PRODUCTION AND OPERATIONS MANAGEMENT
Year:2019 | Volume:9 | Issue:2 (17) |Papers Count:10

Distributing the production activities among the supply chain facilities with regard to the considered criteria can have a significant impact on the productive management. In this paper, a comprehensive mathematical model for reentrant permutation flow shop scheduling via considering a preventive maintenance and distributed jobs on different facilities is proposed. The uncertainty of the time of preventive maintenance operation is handled using robust optimization technique based on the uncertainty budget approach. Job assignment to production facilities and job scheduling are determined in the proposed model by considering multiple objectives include Cmax minimization, production cost minimization, and average tardiness. Due to the NP-hard nature of the proposed flow shop scheduling problem, a new hybrid meta-heuristic based on the novel adaptive large neighborhood search and the simulated annealing is adopted. The obtained results from an extensive numerical experimentation indicate the efficiency of the proposed model and solution algorithm to tackle the proposed problem.

Fuzzy DEA methods have been introduced to deal with the fuzziness of variables. Although, some of these variables are affected by uncertainty and also information granularity, the membership function of fuzzy set is certain and this contrasts with the fuzzy concept as a whole. Type-2 fuzzy sets are introduced because of this and their membership functions have the nature of fuzzy type-1. The calculations of type-2 fuzzy sets are very complicated. However, interval type-2 fuzzy sets which have the characteristics of type-2 fuzzy sets and do not add any complexity to the calculation process can be applied to deal with such a problem. That is why in this paper, it is explored that how an extension of interval type-2 fuzzy dynamic network DEA approach helps to measure airports’ sustainability. Sustainable airports play an irrefutable role in making transportation systems sustainable. Such an integrative approach in DEA models is unprecedented. So, this extension of DEA is valuable from both technical and conceptual aspects.

Nowadays, varieties of warranties are presented by manufacturers. Improving the warranty policy imposes some costs on the producers. As a result, one needs to rely on factual and reliable data as well as the data on defects and repair when it comes to making warranty policy. To this end, this study uses data mining method. That is, an electronic equipment company's warranty data including 3500 defects reports within a 5-year period sample were mined, using association rules. This yielded significant patterns based on the data. Out of total derived rules and patterns, some rules describe the associations between the products and their defects and repairs better than others. So, having these information, we could determine the warranty policy for 24 products. The findings of this study can reduce warranty costs via optimization of warranty policies decisions.

This article presents an integrated production-distribution plan in a reverse supply chain via multi objective mathematical modeling in a high-tech environment. The objectives of the proposed model include 1) minimizing total costs including production, maintenance, inventory and manpower costs, 2) maximizing customer and supplier satisfaction, and 3) maximizing the quality of manufactured products. The supply chain consists of several suppliers, a producer, customers, a repair center to repair the customer's goods and a repair and maintenance center for repairing or disposing products that have passed their warranty period. Among the contributions of this research, we can consider such issues as considering the quality of products manufactured, returned or supplied from suppliers in order to realize the win-win relationship with suppliers, using the maximum capacity of suppliers and supply of parts by each reconstruction center. In order to validate the model, it is solved for some examples using Lingo software and LP metric method.

In this paper, a network of closed-loop supply chain is considered, including external supplier, production/recovery facilities, hybrid distribution/collection centers, disposal centers and customers. Generally, in the closed-loop chains production is done by the returned products, we can’ t achieve a correct analysis by considering one period. In many situations in the real world, we need to consider more than one period, therefore the studied model is assumed in the form of some multiperiod and suppliers are used to supply the shortage of parts. In this paper, it is assumed that parameters of demand, quantity and quality of returns and variable costs are uncertain. To evaluate the uncertainty, two approaches of two-stage and robust stochastic optimization have been used. The results show that performance of robust optimization is better than the two-stage stochastic optimization under uncertainty.

Using cross-functional team (CFT) is a suitable strategy for improving the performance of organizations. The member selection problem is an important aspect of the CFT formation. Several evidences showed the important criteria for choosing true members are: cooperation and coordination, functional expertise, individual abilities, cost, and communication. In this paper, effective features for member selection are identified and a multi-objective 0– 1 nonlinear programming model is developed. This model is developed by using individual and collaborative performance. Afterward, it is converted into the linear form by changing variables to solve it more easily. The proposed model is used in the real example in Census cross-functional team in the statistical center of Iran and required data were collected by surveys and interviews. The results indicate that this proposed model has better performance compared to recommendation of experts and can be used in other fields.

In this paper we consider the inverse of backup 2-median problem. In this problem, a set of weighted points are given and we should change some parameters of the problem such as weights of vertices and edges and coordinates of points such that the two given points be the backup 2-median. We present mathematical models for inverse backup 2-median problems on graphs. In the case that the underlying network is a tree, linear models are presented for the problem with variable edges and weight of vertices. We also consider the continuous case of the problem with variable coordinates of vertices on the plane. In this case, we solve the model by PSO and a hybrid improved PSO methods. Computational results are compared for the varying amounts of parameters.

A project portfolio is a crucial decision-making process used to prepare an optimum collection of vast alternative projects. In most of the previous modeling methods, the focus is directed towards maximizing project efficiency and so, the role of risky aspects in selecting appropriate projects has been neglected. This paper presents an integrated multi-objective mathematical programming (MOMP) based on efficiency-risk for selecting a project portfolio using various techniques including data envelopment analysis (DEA), risk priority number (RPN) and nondominated sorting genetic algorithm (NSGA-Ι Ι ). The proposed model can support both the capability to nominate mutually exclusive projects (conflicting projects that only one of them can be done) or any type of predecessor projects (doing a project depends on another project) and concurrent projects (need to be done at the same time) selection. Another advantage of the model is that the hyper heuristic solutions can be found in the form of several non-dominated cases and it is possible for organization’ s experts to choose the best and the most suitable solutions.

Evaluating productivity and efficiency is an important measure for managerial performance. Appraising and taking advantages of valid mechanisms to evaluate managerial performance is highly important. Malmquist productivity index and Geographical Information System (GIS) are two tools which their integration can provide a pervasive approach for decision making units (DMUs) appraising depend on geographical position. This research uses these techniques to evaluate productivity improvement of an Iranian company’ s sale centers. For this purpose, a mathematical model is designed and also, ordinary productivity variables and GIS data base variables are considered. The results reveal that the most productivity improvement belongs to Store 2 with 24. 7 percent of improvement which is graded as inefficient centers by primary evaluation. The mean total productivity improvement for all sales centers in a given period is recognized as 15. 2 percent by this approach.

In the competitive market of perishable cargoes, determining the price of the product and making opportunities for customers to accelerate the sale of goods through discounts is crucial. Over the life of perishable goods, generally its value reduced to the customer, in this situation, to encourage the purchase, policies such as a discount or reduced price sales policies can be effective. Literature has not provided a model for determining the optimal time to announce a price reduction. While early or late prices announcement could reduce profit, the aim of this paper is to analyze such a model at the level of an enterprise. In the modeling, we assumed that by announcing price discount, tangible changes occur in demand, and demand is a function of price and time. The demand rate in the discount time is decreasing in the beginning of the time and then declining over time. The purpose of the model is determining the optimal price, discount time and order size to maximize the total profit in a single period. After modeling, concavity of the profit function is considered and optimal pricing and discounts are exclusive. Then, a heuristic algorithm derived from the literature was used in order to determine the optimal price, the optimal discount time and the optimal order quantity.