Journal of Industrial and Systems Engineering
Volume:13 Issue: 4, Autumn 2021

In some practical applications, the quality of a process or a product is characterized by a relationship, namely a profile between a response variable and one or more explanatory variables. Assuring the capability of a profile to meet the process requirements is particularly important. Process capability indices (PCIs) are widely used to measure whether a process is capable of reproducing product items within the specification limits (SLs). Background literature on the PCIs for profiles mostly take crisp values for process data. However, in practice, the outcomes of a measurement are often imprecise. So, the basic assumption of crisp data for process capability analysis (PCA) in profiles is not valid. Hence, fuzzy methods are developed to analyze the capability of a fuzzy profile with fuzzy response data. To this end, we extend the functional approach based on fuzzy set theory for the situations in which the SLs and target values of the response variable are imprecise. The performance of the proposed indices is investigated through simulation studies. The simulation results confirm that the proposed method performs well regarding -distance between the estimated value and the true value of fuzzy PCIs. Furthermore, a case study shows the applicability of the proposed method.

In this paper, an adaptive optimization model based on a closed-loop control system is developed to regulate the strategic bidding process of generation companies (GenCOs) in day-ahead electricity markets. Each day, the bidding problem of each GenCO is submitted in the form of a supply function consisting of 24 sub-problems, one for each hour of the next day. The hourly market clearing price and the total demand of the next day are the unknown values in the bidding problem that should be estimated by the concerned GenCO. The GenCOs, as the main players in the market, receive feedback signals for market clearing price and demand for each hour of the previous day, based on which they set their bidding for the next day. In the optimization model, the limitations on the production level and production change rate are considered in terms of the minimum and maximum quantities constraints. To better adapt to the market demand and price dynamics beforehand, we also used an adaptive forecasting algorithm for the next day's demand and clearing price. Using this adaptive dynamic model, the network operator can clear the market based on the bids received from the GenCOs and the consumers. As we concentrated on the GenCO side, as the most influential player of electricity markets, the bids from the demand side are considered here as a whole and modeled by a linear function. Finally, the real market data from the day-ahead Nordic electricity market (Nord Pool) are used as the case study to verify the effectiveness of the proposed model and its adaptive algorithm. The results show that the GenCO that uses the proposed model can gain more profit in comparison to those that take non-strategic behavior (naive strategy) in the market.

Banks, in general, have a direct impact on the macro-economy of all countries. Recognizing the criteria which have momentous influence on bank branches’ efficiency is the main purpose of this research. An artificial neural network approach, one of the most applicable data mining techniques, is adopted to identify the criteria that influence the branches' efficiency the most (according to the result of efficiency evaluation base on MCDM). Then, the optimal group of input criteria is determined in order to achieve the most efficient performance. Branches that enjoy more appropriate inputs would have better conditions to increase their efficiency, possess more acceptable position and gain more adequate results. In this paper, utilizing data mining science, we have endeavored to suggest a suitable method in recognizing the most significant inputs with positive impact on enhancing efficiency of branches by the incorporation of relatively neglected indicators which fit the particular conditions of Iranian banks. The strength of this article compared to other related researches is that it provides a mechanism according to which senior managers in the banking sector will be able to identify the most important indicators and implement the best conditions to achieve the highest level of efficiency in the collection.

In this paper, a bi-level mathematical formulation for a pricing-inventory-routing problem in the context of sustainable closed-loop supply chains is developed. The two levels are entitled as the upper level model and the lower level model. The upper level model (the leader model) tries to minimize greenhouse gas (GHG) emissions while the lower level model (the follower model) focuses on profit maximization. To solve the problem, an enumeration heuristic method based on knapsack problem and genetic algorithm (GA) is devised. The results show that the heuristic method is capable of obtaining high-quality solutions in reasonable CPU-times.

This study investigates the joint production planning and warehouse layout under uncertainty. Today’s competitive business world needs to be investigated by models which are capable of considering uncertain nature of the problems, especially when the historical data is not available or the level of uncertainty is high. Joint production planning and warehouse layout problems is almost a novel and new area in both academics and practice. For warehousing problem, the eventually of rental warehouses and new allocations is enabled in each planning horizon period. A bi-objective MILP model is proposed and fuzzy distributed parameters and chance constraints are taken into considerations. One of the objective functions deals with the cost associated parameters and variables while the second one minimizes the fluctuations of the work labor in each planning period. A simple test problem along with a case study is investigated by the proposed model. The obtained results prove the applicability of the proposed model in real-world scale problems.

This article proposes an integrated approach towards the design optimization and production planning of cellular manufacturing systems as a part of closed-loop supply chains in an effort to make manufacturing enterprises sustainable. For industrial applications both at the system design and operation stages, a mixed integer linear programming (MILP) model, to integrate the production planning problem in cellular manufacturing systems and the tactical planning of a closed-loop supply chain, has been developed. The cellular manufacturing system in the proposed mathematical model has several features including dynamic cell configuration, multi-period production settings, machine capacity, machine acquisition, machine procurements, and multiple units of identical machines as well as considering different cost parameters such as production cost, operational cost of the machines, and subcontracting cost of the part demands; mainly targeted to be used in industry at the operational level. In addition, several activities such as acquisition, disassembly, setup for disassembly, and disposition of the returned products have been considered on the reverse flow of the closed-loop supply chain of the proposed mathematical model, which would lead to further industrial applications mainly at the integrated design stage of manufacturing and supply chain systems in addition to the potential applications at the operational level.

The occurrence of disruptions has undeniable impacts on supply chain (SC) performance and severely affects its costs and revenues. SC resilience (SCR) reduces the impacts of these disruptions. Among the issues in the SCR, although the recovery of the SC after the disruption is of vital importance, it has not been considered as it should be. To fill this gap, this paper enumerates some important issues in SC recovery planning and proposes a dynamic model for it. One of the features of the proposed model is to consider the recovery time and cost in order to achieve the pre-disruption SC performance. Then, we demonstrate the application of this model in the recovery of a two-echelon poultry SC. Since the developed model is a nonlinear dynamic model, we use the direct collocation method to solve it. The outputs of the sensitivity analysis show that changes in many parameters result in significant changes in model variables. Based on the results, it can be said that the development of appropriate models for recovery plays an important role in the analysis of possible alternatives for SC recovery and can help SC managers to deal with disruptions by comparing alternative recovery options.

Demand fluctuations, customer’s behavior and price of products can greatly impact on distribution systems. Here, pricing decisions in a distribution system is focused considering customer behavior in a competitive market. A behavior-based bi-level model is proposed to present the Stackelberg competition among the wholesalers and retailers in a distribution system under two distinct scenarios. The defined scenarios attempt to focus on profit and utility maximization for the distribution system and customers, respectively. In addition, behavior-based price discrimination is used in the current paper considering retail prices and customer geographical zones in the market. The developed model is finally carried on an industrial case problem to derive sensitivity analyses and compare different scenarios to get managerial insights.

In this paper, a novel robustness index is introduced to provide a measure of the robustness of a solution against variations in decision variables and parameters. Most of the proposed robustness measures in the literature consider only magnitude of variations in the objectives space and don’t take into account the direction, or in the other words, the type of variations. In this paper, two types of variation named dominating and Pareto variations are introduced and argued that the Pareto variations are more robust than the other one. An index is also proposed here to help measuring the proportion of dominating variations. We proved that this index is independent of magnitude of variations. A robustness index is developed based on these two measures. The robustness index is then used as an additional objective and constraint function so that the uncertain multi-objective optimization problem is transformed to a deterministic one. The resulting deterministic multi-objective optimization problem is solved by NSGA-III. Moreover, Mont Carlo simulation is used to evaluate solutions during the algorithm and compute the robustness index. Two test problems from the context of engineering design optimization are used to illustrate the applicability and efficiency of our proposed robustness index.

This paper introduces a new method for organizational performance measurement. The model integrates the QEST (Quality factor + Economic, Social & Technical dimensions) and the BSC (Balanced Scorecard) models. Although the model is originally defined to measure performance in the banking industries, it can be used for almost any organization and any multi-attribute decision-making problem. It not only has a new look to the BSC perspectives but enables organizations to obtain a more reliable assessment. The model takes advantage of the QEST-3D and the BSC that lets the proposed model be easy to generalize. The research’s motivation is to obtain performance measurement based on not only the values of the indicators but the proportion of the values ​​of the leading indicators and the lagging ones obtained by BSC. The financial perspective is considered a lagging indicator, while the other indicators are considered leading. To obtain reliability analysis, the model has been tested in 17 branches of a bank in Kurdistan province for three recent years, as a real case. The results that are compared with what is obtained from the pre-qualified TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) successfully illustrate the accuracy and practicality of the proposed model. The results show that based on the correct logic proposed in the proposed method for separating and analyzing the perspectives taken from the BSC, the strategy-oriented ranking made by the proposed method is more reliable and logical than the ranking performed by the TOPSIS.

This study develops a mathematical model for the designing of a supply chain network. The uncertain nature of demand and lead time is incorporated into the concerned model. This motivates us to deploy the queuing concept to deal with uncertainties and analysing the number of orders, number of shortages and average of on-hand inventory. Then, in accordance with the outputs of the queuing analysis, a mixed integer nonlinear programming model is devised to design the distribution network of a supply chain. The decisions to be made are facility locations, demand allocations along with inventory management decisions. The objective function of the model aims at minimising the total supply chain costs encompassing location, transportation and inventory costs. Notably, we assume that each facility manages its inventory policy based on a  policy and stock outs result in lost sales. Inasmuch as the developed problem is difficult to solve by means of exact methods, tailored hybrid solution algorithms based on simulated annealing and genetic algorithm are employed to overcome the computational complexity of the developed model. Finally, using the real information of the Telecommunication infrastructure company, we evaluate the proposed model and the management insights are reported.

The last decade has seen numerous studies focusing on the closed-loop supply chain. Accordingly, the uncertainty conditions as well as the environmental impacts of facilities are still open issues. This research proposes a new bi-objective mixed-integer linear programming model to design a closed-loop supply chain tire remanufacturing network considering environmental issues that improve performance in conditions of uncertainty associated with the tire industry. This model seeks to maximize the total profits of the network, including customer centers, collection centers, recycling centers, manufacturing/remanufacturing plants, distribution centers, and on the other hand, is looking to minimize environmental impact all over the supply chain network. Another novelty of the proposed model is in the solution methodology. By using an exact approach, the augmented ε‑constraint method, and meta-heuristic algorithm, a well-known Grasshopper Optimization Algorithm (GOA), optimal and Pareto solutions have been obtained for medium and large size sample problems. We analyze the effectiveness of these meta-heuristics through numerical experiments. Also, sensitivity analysis has been provided for some parameters of the model. Finally, the results and suggestions for future research are presented.

In this paper, a new genetic algorithm is presented to plan and schedule operating rooms at the operational level to minimize completion time, surgeons’ free time window, and operating rooms’ overtime, idle time, and setup time costs. The duration of surgeries is calculated according to a predetermined time plus an allowance related to the uncertainty of the surgery time. Also, the operating rooms’ setup times depend on the sequence of surgeries. The time window constraint involves resource availability such as surgeons and operating rooms. First, a mixed-integer nonlinear mathematical model is proposed to solve the problem. Thereafter, a genetic algorithm is developed to solve the problem inspired from the role model concept in sociology using simulating and differentiating procedures, namely Role Model Genetic Algorithm (RMGA). The performance of the proposed algorithm is examined by comparing it with a conventional genetic algorithm and a hybrid genetic algorithm proposed for the nearest problem in the literature to the current problem. The results shows that RMGA prepares better results.

Crowdfunding is a fundraising tool to solicit many small amounts of capital from a large number of potential investors. Peer to peer lending is known as a main type of crowdfunding in which lenders and borrowers can interact directly through an online platform. By eliminating the intermediaries and therefore reducing operating expenses, P2P platforms can provide a win-win situation for both borrowers and lenders. However, the absence of intermediaries –such as banks- increases the risk of loan repayment fraud. To avoid such losses, credit scoring methods help lenders to decide on a specific loan by assessing corresponding credit risk. This paper proposes a credit scoring model on a P2P lending platform in Iran. Although data-driven approaches have increasingly used to enhance credit scoring within financial domains, there is a lack of research on assessing the usability of these approaches within P2P crowdfunding scenarios. This research focuses on developing a novel data-driven model that can enhance P2P credit scoring within crowdfunding scenarios. To do so, on the basis of data from an Iranian P2P lending platform, five different tree-based classifiers were developed, among which Random Forest resulted in the best accuracy (97.80%). Lenders in the used platform are businesses, each having a different risk tolerance threshold. A default probability was computed for each loan request to help lenders make decisions based on their own risk tolerance. The results clearly demonstrate how novel data analytics approaches can enhance intelligent decision making about P2P funding within P2P lending platforms.

Volatility in competitive businesses has increased the uncertainty and ambiguity of decision-makings. Uncertainties are known as risks in the literature reviews. The present study developed the model proposed by Kirilmaz and Erol to mitigate risks and ambiguity in decision makings in the green supply chain. An initial multi-objective procurement plan was developed using a robust planning model considering costs, purchase discounts, carbon emissions and uncertainty as the first priority. The paper applies a scenario-based approach to consider an uncertain customer demand in different scenarios. The scenario-based model ensured that regret whereas scenarios are not probability. Moving toward the green supply chain decreases the costs that exert negative and devastating effects on the environment. As the second priority, risk was ultimately incorporated into this plan. A hypothetical data-set was examined and a cost analysis performed to evaluate the quality of the obtained solutions and the performance of the proposed model.