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Scientia Iranica - Volume:25 Issue: 6, Nov - Dec 2018

Scientia Iranica
Volume:25 Issue: 6, Nov - Dec 2018

  • Transactions on Industrial Engineering (E)
  • تاریخ انتشار: 1397/09/09
  • تعداد عناوین: 10
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  • D. Veysmoradi, B. Vahdani *, M. Farhadi Sartangi, S.M. Mousavi Pages 3635-3653
    In this study, the response phase of the management of natural disasters is investigated. One of the important issues in this phase is determining the distribution areas and timely distribution of relief to affected areas in which transportation routing is of a critical matter. In the event of disasters, especially flood and earthquake, terrestrial transportation is not that much easy due to the damage to many infrastructures. For this reason, we propose that delivering relief from the distribution areas to disaster stricken places should be done simultaneously by terrestrial as well as aerial transportation modes to increase route reliability and reduce travel time. In this study, for relief allocation after earthquake, we offer a mixed-integer nonlinear open location-routing model in uncertainty condition. This model includes several contradictory objectives and variety of factors such as travel time, total costs, and reliability. In order to solve this model, a hybrid solution by combining robust optimization and fuzzy multi-objective programming has been used. The performance and effectiveness of the offered model and solution approach has been investigated through a case study on the earthquake in East Azerbaijan, Iran. Our computational results show the solution we have offered for real problems has been effective.
    Keywords: Emergency logistics, Relief distribution, location, routing, Split delivery, Multi-Objective Programming, Robust Optimization
  • Mohammad Reza Maleki, Amirhossein Amiri *, Ali Reza Taheriyoun Pages 3654-3666
    In some profile monitoring applications, the independency assumption of consecutive binary response values within each profile is violated. To the best of our knowledge, estimating the time of a change in the parameters of an autocorrelated binary profile is neglected in the literature. In this paper, two maximum likelihood estimators are proposed to estimate the real time of step changes and drift in Phase II monitoring of binary profiles in the case of within-profile autocorrelation, respectively. Our proposed estimators, not only identify the change point in the autocorrelated logistic regression parameters, but also in autocorrelation coefficient. The performance of the proposed estimators to identify the time of change points either in regression parameters or autocorrelation coefficient is evaluated through simulation studies. The results in terms of the accuracy and precision criteria show the satisfactory performance of the proposed estimators under both step changes and drift. Moreover, a numerical example is given to illustrate the application of the proposed estimators.
    Keywords: Within-profile autocorrelation, step change point, linear trend disturbance, binary profile, Phase II
  • O. Poursabzi, M. Mohammadi *, B. Naderi Pages 3667-3684
    This paper studies the problem of capacitated lot-sizing and scheduling in job shops with a carryover set-up and a general product structure. After analyzing the literature, the shortcomings are easily realized; for example, the available mathematical model is unfortunately not only non-linear but also incorrect. No lower bound and heuristic is developed for the problem. Therefore, we first develop a linear model for the problem on-hand. Then, we adapt an available lower bound in the literature to the problem studied here. Since the problem is NP-hard, a heuristics based on production shifting concept is also proposed. Numerical experiments are used to evaluate the proposed model and algorithm. The proposed heuristic is assessed by comparing it against other algorithms in the literature. The computational results demonstrate that our algorithm has an outstanding performance in solving the problem.
    Keywords: lot-sizing, scheduling, job shop, Sequence-Dependent, Lower Bound, Heuristic
  • Saeed Firooze, Majid Rafiee *, Seyed Mohammad Zenouzzadeh Pages 3685-3699
    The main purpose of emergency medical services is providing fast medical care, as well as transporting patients to the hospital, in the shortest possible time. Healthcare managers try to improve healthcare systems through reducing the response to demand time. In this paper, we seek to propose an optimization model in order to cover as much demand as possible in the shortest possible time using the available ambulance fleet. To do so, considering the response and service time, amount of demand during the time periods, limitation in the number of available emergency vehicles and the capacity of ambulance stations, we have proposed a mixed integer linear programming optimization model, aiming to minimize the total response time. In this paper we take into account fleet relocation and unavailability time, the time interval in which the vehicle is on its way or doing a service at a demand point. Then, a sensitivity analysis is conducted on the model by manipulating the parameters, so as to observe the effects on the outputs. In order to evaluate the model, several arti cial test problems were generated and solved. The results depict the capability of the proposed model in dealing with emergency cases.
    Keywords: Ambulances, Emergency medicalservices, Location, Relocation, Facility location
  • Jafar Razmi, Davoud Haghighi, Reza Babazadeh* Pages 3700-3712
    The coordination and integration of efforts and activities of supply chain (SC) members that is a key component of supply chain management success has become a challenging activity due to conflicts in such systems. Lack of proper detection of conflicts in a SC and therefore mismanagement of them will increase disruption risk in the SC. In this article, a knowledge-based algorithm is presented based on the OTSM-TRIZ (general theory of powerful thinking) problem flow network approach to identify, formulate and solve the conflicts of SCs before they occur and cause harmful effects. The proposed algorithm involves analyzing and developing network of problems (NoP) in order to transfer it into a network of conflicts. This study validated the proposed algorithm through a case demonstration. Through the implementation and application, the result demonstrated that this knowledge- based algorithm was able to identify and formulate supply chain conflicts before they occur and more importantly it greatly increased the coordination between supply chain entities.
    Keywords: Supply chain conflicts, OTSM-TRIZ, Problems flow network, Problems network, Semantic relationships, Conflicts network
  • Zhenhong Li, Bo Li * Pages 3713-3722
    This study considers a novel class of bi-level fuzzy random programming problem about insuring critical path. In this study, each task duration is assumed as a fuzzy random variable and follows the known possibility and probability distributions. Because there doesn’t exist an effective way to solve the problem directly, we first reduce the chance constraint to two equivalent random subproblems under two kinds of different risk attitudes. Then, we may use sample average approximation (SAA) method for reformulating the equivalent random programming subproblems as their approximation problems. Since the approximation problems are also hard to be solved, we explore a hybrid genotype phenotype binary particle swarm optimization algorithm (GP-BPSO) for resolving two equivalent subproblems, where dynamic programming method (DPM) is used for finding the solution in the lower level programming. At last, a series of simulation examples are performed for demonstrating the validity of the hybrid GP-BPSO compared with the hybrid BPSO algorithm.
    Keywords: Insuring critical path, Bi-level fuzzy random programming, Hybrid algorithm, Dynamic programming method, Task duration, Project management problem, Sample average approximation
  • Majid Kalantary, Reza Farzipoor Saen, Abbas Toloie Eshlaghy* Pages 3723-3743
    This paper focuses on assessing sustainability of supply chains. In this paper, at first, we propose network dynamic range adjusted measure (RAM) model. Then, inverse version of network dynamic RAM model is proposed. Our inverse network dynamic data envelopment analysis (DEA) model changes both inputs and outputs of decision making units (DMUs) so that current efficiency scores of DMUs remain unchanged. We change inputs and outputs without any change in efficiency score of DMU under evaluation while inputs and outputs may have large ranges. A case study shows efficacy of our proposed model.
    Keywords: Sustainable supply chain management (SSCM), Data envelopment analysis (DEA), Range adjusted measure (RAM), Inverse data envelopment analysis, Network DEA, Dynamic DEA
  • Yan Yang, Junhua Hu *, Ruixiao Sun, Xiaohong Chen Pages 3744-3764
    Medical tourism has developed rapidly worldwide, especially in Asia, and one of the most important problems facing the patient-tourists is the selection of the optimum destination. In this paper, we present a novel multiple criteria group decision making (MCGDM) methodology to evaluate and rank the medical tourism destinations vague based on vague information. A systematic assessment and selection model was constructed by investigating MCGDM problems with neutrosophic fuzzy preference relations (NFPRs). We began by defining NFPRs which allow the patient-tourists lacking information, time or patience, to express their uncertainty and hesitancy about the given preference values. The additive consistency and acceptable consistency for NFPRs were then proposed. Furthermore, the approach to improve the consistency for NFPRs was also validated and a series of aggregation operators were developed. In addition, we presented a systematic MCGDM method using NFPRs (MCGDM-NFPRs) in this paper to rank the medical tourism destinations. Then our proposed approach was applied to two cases considering different kinds of original data to prioritize medical tourism places. Finally, the applicability and feasibility of proposed approach were verified by the comparison with other previous methods, along with some analyses and a comprehensive discussion
    Keywords: Neutrosophic fuzzy preference relations, Consistency, Aggregation operators, Multiple criteria group decision making, Medical tourism
  • Ebrahim Asadi, Gangraj* Pages 3765-3775
    This research addresses scheduling problem of n jobs on a Hybrid Flow Shop (HFS) with unrelated parallel machines in each stage. A monolithic mixed integer linear programming (MILP) model is presented to minimize the maximum completion time (makespan). As the research problem is shown to be strongly NP-hard, a Lagrangian relaxation (LR) algorithm is developed to handle the HFS scheduling problem. We design two approaches, simplification of subproblems and dominance rules, to solve the subproblems which are generated in each iteration. For evaluation purposes, numerical experiments with small and large size problems are randomly generated with up to 50 jobs and four stages. The experimental results show that the Lagrangian relaxation approaches outperform the MILP model with respect to CPU time. Furthermore, from the results, it can be conclude that the simplification of subproblems shows slightly better solutions in comparison with dominance rules to solve the subproblems.
    Keywords: Hybrid Flow Shop, Makespan, Lagrangian relaxation, simplification of subproblems, dominance rules
  • Mohammad Mahdi Paydar *, Hasan Molladavoodi, Abdul Sattar Safaei Pages 3776-3793
    Logistics planning plays an important role in providing services to the disaster -stricken areas. In this study, a scenario-based multi-objective model is presented to locate the distribution and evacuation centers and distribute the relief commodities with an appropriate allocation. It aims to serve the earthquake-stricken areas that are classified according to their construction qualities. The objective functions of cost, responsiveness and demand coverage are considered in the proposed optimization model. Moreover, due to the uncertain nature of a disaster and uncertainty in some model parameters, a robust optimization approach is utilized. A revised multi-choice goal programming method is applied to solve the multi-objective model. The proposed model is validated through a case study conducted in the city of Amol. The computational results show the efficiency of the proposed model in a real–world disaster situation.
    Keywords: Relief logistics, Responsiveness, Evacuation centers, Robust Optimization, Revised multi-choice goal programming