confidence, and (iii) to enable risk considerations in dispatching strategies.

Paper Nr: 17
Title:

Data-driven Algorithm for Scheduling with Total Tardiness

Authors:

Michal Bouška, Antonín Novák, Přemysl Šůcha, István Módos and Zdeněk Hanzálek

Abstract: In this paper, we investigate the use of deep learning for solving a classical N P-hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a singlepass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic.

Paper Nr: 19
Title:

Optimal Pipe Routing Techniques in an Obstacle-Free 3D Space

Authors:

Marvin Stanczak, Cédric Pralet, Vincent Vidal and Vincent Baudoui

Abstract: This paper introduces two approaches to automate the design of pipe-systems in a continuous 3D space to assist designers to deal with the high number of constraints of the pipe routing problem. This work assimilates a pipe to a succession of straight sections and bends in which possible bends are restricted to a bend catalog containing orthogonal and non orthogonal bends. As a preliminary work, both methods ignore obstacles but take into account pipe section orientations to deal with asymmetrical pipe sections. To do so, a graph of reachable pipe orientations is generated from the bend catalog. Then, the first approach enumerates valid bend combinations using the previous graph and computes the optimal length of straight sections for each combination using a Linear Program. The second one formulates the pipe routing problem as a Mixed Integer Linear Programming problem based on enumerated reachable orientations. Both approaches are tested on realistic scenarios inspired by industrial problems.

Paper Nr: 29
Title:

An Approximate Method for Integrated Stochastic Replenishment Planning with Supplier Selection

Authors:

Rabin K. Sahu, Clarisse Dhaenens, Nadarajen Veerapen and Manuel Davy

Abstract: A practical methodology for integrated stochastic replenishment planning with supplier selection is proposed for the single item inventory system. A rolling horizon strategy is adopted to implement the ordering decisions. Our method works in two stages. The first stage is a general black box stage that gives the minimum expected “coverage period” cost. The second stage uses a dynamic programming approach to compute the minimum expected cost for the rolling horizon. The proposed method is applicable for both stationary and non-stationary demand distributions and even for problems with minimum order quantity constraints. We also propose to examine the benefits of a dynamic supplier selection approach in comparison to selecting a common supplier. We conduct extensive numerical analyses on synthetic data sets for validation.

Paper Nr: 31
Title:

A New Hybrid Salp Swarm-simulated Annealing Algorithm for the Container Stacking Problem

Authors:

Mohamed ElWakil, Mohamed Gheith and Amr Eltawil

Abstract: In container terminals, the shipping containers are stored temporarily in yards in the form of bays composed of vertical stacks and horizontal rows. When there is a need to retrieve a target container, it may not be located on the top of its stack, in such a case, the containers above it are called blocking containers. These blocking containers should be relocated first in order to retrieve the target container. These relocations introduce an extra workload and a challenge to the container terminal efficiency. In the Container Stacking Problem (CSP), a group of containers are to be stacked in a given bay, while considering the future retrieval of these containers with minimum number of relocations. In this paper, a new hybrid Salp Swarm-Simulated Annealing Algorithm (SSSA) is proposed for solving the NP hard CSP. The contributions of this paper are as follows, first, and for the first time, a discrete optimization version of the Salp Swarm Algorithm (SSA) is proposed. The algorithm is different from the original continuous optimization one. Second, the SSA performance is enhanced with a simulated annealing algorithm to improve its exploration capability. In order to examine the performance of the proposed algorithm, computational experiments were performed on benchmark instances that illustrated the competitive performance of the SSSA with respect to the optimal solutions of the instances.

Paper Nr: 61
Title:

Delay Predictors in Multi-skill Call Centers: An Empirical Comparison with Real Data

Authors:

Mamadou Thiongane, Wyean Chan and Pierre L’Ecuyer

Abstract: We examine and compare different delay predictors for multi-skill call centers. Each time a new call (customer) arrives, a predictor takes as input some observable information from the current state of the system, and returns as output a forecast of the waiting time for this call, which is an estimate of the expected waiting time conditional on the current state. Any relevant observable information can be included, e.g., the time of the day, the set of agents at work, the queue size for each call type, the waiting times of the most recent calls who started their service, etc. We consider predictors based on delay history, regularized regression, cubic spline regression, and deep feedforward artificial neural networks. We compare them using real data obtained from a call center. We also examine the issue of how to select the input variables for the predictors.

Paper Nr: 63
Title:

The Bi-objective Minimum Latency Problem with Profit Collection and Uncertain Travel Times

Authors:

Maria E. Bruni, Sara Khodaparasti and Samuel Nucamendi-Guillén

Abstract: This paper introduces a new bi-objective minimum latency problem with profit collection, where routes must be constructed in order to maximize the collected profit and to minimize the total latency. These objectives are usually conflicting. Thus, considering some important features, as the segmentation of the customers into two classes, mandatory and optional, and the presence of uncertain travel times, we follow a bi-objective approach, aiming to compute a set of Pareto-optimal alternatives with different trade-offs for a decision-maker to choose from. In order to address this computationally challenging problem, we propose a Multi-Objective Iterated Local Search. Computational results confirm the practicality of the algorithm, in terms of the quality of the solutions, and its computational efficiency in terms of time spent. We conclude that the algorithm finds good-quality solutions for small and medium-size instances.

Short Papers
Paper Nr: 7
Title:

Online Deterministic Algorithms for Connected Dominating Set & Set Cover Leasing Problems

Authors:

Christine Markarian and Abdul-Nasser Kassar

Abstract: Connected Dominating Set (CDS) and Set Cover (SC) are classical optimization problems that have been widely studied in both theory and practice, as many variants and in different settings, motivated by applications in wireless and social networks. In this paper, we consider the online setting, in which the input sequence arrives in portions over time and the so-called online algorithm needs to react to each portion. Online algorithms are measured using the notion of competitive analysis. An online algorithm A is said to have competitive ratio r, where r is the worst-case ratio, over all possible instances of a given minimization problem, of the solution constructed by A to the solution constructed by an offline optimal algorithm that knows the entire input sequence in advance. Online Connected Dominating Set (OCDS) (Hamann et al., 2018) is an online variant of CDS that is currently solved by a randomized online algorithm with optimal competitive ratio. We present in this paper the first deterministic online algorithm for OCDS, with optimal competitive ratio. We further introduce generalizations of OCDS, in the leasing model (Meyerson, 2005) and in the multiple hop model (Coelho et al., 2017), and design deterministic online algorithms for each of these generalizations. We also propose the first deterministic online algorithm for the leasing variant of SC (Abshoff et al., 2016), that is currently solved by a randomized online algorithm.

Paper Nr: 8
Title:

A Classification Framework for Time Stamp Stochastic Assignment Problems

Authors:

Kees Kooiman, Frank Phillipson and Alex Sangers

Abstract: In this paper Time-stamp Stochastic Assignment Problems are studied. These problems occur in places where an incoming request or task has to be connected to a resource immediately. A definition and framework for these problems is given, in which the different Time-stamp Stochastic Assignment Problems can be categorised. An explicit notation is introduced to distinguish the different categories. Several solution methods for Time-stamp Stochastic Assignment Problems are listed and their advantages and disadvantages are discussed.

Paper Nr: 9
Title:

Immediate Parcel to Vehicle Assignment for Cross Docking in City Logistics: A Dynamic Assignment Vehicle Routing Problem

Authors:

F. Phillipson and S. E. de Koff

Abstract: In this paper we present the Dynamic Assignment Vehicle Routing Problem. This problem arises in parcel to vehicle assignment where the destination of the parcels is not known up to the assignment of the parcel to a delivering vehicle. The assignment has to be done immediately without the possibility of re-assignment afterwards. The problem is defined and various methods are proposed to come to an efficient solution method. Three cases are presented to test this efficiency. An approach using minimum cost insertion with penalty performs best.

Paper Nr: 14
Title:

A Composite Indicators Approach to Assisting Decisions in Ship LCA/LCC

Authors:

Yiannis Smirlis and Marc Bonazountas

Abstract: During of the life cycle of ship, multiple decisions concerning design, operation and demolition must be made. The Life Cycle Assessment/Cost (LCA/LCC) framework applied in ships, mandates that such decisions need to encounter dominant economic and environmental aspects about the ship. In this paper we consider these decisions in the context of Multi-Criteria Decision Analysis and present a methodology to construct composite indicators to assist decision making. For the criteria introduced we propose the use of key performance indicators (KPIs) that quantify economic and environmental dimensions. For the construction, aggregation and weighting of the KPIs we present linear programming models that estimate the weights endogenously from the data. The models developed can discriminate the optimum designs, thus assisting decision making.

Paper Nr: 22
Title:

Joint Optimization of Dynamic Lot-sizing and Condition-based Maintenance

Authors:

Alp Darendeliler, Dieter Claeys, Abdelhakim Khatab and El-Houssaine Aghezzaf

Abstract: This study investigates the dynamic lot-sizing problem integrated with Condition-based maintenance (CBM) for a stochastically deteriorating production system. The main difference of this work and the previous literature on the joint optimization of lot-sizing and CBM is the relaxation of the constant demand assumption. In addition, the influence of the lot-size quantity on the evolution of the equipment degradation is considered. To optimally integrate production and maintenance, a stochastic dynamic programming model is developed that optimizes the total expected production and maintenance cost including production setup cost, inventory holding cost, lost sales cost, preventive maintenance cost and corrective maintenance cost. The algorithm is run on a set of instances and the results show that the joint optimization model provides considerable cost savings compared to the separate optimization of lot-sizing and CBM.

Paper Nr: 23
Title:

The Maximum Feasible Scenario Approach for the Capacitated Vehicle Routing Problem with Uncertain Demands

Authors:

Zuzana Borčinová and Štefan Peško

Abstract: This study deals with the Capacitated Vehicle Routing Problem where customer demands are uncertain with unknown probability distribution. We follow the robust optimization methodology to formulate and solve the Robust Vehicle Routing Problem with Demand Uncertainty. Since the robust solution is a route plan, which optimizes the worst case that could arise, our focus is concentrated on determining the worst-case demands to solve the robust optimization model. The computational experiments examined two proposed strategies to indicate their performance in terms of the extra cost and unmet demands.

Paper Nr: 25
Title:

Robust Emergency System Design using Reengineering Approach

Authors:

Marek Kvet and Jaroslav Janáček

Abstract: A robust emergency service system is usually designed so that the deployment of given number of service centers minimizes the maximal value of objective functions corresponding with the specified detrimental scenarios. If the problem is solved by any solving technique based on the branch-and-bound method, the min-max link-up constraints cause bad convergence of the associated computational process. Within this paper, we try to overcome the drawback following from the link-up constraints by usage of an iterative process applied to a series of surrogate problems. The surrogate problems represent a simple emergency system reengineering under a given scenario and chosen values of reengineering parameters. The results of the surrogate problems are used for considerable reduction of the initial set of possible service center locations. The robust emergency service system is obtained as the optimal solution of the reduced problem. We provide the reader with a comparison of the original min-max problem solution to the suggested approach.

Paper Nr: 32
Title:

DynaQUEST: A New Approach to the Dynamic Multi-satellite Scheduling Problem

Authors:

J. Berger, N. Lo, M. Noel and L. Noutegne

Abstract: Reported dynamic multi-satellite scheduling approaches for Earth observations show many limitations when operating in time-varying uncertain environment. They largely run over predetermined time periods and often offline, assume negligible execution time, improperly account for the passage of time during planning, remain myopic or fail to show “anytime” behavior. A novel approach to solving the dynamic multi-satellite scheduling problem is proposed. The open-loop with feedback DynaQUEST approach includes an event-driven controller monitoring dynamic situation evolution while supervising a co-evolving episodic scheduling problem solver. Reactive to real-time and delayed information feedback, the controller timely enables the problem-solver to stay responsive, interruptible and adaptive, taking advantage of emerging opportunities to timely improve solution quality. The problem-solver continually solves a new static problem shaped by dynamic changes and constrained by current resource commitments to adaptively expand the emergent solution. Problem model formulation is based on network flow optimization using mathematical programming. Departing from mainstream approaches widely promoting an exact objective function coupled with a heuristic problem-solving method, the proposed approach alternatively combines an approximate objective function and an exact algorithm. The approach embraces an extended time horizon relaxing myopic planning. Computational results prove the approach to be cost-effective and to outperform alternate baseline heuristics.

Paper Nr: 33
Title:

An Economic Production Quantity Model with Imperfect Quality Raw Material and Backorders

Authors:

Noura Yassine, Christine Markarian and Raed El-Khalil

Abstract: In this paper the classical EPQ model is extended to account for the cost and quality of the raw material used in the production process and to incorporate the effects of shortages into the model. A production process that uses n different types of raw material is considered. The various types of raw material acquired in batches from the suppliers are assumed to contain a percentage of imperfect quality items of raw material. The proportion of imperfect quality raw material found in a batch is a random variable having a known probability distribution. A mathematical model describing the inventory/production situation is formulated and used to derive a system of equations whose solution is the optimal production and shortage quantities that minimizes the total cost. It is shown that the total cost function depends on the determination of the maximum of a set of n independent random variables obtained from the proportions of imperfect quality raw material. A process for obtaining the probability function of the maximum along with its expected value is described. Expressions for the probability density function and the expected value of the maximum are developed for the case when the random variables are uniformly distributed. A numerical example illustrating the determination of the optimal policy is presented.

Paper Nr: 43
Title:

One Step Ahead Optimal Control of a Single Echelon Supply Chain using Mathematical Programming

Authors:

Amit Bhaya, Eugenius Kaszkurewicz and Luiz B. Roth

Abstract: A single echelon supply chain model problem, consisting of a store with known inventory and shipping capacities, a known delivery delay or lead time and a random demand for a product at the store is formulated as an optimal control problem. In the practical case when only current and past demands are known, using the concept of one step ahead optimal control, the problem is reformulated as the mathematical programming problem of maximizing economic value added (EVA), subject to the dynamics and constraints, such as inventory size. Illustrative examples are given and performance indices are proposed to evaluate the performance of the proposed controller, which exhibits good efficiency and no bullwhip effect.

Paper Nr: 53
Title:

Collaboration Mechanism for Shared Returnable Transport Items in Closed Loop Supply Chains

Authors:

Fatima E. Achamrah, Fouad Riane, Abdelghani Bouras and Evren Sahin

Abstract: This paper addresses a relevant practical approach of collaboration in supply chains including reverse flows of materials. The objective is to simulate a two-stage closed loop supply chains in which two producers use reusable pallets to distribute their finished products to the same retailers. The producers supply raw materials and new pallets they need from suppliers. For each producer, the flows of raw material, loaded/empty pallets and finished products are triggered by information flows. Two simulation models are considered. In the first model, supply chains are non-collaborative. Each producer manages his own pool of pallets. After receiving replenishment orders, trucks deliver loaded pallets and simultaneously pick-up empty ones from retailers to be returned to the producer. In the second model, the two producers share their pool of empty pallets. The results show that collaboration can lead to economies of scale and costs reduction. They also highlight the need for a third party to manage the entire system to promise mutual benefits for the concerned parties.

Paper Nr: 66
Title:

A Variable Neighborhood Search for the Vehicle Routing Problem with Occasional Drivers and Time Windows

Authors:

Giusy Macrina, Luigi P. Pugliese and Francesca Guerriero

Abstract: This paper presents a Variable Neighborhood Search algorithm for a Vehicle Routing Problem variant with a crowd-sourced delivery policy. We consider a heterogeneous fleet composed of conventional capacitated vehicles and some ordinary drivers, called occasional drivers, who accept to deviate from their route to deliver items to other people in exchange for a small compensation. The objective is to minimize total costs, that is conventional vehicles costs plus occasional drivers compensation. Our computational study shows that the Variable Neighborhood Search is highly effective and able to solve large-size instances within short computational times.

Paper Nr: 70
Title:

Towards Multi-objective Optimisation of Quantitative Goal Models using Constraint Programming

Authors:

Christophe Ponsard and Robert Darimont

Abstract: Goal Model are widely used to capture system goals and refine them into operational requirements assigned to human, hardware or software. Such models support alternative goal refinements resulting in a potentially large design space to explore. A given design can be quantitatively evaluated in terms of its fulfilment of a set of non-functional requirements (e.g. reliability, performance) or business goals (e.g. costs, stakeholder satisfaction). Optimisation techniques can be used to explore the design space to determine an optimal design according to a single objective like the cost but also according to multi-objective techniques to propose a set of Pareto-optimal solutions in which a best solution can be selected. In this paper, we show how to translate a goal-oriented requirements model, expressed in the KAOS notation, into a constraint programming (CP) problem. The OscaR.CP engine is used to get, from all alternatives co explored, either global or Pareto-optimal solutions. Our method is implemented as a tool plugin of a requirements engineering platform and is benchmarked on the classical meeting scheduler case study.

Paper Nr: 24
Title:

Efficiency of Meme Usage in Evolutionary Algorithm

Authors:

Jaroslav Janacek and Marek Kvet

Abstract: Emergency medical service system design attracts attention of a broad researcher and practitioner community due to increasing public demand for more safe life. A basic model of the design problem is known as the weighted p-median problem. Large instances of the problem as are hard to solve in general and very often it is necessary to obtain a series of solutions to be able to offer a spectrum of various solutions. For this purpose, an evolutionary metaheuristic seems to be a suitable tool, as it processes simultaneously a family of solutions. A standard evolutionary algorithm is based on developing a population using some nature inspired operations with solutions-members of the population, which produce candidates for population updating. This evolutionary process is characterized by fast improvement of the best-found-solution at the beginning and very slow improvement at the end, when the best-found-solution is near to the optimal one. To improve the first phase of the evolutionary process, numerous authors recommend to plug increasing procedures called memes in the process. Within this paper, we will study an impact of the meme plugin on acceleration of the evolutionary process, when big instances of the weighted p-media problem are solved. The study will be performed on instances of an emergency service system design problem solved by genetic algorithm with elite set and the studied meme will be based on exchange neighborhood searching.

Paper Nr: 26
Title:

Comparison of Continuous and Discontinuous Charging Models for the Electric Bus Scheduling Problem

Authors:

Maros Janovec and Michal Kohani

Abstract: Electric buses offer an alternative to conventional vehicles in the public transport system due to their low operational costs and low emissions. Therefore, the standard problems must be resolved with respect to the nature of the electric buses, which is mainly the reduced driving range and charging time. In this paper, we deal with the electric bus scheduling problem. We propose changes to the previously presented model, which allowed only the continuous charging. These changes will allow the model to describe also discontinuous charging when the electric bus can be unplugged during the charging, then let another electric bus to charge and after that plug-in to charger again. These two formulations are tested by IP solver and the solutions and performance of both discontinuous charging and continuous charging formulations are compared on the datasets generated from the data provided by public transport system provider DPM Zǐn the city of Žilina.

Paper Nr: 27
Title:

Linear Model Adjustment and Approximate Approach for Creating Minimal Overhead Wires Network for Vehicle Schedules

Authors:

Dobroslav Grygar and Michal Kohni

Abstract: Nowadays, there is a significant effort in reducing the environmental impact caused by public transport. This goal can be achieved in many ways. One possible way is in using battery-assisted trolleybuses in cities. Therefore this paper deals with the question of how to create a minimum overhead contact network for such vehicles operation. The article presents the mathematical model of such a problem and tests the impact of the version with the modified condition. It also proposes a suboptimal way to propose needed vire network for operation selected vehicle schedules using individual routes. As benchmarks, some vehicle schedules in Zilina were selected.

Paper Nr: 38
Title:

Approximate Analysis of Transfer Line with PH-service Time and Parts Assemble

Authors:

Yang W. Shin, Gyeong M. Baek, Dong O. Kim and Dug H. Moon

Abstract: We consider a transfer line in which workstations are linked in series. Each station consists of single machine and two buffers of finite capacity, one for product and the other for part to be assembled. The product is supplied to the first workstation of the system and they are processed along the line, and a part is supplied to each station according to independent Poisson processes. The processing time distribution of each machine is of phase type (PH). Blocking-After-Service (BAS) rule is adopted. In this paper, we present an approximate analysis for the system based on the decomposition method. Some numerical examples are presented for accuracy of approximation.

Paper Nr: 40
Title:

Weighted k-Nearest Neighbor Adaptations to Spare Part Prediction Business Scenario at SAP System

Authors:

Eren Esgin

Abstract: In the context of intelligent maintenance, spare part prediction business scenario seeks promising returnon-investment (ROI) by radically diminishing the hidden costs at after-sales customer services. However, the classification of class-imbalanced data with mixed type features at this business scenario is not straightforward. This paper proposes a hybrid classification model that combines C4.5, Apriori algorithms and weighted k-Nearest Neighbor (kNN) adaptations to overcome potential shortcomings observed at the corresponding business scenario. While proposed approach is implemented within CRISP-DM reference model, the experimental results demonstrate that proposed approach doubles the human-level performance at spare part prediction. This highlights a 50% decrease at the average number of customer visits per fault incident and a significant cutting at the relevant sales and distribution costs. According to best runtime configuration analysis, a real-time spare part prediction model has been deployed at the client’s SAP system.

Paper Nr: 42
Title:

Solving the Multi-activity Shift Scheduling Problem using Variable Neighbourhood Search

Authors:

Yi Qu and Timothy Curtois

Abstract: This paper presents a set of benchmarks instances for the multi-activity shift scheduling problem and the results produced using a variable neighbourhood search method. The data set is intended as a resource to generate and verify novel research on an important and practical but challenging problem. The variable neighbourhood search uses four different neighbourhood operators and can produce feasible solutions within short computation times.

Paper Nr: 51
Title:

Dangerous Goods Container Allocation in Ship Stowage Planning

Authors:

Hao Lei and Minjae Ok

Abstract: Ship stowage problem is difficult to solve due to the complex conditions from real-world business. For Dangerous Goods (DG) containers, IMDG code mandates the different types of DG must follow the minimum segregation on vessel and in container yard. In addition, DG containers must meet ship requirements and in-house rules from port and ship owners. However, there is a lack of research on stowage planning including DG containers. In this paper, we suggest a novel method to assign DG to integrate the existing stowage planning model. The proposed method is divided into two parts respectively for bay assignment problem and slot assignment problem. The bay assignment model separates DG containers into segregation level groups based on standard IMDG segregation table and assigns different group of containers into bays according to specific ship structure. The slot assignment model is a search-based heuristic model which able to recommend possible slot for a new coming DG container according to assigned container distribution and segregation rules between the new coming DG class and the existed ones. An empirical evaluation on a real-world dataset obtained from a shipping company demonstrates the effectiveness of our method.

Paper Nr: 59
Title:

Power System Optimization Problems: Game Theory Applications

Authors:

Sudha Balagopalan and Ravishankar S.

Abstract: We look at the conflict situation in power system problems from an optimization perspective and use Game Theory (GT) concepts for modelling and solving the problem. In order to model the conflicts effectively, we first identify the players, the optimizing quantity and the optimizing platform. This paper details two power system problems and present a case study. We also identify two more areas where the same principles may be applied. Though our work focuses on Cooperative Game Theory (CGT), an extension to the Non-Cooperative Game Theory (NGT) platform is possible. Since GT is more relevant to a market structure, we use market engineering principles including multilateral trades, differential pricing, inverse elasticity rule, graph theoretical allocation, etc. as tools for organizing the optimization process. A useful addition for inducing stability in the decision making process is the concept of ‘Power Vectors’ borrowed from sports and game parlance for ceding players. Results are encouraging, with a transmission loss reduction of more than 70% in a five bus and 40% in a 24 bus system. We conclude that both versions of GT, the CGT and NGT are powerful tools for optimization in a practical scenario with conflicts and contradictory incentives.

Paper Nr: 62
Title:

A Production Model with Continuous Demand for Imperfect Finished Items Resulting from the Quality of Raw Material

Authors:

Abdul-Nasser El-Kassar, Manal Yunis and Mohammad N. El Dine

Abstract: The purpose of this paper is to present a production process in which the quality of a single type of raw material used to produce the finished product is considered. The common modeling approach followed by previous research is based on discarding the imperfect quality items of raw material. In this paper, we consider the case where both perfect and imperfect quality items of raw are used in the production process resulting in two types of qualiy of the finished product. It is assumed that both perfect and imperfect quality items of the finished product have continuous demand. This modeling approach has yet to be deployed. Two models that depend on the length inventory cycle of each type of the finished product are developed. Numerical examples are provided to illustrate the determination of the optimal production quantity. Theoretical and practical implications are discussed, and recommendations are presented.

Paper Nr: 69
Title:

A/B Testing Adaptations based on Possibilistic Reward Methods for Checkout Processes: A Numerical Analysis

Authors:

Miguel Martín, Antonio Jiménez-Martín and Alfonso Mateos

Abstract: A/B Testing can be used in digital contexts to optimize the e-commerce purchasing process so as to reduce customer effort during online purchasing and assure that the largest possible number of customers place their order. In this paper we focus on the checkout process. Most of the companies are very interested in agilice this process in order to reduce the customer abandon rate during the purchase sequence and to increase the customer satisfaction. In this paper, we use an adaptation of A/B testing based on multi-armed bandit algorithms, which also includes the definition of alternative stopping criteria. In real contexts, where the family to which the reward distribution belongs is unknown, the possibilistic reward (PR) methods become a powerful alternative. In PR methods, the probability distribution of the expected rewards is approximately modeled and only the minimum and maximum reward bounds have to be known. A comparative numerical analysis based on the simulation of real checkout process scenarios is used to analyze the performance of the proposed A/B testing adaptations in non-Bernoulli environments. The conclusion is that the PR3 method can be efficiently used in such environments in combination with the PR3-based stopping criteria.

Area 2 - Applications

Full Papers
Paper Nr: 36
Title:

Simulated Annealing Approach for the Vehicle Routing Problem with Synchronized Visits

Authors:

Seddik Hadjadj and Hamamache Kheddouci

Abstract: In this paper, we consider a vehicle routing problem in which customers have to be visited by two different vehicles in a certain order. The first vehicle has to visit n customers to pickup or deliver empty containers, while the second has to deliver ready-mixed concrete by pouring it into the previously delivered containers. This problem is a vehicle routing problem (VRP) variant involving synchronized visits and temporal precedence constraints. We propose an ILP formulation and a simulated annealing algorithm to minimize the total travel times of both vehicles, and we provide experiments that show very satisfying results.

Paper Nr: 47
Title:

On the Exploitation of Textual Descriptions for a Better-informed Task Assignment Process

Authors:

Nikos Kanakaris, Nikos Karacapilidis and Georgios Kournetas

Abstract: Human resources always play a crucial role on firms’ profitability and sustainability. For a long time already, the identification of the appropriate personnel possessing the skills and expertise required to undertake a task is mainly performed through simple keyword searches exploiting structured data. In this paper, we build on Natural Language Processing techniques to take this identification to a higher level of detail, granularity and accuracy. Our approach elaborates unstructured data such as descriptions and titles of tasks to extract valuable information about the employees’ capability of successfully engaging with a particular task. Text classifiers such as naive Bayes, logistic regression, support vector machines, k-nearest neighbors and neural networks are being tested and comparably assessed.

Short Papers
Paper Nr: 18
Title:

A Highly Constrained Integer Linear Program with Fuzzy Boundary Conditions for Scheduling in Online Tutoring

Authors:

Quoc T. Au and Faiyaz Hasan

Abstract: Scheduling personnel is an important aspect of many organizations and can rapidly become unsustainable to perform manually. It is crucial that the automated algorithm consistently generates a high quality schedule. This paper discusses a linear integer program with a set of constraints, developed in collaboration with a scheduling expert, required to generate reliable schedules for a live online tutoring platform. The focus in the algorithm is shifted away from the optimization of an objective function to adopting hard constraints that guarantee any solution will be sufficiently good. Furthermore, a graceful degradation protocol to implement fuzzy boundary conditions for the constraints ensure that appropriate compromises can be made in most cases to find a solution. Lastly, a comparison of the automated algorithm to a domain expert shows a 5% higher topic coverage and 10% lower cost in favor of the former which has led to a rapid adoption of the automated scheduler by the organization.

Paper Nr: 21
Title:

Dynamic Assignment Vehicle Routing Problem with Generalised Capacity and Unknown Workload

Authors:

F. Phillipson, S. E. de Koff, C. R. van Ommeren and H. J. Quak

Abstract: In this paper we present a modification to the Dynamic Assignment Vehicle Routing Problem. This problem arises in parcel to vehicle assignment where the destination of the parcels is not known up to the assignment of the parcel to a delivering route. The assignment has to be done immediately without the possibility of re-assignment afterwards. We extend the original problem with a generalisation of the definition of capacity, with an unknown workload, unknown number of parcels per day, and a generalisation of the objective function. This new problem is defined and various methods are proposed to come to an efficient solution method.

Paper Nr: 34
Title:

Self Learning Strategy for the Team Orienteering Problem (SLS-TOP)

Authors:

A. Goullieux, M. Hifi and S. Sadeghsa

Abstract: The Team Orienteering Problem (TOP) can be viewed as a combination of both vehicle routing and knapsack problems, where its goal is to maximize the total gained profit from the visited customers (without imposing the visit of all customers). In this paper, a self learning strategy is considered in order to tackle the TOP, where information provided from local optima are used to create new solutions with higher quality. Efficient deep searching (intensification) and jumping strategy (diversification) are combined. A number of instances, extracted from the literature, are tested with the proposed method. As shown in the experimental part, one of the main achievement of the method is its ability to match all best bounds published in the literature by using a considerably smaller CPU/time. Then, for the first preliminary study using both jumping self learning strategies, encouraging results have been obtained. We hope that a hybridation with a black-box solver, like Cplex or Gurobi, can be considered as the main future of the method for finding new bounds, especially for large-scale instances.

Paper Nr: 39
Title:

A Simulation Study on the Effect of Reconfiguration Strategy in an Automotive Body Shop Considering the Change of Product-mix

Authors:

Dug H. Moon, Dong O. Kim, Young H. Lee and Yang W. Shin

Abstract: In this paper, we consider the manufacturing system of an automotive body shop in which two types of car are produced and one car is substituted by the other car gradually. There are two different underbody lines because the underbody structures of the two types of car are absolutely different. We also consider the reconfiguration strategies for changing the layouts as the changes of the product-mix. The effects of reconfiguration strategies and buffer allocations are investigated by simulation experiments.

Paper Nr: 57
Title:

Choice of Airport in Extinguishing Wildfires: Model and Cases

Authors:

Louise Ekström and Christine Große

Abstract: This paper develops a model to support the optimal choice of an airport as a base for the flying vehicles that are operated to extinguish wildfires and forest fires. Based on experiences from the two largest wildfires in Swedish history, this study models the optimisation as a balanced transportation problem. In both cases, the model selected the airport that is closest to the fire area. If the capacity of the chosen airport was insufficient to host all of the flying vehicles, then the model added a second airport which is also nearby the wildfire area. The cases demonstrate that the total cost of the operation is lower when the extinguishing work is concentrated in an area that has a short distance between the airport and the fire, the fuel depots and the pilots’ accommodation. Improved access to relevant data in the context of crisis management by air could allow for the inclusion of additional parameters and correct data in the optimisation model, which could in turn provide more comprehensive decision-making support.

Paper Nr: 20
Title:

Decision Support for Shipping Spare Parts in Bundles

Authors:

Yizi Zhou, Jiyin Liu, Rupal Mandania, Kai Chen and Rongjun Xu

Abstract: A telecommunication equipment company sends spare parts from local hubs to construction sites or other local hubs in mainland China several times a day through parcel delivery services. Depending on the delivery distance, there are various delivery options such as transportation via air, via road, via sea, via rail and via inland waterways. Many choices named service levels are available within each transportation category. There are three parcel delivery pricing policy: price per shipment, weight ranged price, and continuous pricing. Each spare parts delivery usually has a priority level or delivery time requirement. Spare parts to be shipped from the same hub or nearby hubs to the same or nearby destinations are considered being able to ship in bundles. By observing the delivery pricing structure, it is usually beneficial to bundle spare parts together for shipment. The problem is formulated as a mixed integer liner programming model. Numerical experiments are carried out to observe the benefits and also reflect the features of parcel delivery pricing structure.

Paper Nr: 35
Title:

Regression Analysis of Historical Blood Donors to Improve Clinic Scheduling

Authors:

Geoffrey Pond and Isabelle Turner

Abstract: The Canadian Blood Services (CBS) is responsible for the collection, storage and distribution of blood products throughout the country. Like all civilian hospitals and medical facilities, the Canadian Armed Forces (CAF) Health Services System relies on CBS to provide it with required blood products through the Canadian Armed Forces Blood Distribution System. Under normal circumstances, CBS collects all blood products through organized events including mobile and permanent clinics, where prospective donors attend via either pre-booked appointments or unscheduled walk-ins. Of those who make appointments, only a portion show-up for their appointment and of these only some yield a successful donation. As donation clinics are capacity-constrained by both the labour-force and infrastructure, CBS is motivated to maximize the utilisa-tion of existing resources through implementation of an overbooking policy. Leveraging historical data, a statistical analysis was conducted to identify factors influencing conversion rates to aid in developing an improved scheduling policy. The location of the centre, the day of the week as well as demographic groups were included as candidate independent variables in a regression model to forecast the proportion of pre-booked appointments that are attended and yield a collection.

Paper Nr: 48
Title:

An Optimization Method for a Multi-day Distribution Problem with Shortage Supplies

Authors:

Netiphan Amphaiphan and Wasakorn Laesanklang

Abstract: We investigated a multi-day distribution problem while supplies are limited. This scenario can be found in post-natural disasters or economic crisis such as floods, earthquakes, palm oil shortage crisis, etc. The objective function of this problem is to minimize total traveling distance, unsatisfied cost, and variance of supply delivery proportion. In order to solve this multi-day problem optimally, it requires large computing memory and takes a long computational time. Therefore, we divided these large problems into multiple daily sub-problems and solved the sub-problems with the exact method. The sub-problems were solved sequentially for which the prior daily sub-problem is to be tackle first and the following daily sub-problems are defined based on the prior daily sub-problem solution. Changes were applied to update demands and to adjust delivery priority. There are three delivery priority setups proposing in this paper. Also, we present an experiment using the three proposed methods to solve modified Solomon’s vehicle routing problem datasets which extended a single period vehicle routing problem with time windows to be seven-day routing problems.

Paper Nr: 58
Title:

Coalitional Power Indices Applied to Voting Systems

Authors:

Xavier Molinero and Joan Blasco

Abstract: We describe voting mechanisms to study voting systems. The classical power indices applied to simple games just consider parties, players or voters. Here, we also consider games with a priori unions, i.e., coalitions among parties, players or voters. We measure the power of each party, player or voter when there are coalitions among them. In particular, we study real situations of voting systems using extended Shapley–Shubik and Banzhaf indices, the so-called coalitional power indices. We also introduce a dynamic programming to compute them.