نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 15 (پاییز و زمستان 1398)

This study develops a new multi-objective programming model to design a four-echelon pharmaceutical supply chain (PSC) network for several perishable products over multiple time periods. Supply chain consists of four echelons, including suppliers, manufacturers, distribution centers, and retailers. This model proposes an integrated decision-making approach for the location of facilities (pharmaceutical production and distribution sites) and their most suitable allocation to each other for a reliable transportation of products between echelons. It also determines the optimal amount of production and transportation among facilities and the required number of labours. A varying level of technological expertise is required for the establishment of production and distribution systems. The problem aims to reduce costs and unemployment and pharmaceutical supply gap between regions and to increase their satisfaction rate with an emphasis on the importance of providing a large supply of pharmaceutical products. Given the fact that the problem is a NP-hard one and accurate methods are inefficient, a genetic algorithm-based meta-heuristic is developed for problem-solving and its performance is analyzed on a wide range of single- and two-objective problem instances. The results show that an increase in the satisfaction rate of regions and a reduction in its gap between regions as two objectives are of great importance in pharmaceutical supply chain. Moreover, a reduction in unemployment gap between regions improves the level of employment, and it provides a right balance between social responsibilities. The developed algorithm also provides an optimal solution for large-sized single- and two-objective problems in a short time period.

Nowadays, many researchers focus on reverse logistics and closed-loop supply chain network design. The main reason is the ever-increasing importance of environmental issues, customer satisfaction and economic benefits. In this paper, a scenario-based optimization model is proposed to deal with the closed-loop supply chain network design problem considering sale and leaseback (SLB) transactions. SLB transactions increase the liquidity of total assets and provide monetary resources for other activities. The proposed model, which is formulated based on sales accounting standards, maximizes the supply chain’s benefit after tax. The proposed model is solved to optimality. Finally, a sensitivity analysis on the safety stock coefficient, the fair value of the leased asset, the interest rate implicit in the lease and lessee's incremental borrowing rate is performed to assess the impact of these parameters on the expected value of supply chain’s benefit after tax.

Cause-selecting charts (CSCs) are the main tool for statistical quality control in multistage processes. The establishment of these control charts, which use regression models and residuals to remove the effect of previous stages from current stage, needs some critical assumptions like the linear independence among incoming quality variables. When this assumption is violated, which is quite common in real practice and called multicollinearity, the variance inflation in regression parameters occurs. Subsequently, this leads to some crucial problems in the performance of traditional CSCs. To tackle the mentioned problem, there exist some statistical and practical methods and the application of ridge regression is one of the most efficient approaches. In this paper, modeling and designing a novel control chart based on ridge regression has been addressed and extensive simulation studies have been conducted to investigate the performance of the suggested monitoring procedure compared with the traditional control chart in the literature. The results reveal that the proposed control chart outperforms the existing control chart in the presence of multicollinearity.

Most of the terrorist activities that have taken place over the past two decades have been based on accurate information, which has led to disturbances in the security and some extensive damages and it is a major threat to public and government infrastructures. The dramatic expansion of such activities has shown the necessity and importance of the correct location and protection of these infrastructures in order to reduce the damage caused by the attack to increase the reliability of facilities for providing services. In such cases, a Stachelberg game is formed between the system designer and the attacker. Due to the high value and the lack of accurate information in the context of confliction, in this research, we are going to model the location-interdiction-protection problem under asymmetric information as a bi-level programming model and explore the advantages and risks of neglecting the information asymmetry in decision-making. In order to solve the suggested bi-level model, two solution methods are proposed. At first, Karush-Kuhn-Tucker conditions are used to convert the model to a single level model.Then for large size problems, we develop a matheuristic which searches the solution space of the upper level problem according to tabu search principles, where a hash function calculates and records the hash values of all visited solutions for the purpose of avoiding cycling, and resorts to a CPLEX based exact solution technique to tackle the lower level problem. Test results show efficiency and effectiveness of the proposed heuristic algorithm.

Nowadays environmentalism has been become an important global issue. In recent years, the closed-loop green supply chain management has grown considerably and its result be important for managers. In this paper, we have proposed a mathematical optimization mixed-integer programming model for designing a single objective closed-loop green supply cahin network consisting of production and recovery centers, distribution centers, inspection centers,disposal centers and customers, which, in addition to reducing system costs includes the fixed cost of openning plant and distribution centers, the variable cost of product production with different technologies and shipping cost, taking into the carbon tax rate, the amount of carbon emissioned by production, transportation and establishing. Due to in real-world issue the parameters are uncertain. Moreover, the model has been developed using a robust fuzzy programming approach to examine the effects of uncertainties of production cost, remanufacturing cost,distribution process cost, inspection process cost and disposal process cost, amount carbon emission, facility capacity and the demand rate on the network design. Gams software has been used to obtain an optimal solution to the problem. The numerical results shows the proposed model is capable of controlling uncertainty and the robustness price is imposed on the system, therefore, the value of the objective function in a probability 5% is lower than the robust fuzzy possibilistic.

In the classic models of scheduling problems, researchers mostly concentrate on the objectives considering jobs completion time. Due to the relation among economy, energy and environmental concerns, attention to the energy use of machines have been considered by researchers in the field of scheduling in recent years. Also, In the literature of scheduling problems, it is mostly assumed that one agent try to optimize the problem. But, occasionally there are several agents that each has their own jobs and they must use a series of common resources to process them. In this study, a two-agent heterogeneous parallel-machines scheduling problem is studied in which the process speed of each job on each machine is adjustable. Since there is a direct link between the energy used in machines and process speed, the used energy costs affect on scheduling problem. In this study, the first agent is tried to minimize total tardiness penalty as well as energy costs of production machines and the second agent is tried to minimize total tardiness and earliness. The suitable schedule should be considered to allocate and sequence jobs of agents to the common resources to optimize appropriately the agent’s objective functions. Since the proposed problem is Np-hard, in order to solve it in large scale problems, a Memetic algorithm is developed and to verify the performance of this algorithm, we take into comparison the results of Memetic algorithm with the results of GAMS software and of another meta-heuristic algorithm.

In inventory models based on the Economic Order Quantity, Demand rate and holding cost are assumed have constant value and the purchase cost is assumed constant regardless of the order size. In actual applications, the demand rate can be affected by many variables such as seasonality, selling price, and availability. Moreover, the holding cost tends to be higher for extended storage periods. Additionally, the unit purchase cost is generally lower for larger order sizes due to quantity discounts. The purpose of this paper is to develop an inventory and pricing model of a product in a supply chain consisting of multiple suppliers, multiple producers and multiple customers that have linear functions price-dependent demand, time-dependent holding cost, and cost of purchase depends on the size, so that three cases are considered simultaneously in the model. Based on these assumptions, a formulated mathematical model and an optimal solution solution algorithm are developed to maximize total profits, a case study of the problem in a manufacturing plant was carried out using real data Modified, a numerical example is given, and sensitivity analysis is performed on the effect of different parameters on the optimal solution. Our findings indicate that, first, demand function parameters and then cost and purchase parameters are the most influential factors on the profit function.

An appropriate supply chain is consisting of foreign suppliers, facilities of productions, distribution centers, sales, demands and transportation. A system may be used for both reducing costs in supply chain and helping to respond customers' demands quickly. In the present study, designing a closed-loop supply chain as strategic decision considered, and as integrate designing of direct-reverse logistic system cause to prevent sub-optimization resulted by designing separated to systems, and closed-loop model used. The studied system in the research was modelling an integrated direct and reverse logistic system as multi surfaces, multi-objective, multi-production and multi-stages optimization through limited capacities and lack of assuring in demands, costs and return. So, in order to oppose lack of assuring, two stable and potential optimization strategies considered. Firstly, regarding to parameters such as demands, costs and potential return and using normal distribution, modelling suggested, and as potential strategy may loss efficiency in large sizes, stable optimization proposed to use, therefore, mix integer nonlinear programming model used, then its stable equivalent considered. The aim of present study is to minimize costs and increase quality of recyclable productions. Finally, two multi-objectives metaheuristic and genetic algorithms solved the problems through non-dominated sorting and particle swarm optimization algorithms, then the results compared. According to the obtained results, NSGA II is more suitable than MOPSO.

Process capability indices are widely used in different industries for quality assurance purposes. An appropriate analysis of the process capability index can result in improving quality level and satisfying customer requirements. Recently, some control charts have been developed for continuous evaluation of process capability in producing conforming products. However, these charts are designed to monitor a single index and cannot directly show the relation between parameters of the quality characteristic distribution and the process capability index. In fact, average and dispersion of a process are combined into an index and control charts monitor this individual index. In this research, control charts are developed for monitoring the capability of a process in producing conforming products. In these charts, the capability of a process in producing conforming products is monitored in terms of the average and dispersion of the process. Therefore, interaction between these measures can also be considered into account. Control charts are developed for Normal, Lognormal and Weibull distributions and their performances are evaluated through simulation and Average Run Length (ARL) and Standard Deviation of Run Length (SDRL) measures. The results show that the proposed control charts can lead to an appropriate monitoring of the process capability.

Pricing is one of the most important decisions a company or organization can take, because the reflector price is the amount of value that a product has and the customer is willing to pay. This issue will become even more important for perishable products. Therefore, in this paper, we will examine the pricing of non-instantaneous perishable products, as demand is a nonlinear function of the price and age of the product. Also, the price is a function of the product's age. The model is a bi-objective model, which, in addition to maximizing profit, attempts to maximize this issue, considering the satisfaction of chain centers and customers as a goal. Genetic Algorithm and Vibration Damping Optimization has been used to solve the problem. Results show the validation of the model and the performance of the developed algorithms.

In the twenty-first century especially in the recent decade, due to concerns about the environment and social responsibility, the Closed-loop supply chain has attracted more attention. In this paper, a closed loop supply chain consisting of a manufacturer, a retailer, and a third-party collector is considered. The manufacturer also produces new products from raw materials and simultaneously remanufacturing the used products. In the direct channel, the manufacturer introduces new products and remanufactured products to the market through a retailer, and in the reverse channel, the retailer and third-party collector competitively collect the products used. To analyze the optimal pricing and collecting decisions under different channel leadership, Four different scenarios based on game theory have been developed - a centralized model and three decentralized models based on the Stackelberg game including Manufacturer-led, Retailer-led, and Third Party collector-led. Then, using numerical analysis, we compared and analyzed the optimal decisions in different scenarios and examined the effect of competition intensity between the dual recycling channel of the retailer and third-party collector on decision variables, profit of members and total profit. We concluded that the profit of chain Supply in the centralized scenario is larger than decentralized models, In addition it can be concluded that the retailer-led decentralized model is often the most effective scenario in the CLSC.

Determination of degradation status and estimating remaining useful life are two main activities in prognostics and health management. These two main activities can be perceived as the problem of multi-sensor data fusion. These sensors contain information including speed, pressure, and temperature. In terms of evidence theory, the information obtained from each of these sensors can be regarded as a part of the evidence and determination of degradation status and estimating remaining useful life based upon this information can be considered as the problem of multi-sensor data fusion. In this article, the Dempster-Shafer theory has been employed as a tool for modeling and multi-sensor data fusion as an indicator of the health status of the turbofan. In this regard, initially, the theory of evidence has been reviewed and then how to model the remaining useful life estimation problem by multi-sensor data fusion within the framework of the concepts of this theory including mass function, focal elements and rules of evidence composition is explained. This paper has introduced a new approach for combining the results of different estimates of remaining useful life through determining the weighs. Furthermore, it has offered two different scenarios to specify the similarity between the system under study and the available evidence. Finally, to appraise the proposed method, the turbofan engines data set (C-MAPSS) has been used as it has been extensively addressed in literature by researchers. According to the results, the proposed method revealed better score and performance compared with other available methods in the literature.