نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 16 (بهار و تابستان 1399)

Machines and equipment continuous operation exposes them to failure (such as aging, wear, corrosion and creep). Consequences of such deteriorations are not limited to efficiency, reliability and life cycle reduction, they also may lead to environmental degradation. Investigating adverse effects of system’s failures have led to many academic studies on condition-based maintenance strategies. One of prominent approaches in this area is   maintenance policy based on the system’s performance and status, which reduces the overhead costs of the associated penalties by controlling environmental damages. The purpose of this study is to model an optimal maintenance policy including production and inspection periods scheduling with minimal average total cost, under uncertainty conditions. In this article, the threshold exceeding period, time to failure from exceeding threshold point and the delay time to start maintenance activities are considered random variables and their effects have been studied. Numerical examples of the proposed maintenance model have been solved by Nelder–Mead, PSO and Genetic algorithms, and optimal inspection times are computed. Sensitivity analyses have been done numerically to study the effect of changes of different parameters on the optimal inspection times.

Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. The level of participation in preventive health care programs is a critical factor in terms of their effectiveness and efficiency of these programs. This article presents a methodology for locating preventive health care facilities (PHCFL) in order to increase the accessibility to potential clients and thus maximize participation in preventive healthcare programs. Due to capacity constraints in preventive health care facilities and the importance of waiting time in queues, we assume that each facility acts as M/M/ /  queuing system. We present an Integer nonlinear programming (INLP) model in order to maximize the participation rate and minimize the cost of building facilities and allocating medical equipments to open facilities. Because of the importance of other objective functions, supplements have been introduced to bring about equitable access to preventive health care centers and to balance the hours of servant unemployment.The model is known as NP-Hard models, hence we propose two meta-heuristics algorithms to solve this problem with multi-objective functions. Meta-heuristics algorithms are consists of: non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm. In order to increase the speed and performance of the combinatorial optimization algorithms, new coding is used in these solution algorithms. Finally, we used Taguchi method to tune the parameters of two algorithms and test problems with different size were generated and analyzed. According to the results, non-dominated sorting genetic algorithm is better.

This study considers optimal warranty and maintenance services from service agent and consumer view points for vehicles under two dimensional warranty policy. The interactions between the agent service and consumers are modeled by estimating their profits and then are examined by game theory in the base warranty and extended warranty. Maintenance policies are performed as period preventive maintenance (PM), home and road corrective maintenance (CM). Then, the agent and the consumer’s profits are optimized and the number of period preventive maintenance (PM) and the price of warranty services are estimated by the stackelberg equilibrium. For demonstrating the applicability of this research, finally the proposed model is investigated by using of real data from a truck service agent.

Selecting effective green manufacturing practices (GMPs) for a specific industry is a complicated process due to the existence of diverse alternatives, managerial objectives, and resource limitations. In this study, we seek to propose a novel decision-making approach that is capable of formulating the objectives and resource limitations of the organizations in their GMPs selection decision-making problems. The proposed approach is a combination of methods including Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP), Additive Ratio Assessment (ARAS) along Zero-one Goal Programing (ZOGP) model. We validate the proposed approach through a case study of selecting effective GMPs under resource constraints for a steel manufacturing complex in Iran. In this empirical study, a set of criteria categorized in four dimensions including environmental perspective, operational perspective, organizational perspective, and external perspective is defined for evaluation of GMPs. The results indicated that among the decision dimensions, an external perspective was identified as a leading factor. This research concludes that the implementation of industrial wastewater treatment plants and establishing a waste management system are the preferred strategies among the suggested alternatives considering the available resource of the factory, at the moment.

Proper performance of production/supply centers in a multi-factory production (MFP) network requires undisrupted equipment. The design and implementation of a maintenance system is important for two reasons - firstly, extending the life of the equipment, and second, reducing the MFP network disruption and associated costs. The greater productivity of an MFP system is achieved by integrating maintenance and production decisions. In this study, considering a scenario-based uncertainty in the demand and failure rate, a robust scenario-based stochastic programming (RSSP) model has been presented. proposed RSSP model integrates the strategic and operational decisions of production and the maintenance, and takes into account the MFP disruption costs. We suggest three preventive maintenance strategies such as maintenance outsourcing, deployment of backup equipment, as well as periodic preventive maintenance for the MFP network. The objective function of the proposed model is to maximize the total profit, subject to, constraints such as limited capacity of production, storage, access to service centers, and budget should be satisfied. The proposed RSSP model is formulated as mixed linear program which can be solved in small-scale instances by the CPLEX Solver. Furthermore, Benders decomposition solution method is proposed for large-scale instances. Finally, a numerical study of CNG stations, as an MFP network, is conduct to demonstrate the applicability of the proposed model and analyze the results.

This paper deals with a two-period inventory planning and control problem with stochastic demand. In this system, a distributor can order the products in a preseason period and sell them in two subsequent selling periods. Demand during the first period is a random variable with a known probability distribution, while the demand for the remaining products at the end of the first period depends on their selling price during the second period. Therefore, the selling price of end-of-period products is very important so that a low price will achieve more sales. It is also assumed that the purchase price of the products in the preseason period is a time-dependent and follows a random process. For the considered problem, a mathematical model is suggested and then using the probabilistic dynamic planning approach, the optimal selling price at the second period, the optimal ordering quantity, and the purchase price of the products are determined to maximize the system profit. Finally, a numerical example is presented to investigate the performance of the model, and the sensitivity analysis of the main parameters.

Today, supply chains are exposed to a variety of risks. Ignoring these risks can cause irreparable damage to them. On the other hand, the subject of redesigning is essential when the supply chain loses its optimality or needs to be altered due to changing conditions. In this paper, in contrast to most researches done in the literature, the problem of resilient supply chain network redesign is investigated under operational and disruption risks. The network structure addressed in this paper is a mixture of open and closed loop schemes, which has been rarely considered in the literature. A novel stochastic robust optimization model is developed to manage the uncertainty of the problem. The problem is formulated as a linear mixed-integer programming model with the objective function of profit maximization. Due to the high complexity of the model and the challenge to solve it in large-scale dimensions, a Lagrangian relaxation algorithm is developed, and its excellent performance is shown by the relevant calculations. In order to measure the efficiency and validity of the model, a case study has been presented in the automotive tire industry. The results show that using resilience strategies is very effective in improving the profitability of the supply chain and preventing losses. In addition, the use of a mixed supply chain network increases the overall profitability of the supply chain in comparison to a forward supply chain network.

In this paper, an inbound and outbound truck scheduling problem is investigated in a multi-door cross-dock system considering due date assumptions for outbound trucks as a strict constraint. Part of the products is placed inside outsourcing trucks to ensure timely delivery, customer satisfaction, and product quality enhancement. The problem under consideration is formulated as a bi-objective mathematical programming problem whose objectives are to minimize operational costs and maximize the quality of products delivered in cross-dock. In this paper, the sequence of trucks behind the inbound and outbound doors in the cross-dock is also regarded. A mixed-integer programming model is formulated for the proposed problem. Since the real-world uncertainty is inevitable; therefore, an improved IVF-SO fuzzy method is presented in this paper to solve the model. In this paper, to illustrate the efficiency of the proposed model and also the introduced approach, a real-world example is presented to justify the performance. The numerical results of the case study demonstrate the superiority of the proposed approach over previous methods.

In this paper, the economic-statistical design of the Max EWMAMS control chart under measurement errors and multiple measurements for joint monitoring of mean and variability of the process is investigated. The traditional approach for monitoring mean and variance of the quality characteristic is using two separate control charts. This approach leads to an increase in the probability of Type I error. To overcome this problem, researchers have proposed control charts for joint monitoring of mean and variability of the process. Also in practice, the measurement errors exist in the sampling process. The sampling with multiple measurements is a way to reduce the deficiency of the measurement error and increasing power of the control chart. However, the multiple measurements cause to increase the sampling costs. Hence, this factor should be considered in the economic-statistical design of control charts as well. In the proposed cost model, the Lorenzen-Vance cost function is developed and a genetic algorithm is applied to obtain model parameters that minimize cost function. Finally, the performance of the proposed model is evaluated by a numerical example.

The expansion of urban infrastructure and the development of urbanization, on the one hand, and the increasing demand and growth of distribution companies in urban and inter-urban environments on the other hand, have led to complexities in logistics and good transport. In this regard, many companies and factories produce and distribute food products that their qualities decrease by increasing the duration of distribution. In this study, we present and investigate the locating-routing model in multigraphs where parallel paths have different traffic congestions according to the time of the day. The proposed model tends to reduce the costs of locating and logistics in addition to reduce pollution generated by vehicles and maximize the quality of food products. In order to approach the model to real conditions, the problem is modeled under uncertain conditions and warehouses failure is possible in the transport network. In the following, the proposed model is solved and examined. The results show that the use of multigraph representation not only reduces transportation costs and environmental impacts but also enhances the quality, novelty of shipped products, and helps to design an efficient logistic network for perishable products.

;Due to environmental concerns along the world, reverse logistics now is becoming an important strategy to decrease resource extraction. This research develops a generic mixed integer linear programming model for reverse logistics network design. This is a multi-echelon reverse logistics model. It maximizes total profit by handling products returned for reuse, refurbishing, remanufacturing, recycling and sale of spare parts. Also considering product variety and bill of material are model features. A hybrid algorithm constructed by genetic algorithm and branch and cut algorithm is proposed to solve the constructed problems. The designed model is validated and tested by using data generated in various size. Sensitivity analyses are conducted on various parameters to illustrate the capabilities of the proposed model.

Mixed model two-sided assembly lines are widely used in various industries because of their increased capability to meet a wide range of customer demands. Assembly Line Balancing is a fundamental design issue for industries. On the other hand, Mixed model Two-sided Assembly Line Balancing is a NP-hard problem. Therefore, it is necessary for researchers to design effective approaches to solve this problem. In this paper, a genetic algorithm is applied to balance the assembly line of the "Beh Afarinan Datis Tiva Company" which is of MTALB type. Because of the randomness of tasks time, the simulation-based optimization approach has been used to get closer to the real situation. Finally, by comparing the minimizing the number of mated stations, the total number of stations, the Weighted Line Efficiency and the Weighted Smoothness Index criterion for a specific cycle time before and after assembly line balancing, the effect of balancing on the assembly line performance is shown.