فهرست مطالب seyed jafar sadjadi

In this paper, we present a comprehensive approach for evaluating efficiency in complex networks by integrating network data envelopment analysis (NDEA) with the Malmquist productivity index. The proposed method addresses the inherent challenge of accommodating negative data within the network efficiency evaluation framework, which is a common occurrence in real-world network operations. Through the introduction of a two-stage structure, the model not only effectively manages the presence of negative values, but also provides a robust and insightful assessment of network efficiency. A case study from banking sector is employed to demonstrate the efficacy of the proposed approach, showcasing its capacity to offer valuable and actionable insights for decision-making in complex network environments. The results highlight the practical applicability and importance of the extended approach in addressing the complexities associated with evaluating efficiency in diverse network settings.

In the ever-evolving realm of finance, investors have a myriad of strategies at their disposal to effectively and cleverly allocate their wealth in the expansive financial market. Among these strategies, portfolio optimization emerges as a prominent approach used by individuals seeking to mitigate the inherent risks that accompany investments. Portfolio optimization entails the selection of the optimal combination of securities and their proportions to achieve lower risk and higher return. To delve deeper into the decision-making process of investors and assess the impact of psychology on their choices, behavioral finance biases can be introduced into the portfolio optimization model. One such bias is regret, which refers to the feeling of remorse that can induce hesitation in making significant decisions and avoiding actions that may lead to unfavorable investment outcomes. It is not uncommon for investors to hold onto losing investments for extended periods, reluctant to acknowledge mistakes and accept losses due to this behavioral tendency. Interestingly, in their quest to sidestep regret, investors may inadvertently overlook potential opportunities. This research article aims to undertake an in-depth examination of 41 publications from the past two decades, providing a comprehensive review of the models and applications proposed for the regret approach in portfolio optimization. The study categorizes these methods into accurate and approximate models, scrutinizing their respective timeframes and exploring additional constraints that are considered. Utilizing this article will provide investors with insights into the latest research advancements in the realm of regret, familiarize them with influential authors in the field, and offer a glimpse into the future direction of this area of study.  The extensive review findings indicate a growth in the adoption of the regret approach in the past few years and its advancements in portfolio optimization.

The high popularity and profitability of gift cards encourage many sellers to use them to sell their goods. Retailers have also been encouraged to use independent third parties to sell their gift cards for increasing their sales channel and taking advantage of it. This paper develops a two-echelon supply chain for gift card incentive policy, with a third party and retailer at the first level and a supplier at the second one. The most important research questions are as follows: order amount of chain members to maximize their own and the whole chain profit, gift card prices by the retailer to its customers, gift card prices by the retailer to third party, and gift card prices by the third party to customers. Stackelberg's approach is used to solve the model, assuming that the third party is the follower and the retailer is the leader. In addition, by proving the concavity of the objective function, obtaining the closed-form solution for variables, and proving the resulting solutions, an algorithm has been developed to achieve the optimal answer. Findings showed that the use of cards in the case of economic order models increases the demand for retail and on the other hand attracts more customers and better brand expansion. A numerical example as well as a sensitivity analysis are performed to describe the model. Finally, conclusions as well as suggestions for future research are provided.

Venture capital (VC) financing is associated with the challenges of double-sided moral hazard, and uncertainty, which leads to the difficulty in estimating the venture's value accurately and consequently the impossibility of determining the optimal equity sharing between the entrepreneur and investor. Traditionally, convertible preferred equity mechanisms used to be implemented as an incentive to decline moral hazard. However, despite the emphasis on investor risk-taking, such mechanisms transfer the investor risk to the entrepreneur and do not mitigate the incentive of opportunistic behaviors. Furthermore, according to the literature review, and to the best of the authors’ knowledge, there has not been developed any practical mechanism for equity sharing in VC financing up to now. This paper proposes a fair equity sharing mechanism, which alleviates the above-mentioned deficiencies. It adjusts both parties' share during the equity dilution in each stage of financing, regarding the difference between the venture's ex-ante and ex-post values. Moreover, it manages uncertainty by applying staged financing and the option of abandonment at the end of each stage. The proposed mechanism has been verified by using the mathematical tools and drawing its curves for a case study.

One of the most complex and costly systems in the industry is the Gas turbine (GT). Because of the complexity of these assets, various indicators have been used to monitor the health condition of different parts of the gas turbine. Turbine exit temperature (TET) spread is one of the significant indicators that help monitor and detect faults such as overall engine deterioration and burner fault. The goal of this article is to use data-driven approaches to monitor TET data to detect faults early, as fault detection can have a significant impact on gas turbine reliability and availability. In this study, the TET data of v94.2 GT is measured by six temperature transmitters to show a detailed profile. According to the statistical tests, TET data are high dimensional and time-dependent in the real world industry. Hence, three distinctive methods in the field of the gas turbine are proposed in this study for early fault detection. Conventional principal component analysis (PCA), moving window PCA (MWPCA), and incremental PCA (IPCA) were implemented on TET data. According to the results, the conventional PCA model is a non-adaptive method, and the false alarm rate is high due to the incompatibility of this approach and the process. The MWPCA based on V-step-ahead and IPCA approaches overcame the non-stationary problem and reduced the false alarm rate. In fact, these approaches can distinguish between the normal time-varying and slow ramp fault processes. The results showed that IPCA could detect fault situations faster than MWPCA based on V-step-ahead in this study.

Security of gas supply is vital for socio-economic health and sustainable development. Occurrence of operational, physical and organizational disturbances at the level of provincial gas companies has made it necessary to strengthen governance to overcome them and support the security of supply and provision of public services. In this study, which has been implemented for Tehran Gas Company, governance as a multidimensional and hierarchical structure is analyzed to develop its main elements along with reliable measurement metrics of each dimension. The results of the study, which was conducted by confirmatory factor analysis, show that governance at the level of the gas distribution company includes three main dimensions: policy-making and regulation, planning, monitoring, and finally the structure and systems. Transparent authority levels, information sharing, and risk management guidelines are emphasized to strengthen internal controls and reduce disruption, and therefore a continuity program will be effective in creating resilient distribution. The results of this study provide scientific and practical insights into hard (physical) and soft (non-physical) enablers of governance development and improve effectiveness, accountability, informed decision making, and risk management systems and structures to reduce fluctuations. Gas access and supply security support.

In recent years researchers have been interested in inventory models for deteriorating items along with determining the price of these items; given that in many real-world problems, changing the price can affect the demand level. Meanwhile, there are some other demand stimulation like advertising, multiple post-payments and discounting which can change and control the demand level of the items. In this paper, a new deteriorating inventory model was developed that considers these three demand stimulation along with pricing. The model has been converted and solved using geometric programming approach. Meanwhile, genetic algorithm was used as an alternative method to test the performance of GP approach. The model was implemented in real case study of food industry and numerical results and sensitivity analyses demonstrate the superiority of developed approach.

The supplier selection process, as one of the components of the supply chain management (SCM), refers to evaluating and selecting suitable suppliers based on relevant criteria. This study presents two supplier selection models to supply complementary, substitutable, and conditional products. For this purpose, two multi-objective mixed-integer non-linear programming (MOMINLP) models are formulated to select the suppliers with the highest scores, the lowest total cost, and the highest quality. To identify the criteria weights and to score the suppliers, first, one of the effective multiple criteria decision-making (MCDM) methods, called the Best-Worst Method (BWM), is employed. Then, the weighted relative deviations from the ideal values of the criteria are minimized to solve the multi-objective models. Finally, two case studies are represented to show the practical application of the proposed methodology in the decision-making process.

One of the most important problems in portfolio selection models is the ability to provide the optimal number of each share. Therefore, in some cases, it interferes with portfolio optimization in converting the desired weight per share to the desired number per share, unless the results are an integer. Moreover, by applying the appropriate strategy, it seems possible to discover the optimal stock allocation for significant cases with comparatively large stock value. In this regard, this study presents a multi- objective portfolio selection model considering cardinality, quantity and budget constraints based on a new improved knapsack problem. Value-at-Risk (VaR) is considered as the second objective function of risk assessment in the knapsack-based portfolio selection model. We consider parametric (variance- covariance matrix) and non-parametric (historical) approaches to measure VaR. The study also uses the best GARCH family models to estimate the conditional volatility of return in the variancecovariance matrix, which is based on measuring and comparing different criteria under various types of GARCH family models. Finally, a Non-dominated Sorting Genetic Algorithm II (NSGA II) is planned to solve the problem. An actual portfolio of the Iran stock market is solved to demonstrate the application of the suggested model.

Technology valuation, especially in the early stages of new technology-based firms (NTBFs) growth is one of the most critical challenges, which most often hinders the investor and entrepreneur's deals during the venture capital (VC) financing process. It is clear that uncertainties arising from the likelihood of implementing public policies could significantly affect the volatility of NTBFs cash flows in the field of cleaner production. Commonly, these kinds of technologies require public supportive policies for achieving success. Consequently, their technology valuation is more challenging and traditional valuation methods are not suitable anymore because of the definitive assumption of cash flow and ignoring the investors’ flexibilities and uncertainties. Therefore, this paper proposes a method by introducing a framework based on the decision tree and the real options analysis which is tailored to meet the technology valuation of such firms during all stages of their growth. Furthermore, unlike previous papers that have utilized the compound options, option to choose has been used to apply investors’ flexibilities. Then, the proposed framework is supported by a case study, which has been conducted to verify and validate it. Finally, the conclusion section discusses the contributions and limitations of the study and provides directions for future research.

This paper aims to conduct a research on the labor-management negotiation in chicken evolutionary game models through catastrophe theory. The both players can compromise or not during the negotiation. The "no compromise" strategy for labor means threat to strike and for management is ignoring labors' demands. Since the model of this research is chicken game, if on player decides to dig in, the optimum decision for other is to compromise, however it is costly to be calling a chicken by the rivals. In the process of evolution, players reevaluate their options to update the payoffs in case of gradual and continuous changes which may happen in effective variables of strategy selection. The continuous changes could cause a catastrophic change in system’s state and its collapse by a strike or lockout. ESS analysis and determining catastrophe threshold in the chicken evolutionary game will be done with the aim of giving managerial insights that help the players to prevent making decisions that could cause unsuccessful negotiation.

Determining supplier and optimum order of the quantity is an issue of great importance in logistics management for many companies. In this regard, it is crucial to determine the best decisions for the order quantity as well as the most suitable supplier through considering existing limitations and uncertainties. To optimize a multi-product, multi-period model with select supplier for deteriorating products, while uncertainty of future economic conditions directly affects the problem conditions. In this regard, a mixed integer definite programing model is introduced, and afterwards, the proper robust structure is established through a two-phase scenario-based approach. The behavior of the main features of the inventory system elaborated upon in this article, that is, multi-product, uni-level, multi-period inventory system, has been modeled under the influence of uncertain economic environment. In the final phase pattern, search method is employed to determine proper answers, the results of which are analyzed, to shed light on various aspects of the solving procedure, as well as the problem itself. The applicability of the proposed model is shown by an illustrative example.

In this paper, a new competitive location problem for a chain is considered. The chain’s owner can offer a variety of products. The model’s objective is to determine both the location of the new facilities and the optimal product type for each opened facility. The patronizing behavior of the customers is based on Huff rule and the location of new facilities is selected from a set of potential sites. As a result, the model is a nonlinear integer programming problem and for solving the proposed model, the problem is reformulated as a mixed integer linear programming and therefore a standard optimization solver can be used for obtaining the optimal solutions for small and medium-size problems. To cope with large-size problems, we develop two methods 1) a heuristic method for a special case and 2) a hybrid heuristic-firefly algorithm for general cases. By using the proposed model, it is shown numerically that in multi-product industries in which owner of the facilities is able to offer different types of products, in addition to the optimal location, it is necessary to determine the best products. In the end, a real-world case study for locating a new bakery is presented.

During the past few decades, there have been tremendous efforts in cooperative advertising. In spite of many practical applications in real life, cooperation in advertising and pricing strategies in a one-manufacturer and multi-retailer supply chain is almost overlooked in the literature. Hence, this paper seeks to investigate optimum co-op advertising and pricing decisions in a B2B relationship for a supply chain consist of a manufacturer and numerous multiple retailers in Iran as a case study. This paper introduces a game theoretic model containing pricing and cooperative advertising in a one-manufacturer and multi-retailer structure. Non-cooperative and cooperative game structures are used for analyzing the proposed model. The non-cooperative game structure uses Stackelberg game among the echelons and Nash game in the retailer echelon. Motivated by a real case study including an Iranian supply chain data of one manufacturer and 150 retailers, a novel model proposed to tackle the similar condition occurred in real life. The results indicate that the manufacturer prefers to suggest higher participation rate to smaller retailers. Sensitivity analysis is presented, and some managerial insights are finally derived from the results.

This paper examines the impacts of three factors include the service, price, and discount on the supply chain’s profit. We consider a supply chain, including one traditional retailer and two manufacturers. By using the game-theoretic approach, we derive optimal solutions and analyze competition between members under two scenarios: (1) the retailer buys the products from two manufacturers without price discount contracts, i.e. no products are sold with price discount contract. (2) The retailer buys the product from one of the manufacturers with price discount contracts. We find that the price discount rate and the service level are very effective on the demand and profit of supply chain members and determining the appropriate discount rate is very important. The results show that increasing the service provided by the retailer does not necessarily increase the profit of the manufacturer and he should set an appropriate discount rate to increase his profit. Our work contributes to three aspects: (1) joint and simultaneous examination of competition in the supply chain under the three factors of price discount, price, and service level; (2) examination of competition in the supply chain where the retailer and a manufacturer provide free service to consumers; and (3) analysis and comparison of the numerical example results in the two above scenarios according to the sensitivity analysis of various parameters.

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 model of two-echelon periodic supply vessel planning problem with time windows mix of facility location (PSVPTWMFL-2E) in an offshore oil and gas industry. The new mixed-integer nonlinear programming (MINLP) modelconsists ofa fleet composition problem and a location-routing problem (LRP). The aim of the model is to determine the size and type of large vessels in the first echelon and supply vessels in the second echelon.Additionally,the location of warehouse(s),optimal voyages and related schedules in both echelons are purposed.The total cost should be kept at a minimum and the need of operation regions and offshore installationsshould be fulfilled.A two-stage exact solution method, which is common for maritime transportation problems, is presented for small and medium-sized problems. In the first stage, all voyages are generated and in the second stage, optimal fleet composition, voyages and schedules are determined. Furthermore, optimal onshore base(s) to install central warehouse(s)and optimal operation region(s) to send offshore installation’s needs are decided in the second stage.

In this paper, a fuzzy multi-objective model is presented to select and allocate the order to the suppliers in uncertainty conditions, considering multi-period, multi-source, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects. Partial and general coverage of suppliers in respect of distance and finally supplier's weights make the amounts of products orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, multi-objective particle swarm optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm applies to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analysis.

This paper aims at providing a new approach to optimize location and design (quality) decision for new facilities as a leader-follower competitive configuration under the condition that competitor’s reaction is unknown. A chain is considered as a leader in the first level and tends to open a new facility in a specific market where similar competitor facilities as follower already exist. In the second level, the follower decides on locating and designing some facilities through the market subject to the location and design of leader’s facilities to keep or capture more market share. The market share captured by each facility depends on its distance to customers and its quality based on probabilistic Huff-like model. In facts, the leader decides on location and quality of its own new facility based on the follower reaction strategies to maximize its profit. Since the number of the follower’s new facilities are unknown for the leader, "robust optimization" is used for modeling this problem. A case from two chain stores in the city of Tehran, Iran, is studied and the proposed model is implemented. The computational results display the robustness and effectiveness of the model and highlight the importance of using robust optimization approach in uncertain competitive environments.

Proper and realistic scheduling is an important factor of success for every project. In reality, project scheduling often involves several objectives that must be realized simultaneously, and faces numerous uncertainties that may undermine the integrity of the devised schedule. Thus, the manner of dealing with such uncertainties is of particular importance for effective planning. A realistic schedule must also take account of the time-based variations in the capacity of renewable resources and the amount of resources needed to undertake the activities and the overall effect of such variations on the schedule. In this study, we propose a multi-objective project scheduling optimization model with time-varying resource requirements and capacities.This model, with the objectives of minimizing the project makespan, maximizing the schedule robustness, and maximizing the net present value, considers the interests of both project owner and contractor simultaneously. Two multi-objective solution algorithms, NSGA-II and MOPSO, are modified and adjusted with Taguchi method to be used for determination of the set of Pareto optimal solutions for the proposed problem. The proposed solution methods are evaluated by the use of fifteen problems of different sizes derived from Project Scheduling Problem Library (PSPLIB). Finally, solutions of the algorithms are evaluated in terms of five evaluation criteria. The comparisons show that NSGA-II yields better results than MOPSO algorithm. Also, we show that ignoring the time-based variations in consumption and availability of resources may lead to underestimation of project makespan and significant deviation from the optimal activity sequence.

The present paper aims to propose a fuzzy multi-objective model to allocate order to supplier in uncertainty conditions and for multi-period, multi-source, and multi-product problems at two levels with wastages considerations. The cost including the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects, partial coverage of suppliers in respect of distance and finally, suppliers weights which make the products orders more realistic are considered as the measures to evaluate the suppliers in the proposed model. Supplier's weights in the fifth objective function are obtained using fuzzy TOPSIS technique. Coverage and wastes parameters in this model are considered as random triangular fuzzy number. Multi-objective imperial competitive optimization (MOICA) algorithm has been used to solve the model,. To demonstrate applicability of MOICA, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms and results are analyzed using quantitative criteria and performing parametric.

In this paper, a hybrid meta-heuristic approach is proposed to optimize the mathematical model of a system with mixed repairable and non-repairable components. In this system, repairable and non-repairable components are connected in series. Redundant components and preventive maintenance strategies are applied for non-repairable and repairable components, respectively. The problem is formulated as a bi-objective mathematical programming model aiming to reach a tradeoff between system reliability and cost. By hybridizing a standard multi-objective fire fly (MOFA) and differential evolution (DE) algorithms, a powerful and efficient approach called MOF-DE algorithm which has inherited the advantages of the two algorithms is introduced to solve this problem. In order to achieve the best performance of MOF-DE, response surface methodology (RSM) is used to set proper values for the algorithm parameters. To evaluate the performance of the proposed algorithm, various numerical examples are tested and results are compared with methods like NSGA-II, MOPSO and standard MOFA. From the experiments, it is concluded that the performance of the MOF-DE algorithm is better than other methods at finding promising solutions. Finally, sensitivity analysis is carried out to investigate behavior of the proposed algorithm.

One of the primary assumptions in most optimal pricing methods is that the production cost is a non-increasing function of lot-size. This assumption does not hold for many real-world applications since the cost of unit production may have non-increasing trend up to a certain level and then it starts to increase for many reasons such as an increase in wages, depreciation, etc. Moreover, the production cost will eventually have a declining trend. This trend curve can be demonstrated in terms of cubic function and the resulted optimal pricing model can be modeled in Geometric Programming (GP). In this paper, we present a new optimal pricing model where the cost of production has different trends depending on the production size. The resulted problem is formulated as a parametric GP with five degrees of difficulty and it is solved using the recent advances of optimization techniques. The paper is supported with various numerical examples and the results are analyzed under different scenarios.

The basic assumption in the traditional inventory model is that all outputs are perfect items. However, this assumption is too simplistic in the most real-life situations due to a natural phenomenon in a production process. From this it is deduced that the system produces non-perfects items which can be classified into four groups of perfect, imperfect, reworkable defective and non-reworkable defective items. In this paper, compared with classic model, a new integrated imperfect quality economic production quantity problem is proposed where demand can be determined as a power function of selling price, advertising intensity, and customer services volume. Furthermore, as novelty way the unit cost is defined as a cubic function of outputs which is similar to real world. Also, a geometric programming modeling procedure is employed to formulate the problem. Finally, a numerical example is illustrated to study and analysis the behavior and application of the model.

In this paper, a bi-objective mathematical model is presented to optimize supply chain network with location-inventory decisions for perishable items. The goals are to minimize total cost of system including transportation cost of perishable items from centers into DCs, DCs to ultimate center, transportation cost of unusual orders, and fixed cost of centers as DCs as well as demand unresponsiveness. Considering special conditions for holding items, regional DCs, and determining average of life time items assigned to centers are other features of the proposed model. With regard to complexity of the proposed model, a Pareto-based meta-heuristic approach called multi-objective imperialist competitive algorithm (MOICA) is presented to solve the model. To demonstrate performance of proposed algorithms, two well-developed multi-objective algorithms based on genetic algorithm including non-dominated ranked genetic algorithm (NRGA) and non-dominated sorting genetic algorithm (NSGA-II) are applied. In order to analyze the results, several numerical illustrations are generated; then, the algorithms compared both statistically and graphically. The results analysis show the robustness of MOICA to find and manage Pareto solutions.