Journal of Industrial and Systems Engineering
Volume:14 Issue: 1, Winter 2021

The post-disaster response phase aims to reduce casualties by accessing critical areas to transfer relief aid, search and rescue operations to the injured as soon as possible. Debris from the disaster blocks roads and prevents rescue teams from reaching critical areas. It is crucial to decide which routes should be cleared for relief aid transportation to reduce the negative effects of the disaster. In this study, a model for debris removal is presented to minimize access time to critical areas such as hospitals and maximize coverage of the areas. The AUGMECON 2 method has been used to solve this problem. Also, the efficiency of this solution method in Tehran has been studied, and its results have been analyzed. The results of this study indicate the importance of considering a comprehensive plan and several sites for debris removal in the disaster response phase.

The outer shell is the primary protection of the building against adverse weather conditions and determines the heat exchange rate to the environment. Evaluating and optimizing the outer shell design of residential buildings due to multiple and conflicting criteria such as energy consumption, costs, and environmental impacts is a multi-objective challenge. In this paper, a bi-objective model is presented to evaluate different methods of constructing the outer shell of residential buildings to reduce energy consumption at the lowest possible cost significantly. So that, minimizing the heat transfer from the outer shell as a function of the energy target and minimizing the cost of fabricating the components of the outer shell as a function of the cost and the augmented epsilon constraint method are used to solve the model and determine Pareto’s solutions. The results show that by determining the appropriate thickness and density of the walls and the appropriate ratio of walls’ permeable surface while spending reasonable costs, it is possible to reduce required energy for cooling and heating the house.

Novel marketing theories that focus on service dominant approaches require to deeply consider customer specifications and needs within using products and services by customers. In this way, data driven approaches that focus on analyzing customer behavior are critically important to realize service dominant logic of marketing. Although previous studies have proposed different approaches to enhance dynamic and customer centric value propositions, there is not a comprehensive view on data-driven approaches that can be used within this context. The main research question that is addressed in this paper is "what are the data-driven approaches, concepts, and practical domains that are addressed for customer centric value propositions to enable service ecosystems to co-create value with customers”. To answer this research question, a systematic literature review is conducted. Based on the relevant evidence extracted from 124 papers, the approaches, core concepts, and key practical domains of customer centric value propositions are described. The paper aims to systematically bridge between prescriptive approaches and tools that have emerged in the field of data analytics and descriptive concepts that have introduced by novel marketing theories.

In this paper, risks in oil construction projects are identified, analyzed and considered for buffers in system dynamics modeling. To do so, all important and effective variables and risks within an oil construction project are determined, including internal and external variables and then, conceptual model is developed using system thinking approach. Based on the conceptual model, state and flow model is obtained and the relations between variables are established. In order to show efficiency and effectiveness of the proposed method, a real case study is considered and solved. Moreover, sensitivity analysis is provided, by using real data and this model. The primary goal of this research is to investigate the impact of different risks that exist in oil construction risks on key variables; these key variables include human resources, facilities and materials. The results demonstrate that the initial plan of each resource is not consistent with the actual need of them. In other words, based on the existing risks in the model, the proposed approach determines what level the actual resources requirement would be placed at and given existing risks, which buffer should be considered for each resource. Furthermore, the impact of risks in performing activities is forecasted and the model shows what impact the risks have on the delay in initial progress of the project as time passes. Finally, it is studied how changes in key and input variables affect the all project.

Nowadays, due to the environmental issues, governmental regulations and economic benefits, focus on collecting and recovery of products has increased. Recovered products can be reused or sold in secondary markets. In this paper, we consider a given structure for a closed loop supply chain, including a manufacturer, distributer and retailer in the forward logistic; the original products are given to the primary market. In the reverse logistic of the given structure, the returned products are disassembled and some obtained parts are used in the manufacturer. We assume that the produced products from returned parts can be given to a secondary market. A minimum quality level is considered for the returned parts. A collection site, and a repair site is added to the initial structure and it is assumed that the disassembled parts to be categorized into end-of-use, end-of-life and disposals. Some products called commercial returns are not assembled and can be given to the secondary market after a simple repair. Furthermore, uncertainty on the demand and return rates are considered and the operational decision variables of the models which are mainly the flow values in the chain and opening some facilities are determined. Electronic devices such as mobile phones and printers are suitable examples for the studied supply chain. The robust counterpart of the model is developed and a solution approach based on the Lagrangian relaxation is developed for solving the problem. Two heuristics based on partial derivations are developed to solve the sub problems and results are analyzed.

Resilient natural gas production and transmission pipeline for minimum cost and minimum the maximum cumulative fraction of unsupplied demand related to the met demand before disruption) are two essential goals of natural gas transmission network design. This paper develops a multi-objective multi-period mixed possibilistic-stochastic programming model to form a trade-off between resiliency and cost. In the presented model, the uncertainty of natural gas consumptions is considered as an operational risk while disruption risks are accounted for the failure of refinery production capacity and pipeline transmission capacity. The proposed model utilizes mitigation strategy such as extra capacities in the refinery, backup and fortified pipelines before disruption event and recovery strategy for restoring lost capacities of facilities to reach normal performance after disruption event. Finally, the performance of the proposed model is validated by executing a computational analysis using the data of a real case study. Our analysis shows that the efficiency of the natural gas transmission network is highly vulnerable to failure of pipeline and refinery capacity as well as demand fluctuations. Also, results indicate that utilizing extra refinery production capacity, fortified pipeline and backup pipeline options have numerous influences in raising the resiliency of the NG network.

Macroeconomic investments in recent years has grown dramatically. Since the number of sources are usually less than the number of proposing projects to the organization, project selection and decision-making in this regard is considered as an inevitable issue. Wrong selection, will have negative consequences, such as wasting resources and also eliminate resources which can be properly used in a more appropriate project results in benefits for the organization. Therefore, a method for selecting project portfolio using a mathematical model and focusing on sustainability factors is proposed. In this paper, we present a multi-objective mathematical programming model that is a comprehensive and also a practical model for portfolio selection of construction projects because it uses sustainability criteria to evaluate projects as one of the objective functions. Multi-objective models can also be used to contrast the objectives with each other in project portfolio selection. Other innovations of the proposed model in this paper are multi-period modeling that specifies the precise timing of the selection of selected projects over 10 defined periods. A robust model is then proposed in order to considering the uncertainty, in this paper contains the uncertainty is the duration of the project. The results show that the robust model in terms of mean objective function under different realizations performs better than the deterministic model and may be because the robust model unlike the deterministic model considers the uncertainties caused by the disturbances.

The industry life highly depends on spare parts since it is vital to perform maintenance operations, especially in strategic industries. The expensive and low-demand spare parts are a must for the continuation of the production; therefore, they are held in warehouses to meet unexpected demand. These spare parts cause high inventory costs also they require human resources, energy, and budget for the repair operations. It is important to point out that separate optimization of decisions in spare part supply chain leads to sub-optimality so, an integrated mathematical model can outperform a routine model. In this paper, we present a network design and planning model that is integrated with the METRIC model (Multi-Echelon Technique for Recoverable Item Control) that formulates inventory management decisions of the repairable spare parts. This model covers different decisions such as supplier order assignment, stock level in warehouses, flows among the facilities, and location of facilities. Due to the np-hardness of the problem, a hybrid approach is presented that incorporates heuristic and meta-heuristic methods. This approach is used to solve the proposed model that has been never applied in previous researches for such a model.

Nowadays, healthcare waste (HCW) management has been received attention by increasing the rate of the population and the usage of services. Meanwhile, one of the significant challenges is to select the appropriate treatment technology for decision-makers (DMs) in the HCW industry. In this respect, this paper proposes a new multi-criteria decision-making (MCDM) approach to compute criteria weights, DMs' weights, and alternative ranking methods for assessing and selecting the best HCW treatment technology from various stakeholders. The proposed structure deals with uncertain evaluations of alternatives by using intuitionistic fuzzy (IF)’ linguistic variables to show criteria weights and to extend two new weighting and ranking methods to obtain DMs' weight and rank the HCW disposal alternatives based on uncertain conditions. Eventually, an empirical case in Shanghai, China, from the recent literature, is applied to determine the feasibility, validation, and effectiveness of the proposed model. Results demonstrate that the introduced model is proper and efficient to handle the HCW treatment technology selection problem under an uncertain information condition. According to the final comparative results, the first alternative and the first DM have a high preference than others, respectively.  Furthermore, the sensitivity analysis determines that the final ranking results are reliable with changing the criteria' weights regarding four various kinds of states.

Bi-level linear programming (BLP) is a problem with two decision makers and two levels:  the Leader in the upper and the Follower in the lower levels. Decision on one level affects the other one. In this respect, finding an optimal solution for BLP problems with inexact parameters and variables (proposed in many real-world applications) is non-convex and very hard to solve regarding its structure. In the present study, Multi-Objective Linear Programming (MOLP) is applied to offer a new approach is proposed to find an optimal fuzzy solution for the BLP problems, in which all parameters and variables have fuzzy nature. The main contribution of this research can be described as follows. First based on lexicographic ordering and using triangular fuzzy numbers, the given fully fuzzy BLP problem is converted into its equivalent multi-objective BLP problem. Then, the lexicographic method is used to solve the obtained model in the previous step. Subsequently, the optimal solution of the multi-objective BLP problem is obtained. However the answer to the main question is given in Theorem 1 if  the optimal solution of the multi-objective BLP problem can be considered an optimal solution of the fully fuzzy BLP problem.. Finally, to demonstrate the applicability of the proposed approach, it is run to solve some examples, and its results are compared with one of the existing methods.

In recent years, research has shown that biomass as an alternative energy source for fossil fuels can be effective in decreasing recent environmental crises. Next, the researchers examined how biofuels are produced through the oil supply chain infrastructure and came up with useful results. This paper is the first study to present the decisions of both chains simultaneously through a mathematical optimization model for the gas oil and biodiesel supply chains. The model proposed in this paper determines the connection point of two chains and other decisions related to network design with a sustainable development approach. The method used in this paper for solving the multi-objective model is the augmented epsilon constraint method. Also, to consider the uncertainty in export demand, the two-stage scenario-based stochastic programming method has been used. Finally, the performance of the mathematical programming model has been investigated through a case study in Iran, and its sensitivity analyzes have been performed.

This paper deals with the coordination of a two-stage supply chain, including a supplier and a retailer. The final demand is sensitive to the sales promotion and the quality improvement done by the retailer and the supplier, respectively. In the standard newsvendor setting, a buyback contract integrates the decentralized system where both members try to optimize their own profit. We showed that a buyback contract could not thoroughly coordinate the supply chain even though it enhances the whole supply chain profit. Therefore, in this research, we extended a new contract based on a buyback contract with which both members share the costs of efforts. The results showed that this contract can coordinate the channel and provide a win-win condition for supply chain components. The numerical example is used to indicate the results and obtain more insights. The optimal sales and quality efforts and the optimal order level are also determined, resulting in the optimal supply chain profit. Sensitivity analyses are performed in order to investigate the effects of different parameters on decision variables and profit. The results showed that the supply chain performance decreases by incrementing the cost coefficients of sales effort and quality efforts.

Healthcare is considered as one of the most important issues of today's societies. In recent years, healthcare economy has found a special status worldwide. Meanwhile, hospitals as the important arm of providing healthcare services and the first level of referral for healthcare services, with their specific areas and responsibilities, are considered the most important healthcare Institute in any country. This paper examines key performance indicators (KPIs) of HSE in the hospital management system through creating a strategic agenda and set of strategic decisions during corona virus pandemic. Using the available multiple decision-making tools based on the criteria of interest to patients, the research first deals with selecting the effective indicators in assessing the HSE management system of the hospital management by experts through a bank of collected indicators. It then ranks the KPIs of HSE using fuzzy TOPSIS method. The results indicated that TOPSIS algorithm is one of the most reliable, scientific, and managerial methods for decision-making. Also, based on the results, the most important factor in the HSE performance in the hospital management system was determined as the Absenteeism from work due to illness indicator.

In a truncated life testing, the test of each item is terminated at a predetermined time which is usually a coefficient of mean, median or other percentiles of lifetime. Life testing and acceptance sampling plans are two major fields of reliability theory and statistical quality control. In a reliability acceptance sampling (RAS) plan the quality characteristic of interest is lifetime. Thus, in designing RAS plans, two subjects of life testing and acceptance sampling plans should be taken into consideration. In this paper, one type of sampling plans, which is known as resubmitted sampling (RS) plans, is proposed for truncated life testing. The items are considered Weibull distributed with a known shape parameter. To obtain the operating characteristic (OC) curve of the RS plan, an equation is derived and to optimize the value of average sample number (ASN), three models are proposed: (I) minimizing ASN in acceptable quality level (AQL), (II) minimizing ASN in limiting quality level (LQL) and (III) minimizing ASN based on the both AQL and LQL. In optimizing the models, the constraints related to the consumer’s and producer’s risks are taken into consideration. Finally, numerical examples and sensitivity analyses are conducted. According to the results of comparison of RS and single sampling (SS) plans, it cannot be concluded that one scheme monotonically outperforms the other.  Moreover, from the aspect of OC curve, the acceptance probability of a given lot under the RS plan is slightly larger than the corresponding value in the SS plans.

In Iran, the energy price is very much influenced by the dollar price. However, this price is highly fluctuated due to various reasons. The emergence of the pandemic, the covid-19, from one part and the financial sanctions on the economy from another, cause the high volatility on this foreign currency. First, in this study, we converted the IRR (Iranian currency) into the same dollar rate of the year, contributing to the impact of exchange rate volatility in the model. Then, we forecast the price of all three principal fuels that affect the cost of electricity production, and then we forecast the electricity prices using ANN_GA and the historical data. This study also examines the fundamental and exacerbating causes in recent years, especially in 2018 when we faced an unprecedented increase in dollar prices in the Iranian market when the U.S. withdrew from the joint comprehensive plan of action (JCPA), and its effects are still visible. We intend to investigate the impact of these fluctuations on the future electricity market. In the end, we examine that which variables (fuel prices) would affect electricity prices the most using a linear regression model.