Scientia Iranica
Volume:26 Issue: 3, May-Jun 2019

This paper presents a framework for solving risk response action selection problem by considering: (1) the impact of risk events on the project objectives, (2) the interactions between risk events and (3) management criteria and preferences. For these purposes, a framework is developed by combining an optimization-based model and a Multi Criteria Decision Making (MCDM) approach. First, in the optimization-based model, Ant Colony Optimization (ACO) is used to find the best combination of response actions which have more effects on time, cost and quality. Also, in this model, to overcome the imprecision situation resulting from lack of knowledge or insufficient data, risk parameters are determined using the fuzzy set theory.  Moreover, the Design Structure Matrix (DSM) is used to capture the effect of interactions between risk events. Second, the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method is used to analyze the obtained solutions by ACO, based on the other management criteria. Finally, the efficiency of the proposed framework is examined by its implementation in a real building construction project. Discussions through the case study show that using the proposed framework decision makers can evaluate more aspects of response actions.

Against the background of supply chains, this paper constructs a class Hotelling model to describe and explore sequential auctions of close substitutes with slightly more general associated valuations. In this generalized model, both close substitutes and bidders are hypothetically distributed in the interval [0, 1], types of bidders are continuous, and each bidder’s valuations for close substitutes are not independent. And with the aid of this model, equilibriums are explored and efficiencies of the auctions are analyzed under second-price sealed-bid auction formats. Further considering two typical information policies, we investigate some concrete bids and revenues of the efficient sequential auctions while bidders’ valuations are linear functions of distances between them and close substitutes. Results show that efficiencies of the sequential auctions are conditional, and influences of information policies on revenues of the auctions are related to both numbers of bidders and locations of items.

This article is about Bayesian estimation of parameters of a heterogeneous 3-component mixture of Rayleigh distributions (3-CMRD) generating a mixture data. Being the most popular and reasonable sampling scheme in reliability and survival analyses, the doubly censored sampling scheme is considered. The Bayes estimators and their posterior risks are derived under various situations. In addition, elicitation of hyperparameters is presented. Algebraic expressions for posterior predictive distribution and Bayesian predictive intervals are derived.  Assuming the informative and the non-informative priors, a comprehensive Monte Carlo simulation is conducted to examine the performance of the Bayes estimators under symmetric and asymmetric loss functions. Finally, to highlight the practical importance, the proposed 3-compnent mixture model is applied to a doubly censored lifetime data from a real life situation. It is observed that the analysis of doubly censored data in Bayesian framework, the SRIGP paired with SELF (DLF) is suitable choice for estimating mixing proportion (component) parameters.

Master production scheduling is an effective phase of production planning which leads to scheduling and magnitude of different products production in a company. This problem requires investigating a wide range of parameters, regarding demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty, in which demands, as time-dependent variables, are considered as stochastic variables, while cost and utilization parameters, with cognitive ambiguity, are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a practical problem of a polyethylene pipe and fitting Co. in Iran. The result showed a high degree of applicability.

In this paper, we address the surgical case scheduling problem in multi operating theater environment with uncertain service times in order to minimize makespan. In surgical case scheduling, not only the hospital resources are allocated to surgical cases but also the start time of performing surgeries is determined based on sequence of cases in a short-term time horizon. We consider fuzzy numbers for duration times of all stages and hereafter the problem called fuzzy surgical case scheduling. Since the operational environment in the problem is similar to no-wait multi-resource fuzzy flexible job shop problem, we consider constraints of that for formulating and solving problem. This problem is strongly an NP-hard optimization problem, hence we employ ant system algorithm to tackle problem. The proposed approach is illustrated by detailed examples of three test cases, and numerical computational experiments. Therefore, the performance of proposed algorithm is compared with a schedule constructed by first-come-first-service rule on all test instances. Also, a real case is provided from Isfahan’s hospital to evaluate proposed algorithm.  Consequently, computational experiments state that algorithm outperforms results obtained by hospital planning as well as fuzzy rule, and these indicate efficiency and capability of our algorithm for optimizing the makespan.

Although after an earthquake the injured person should be equipped with food, shelter and hygiene activities, before anything must be searched and rescued. But disaster management (DM) has focused heavily on emergency logistics and developing an effective strategy for search operations has been largely ignored. In this study, we suggest a stochastic multi-objective optimization model to allocate resource and time for searching the individuals who are trapped in disaster regions. Since in disaster conditions the majority of information is uncertain, our model assumes ambiguity for the locations where the missing people may exist. Fortunately, the suggested model fits nicely into the structure of the classical optimal search model. Hence, we use a stochastic dynamic programming approach to solve this problem. On the other hand, through a computational experiment, we have observed that this model needs heavy computation. Therefore, we reformulate the suggested search model as a multi-criteria decision making (MCDM) problem and employ two efficient MCDM techniques, i.e. TOPSIS and COPRAS to tackle this ranking problem. Consequently, the computational effort is decreased significantly and a promising solution is produced.

This paper presents a new cell formation and cell layout problem considering multiple process routings and subcontracting using the principles of queuing theory. It is assumed that each machine operates as an M/M/1 queuing system and a queuing network is used to obtain in-process inventories and machine utilization. The problem is formulated as a mixed-integer nonlinear program with the objective of minimizing the total costs, including the production, subcontracting, material handling, machine idleness, and holding costs. Due to the computational complexity of the problem, a heuristic method is suggested to effectively solve the problem. A numerical example is given to clarify the proposed approach, and finally, further instances are solved to verify the performance of the solution method and to accomplish comparisons. The computational results show that the proposed heuristic is both effective and efficient.

This paper presents a sustainable multi objective routing and scheduling for maritime transportation considering ship variable speeds under uncertainty. The proposed model is aimed to satisfy three individual dimensions of sustainability including economic, environmental and social aspects simultaneously while it is finding the best routes and schedule for each ship. The first objective is placed to meet economic goal by minimizing shipping cost. The second objective goes to social respect of sustainability by maximizing job creation due to number of intransitive workers in ships and ports and, the third one is minimizing CO2 emission to cover environmental target. Several test problems are applied to validate the proposed model and sensitivity analysis is used to demonstrate effects of model’s parameters on objective function value. Augmented ɛ-constraint is implemented as a solution method to solve the multi-objective mathematical model. This is the first ship routing and scheduling paper which is considered three aspect of sustainability under uncertainty and solved by augmented ɛ-constraint. To solve the model in larger size, factual input data from a real case study is considered. Computational results show a significant positive managerial effects of this paper contributions.

In recent years, the theory of complex fuzzy sets has captured the attention of many researchers, and research in this area has intensified in the past five years. However, almost all of the researchers in this area have focused on the development of various complex fuzzy based models as well as constructing decision making processes using current decision making approaches and tools. In this spirit, this paper focuses on developing the algebraic structures pertaining to groups and subgroups for the complex intuitionistic fuzzy soft set model. This paper was constructed based on the complex intuitionistic fuzzy soft set model which is characterized by a membership and a non-membership structure for both the amplitude and phase terms of the elements. This model was chosen due to its dual-membership structure that is better able to handle the uncertainties and partial ignorance that exists in most complex data, whilst retaining all the characteristics and advantages of complex fuzzy sets. Besides examining the properties and structural characteristics of the algebraic structures, the relationship between the algebraic structures introduced here and the corresponding algebraic structures in fuzzy group theory and classical group theory were also discussed and verified.

Although the literature of the supply chain is teemed with the analysis of the bullwhip effect, few studies regarding the impact of the bullwhip effect or demand distortion on the supply chain profit have been done. Hence, we introduce the concept of Distance to Loss (DL), which is a function of the retailer’s selling price, the manufacturer’s wholesaler price, the end item’s salvage value, the retailer’s expected demand and the retailer’s variance of demand. This concept can perfectly model both stock-out loss and overstocking loss emanated by the bullwhip effect and combines both the newsvendor model and credit risk concepts. Our findings are based on an experimental design and are profoundly in line with previous research. In particular, our model indicates that variations in demand parameters, retailer’s selling price and manufacturer’s wholesaler price impinge on the retailer’s DL, whereas a slight increase in the salvage value negligibly affect the retailer’s DL.