Technical Sessions A1 - G1

SESSION A1: Applied Operations Research

Süleyman Mete, Zeynel Abidin Çil, Eren Özceylan and Kürşad Ağpak

The disassembly line balancing (DLB) problem is the process of allocating a set of disassembly tasks to an ordered sequence of work stations in such a way that optimizes some performance measures (e.g., cycle time, number of stations, number of hazardous components, work load). Since DLB problems belong to the class of NP-hard problems, many heuristic and meta-heuristic algorithms are applied to cope with the complexity of DLB problems and to obtain acceptable solutions in reasonable amount of time. To do so, a beam search based approach for the DLB problem is developed in this paper. Minimization of number of work stations is used as the performance measure. The proposed algorithm is compared with the optimal solutions of well-known real case and some test problems. The results indicate that the beam search technique which is the first application in DLB literature is a very competitive and promising tool for further researches.

Giboulot Valentin, Chassaing Maxime, Lacomme Philippe, Alain Quilliot and Ren Libo

This paper deals with a dynamic dial-a-ride problem. The Dial-a-ride problem (DARP) involves the satisfaction of a set of transport requests (which are known in advance) using a fleet of homogeneous vehicles. Each request has constraints on time windows and on maximal transport duration. In a dynamic DARP, a sub-set of requests are not known during the phase of routes planning and they arise dynamically during the routing process. In this paper, we introduce a new approach to determine the robustness of solutions favoring routing of the dynamic requests. A robust solution must be able to consider how to include the dynamic requests into planned trips. We proposed an ELS heuristic for initially solving the problem with static requests and an insertion algorithm with a simulator for handling dynamic requests. As preliminary results, the proposed approach is performed on a set of scenarios generated from real-road networks instances.

[105] A Simulation-Optimization Approach for Robust Aircraft Routing
Mohamed Ben Ahmed, Farah Zeghal Mansour and Mohamed Haouari

To mitigate the impact of unpredictable disruptions, airlines are seeking to proactively design schedules that incorporate some robustness features. In this paper, we propose a novel simulationoptimization approach using a particle swarm optimization algorithm for solving the robust aircraft routing and flight retiming problem. The approach requires inserting buffer times between departure times of flights to improve the robustness of both aircraft and passengers connections. The proposed approach is based on discrete-event simulation for the evaluation of the solutions performance and a Particle Swarm Optimization (PSO) routine for guiding the search towards enhanced solutions. The results of computational experiments that were carried out on a real data are presented.

Arnaud Laurent, Nathalie Grangeon, Laurent Deroussi and Sylvie Norre

This article proposes an extension of the Resource Constrained Project Scheduling Problem: the Multi-Site RCPSP with resource pooling between several sites. This extension considers new constraints for the RCPSP like transportation times and choice of the site where tasks are executed. A linear program of this problem is given. Four methods of resolution are described: local search, simulated annealing and Iterated Local Searches with two different acceptance criteria: simulated annealing type acceptance criterion and better acceptance criterion. We compare the results obtained with each method. ILS with simulated annealing type acceptance criterion gives the best results.

SESSION B1: Integrated Maintenance and Logistics Problems

Imene Djelloul, Abdelhakim Khatab, El-Houssaine Aghezzaf and Zaki Sari

This paper deals with selective maintenance of systems required to perform missions with finite breaks between consecutive missions. During breaks, maintenance actions may be performed on system's components. For each component, a list of maintenance actions is available, from minimal repair to replacement, trough imperfect maintenance. Due to limitations on maintenance resources such as time and budget, it may not be possible to perform all necessary maintenance actions. The selective maintenance problem consists then to select a subset of maintenance actions to be performed on some components to maximize the next mission reliability. In this paper, the selective maintenance problem is investigated for a series-parallel system. Missions operated by the system are considered of uncertain durations and modeled as random variables. A mathematical optimization model is then proposed where the objective is to maximize the reliability of executing the next mission, taking into account both budget and time maintenance constraints. A numerical example is then provided and discussed. The results obtained allow to show the benefit of considering stochasticity of missions' durations.

[207] An optimal maintenance policy for transport vehicles in a supply chain under infrastructure/environment constraints
Asma Troudi, Sofiene Dellagi and Sid-Ali Addouche

This paper treats an optimal vehicle routing and maintenance problem. Different variations of the basic vehicle routing problem are examined which involve the consideration of additional constraints. Our objective consists in establishing an optimal maintenance strategy in the context of the transport in the supply chain. The vehicle routing problem aims to find a set of routes with a minimal cost, so that the known demands of all nodes are fulfilled. Some formulations also present constraints on the maximum travelling time. For this reason, we apply a preventive maintenance strategy that takes the influence of crossed distances and climatic conditions into account for the means of transportation. An objective function, including transport and maintenance costs, is developed analytically and optimized in order to establish simultaneously the vehicle routing distance as well as the maintenance strategy. This maintenance strategy is defined by the optimal number of preventive maintenance to do over the vehicle routing.

Bermawi P. Iskandar, Andi Cakravastia and Hennie Husniah

In this paper, we study a two dimensional lease equipment contract with involving imperfect preventive maintenance. The two dimensional lease contract is characterized by two parameters – age and usage limits. The lessor performs both preventive and corrective maintenance and a penalty cost incurred to the lessor when the time required to perform a corrective maintenance exceeds a pre-specified target. Failures are influenced by the age and usage of the equipment and modelled by using a one dimensional approach. We consider an imperfect preventive maintenance (PM) policy that reduces the equipment failure rate and hence it will decrease the penalty cost and maintenance cost during the lease contract. We find the optimal PM degree (reduction in failure rate) that minimizes the expected total cost.

SESSION C1: Data Analytics and Decision Making for Smart Management

[199] The Effectiveness and Validation Scheme on Multiple Criteria Decision Making Methods
Jei-Zheng Wu and Pei-Jen Tiao

Multiple criteria decision making (MCDM) methods have various applications in real life. We face MCDM problems every day and their criteria very often conflict with each other. MCDM methods have been developed to support decision makers to enhance decision quality. MCDM methods use various calculation methods to evaluate the rank of alternatives. Yet, little evidence could support that the best alternative chosen by MCDM method is consistent with the decision maker’s ideal alternative in mind. Therefore, this study aims to develop an operational validation scheme to examine and compare existing common MCDM methods including TOPSIS, VIKOR, ELECTRE, the piecewise linear prospect theory method (PLP), and Analytic Hierarchy Process (AHP) in terms of effectiveness. Controlling variables include number of alternatives, number of criteria, distribution of data set, and the options between non-dominated data set, i.e., Pareto efficient frontier, and complete data set. We also add three different weight distributions, including uniform weights, rank order centroid weights, and rank sum weights, in these experiments to see how weights affect the MCDM methods. We test four different utility functions including linear, quadratic, Chebycheff, and prospect utility functions. Except the compensatory, non-compensatory and portion of compensatory utility functions, we also use the prospect theory utility function. In addition, the parameter settings such as normalization methods, distance functions, VIKOR’s v, and ELECTRE’s thresholds will also be examined. With the aforementioned settings, we compare the MCDM methods’ ranks with decision maker’s ranks by using assumed preference utility functions. Total absolute differences and the Kendall’s statistical test are applied to examine the effectiveness of the compared methods.

Sadegh Abedi, Reza Radfar, Tara Salek and Mitra Saboori

Local Content has become the focus of attention by many industrial companies in developing countries. These countries implement different policies to increase the share of Local Content in their industries. Iran’s automotive industry has become a key industry because of the type of the product cycle and its utilizing many supporting industries, thus playing an important role in the country’s economy. Therefore, the factors influencing the product Local Content for automakers seems necessary. In this study, first, a conceptual model is proposed regarding an assessment of the theoretical framework for the research on factors influencing automotive product local Content in the developing countries. Then, analyzing small automakers, namely Bahman Group and Pars Khodro, the correlation between factors influencing parts local Content in Iranian automakers is evaluated. In this research by using data analysis, the right combination of local content indicators relating to the auto industry is presented. The results indicate that product innovation in the foreign contracting companies and production time with regard to the product life cycle are among the important factors influencing Local Content in the two Iranian automakers.

[284] Analysis of the Consumption Patterns From Smart Meter
Hsiao Fan Wang and Chia-Yu Shen

Smart grid recently becomes a common technology in many countries, and the smart meter is one of essential component in this system. Compared to the traditional meters read on a monthly basis, the smart meters record consumption of electric energy in intervals of an hour or less. The consumption data gathered from smart meters are the opportunities to allow electricity companies to do things they never could before, which provides better understanding of customer behavior and forecasting electricity usage in the future time. In our study, we propose a forecasting model, which combines the support vector regression (SVR) with genetic algorithm (GA). As various factors affect the consumption patterns, we have considered the impact factors such as the weather variations and features of particular time to improve accuracy of our model. Our study will be beneficial to the residential or industrial electricity consumers, where they can save on demand and electricity bill by combining the smart meters to the short-term demand forecasting techniques.

Chia-Yu Hsu and Yi-Ting Wang

SESSION D1: Manufacturing

Simge Yelkenci Kose and Ozcan Kilincci

In this study, we investigate buffer allocations in various reliable open serial production line designs i.e., balanced, unbalanced, upper limits for buffers, and some bottleneck position conditions. The main objective is to analyze the buffer size configurations obtained by genetic annealing algorithm, which is presented in our previous work, using buffer utilization-based search methods if any could help to improve the capacity of the production line. Buffer utilization-based search methods are used as a search tool to create buffer size configurations. As an evaluative tool, discrete event simulation modelling is used to obtain the average production rate of the line. Computational results show the efficacy of the utilization-based search methods and genetic annealing algorithm for the buffer allocation problem in reliable serial production lines.

Kazuhiro Izui, Xiaobo Bai, Takayuki Yamada, Shinji Nishiwaki, Akio Noda and Tatsuya Nagatani.

This paper proposes a bi-level multiobjective optimization method for the design of robotic cellular manufacturing system layouts. In this method, a layout is coded using sequence-pair representation at the first level of the optimization process, and a genetic algorithm is used to obtain non-dominated solutions. Then, using such solutions as initial points, the detailed positions of the components are optimized using multiobjective local optimization techniques in the second level of the optimization process. A three-robot manufacturing system design problem is used to illustrate the effectiveness of the proposed method.

Nassima Aissani and Damien Trentesaux

Decentralized decision-making processes in manufacturing control offer seeking fault-tolerance, scalability, flexibility, adaptability and reactivity. However, many convergence problems appear: decisional entities do not have a clear vision whether or not the system is heading towards its objectives.They also encounter difficulties to consider future state of the system. In this paper, we consider the routing problem in FMS distributed control.The routing problem means: how products choose their routes in the FMS to achieve the allocated resource at the right time to execute their task. For that reason, the remaining occupancy time (RT) of resources, as the data representing the future state of the system, is used. This time (RT) is used by the product to choose the best path that will bring it to the resource at the exact moment when it is released by means of waiting loops. Experiments show that this approach reduces average waiting time of products and consequently the entire production time.

SESSION E1: Quality Management/Engineering, Reliability and Maintenance

Shu Wu and Ying Zhang

Run Rules are used to indicate out-of-statistical control situations. It can increase the power of the standard Shewhart chart for detecting small shift. The waiting time distribution for a special run given a sequence of independent success, the parameters of the distribution is usually evaluated under the assumption of known parameters. However, in practice, the process parameters are rarely known and have to be estimated from an in-control historical Phase I data set. This paper discusses the Run Rules np control chart with estimated parameters, we investigate the number m of phase I samples that would be necessary for these charts in order to obtain similar in-control average run lengths (ARL) as in the known parameters case. We also propose new specific chart parameters that allow these charts to have approximately the same in-control average run lengths as the ones obtained in the known parameter case.

Huadong Mo, Zhihui Fang, Yong Wang and Min Xie

Digital networked control systems are of practical importance and perform indispensable function in safety-critical systems. Because of the degraded communication networks, the systems may fail to deliver required performance. Modeling such systems needs to specify the relationship between dynamic control system and uncertain networked degradations. A time-varying model is proposed to study these features simultaneously, and consider data transmissions in both communication channels. Laplace Transform method is introduced to avoid computing the convolution of modules. The behaviors of networked degradations are described by Markov chains subject to uncertainties. An event-based Monte Carlo method is applied to estimate the system reliability. The reliability can be improved via searching the optimal control strategy through the combined Genetic Algorithm and Annealing Algorithm. A case study on a heat exchange system is provided to illustrate the correctness and the efficiency of the proposed framework.

[70] An improved method with interval-valued intuitionistic fuzzy setting to failure mode and effects analysis based on complex proportional assessment
Z. Hajighasemi, S.M. Mousavi and A. Siadat

Failure mode and effects analysis (FMEA) is a bottom-up analytical method that helps industrial companies to identify and prioritize their potential failure modes. Identifying serious failure modes and performing corrective actions on the right spot should be done to prevent the customer experience the failure. Despite of the extended use of FMEA in various industries; the conventional FMEA is debatable due to some deficiencies reported in some studies in the field such as possessing equal weights for all three risk factors which may result in biased results in the final prioritization. Furthermore, considering input judgments as crisp data makes it inadequate, in term of considering human uncertainties and ambiguities in FMEA calculation. Therefore, this paper proposes a new method based on group decision making concept that extends complex proportional assessment (COPRAS) method to improve calculations in FMEA. In addition, this method regards human judgment uncertainty as fuzzy inputs under interval-valued intuitionistic fuzzy environment, as well as Einstein geometric operators for computational tasks. A practical manufacturing example is used to examine applicability and effectiveness of this method. As it will be illustrated, this method has a capability to overcome FMEA’s shortage and improves FMEA and decision results.

[240] A study of linear multi-state systems with interval-valued states
Wei Wang, Junlin Xiong and Min Xie

The paper proposes a new model that generalizes the linear multi-state system with interval-valued states. The system consists of independent linear ordered multi-state components. The interval-valued states of each component are represented as interval numbers with upper bounds and lower bounds. It means that a component in the interval-valued state can provide arbitrary contribution between the lower bound and the upper bound to the entire system. The system fails, if the sum of the interval-valued performances of all the components does not satisfy the predetermined demand with a maximum bound and a minimum bound. The performance degradation of system components is considered, and it is described as the degradation of the bounds of component’s states. Interval universal generation function is introduced to evaluate the system reliability. The components’ reliability importance indices are also studied to identifying the most influential components of the new proposed system. An example of a telescope antenna is presented for demonstration.

SESSION F1: Supply Chain Management & Logistics

Driss Serrou and Abdellah Abouabdellah

Following the multiplicity of care services that a hospital and a result of factors that come into play during its operations, such as cost control, quality assurance and security guarantees, its mission remains complex and very difficult to manage. The control of hospital spending remains a major concern in the majority of managers of hospital systems, which always leads to the search for a rational and optimal solution for physical and information flows related to their activities. This flow summarizes hospital logistics term. In this paper, we are interested in a study that shows the interest of the grouping of pharmacies and their impact on the performance of the hospital logistics. The first part of the article shows the interest of the hospital logistics in a hospital as well as the review of the literature on the evaluation of performance. The second part describes the steps for setting up the study. We finish our work by applying a case study methodology.

[46] Swift Trust in Humanitarian Logistics Operations: An Ongoing Work
Qing Lu, Mark Goh and Robert de Souza

Trust is essential for any teams working together. In humanitarian logistics operations, relief organizations often have to work collaboratively in emergent response groups or hastily formed networks. Trust in such a context, called “swift trust” in the literature, is an important but less explored topic. This paper attempts to fill the research gap first by developing a testable research framework for empirical investigation. The framework incorporates various factors affecting the forming of swift trust as well as its impact on team coordination and effectiveness. We further conduct an empirical examination among humanitarian players in Southeast Asia to test the research framework. While the empirical analysis is still ongoing, we expect that this study would enrich our understanding on swift trust in relief networks, which may in turn improve humanitarian logistics operations in rapid onset disasters.

[50] Risk-averse procurement strategy in presence of strategic customer
Lei Shu and Feng Wu

Risk-averse procurement strategy in presence of strategic customer "Abstract: Arising in online environment lately due to frequent dynamic pricing in presence of strategic customer, pulsed demand increases the risk of a retailer as it is large and highly uncertain. Moreover, it shifts between periods because of customer strategic behavior. This paper, from the perspective of a risk-averse retailer, establishes a procurement model and a strategic customer demand model, to analyze the effects of strategic customer, pricing and risk aversion on order quantity, profit and risk. Some useful conclusions for procurement risk control are drawn in the setting of two-period. Selling price of period 1 affects order quantity of period 2, when strategic customer ratio is independent of prices; while all selling prices affect all order quantities when dependent. Order quantity in period 1 (period 2) decreases (increases) in strategic customer ratio, which would be less sensitive when the retailer is more risk-averse. Besides, risk-aversion procurement can effectively and significantly mitigate risk without much loss of profit, especially when strategic customer ratio is large.

SESSION G1: supply chain management & logistics

[11] Exergy analysis: A new paradigm for modelling inventory systems
Hussam Jawad and Mohamad Y. Jaber

Arising in online environment lately due to frequent dynamic pricing in presence of strategic customer, pulsed demand increases the risk of a retailer as it is large and highly uncertain. Moreover, it shifts between periods because of customer strategic behavior. This paper, from the perspective of a risk-averse retailer, establishes a procurement model and a strategic customer demand model, to analyze the effects of strategic customer, pricing and risk aversion on order quantity, profit and risk. Some useful conclusions for procurement risk control are drawn in the setting of two-period. Selling price of period 1 affects order quantity of period 2, when strategic customer ratio is independent of prices; while all selling prices affect all order quantities when dependent. Order quantity in period 1 (period 2) decreases (increases) in strategic customer ratio, which would be less sensitive when the retailer is more risk-averse. Besides, risk-aversion procurement can effectively and significantly mitigate risk without much loss of profit, especially when strategic customer ratio is large.

Mohamad Y. Jaber and Hussam Jawad

Just-In-Time (JIT) advocates that inventory is a waste and should be reduced. However, having smaller and more frequent shipments generate more waste, consume more resources and congests a supply chain rendering the JIT policy unsustainable. This paper uses the second (entropy) law of thermodynamics to calculate the entropy generated in each of two systems; EPQ and JIT. To make the comparison meaningful, the EPQ model is modified to capture some of the costs that will work against/for EPQ and JIT. It then adds an entropic component to the cost functions of EPQ and JIT to capture the costs of disorder (entropic) that are usually not accounted for in classical inventory analyses. The results show that a JIT policy is more expensive to operate than an EPQ policy.

Nie Duxian, Qu Ting, Kang K, Chen Xin and Huang George Q.

Oriented to the decision autonomy of typical cluster enterprises, a cluster assembly supply shain configuration decentralized decision model is established. Augmented Lagrange Coordination (ALC),which supports open-structure optimization,is applied for solving the problem. CASCC decision model is solved by ALC collaborative optimization and results have proved the effectiveness of ALC for CASCC problem. From the perspective of supply chain management, a set of sensitivity analysis for profit of each collaborative enterprise is conducted to obtain the managerial insights.