Technical Sessions A7 - G7
SESSION A7: Applied Operations Research
 A SMART production planning approach of a multi-sites company
Rihab Khemiri, Khaoula Elbedoui-Maktouf and Belhassen Zouari
This paper proposes a generic model of multi-sites planning. The mathematical modeling can generate decisions relating to production based on the customer demand and at lower cost. Experiments are presented to validate the approach and showing its coherence.
Nahla Chabbah Sekma, Ahmed Elleuch and Najoua Dridi. Prediction of CPU Availability in Volunteer Computing Systems using Multivariate Time Series Modeling
Abstract: Computing resources in volunteer computing grid represent a big under-used reserve of processing capacity. Predicting CPU availability can help to better exploit these resources and make effective scheduling decisions. Autoregressive models are among the simplest prediction techniques that may perform better than the most complex competitors. To address their limitations, we propose an automated approach to check whether time series meet the model assumptions and to construct a prediction model by identifying its appropriate order value. At each prediction, we consider autoregressive models over three different past analyses: first over the recent hours, second during the same hours of the previous days and third during the same weekly hours of the previous weeks. We compare multivariate vector autoregressive models (VAR) and pure autoregressive models (AR), constructed according to our approach, against the tendency prediction technique using traces of a large-scale Internet-distributed computing system, termed seti@home.
 AN EMPIRICAL STUDY OF PRODUCT CONFIGURATION DESIGN BASED GSA
Runliang Dou and Chao Zong
Genetic algorithm is easy to fall into local optimum and large amount of calculation, which is hard to find the best configuration design in most case. For this, a hybrid of genetic and simulated annealing algorithms(GSA) is put forward in this paper to make up for the deficiency and defects of the genetic algorithm, and combining with the advantage of simulated annealing algorithm to optimize the performance of the algorithm to a great extent. With car console conceptual design as the instance, the performance of this hybrid heuristic method is compared to the traditional genetic algorithm. A comparative analysis on the simulation experiment shows that our hybrid algorithm can more effectively solve the problem of product configuration design optimization and has wide application prospects.
 A GENETIC ALGORITHM FOR SOLVING THE MIXED ORIENTEERING PROBLEM
Mekki Mariem and Dammak Abdelaziz
This paper proposes a new combination between routing problems especially among the Orienteering Problem and the Arc Routing Problem with Profits. This problem is called “ Mixed Orienteering Problem “which is to find an optimal subset that maximizes the total profits collected from the nodes and arcs minus travel costs under the constraint of time of the tour. We hereby present a literature review of this problem, and we propose the mathematical model. Two process of genetic algorithm approach is developed to solve the Mixed Orienteering Problem, which differs from the selection operator.
 SUSTAINABLE PROJECT SELECTION IN A PORTFOLIO BY A NEW UNCERTAIN MATHEMATICAL PROGRAMMING
V. Mohagheghi, S.M. Mousavi and A. Siadat
Project portfolio selection is one of the most important tasks carried out by organizations in order to keep their competitive advantage in today’s changing and highly competitive market. On the other hand, the contemporary social, economic and environmental conditions have made it insufficient to solely consider economic factors in project selection problems. In this paper, the concept of sustainability is integrated into project portfolio selection problem. A new method of project portfolio selection is proposed that simultaneously considers economic, social and environmental factors. Moreover, in order to consider the uncertain environment of projects and the lack of knowledge at the initial phases of them, interval-valued fuzzy sets (IVFSs) are used. This method consists of two main parts: in the first part, a modern decision index is adopted to utilize funneling down the proposed projects. In the second part, a sustainable project portfolio selection model is proposed based on the concept of IVFSs. This mathematical programming model considers three pillars of sustainability in addition to risk. Finally, in order to illustrate the effectiveness and efficiency of the proposed uncertain mathematical programming, it is used to solve a practical example and the results are presented.
SESSION B7: Cellular manufacturing
 ROBUST CELLLULAR DESIGN WITH PRODUCT LIFE CYCLE CONSIDERATION
Berna Ulutas and Gürsel Süer
This study focuses on the signiﬁcance of considering product life cycle (PLC) stages at the production ﬂoor to cope with unstable and stable demand periods. A three-phased hierarchical methodology is developed. The first phase considers demand variability and aims to identify part clusters with high, medium, and low demand variability. In the second phase, process similarity is taken into account. Also, each part's expected and standard deviation of processing time on each operation is utilized. The third phase considers both demand variability and process similarity. To assess the validity of the methodology, data is generated based on a jewelry manufacturing for thirty products processed in fifteen machines. For each planning time period in concern; it is concluded that the proposed methodology can improve cell utilization with reasonable number of cells. Based on the promising results; it can be stated that the methodology has a potential for application in various industries where short PLC stage lengths are critical.
 OPTIMISATION OF MANUFACTURING CELL FORMATION ALGORITHM THROUGH PARAMETERS OF GENETIC OPERATORS
Vladimir Modrak and Pavol Semanco
Genetic algorithms are frequently used for a number of optimization problems in operations research including manufacturing cell formation problem. In this paper are explored possibilities of Taguchi experimental design for a calibration of genetic algorithm parameters. For this purpose two groups of experiments will be constructed and compared. This work provides further evidence that the efficiency of genetic algorithm is dependent on initial parameters.
STATIC COMPLEXITY IN DESIGN OF MANUFACTURING SYSTEMS
Vladimir Modrak, Slavomir Bednar and Jan Modrak
One of the approaches that contribute towards reducing the overall complexity of manufacturing systems is based on so called static complexity. In this context it is useful to establish analytical framework for static complexity of manufacturing system design. Its practicability can be seen in early stage design phase to assist manufacturing planners in managing appropriate levels of complexity within manufacturing system. For this purpose we propose two new approaches to measure static complexity based on Shannon’s and Boltzmann’s entropies. Proposed indicators will be subsequently verified using the static manufacturing models. Empirical study uses a mechanical job shop and its transformation into cellular manufacturing.
 SIMOGGA, THE USER-FRIENDLY SOLUTION TO OPTIMIZE AND ANALYZE THE FACTORIES LAYOUT USING GENETIC ALGORITHMS
Emmanuelle Vin and Abdelkrim Boujraf
If the interest of the cellular manufacturing is widely demonstrated in the literature, this model is not widely used in the industry. Why? The advantages of the cellular manufacturing are difficult to provide. The major advantages are observed after implementing the new layout. At a first glance some industrial managers are not comfortable with this method and aren’t willing to change their factory layout. SIMOGGA, the software applying material-flow optimization through Cellular Manufacturing methods helps the users to achieve this process, quickly.
SESSION C7: Data Mining, Knowledge Discovery and Computational Intelligence
 COUPLED CO-LINEARITY INDEX AND PCA SCORES PROJECTION APPROACH TO DISCOVER PRODUCT SPECIFIC PROCESS KNOWLEDGE TO SUPPORT PROCESS FMEA IN THE FOUNDRY.
Raed Batbooti, Rajesh Ransing and Meghana Ransing
A Process Failure Mode Effects Analysis (PFMEA) is an integrated analytical tool used to improve manufacturing processes systematically by identifying and evaluating the potential failure modes of manufacturing processes. Foundry process is a complex process with multiple root causes. As a result, there is more than one factor that would have an effect on a specific defect or any other product property such as tensile strength.
The previously proposed co-linearity index infers correlations among process parameters and a novel principal component analysis scores interpretation is used to find the optimal settings for factors. The discovered knowledge can be used as an actionable tool in FMEA to achieve process improvement. The proposed procedure is applied on real case study for a Nickel based alloy casting process.
 THE APPLICATION OF SEPARABILITY ANALYSIS IN FEATURE SELECTION OF THE SERIAL CRIME LINKAGE PROBLEM
Zhihong Lin, Hong Chi, Mengyi Sha, Baoguang Xu and Mingang Gao
As an important content of data mining, the accuracy and generalization ability of classification is influenced by the complexity of data sets and the selected classification algorithm. In the criminal data mining, in order to link serial crime, we often build discrimination model to determine whether two cases were committed by same criminal according to the similarity data of this cases-pair. It is a typical binary classification problem. However, there are multiple features in the similarity data set and only a few features contribute to classification. Thus, in order to classify the data effectively, we are supposed to select features. This paper presents our ongoing study on feature selection in the similarity data set based on data separability. Indices of feature’s separablility and data set’s separability are introduced and we use the data set’s separation rate as an evaluation criterion in feature selection. The reasonability of feature selection is validated by doing k-fold cross validation and making predictions on a new data set.
ASSOCIATION RULE MINING OF MANUFACTURING DATA TO ENHANCE MAINTENANCE AND REPAIR
Emad Abouel Nasr, Hisham Al-Mubaid, Adel Al-Shayea and Ali Kamrani
In the successful manufacturing and industrial organizations, maintenance and quality of products are of utmost importance to the organization. It has been discovered and confirmed, from long time ago, that these two aspects of any industrial organization, namely maintenance and quality of products, are highly correlated. However, there is no extensive research to analyze and investigate the relationship between maintenance and quality of products in a given manufacturing plant. In this paper we conduct a study in this domain and investigate the relationship patterns between maintenance and part quality using maintenance and product quality data. Specifically, we use association rule mining with large and extensive datasets of product quality, repair, and maintenance data. The main goal is to discover the interesting and non-trivial association rules for feature failure in the presence of product with unapproved quality that might require repair or maintenance. The evaluation results and outcomes are very encouraging. The resulting association rules with high confidence and lift values suggest some important associations between product features causing the failure, and such findings were not known and utilized before. It can assist the maintenance and quality engineers to improve maintenance and repair and reduce manufacturing cost.
 WAVELET BASED RADIOMICS FOR BRAIN TUMOUR PHENOTYPES DISCRIMINATION
Ahmad Chaddad, Ahmed Bouridane, Lama Hassan and Camel Tanougast
This paper proposes a novel approach for brain tumour phenotypes by using texture feature extraction based on 2-D discrete wavelet transform (DWT). A novel approach is proposed to analyze the phenotypes heterogeneity for necrosis, active tumor and edema. Texture features were computed from the Daubechies (db), Symlets (sym) and Coiflet (coif) wavelet filters. Naïve Bayes (NB) classifier was employed to discriminate between the three phenotypes. The simulation results showed that there are 69 significant features with p-value < 0.05 from a total of 264 DWT texture features. Preliminary results showed a high accuracy classifier between phenotypes of 76.74% based on db feature. Promising accuracy of 82.55% was found for predicting edema/invasion based on selected coif features. Comparative study demonstrates the feasibility of DWT based feature for detecting the brain tumour phenotypes.
SESSION D7: Simulation
 MODELING AND SIMULATION IN DIALYSIS CENTRE OF HEDI CHAKER HOSPITAL
Jridi Ichraf, Jerbi Badreddine and Kamoun Hichem
Research and studies on modeling and simulation have many and varied applications. They attached great importance to the health care field which is a rich field of investigation, fertile and controversial, demonstrating the complexity of the difficulties of this subject.
Simulation and modeling have become well-established tools at least to aid healthcare decision makers in evaluating alternative system design in order to improve hospital performances.
The implementation of these two approaches has become more prevalent in order to support decision making, to provide adequate information to physicians and managers, in order to predict and assist in effective future planning...
The main objective of this paper is to build a Discrete Event simulation model that includes the different stages that may go through a renal failure patient in the renal unit at Hedi Chaker hospital of Sfax in Tunisia. These stages start by patient arrival, then the first choice of treatment and the transfer between modalities of treatment (hemo-dialysis, peritoneal dialysis and transplant), and end by the death of the patient or by a successful kidney transplant. During the course of being treated for end stage renal disease, patients may switch a number of times between the different modalities. For example, a given patient might move from peritoneal dialysis to transplantation and, after transplant failure, to hemo-dialysis and perhaps to a second transplant. To construct the model, we used real data extracted from the register support of patients in the region of Sfax on the long of the period from the year 2000 to the year 2010. The developed model was verified and validated through many iterative implementations with ARENA simulation software.
Results show that the number of patients undergoing hemo-dialysis, peritoneal dialysis or kidney transplant will continue to increase each year, which implies the increasing demand for treatment.
 A HEURISTIC APPROACH TAKING OPERATORS’ FATIGUE INTO ACCOUNT FOR THE DYNAMIC ASSIGNMENT OF WORKFORCE TO REDUCE THE MEAN FLOWTIME
Aicha Ferjani, Achraf Ammar, Henri Pierreval and Abdelwaheb Trabelsi
In order to cope with the frequent unpredictable changes that, may occur in manufacturing systems, and to optimize given performance criteria, the assignment of workers can be decided online in a dynamic manner, whenever the worker is released. Several studies, in the ergonomics literature, have shown that individuals' performances decrease because of their fatigue in work. In manufacturing context, the workers’ fatigue impacts the task durations. Therefore, we propose to solve the online workers assignment problem through a heuristic, which takes this workers' fatigue into consideration, so as to minimize the mean flowtime of jobs. This approach suggests computing more realistic task duration in accordance with the worker's fatigue and it uses multi-criteria analysis in order to find a compromise to favor short durations and to avoid congestions. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to select where to assign the worker. A learning process through simulation optimization is used to adapt the weights, in TOPSIS, to better fit with the system characteristics. The approach is illustrated with a simulated Job-Shop system. Experimental results comparing our approach with the rule Shortest Processing Time (SPT), which is known as efficient on the mean flowtime, show the effectiveness of the heuristic.
 A SIMULATION-OPTIMIZATION APPROACH FOR MANAGING THE SALES AND OPERATIONS PLANNING IN THE AUTOMOTIVE INDUSTRY
Lâm Laurent Lim, Gulgun Alpan and Bernard Penz
The sales and operations planning (S&OP) is crucial for efficiently balancing production capacities with the volatile market demand. In this article, we propose an original S&OP model in order to improve the trade-off between the supply chain costs and the customer satisfaction. The problem is formulated as a multi-objective optimization model with _-constraints and is solved by a simulation- optimization approach. A static policy for managing the parts procurement and the flexibility offered to the sales function are presented. The model and the proposed solution are illustrated with the case study of Renault. Several optimization algorithms are compared in terms of system performance and computation time. Managerial insights are derived based on these results.
 Simulation platform for multi agent based manufacturing control system based on the Hybrid agent
Ali Vatankhah Barenji, Reza Vatankhah Barenji and Amir Shaygan
Agent based distributed manufacturing control and scheduling systems subsets of new manufacturing systems. Multi agent systems (MAS) not only drive design and engineering control solutions but also influence flexibility, agility, and re-configurability, which makes MASs better than traditional centralized systems. However, implementation all MASs in the real factories are timely, also so costly. A simulation environment that would allow independent development and testing of the services and business processes of the related manufacturing hardware is needed. This paper presents the design and implementation of a user-friendly simulation platform for multi agent based manufacturing control system by considering shop floor level. The proposed simulation platform can be simulate software level by considering hardware level of the factory. An example of the simulation platform presented for a flexible manufacturing system, which is located in EMU CIM lab.
SESSION E7: Multi-Criteria Decision Making and Decision Analysis
A STUDY OF THE STANDARD SETUP TIME QUOTA BASED ON EMPLOYEES’ KNOWLEDGE LEVEL
Haicao Song, Shuping Yi and Hongyu Shen
For the multi-item and small lot size production mode, many setup times are difficult to estimate accurately, which influences the ability to achieve accurate production cycles and costs of products. The survey shows that the length of setup time depends on the employee’s knowledge level. Therefore, a method for determining the standard setup time quota based on the employees’ knowledge levels is proposed. First, an evaluation index system for the employee’s knowledge level is built; the employee’s knowledge level is estimated by the masses and experts fuzzy comprehension evaluation and entropy method. Second, the range and definition of the employees’ knowledge level index are developed. Third, the relational model of the employee’s knowledge level and the employees’ knowledge level index is constructed through the least squares method.
A FRAMEWORK FOR DECISION SUPPORT IN SYSTEMS WITH A LOW LEVEL OF AUTOMATION
Matthias Becker, Sinan Balci and Helena Szczerbicka
We present a framework for realization of a decentral decision support system for deployment in
production scenarios with a low level of automation, such as in multi-site construction. Our solution
shows how to realize a central control and decision support station. Special focus is on easy
connection to the possible multiple construction sites and on high usability for non-experts. Our
framework is able to connect to a large number of modeling, simulation and analysis tools. For sake
of usability it turned out, that only dialogue-based communication with the end user seems
applicable in those scenarios, where often only simple devices are present.
STUDY OF R&D CAPABILITIES CHARACTERISTICS: CASE STUDY AUTO INDUSTRY
Sadegh Abedi, Javad Mehrabi and Mohammad Reza Soroush
In successful developing countries, experience has shown that R&D is the main key for any kind of development. Import technology and R&D has important roles in the development of local technological. To develop and application of new technologies, we must first evaluate the existing capabilities and Based on the capabilities and opportunities to choose proper strategies. In this research by using FDM, the right combination of R&D Characteristics indicators according to the auto industry is presented. Finally, with modeling evaluation of R&D Characteristics by using FIS and check its reliability, five levels were determined to evaluate the R&D Characteristics. Field of company activity indicators has been divided into four categories, Suspension group, Electrical group, Engine groups and trims group. The results show that half of the surveyed companies had R&D capabilities in Level 1 and 2 or in other words very low and low level of R&D capabilities.
SESSION F7: Project Development & Management
An Evaluation of Archimate as an Architecture Framework for Business-IT Alignment
Michaël Petit and Virginie Goepp
Business-IT alignment (BITA) is considered as a crucial challenge faced by enterprises. Several solutions Enterprise-Architecture based solutions (that we call Enterprise Architecture Frameworks) have been proposed in order to help to address this problem. One of them, Archimate, provides a detailed modelling language. However, currently, there are no systematic evaluation of EAF to check if they are appropriate for BITA. In this paper, we propose a contribution to help in this evaluation by proposing a framework of requirements for EAFs for BITA by combining requirements coming from the ISO 15704 standard and some that we derive from the well-known Strategic Alignment Model of Hendreson and Venkatraman. We then apply this evaluation framework to the de-facto standard EAF Archimate proposed by the Open Group. The analysis allows us to identify a potential extension of Archimate to make it more appropriate in a BITA context.
Rachid Benmansour, Oliver Braun and Abdelhakim Artiba. Mixed integer programming formulations for the single processor scheduling problem with time restrictions
Abstract: In the single-processor scheduling problem with time restrictions that has been formulated at first by Braun, Chung and Graham , n independent jobs have to be processed non-preemptively on a single processor that can handle only one job at a time, subject only to the following constraint: during any time period of length a > 0 the number of jobs being executed is less than or equal to a given integer value B>=2. This constraint reflects the condition that each job needs one of B additional resources for being processed and that a resource has to be renewed after the processing of a job has been finished. The jobs are simultaneously available for processing at the beginning of the planning horizon, and their processing times are fixed and known in advance. The objective is to minimize the completion time of the last job in the optimal sequence (i.e. the makespan).
In  it is shown that this problem is NP-hard when the value B is variable and a
classical worst-case analysis of List Scheduling for this situation has been carried
out. We present two mixed integer programming (MIP) formulations to solve the single-processor scheduling problem with time restrictions exactly. The first model is based on time indexed variables whereas the second is based on assignment and positional date variables. The performances of these models are tested by running them on randomly generated instances. Extensive computational studies show the effectiveness (in the number of problems solved) and efficiency (in computational time) of the assignment and positional date variables formulation for the randomly generated instances with up to n = 500 jobs, B = 10 number of resources and processing times out of the interval 1 up to 1000.
A study on how to improve PMBOK guidelines performance by simulation Case Study: National Gas Company of Lorestan province
Fatemeh Jaferi, Seyed Mojtaba Sajadi and Heshmatolah Shams Khorramabadi
The project-oriented organizations are more appropriate for sustainable environments. Any effective project-oriented organization should institutionalize its project management processes in such a manner to yield the greatest possible profits. The aim of this paper is to study the relationship between the project management PMBOK guideline (Project Management Body of Knowledge) and simulation technology in project-oriented organizations. The methodology involves using five steps for applying these two tools aimed at enhancing project management processes in the Lorestan Gas Corporation, as one of the project-oriented organization. Results show the implementation of such management approach leads to a 5% performance improvement and using PMBOK can be instrumental in effective delay management. The implementation of the aforementioned improvement package was effective in improving the efficiency of organizational processes; in terms of optimizing the resource utilization that has manifested itself in resource losses and cost reductions.
SHIPYARD REPAIR PROJECTS SCHEDULING USING CASE BASED REASONING AND GENETIC ALGORITHM
Yaser Elkady, M. Nashat Fors and Amin Shoukry
Repair shipyard management deals with many repair projects with different scope of work at the same time. These projects consist of many tasks that must be planned and executed in different workshops which are considered as a job shop systems. Repair work interruption could occur due to many factors including emergency repair work priority. These interruptions could leads to delaying of activities or unintentionally postpone them. This effects is rarely noticed or recorded by the used management program for rescheduling of activities at a proper time. The present paper is aiming to introduce an algorithm that able to integrate the projects schedule operation with the job shop processes. The algorithm introduces the dynamic changes occur in the production process into the project scheduling program. Case based reasoning technique and expert systems concepts are used for this algorithm to generate project schedules and assign needed resources. The allocation of the available resources is adapted using genetic algorithm technique in order to increase the repair shipyard productivity and profit. The case based reasoning and the genetic algorithm is constructed using c# language. A case study is introduced to validate the algorithm using more than 1000 cases and shows satisfactory results.
FRAMEWORK AND MODEL FOR BUILDING EFFECTIVE INFORMATION SYSTEMS PROJECT TEAMS
Ghada El Khayat, Mohamed Abougabal and Yasser Hanafy
Building effective information systems project teams is crucial to ensure projects success. Research on forming information systems project teams is somewhat limited and the analytical models available do not capture all the factors affecting team effectiveness. By team effectiveness, we refer to realizing project objectives and increasing the tendency of members to work together. This paper presents a framework and a model for building information systems project teams based on the team input-process-output (IPO) model known in the management literature and targeting team effectiveness. To our knowledge, this is first framework that addresses building information systems teams. The review of the literature confirmed the need for a multifactor model to build an effective team. An integer programming model that captures the different factors relating to team effectiveness is contributed in this paper. The model application using real data is the subject of future work.
SESSION G7: Transportation Systems
LOCATION ROUTING PROBLEM WITH SIMULTANEOUS PICKUP AND DELIVERY OF DEMANDS WITH LEAKING CHARACTERISTIC
Mahdi Bashiri and Azar Balaee
This paper addresses a location routing problem with simultaneous pickup and delivery of demands in which delivery products have leakage, this characteristic leads to decreasing products volume during transportation. The amount of leaking products depends on traveled distance between nodes, volume of loaded products on vehicle and property of traveled arc such as weather condition. The problem has a number of applications in real life while products have leakage during the delivery distribution. The goal of this study is to determine location, allocation and routing decisions to minimize the total network cost including of location, routing and operational cost such as the product leakage cost. We develop a mathematical model for this problem. Some numerical examples on well-known data sets are presented to evaluate efficiency of the proposed model. Moreover some sensitivity analysis is performed to confirm model validity.Location routing problem, Pickup and delivery, Leakage, Mixed integer nonlinear programming.
APPLICATION OF HYBIRD ALGORITHM TO JOINT DECISION MAKING IN HINTERLAND BARGE TRANSPORT PLANNING
Fan Feng, Yusong Pang and Gabriel Lodewijks
The paper studies the problem of hinterland barge transport planning in the port of Rotterdam. We address the issue of vessel allocation to terminals (put an effect on terminal berth utilization), decision of time window selections (determine the turn-around time of vessel operation) and the choice of objectives used in the planning algorithm (should we emphases on minimizing the vessel turn-around time or maximizing the terminal berth utilization) in the domain of hinterland transport. The planning problem is modeled as a leader-follower joint decision making model. A hybrid genetic-simulated annealing algorithm is developed to consider conflict objectives simultaneously, thus capable of finding non-dominant solution that beneficial for all joined parties. A case study will be presented to demonstrate the performance of the proposed solution.
Xianchun Tan, Lele Dong, Baoguang Xu, Baihe Gu and Dexue Chen. EMPIRICAL RESEARCH ON KEY INFLUENCING FACTORS OF CO2 EMISSION IN THE RUNNING PROCESS OF URBAN RESIDENTIAL BUILDINGS
Abstract: Urban residential buildings, whose CO2 emission always account for about 24% of that in China’s construction department, have become one of the key areas to control CO2 emission growth by China’s local building department. According to the energy using characteristics of urban and rural buildings, the paper firstly determines the calculation boundaries and content of CO2 emissions in the running process of urban residential buildings and then adopts LMDI emissions factors decomposition model to identify the key influencing factors of urban residential buildings. Finally, we verify the result through Granger causality test. The research results show that compared to 2000, the CO2 emissions in 2011 in the running process of urban residential buildings of the case city have increased by 121.6%. The key influencing factors of it are energy consumption per unit area, per capita living space and the urbanization rate.
A BI-OBJECTIVE P-HUB LOCATION PROBLEM WITH A QUEUE SYSTEM AND TRANSPORTATION MODES
Mohammad Zhalechian, Reza Tavakkoli-Moghaddam and Yaser Rahimi
This paper presents a mixed-integer p-hub median model for the hub-and-spoke network design. This model consists of bi-objective mathematical programming that aims to minimize the total transportation cost of the hub network and the maximum transportation time between each pair of origin-destination nodes simultaneously. It includes an M/M/c/n queuing model and different transportation modes. In order to solve the presented model, a TH method is utilized to find a compromise solution. Additionally, an imperialist competitive algorithm (ICA) as an efficient meta-heuristic algorithm is developed to solve large-sized problems. Furthermore, several computational experiments are carried out to validate the presented model and the developed meta-heuristic algorithm.
INTEGER PROGRAMMING MODEL FORMULATIONS FOR OVER CONSTRAINED FLIGHT – GATE ASSIGNMENT PROBLEM
Cemalettin Ozturk, Arslan M. Ornek and Ipek Sugut
Flight – Gate assignment problems are complex real world problems involving different constraints. Some of these constraints include plane-gate eligibility, assigning planes of the same airline and planes getting service from the same ground handling companies to adjacent gates, buffers for changes in flight schedules, night stand flights, priority of some gates over others, and so on. In literature there are models to solve this highly complicated problem and tackle its complexity. In this study, we propose two different Integer programming (IP) models, namely, timetabling and assignment based models to solve the problem to optimality. These models prove to be highly efficient in that the computational times are quite short. We also present the results for one day operation of an airport using real data. Although, the research is still in progress, we finally present our conclusions based on our study done so far.