International Journal of Industrial Engineering & Production Research

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  The problem of lot sizing, sequencing and scheduling multiple products in flow line production systems has been studied by several authors. Almost all of the researches in this area assumed that setup times and costs are sequence –independent even though sequence dependent setups are common in practice. In this paper we present a new mixed integer non linear program (MINLP) and a heuristic method to solve the problem in sequence dependent case. Furthermore, a genetic algorithm has been developed which applies this constructive heuristic to generate initial population. These two proposed solution methods are compared on randomly generated problems. Computational results show a clear superiority of our proposed GA for majority of the test problems.

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One of the most important issues in urban planning is developing sustainable public transportation. The basic condition for this purpose is analyzing current condition especially based on data. Data mining is a set of new techniques that are beyond statistical data analyzing. Clustering techniques is a subset of it that one of it’s techniques used for analyzing passengers’ trip. The result of this research shows relations and similarities in different segments that its usage is from strategic to tactical and operational areas. The approach in transportation is completely novel in the part of trip patterns and a novel process is proposed that can be implemented in highway analysis. Also this method can be applied in traffic and vehicle treats that need automatic number plate recognition (ANPR) for data gathering. A real case study has been studied here by developed process.

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The analytic center cutting plane method (ACCPM) is one of successful methods to solve nondifferentiable optimization problems. In this paper ACCPM is used for the first time in the vehicle routing problem with time windows (VRPTW) to accelerate lagrangian relaxation procedure for the problem. At first the basic cutting plane algorithm and its relationship with column generation method is clarified then the new method based on ACCPM is proposed as a stabilization technique of column generation (lagrangian relaxation). Both approaches are tested on a benchmark instance to demonstrate the advantages of proposed method in terms of computational time and lower bounds quality.

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The locomotive assignment and the freight train scheduling are important problems in railway transportation. Freight cars are coupled to form a freight rake. The freight rake becomes a train when a locomotive is coupled to it. The locomotive assignment problem assigns locomotives to a set of freight rakes in a way that, with minimum locomotive deadheading time, rake coupling delay and locomotive coupling delay all freight rakes are hauled to their destinations. Scheduling freight trains consists of sequencing and ordering freight trains during the non-usage time between passenger trains but with no interference and with minimum delay times. Solving these two problems simultaneously is of high importance and can be highly effective in decreasing costs for rail transportation. In this paper, we aim to minimize the operational costs for the locomotive assignment and the freight train scheduling by solving these two problems concurrently. To meet this objective, an efficient and effective algorithm based on the ant colony system is proposed. To evaluate the performance of the proposed solution method, twenty-five test problems, which are based on the conditions of Iran Railways, are solved and the computational results are reported.

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In this paper, a new distribution that is highly applicable in the fields of reliability and economics is introduced. Also the parameters of this distribution is estimated using two methods of Maximum Likelihood and Bayes with two prior distributions Weibull and Uniform, and these two methods are compared using Monte-Carlo simulation. Finally, this new model is fit on the real data(with the failure time of 84 aircraft) and some of comparative criteria are calculated to confirm superiority of the proposed model compared to other models.

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The present work addresses inventory-routing rescheduling problem (IRRP) that is needed when some minor changes happen in the time of execution of pre-planned scheduling of an inventory-routing problem (IRP). Due to the complexity of the process of departing from one pre-planned scheduling IRP to a rescheduling IRP, here a decision-support tool is devised to help the decision-maker. This complexity comes from the issue that changes in an agreed schedule including the used capacity of the vehicle, total distance and other factors that need a re-agreements negotiation which directly relates to the agreed costs especially when a carrier contractor is responsible for the distribution of goods between customers. From one side he wants to stick to the pre-planned scheduling and from the other side, changes in predicted data of problem at the time of execution need a new optimized solution. The proposed approached applies mathematical modeling for optimizing the rescheduled problem and offers a sensitivity analysis to study the influence of the different adjustment of variables (carried load, distance, …). 

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In this paper, a new five-parameter distribution is proposed that is called MarshallOlkin Gompertz Makeham distribution(MOGM). This new model is applicable in analysis lifetime data, engineering and actuarial. In this research, some properties of the new model such as mode, moment, Reyni entropy, Tsallis entropy, quantile function and the hazard rate function which is decreasing and unimodal, are studied. The unknown parameters of the MOGM distribution are estimated using the maximum likelihood and Bayes methods. Then these methods are compared using Monte Carlo simulation and the best estimator is proposed. Finally, applications of the proposed model are illustrated to show its usefulness.

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Innovation is an essential tool for the supply chain to gain its competitive advantage and improve its performance. Many researchers have remarked that supply chain innovation is a vital tool for improving the performance of a supply chain and can be very productive. This research attempts to identify and analyze the effective factors on the innovation supply chain in the health sector. The effective factors on the innovative supply chain were extracted by reviewing the literature, similar studies, and experts’ surveys. In this regard, 49 criteria were determined in eight dimensions. Intuitive fuzzy DEMATEL (IFD) and AHP methods were used to determine the weight and the relationships between them. The results indicated organizational innovation as the most important dimension, government support innovation as the most effective dimension, and process innovation as the most affected dimension. Some researchers believe that this period guarantees the survival and success of service organizations in this competition. However, the logistics and communication network of a business is required as a new and innovative landscape to use the competitive advantage opportunities to perceive the global era.

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‎Recently, generalized distributions have received much attention due to their high applicability and flexibility. This paper introduces a new five-parameter distribution called Kumaraswamy-G generalized Gompertz distribution, which is widely used in the field of survival and lifetime data. In introducing a new distribution, it is important to study the statistical properties and the estimation of its parameters. Therefore, this paper studies the statistical properties of this new distribution. In addition, the parameters of this distribution are estimated by three methods. Finally, using a real dataset, the performance of the introduced distribution is investigated.