Scientia Iranica
Volume:27 Issue: 1, Jan-Feb 2020

This paper studies the multi-project scheduling problem which involves multiple projects with different importance weight; with predefined assigned due dates; with activities that have uncertain durations; and with renewable resources that are constrained. The resource sharing policy is applied to share the resources among projects. Due to the environmental rapid changes and also the uniqueness of projects, the probability distribution function of uncertain durations cannot be estimated with confidence. Besides, the multi-project scheduling problem with its large scale investment dictates a conservative approach to deal with the existing uncertainty. Therefore, the Robust Resource-Constrained Multi-Project Scheduling Problem (RRCMPSP) is studied in this paper while the maximum total weighted tardiness of the projects should be minimized. A scenario-relaxation algorithm is implemented which results in optimal solutions for the RRCMPSP. The aim is to find an optimal structure containing all the projects in such a way that it transfers the resources between the activities based on the resource sharing policy while the maximum weighted differences between the projects finish times and their assigned due dates will be minimum.

This paperproposes a multi-objective, multi-stage programming model to design a sustainable closed-loop supply chain network considering financial decisions. A multi-product, sustainable closed-loop plastic supply chain network design problem which encompasses economic, environmental and social objectives is modeled in a mathematical manner. The decisions to be made are concerned with location of facilities; the flow of products, loans to take and investments to make. Uncertainty issue is about demand of customers and investment's rate of return. The decision making model is formulated as a multi-objective, multi-stage mixed integer linear programming problem and is solved by implementing path formulation and augmented Ɛ-constraint methods. Computational analysis, is provided based on the subject company to determine the significance of the proposed model and the efficiency regarding integrating financial decisions with supply chain network design decisions.

The objective of this work is to present a novel multi-criteria group decision making (MCGDM) method under cubic intuitionistic fuzzy (CIF) environment by integrating extended TOPSIS method. In the existing studies, the uncertainties which are present in the data are handled either an interval-valued intuitionistic fuzzy sets (IVIFS) or an intuitionistic fuzzy set (IFS) information, which may lose some useful information of alternatives. On the other hand, CIF set (CIFS) handles the uncertainties by considering both the IVIFS and IFS instantaneously. Thus, motivated by this, in the present work, we presented some series of distance measures between the pairs of CIFSs and investigated their various relationship. Further, under this environment, a group decision-making method based on the proposed measure is presented by taking the different priority pairs of the decision makers. A practical example is provided to verify the developed approach and to demonstrate its practicality and feasibility, we compared their results with the several existing approaches results.

Increasing the number of disasters around the world will decrease the performance of the supply chain. The decision makers should design resilience supply chain network which could encounter with disruptions. This paper develops an integrated resilience model of supplier selection and order allocation. Resiliency measures including quality, delivery, technology, continuity, environmental competences are explored for determining the Resilience Weight of suppliers. Fuzzy DEMATEL and ANP methods are applied to find overall performance of each supplier. Then, the developed mathematical model maximizes overall performance of suppliers while minimizes total cost of network. The proposed mathematical model helps the decision makers to select supplier and allocate the optimum order quantities by considering shortage. Since the disruptive incidents are inevitable events in real world problems, the impact of disruptions on suppliers, manufactures and retailers has been considered in the proposed model. Inherent uncertainties of parameters are taken into account to increase the compatibility of the approach with realistic environments. To tackle the uncertainty and multi-objectiveness of the proposed model, interval Method and TH aggregation function is adapted. The proposed model is validated through application to a real case study in a furniture company. Results demonstrate the usefulness and applicability of the proposed model.

Statistical process monitoring, maintenance policy, and production cycle length usually have been investigated separately while they are three dependent aspects in the industrial systems. Moreover, most of the papers that integrated simultaneously these aspects, suffer from three major drawbacks as follows: (1) Optimizing the production cost without considering the time value of money to simplify the model; (2) Considering the fixed shift size while it is a random variable in the real condition; (3) Economic design of control charts ignoring the statistical properties that lead to reduce the control chart power, extremely. To eliminate these weaknesses, this paper presents an integrated model of production cycle length, maintenance policy, and economic-statistical design considering the time value of money and the stochastic shift size. Furthermore, to maintain the reliability of the system at an acceptable level, the presented model uses non-uniform sampling. Finally, three comparative studies on the main contributions are presented to illustrate the advantages of the model and a sensitivity analysis is implemented on the several parameters to extend insights into the matter.

In the current competitive economy, the investors are facing increased uncertainty while evaluating new investment projects. This uncertainty caused from existence of insufficient information, oscillating markets, unstable economic conditions, obsolescence of technology and so on, and hence uncertainty is inevitable in reality. In such conditions, the deterministic models, while easy to use, do not perfectly represent the real situations and might lead to misleading decisions. When the cash flows for an uncertain investment project, over a number of future periods, are discounted using the traditional deterministic approaches, it may not provide investors with an accurate estimation of the project value. Therefore, this paper utilizes the probability theory tools to derive closed-form probability distribution function (PDF) and related expressions of the net present worth (NPW), as a useful and frequently used criterion, for cost-benefit evaluation of projects. The random cash flows follow normal, uniform or exponential distributions in our analysis. The probability distribution function of the NPW is an important tool that helps investors to accurately estimate the probability of being economic for projects, and hence, it is important tool for investment decision-making under uncertainty.

It is accepted that project breakdown into several independent subprojects can help to have a successful and effective project management. On the other hand, it can lead to inefficiently use of some renewable resources, and increase the total project cost and time. This article studies the benefits of the horizontal partnering among contractors assigned to subprojects through the sharing renewable resources and proposes a model based on cooperative game theory to solve it. The improvement of the net present value of the project is considered as the benefit of the cooperation among contractors. Therefore, a mixed-integer non-linear programming (MINLP) model is developed for the resource constrained project scheduling with objective function of maximizing the net present value (NPV) of each coalition. Seven widely used cooperative game theory solution methods are used to solve the benefit (NPV) allocation problem and then the stability criteria are suggested to find the best allocation scheme. Finally, an example is represented to more comprehensively illustrate the problem.

This paper introduces a supplier selection and order allocation problem in a single-buyer-multi-supplier supply chain in which appropriate suppliers are selected and orders allocated to them. Transportation costs, quantity discount, fuzzy type uncertainty and some practical constraints are taken into account in the problem. The problem is formulated as a bi-objective model to minimize annual supply chain costs and to maximize the annual purchasing value. The fuzzy weights of suppliers, which are the output of one of the supplier evaluation methods, are considered in the second objective function. Then, we propose a novel fuzzy multi-objective programming method for obtaining Pareto solutions. The method is the extension of a single-objective method exist in the literature. This method is based on the decision maker's degree of satisfaction from each fuzzy objectives considering the fulfillment level of fuzzy constraints. In the proposed method, the problem remains multi-objective and, unlike existing methods, does not transformed into a single-objective model. At the last stage of proposed method, the fuzzy results are compared with an index, and decision maker can identify the appropriate or inappropriate solutions. To solve the problem, non-dominated sorting genetic algorithm (NSGA II) is designed and computational results are presented using numerical examples.

In this paper, we consider a manufacturer that produces products in a finite horizon time and sells products with non-renewing free replacement warranty policy. The manufacturer is responsible to provide spare parts for failed products, whether the products are under or out of warranty. Previous research on warranty optimization has focused on maximizing manufacturer profit without considering the spare part market for out-of-warranty products. This study proposes a novel nonlinear model that maximizes manufacturer profit by optimization of price, warranty length and spare part inventory for under- and out-of-warranty products in a manufacturing/remanufacturing system. Due to the model’s unique structure, we propose a new two-stage approach that combines metaheuristic and an exact method, in which the first stage is to determine product’s prices and warranty length with metaheuristic algorithm and in the second stage the remaining inventory related problem is transferred to a Minimum Cost Network Flow Problem which is solved for spare part inventory control. To illustrate the effectiveness of the suggested method, the model is solved for a case study of Iranian SANAM electronic company with two different metaheuristic algorithms and a sensitivity analysis is conducted to study the effect of various parameters on the optimal solution.