Showing papers presented at "International Conference on Service Operations and Logistics, and Informatics in 2015"

A comprehensive evaluation of air pollution prediction improvement by a machine learning method

Abstract: Urban air pollution prediction is one of the most important tasks in the treatment of urban air pollution. Due to the disadvantage that source list updated not in time for WRF-Chem which is a numeric model, the prediction result may be not good enough. In this paper, we take full advantages of forecast on pollution, weather, chemical component from WRF-Chem model as input features, design a comprehensive evaluation framework to improve the prediction performance. Experiments are implemented with different features groups and classification algorithms in machine learning method for 74 cities in China, to find the best model for each city. From experiments, for different city, the best result can be obtained by different group of feature selection and model selection. Experimental results indicate that the more feature we used, the more possibility to enhance the accuracy. For method aspect, the result from combined model is better than the unique model.

Autonomous indoor navigation of low-cost quadcopters

Abstract: Many researchers from academia and industry are investigating closely how to control an autonomous mobile robot, especially Unmanned Aerial Vehicles (UAV). This paper shows the ability of a low-cost quadcopter, "Parrot AR-Drone 2.0", to navigate a predetermined route. The path is obtained by optimized path planning algorithm. A generic Simulated Annealing (SA) optimization algorithm is implemented to generate the obstacle-free path. This path is divided into several waypoints, which are navigated by the drone in various experiments. The position and orientation of the quadcopter are estimated with the incremental motion estimation approach using Inertial Measurement Unit (IMU), which is mounted on the drone. The quadcopter is controlled via Simulink model with PID, which manipulates the drone internal controller for the pitch, roll, yaw and vertical speed. Four different experiments were tested to evaluate the performance of the proposed algorithm and the obtained results indicate the high performance of the quadcopter and its applicability in various navigation applications.

Cloud operating system for industrial application

Abstract: With the rapid development of latest information technology, it is inevitable to apply IoT (Internet of Things), cloud computing and big data into the industrial fields of national key sectors including transportation, electricity, metallurgy, petroleum, chemical, manufacturing, military and so on. Wireless sensor network, industrial Internet, embedded system, software for industrial control and management, and smart terminal are gradually introduced into the industrial systems, which would make the past relatively closed industrial systems more open and intelligent, and contribute to the coming forth industrial revolution. In this paper, the authors mainly discuss issues about cloud Operating System (OS) for industrial application, including cloud computing and cloud operating system introduction, current status analysis of cloud OS and the transformation trend to industrial 4.0. Then, we independently design the main content of this cloud OS, and its application prospect and expected result are given. The study provides theoretical guidance and practical challenge for the development of cloud OS oriented to industrial area.

Towards the electrification of urban freight delivery using modular vehicles

Abstract: This paper introduces the electric Modular Fleet Size and Mix Vehicle Routing Problem with Time Windows (eM-FSMVRPTW), which deals with introducing new types of electric vehicles for urban freight delivery taking into account the possibility of recharging at a customer location. These electric vehicles differ from battery electric vehicles because their modules are autonomous in terms of consumption and electric charging. Therefore, the eM-FSMVRPTW is a completely new problem. Its objective is to minimize the acquisition cost, the total distance travelled and the recharging costs taking into consideration several constraints such as modularity, electric charging, time windows, capacity limit and others. As a solution method, we propose an approach based on a genetic algorithm. The experimental results demonstrate that with the modularity feature, using electric vehicles for freight delivery in urban environment is interesting economically.

Optimal assignment of autonomic managers to cloud resources

Abstract: There has been an increasing number of companies moving towards cloud computing due to its economic model based on the so-called pay-as-you-go. The cloud is known as a dynamic and scalable environment. These characteristics make the management of this environment a complex task. Using autonomic management potentially helps to solve the complexity of managing large number of provisioned cloud resources. Since using one autonomic manager (AM) might result on inefficiency in the management of the system, we propose in this paper to use a decentralized approach for autonomic management. The problem that we are solving herein is to determine how many AMs to use in order to maximize the performance of the management and minimize the cost of the used AMs. We propose a mathematical model that allows to determine the optimal assignment of resources and AMs in different availability zones taking into account the different costs of the involved AMs as well as the communication overhead. We also give an overview of the implementation of the proposed mathematical model.

The impact of practical network constraints on the performance of energy-aware routing schemes

Abstract: Power consumption and CO2 emission have become a major concern over the last few years. Several recent studies have shown that servers and network equipments consume up to 45% of the energy consumption of data centers [1]. Software-defined networking is a new networking paradigm that decouples the control and data functionalities; thus, makes networks easily manageable and programmable. In software-defined networks (SDNs), the central controller has a global view of the network topology, traffic matrices and QoS requirements, which allows it to optimize the energy consumption of the network through energy-aware routing. In this paper, we investigate the impact of practical constraints, discreteness of link rates and limitation of flow rule space, on the performance of energy-aware routing schemes in SDN. The energy-aware routing problem is modeled as an integer linear program (ILP) with discrete cost function. The problem is modeled in GAMS and solved by CPLEX under real network settings and practical constraints. Results show that considering these constraints is critical in order to exploit the energy saving margin of SDNs.

A context model development process for smart city operations

Abstract: Systems enabling smart city operations are highly adaptive complex systems that pose great challenges in their development and operation. Current user-driven techniques for system domain modeling and requirements engineering are not adequate for supporting the development of such systems. In this paper, we propose a development process and an environment-driven modeling approach for the Requirement Engineering Context Awareness methodology to be used for smart city applications. To this end, we propose the use of ontologies to build the environment context model. We show that the environment dimension is the most important dimension of context with the highest impact of changes in a dynamic context. We illustrate our approach by presenting an ontology-based context model of I-Parking. We present dynamic models of typical scenarios of interactions. We propose our approach as an important step in developing highly adaptive context-aware systems for smart city operations where uncertainty and changing conditions in the environment need to be carefully modeled and addressed.

Research on Cross-border E-commerce platform selection in China small & medium-sized enterprises

Abstract: This paper establishes the evaluation criterion system of Cross-border E-commerce platform selection based on DeLone & McLean's e-commerce success model and MA-OWA operator, using triangular fuzzy analytic hierarchy process to determine the weight of each level of criteria. Further, the fuzzy comprehensive evaluation model is established. Finally the case study proves scientificness and feasibility of the evaluation criterion systems and the validity of the evaluation method.

Inventory routing problem for perishable products by considering social issue

Abstract: Inventory Routing Problem (IRP) for perishable products is one of the complex subjects in IRPs. Moreover, considering other criteria and constraints, such as respecting time window, managing the amount of expired products, fuel consumption and environment and social criteria bring this problem more complex. This paper presents a new model considering economic and social aspects in IRPs. We study the effect of speed of vehicle in distribution of perishable products in IRPs. This consideration allows to reduce delivery time, impacting several economic criteria, but increase driver injury (risk of accident for driver), considerate in this model as social issues. In order to find a tradeoff between these issues, a new bi-objective mathematical model has been proposed. First objective focuses on traditional cost of inventory and distribution as well as age and price strategy, backorder cost and discount. Second objective function concerns in minimization of driver injury (social issue), calculated by considering the speed of vehicle in different existing paths between retailers. In fact, quicker path allows to decrease some costs but increase accident risk. It is noteworthy that other economic and social criteria can also add to proposed model. Finally, sensitivity analysis are performed to investigate the importance of each objective function as well as the effect of variation of inventory holding cost against profit and driver injury.

Autonomous unloading of heavy deformable goods: Market opportunities

Abstract: The EU funded project RobLog recently developed a system able to autonomously unload coffee sacks from a standard container. Being the first of its kind, a further development is needed in order for the system to be competitive against manual labor. Financing this development entails a risk, hence a justified skepticism, which can be overcome by the longsighted view of the existing market potential. This paper presents a method to estimate the market potential of autonomous unloading systems for heavy deformable goods. Starting from the analysis of the coffee trade, first the current coffee traffic is investigated in order to calculate the number of autonomous systems needed to handle the imported sacks; Results are validated and the method is extended for the calculation of the potential of other market segments, where the same unloading technology can be applied.

A design of intelligent route guidance system based on shortest path algorithm

Abstract: With the accelerated urbanization and the development of automobile industry, traffic congestion has become serious gradually for many metropolises. Thus, it is an important and extensive method in many countries to use the intelligent transportation systems (ITS) to control traffic and to induce vehicle flows so that road congestion may be mitigated and the traffic may be more efficient. As an important part of intelligent transportation, an intelligent route guidance system (IRGS) not only regulates traffic flow of every crossroads intersections to make full use of insufficient road infrastructure, but also reduces running distance and average waiting time for every driver to reach region traffic balance. This paper explores a design of IRGS and proposes a way to address the task decomposition of traffic guidance and path selection issues. Results from the simulation experiments suggest that IRGS with shortest path algorithm can achieve better network performance and increased traffic control system efficiency. The study provides theory and method guide for the development of real application system.

The critical flaw in the implementation of GPS tracking system in express bus industry

Abstract: Land Public Transport Commission (SPAD) in Malaysia has made it mandatory that GPS tracking systems installed in every express buses since 2008 so that the speed can be monitored and recorded based on real-time. Nonetheless, the effectiveness of the system is under question when Malaysia was startled by a series of bus accidents each killed close to 10 lives while several others were seriously injured. This study is undertaken to identify the critical flaw in the implementation of the GPS monitoring system and propose a way to improve the existing monitoring system. Result from the interview session on top management of three express bus companies has found that the real-time record-keeping, which is a corrective measure, have been carried out instead of real-time monitoring, a preventive measure. Therefore, the existing model of speed management needs to be remodeled to alert the bus driver and the passengers who really concern about their own safety. A device and its main features which exploit the real-time monitoring ability of GPS are proposed for effective and systematic implementation of this new model.

MR-SPS: Scalable parallel scheduler for YARN/MapReduce platform

Abstract: In the Big Data era, MapReduce has been the most utilized model in academia and industry. The main objective of MapReduce and its well-known implementation Hadoop is to run distributed applications to analyze huge amount of datasets, on very large clusters of commodity machines. This can be very time consuming. Also, the cluster that runs MapReduce applications has to be very scalable. In order to improve the performance of the MapReduce model, efficient scheduling algorithms have to be developed. Now day, Scalability is a major concern for applications that works on datasets measured by petabytes. Schedulers for such applications must allow a high scalability and an optimal utilization of resources in order to minimize the execution time. For this end we propose MR-SPS, a scalable parallel scheduling algorithm that takes care of scalability of the cluster and its performance by managing workload and data locality. Our scheduler works in a parallel manner, which allows a higher use of the capacities that the Resource-manager can eventually provide, this will increase the number of node that it can manage. For experiments, we have used CloudSim to simulate MR-SPS and other scheduling algorithms designed for MapReduce model. The results show the superiority of our implementation.

Barriers to implementing reverse logistics in Tunisian companies

Abstract: Despite the progress that has been made over recent years, reverse logistics (RL) remains a challenge to many developed and specially developing countries. The overall purpose of this work is to identify and evaluate the main barriers that impede or hinder the implementation of reverse logistics in Tunisian companies in order to help overcome it, to find out the interaction among identified barriers of reverse logistics using multivariate statistics descriptive and inference and to provide possible suggestions. The results showed that the most important obstacles to successful reverse logistics implementation are: lack of awareness of reverse logistics, high operating cost, demands from customers, lack of top management commitment and lack of the adequate infrastructure to collect and store the products from end users.

A hybrid model for short-term air pollutant concentration forecasting

Abstract: The paper focuses on short-term forecasting of air pollutants including SO2, NO2, O3 and PM2.5. A hybrid model of nonlinear autoregressive with exogenous input (NARX) network and autoregressive moving average (ARMA) is applied. The NARX network is used to solve the problem of nonlinear and multidimensional while the ARMA model is aimed to improve the flexibility for different pollutants. The performance of the hybrid model is evaluated by data of pollutant concentration as basic input, and observed/forecast weather condition as exogenous input. The model overcomes the nonlinear and multidimensional problem and shows promising overall results for all the pollutants over traditional method.

A novel approach to solve K-center problems with geographical placement

Abstract: The facility location problem is a well-known challenge in logistics that is proven to be NP-hard. In this paper we specifically simulate the geographical placement of facilities to provide adequate service to customers. Determining reasonable center locations is an important challenge for a management since it directly effects future service costs. Generally, the objective is to place the central nodes such that all customers have convenient access to them. We analyze the problem and compare different placement strategies and evaluate the number of required centers. We use several existing approaches and propose a new heuristic for the problem. For our experiments we consider various scenarios and employ simulation to evaluate the performance of the optimization algorithms. Our new optimization approach shows a significant improvement. The presented results are generally applicable to many domains, e.g., the placement of military bases, the planning of content delivery networks, or the placement of warehouses.

The detection of the suitable reduction value of Douglas-Peucker algorithm in online handwritten recognition systems

Abstract: The typical Online Handwritten Text Recognition System contains four main phases which are: preprocessing, feature extraction, recognition, and post-processing phases. Preprocessing phase aims to reduce or remove imperfections caused during acquisition step. This phase is also used to minimize handwriting variations irrelevant for pattern classification which may exist in the text acquisition. The preprocessing phase has a great influence on subsequent processing, and a real impact on the recognition rate. This phase could include a number of steps like resizing, centering, simplifying, and smoothing the text. This paper presents an experimental study of using a simplification technique which is used in the preprocessing phase in Online Handwritten Text Recognition systems. The proposed system is designed to deal with any type of text. However, the study is limited to deal with acquired digits which will give a deep understanding of using preprocessing steps in this field.

Community detection in social network: Literature review and research perspectives

Abstract: Social network community detection has occupied an important place in many scientific fields like biology, sociology, or computer science. This problem still attracted a lot of work. The challenge is how to identify inside these networks, groups of persons strongly linked and sharing the same preferences. As in the literature, there are many works trying to detect communities we tried in this paper to do a review of the most important ones and we tried to present some tips for improvement of obtained results.

Industrial ecosystem of the territory: Strategies and perspectives

Abstract: The globalization term has become one of the most used concept after 1990. It is frequently used in social, political, economic and environmental domains. Globalization of the economy is accompanied with increasing international trade. In the framework of our work we are interested in the economic sectors. The future of the economy in its components; industrial, trade, service and financial, is linked to the efficiency of two concepts : transport and logistics. For this reason, the concepts « development of sustainable logistics » and « development of sustainable freight transport» have become a priority of investments for the state.

Vehicle type and route selection for emergency logistic management under road damage

Abstract: Roads in the affected areas are usually damaged during big natural disasters, and it results in blocked road, reduced road capacity, lower vehicle speed and severe restrictions for travelling. Besides, vehicles are donated temporarily by numerous groups and various in their performance. This paper takes the above problems caused by road damage into consideration, and the vehicle route decision are made. Then a multiple vehicles type with different load capacity and travelling performance is considered, and the suitable vehicle type is selected for each route in order to confirm that the vehicles can pass though the assigned route safely. A mixed integer linear model is proposed for the decisions, with the aim of minimizing deliver time for relief goods as well as minimizing the number of vehicles used in the relief, and an algorithm based on Label Setting Algorithm is designed to solve the problem. Finally, a 15-node network example is adopted to numerically verify the proposed models, and the selection truck-load transportation and less-than-truck-load transportation is discussed.

Scheduling workforce for retail stores with employee preferences

Abstract: Traditional brick-and-mortar retailers are facing increasing pressures due to the rapid growth of E-commerce and high employee turnover inherent to the retail industry. To drive continuous growth and profits, brick-and mortar retailers need effective store workforce scheduling methods that cope with employee preferences in order to simultaneously cut cost and improve employee satisfaction. In this paper, a mixed integer programming model is proposed for this purpose. Employee's satisfaction is maximized by satisfying employee's preferences for leave and working partners to the largest extent, and balancing working hours among employees. The proposed model is tested on an apparel retail store in China. The results demonstrate the effectiveness of the proposed model.

A decision support system for the AirCrew Rostering Problem case of TunisAir

Abstract: This paper presents the results of a built Decision Support System (DSS). The main scope of the proposed approach was to solve a Bi-Objective Highly-Constrained AirCrew Rostering Problem (BO-HCACRP). To tackle this problem, we designed a new meta-heuristic based on the Variable Neighborhood Search (VNS). We aimed to satisfy the flight crew members through balancing both the number of occurrences per destination and the total layover of all the planned rotations. To ensure a good exploration of the search space, a two-level mathematical formulation is stated: we started with a well-constructed initial solution solved with the Generalized Assignment Problem (GAP). To tend towards the optimality of the proposed approach, an improved solution through variable neighborhood structures is generated. All the used instances are real world applications provided by the tunisian airline company TunisAir. Experimental investigation proved the effectiveness and the efficiency of the proposed approach.

Research on the optimal parameters of ACO algorithm for a human resource allocation problem

Abstract: In this work, a human resource assignment problem, in an assembly line, is presented and solved. We have to optimize the allocation of workers to tasks and to stations, in order to increase the productivity of the production line. This problem is called Assembly Line Worker Assignment and Balancing Problem (ALWABP). To solve the problem, an Ant Colony Optimization (ACO) algorithm is applied. A Numerical study is carried out to investigate the effect of some parameters of the ACO method on the quality of the obtained solution.

Probabilistic model for evaluating a proactive fault tolerance approach in the cloud

Abstract: Cloud computing is an emerging paradigm where computing services are provided across the web. Virtualization powers the cloud by mutualizing physical resources thus ensuring flexibility and high availability of the cloud. Certainly fault tolerance like load balancing or advancement programming security aim to foster availability but classic reactive fault tolerance techniques prove to be greedy in terms of memory and recovery time. Elsewhere, proactive fault tolerance is possible by preemptive virtual machine migration requiring a strong and accurate failure predictor. In quest of an effective approach for proactive fault tolerance we introduce in this paper a probabilistic model of the cloud with a failure generator for evaluating a proposed approach based on three scenarios of virtual machine migration.

Decrease interference using adaptive modulation and coding

Abstract: The limitations of channel bandwidth, time varying channel and fading are significant and fundamental problem in wireless communication, which leads to the difficulty of providing high Quality of Service. The traditional wireless communication systems are designed to provide good quality of services at the worst channel conditions. Wherein any attempts to increase the channel bandwidth, interference arises along with these attempts. And result in inefficient utilization of the full channel capacity. One of the efficient techniques to overcome to these problems is known as adaptive modulation and coding (AMC). This paper was prepared as simulation study of interference decreasing using AMC algorithm where a comparison between ordinary modulation techniques is compared to Multicarrier Modulation MCM and adaptive modulation, results of the simulation was observed and compared for above scenarios.

Evaluating and measuring knowledge management's impact on supply chain performance using HOQ

Abstract: Supply Chain Performance (SCP) depends on several factors additionally its length and the yield of its members. However, manage a Supply Chain (SC) means well managing knowledge as sharing them between actors involved in this process. Knowledge Management (KM) has, of appearance, a direct impact on SCP, but its formalization remains always ambiguous. In this context, this paper aims to determining the KM impact on the SCP through highlighting the relation between KM Elements (KME) and SCP Evaluation Criteria (SCPEC). Our approach consists, in the beginning, to position the knowledge concept in the SC through a conceptual model that identifies the KM impact on SCP based on mutual relations. Afterward, we adopt the House Of Quality (HOQ) to evaluate and measure the impact of each KME on whole SCPEC, similarly, the impact of whole KME on each SCPEC. An implementation example is proposed in the end.

A multi-objective optimization algorithm for the integrated product line selection and supply chain configuration problem with quality considerations

Abstract: The integration of product platform development and supply chain configuration decisions is a problem that must be resolved in order to achieve mass customization. This paper extends a mathematical model formulated for this problem and introduces quality as a third dimension in addition to cost and revenue. A multi-objective simulated annealing algorithm (AMOSA) is proposed and implemented to solve the model in a way that provides the decision maker with a wider view of alternatives rather than a single solution.

Network design and planning with resource pooling: The context of merging two logistics entities

Abstract: Logistics pooling is relatively a new concept in transportation and logistics optimization, but is very important in practice. It has been identified in literature with different facets reflecting its evolution. Collaborative transportation and consolidations in storage, transfer centers, paths, vehicles loading in one side, and in supply plans in the other side, seem to constitute together a promising alternative to model and to optimize. This research focuses on the case of merging several separate logistics entities and processes, and highlights pooling effects as a result. It is inspired from a real case of logistics service providers in western of France in the field of food distribution according to a Location-Allocation Problem (LAP) with a single-echelon network. The design of logistics network is recalculated by searching possible synergies in order to reconfigure delivery profiles (cadences and amounts) and consolidate flows of the independent operators into a shared organization. Several Mixed Integer Programs are provided to model new optimized Logistics Master Plans (LMP) that lead tactical aspects to the strategic ones. Experimental pooling effect has been shown in the middle of the models performance measure.

Empirical study of customer relationship proneness in hairdresser's context

Abstract: This article discusses the influence of customer relationship proneness in hairdresser's context. Data are collected using online survey. A conceptual model about customer relationship proneness, relational benefits, customer commitment and customer loyalty is proposed to explain the influence. Structural equation modelling is used to analyze the data. Empirical results show that customer relationship proneness is directly and positively related to perception of relational benefits, customer commitment and customer loyalty. Relational benefits have significant influence on customer commitment and loyalty. The direct effect form customer commitment to customer loyalty is also confirmed.

Visual analysis tool for hierarchical additive time-series data

Abstract: The authors developed a visual analysis tool for hierarchical additive time-series data. Additive time-series data are visualized via stacked graphs, and users can easily drill-down into the hierarchical structure by clicking a bar on the stacked graphs. The most important feature of the tool is showing multiple stacked graphs in parent-child relationship simultaneously.