Journal of Industrial Engineering and Management Studies
Volume:8 Issue: 1, Winter-Spring 2021

Hubs facilitate aggregation of connection, and switching points of material and people flow to reduce costs as well as environmental pollution. Hub Location Problem (HLP) is a relatively new research field of classical location issues. In this regard, this paper provides a tourist hub location problem to procure essential commodities, which characterized with non-negligible dynamics of demand. Dealing with a high level of change in demand for these goods over time, the possibility of establishment, renovation, or renting the distribution centers have been formulated in the proposed mathematical model. Finding the best location for distribution centers, the model aims to minimize the routing cost between production centers and retailers, along with emitting pollution from vehicles as less as possible. As the proposed model is bi-objective, that is minimizing costs and pollution emission, two Pareto-based solution methodologies, namely the non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA), are used. Since the obtained results from these algorithms are highly dependent on the value of parameters, the Taguchi method is adopted to tune the parameters of two solution methodologies. Finally, to verify the proper performance of two solution methodologies, numerical examples in different scales are generated. The obtained results from all scales and solution methodologies indicate that the new modeling approach to the possibility of establishment, renovation, or renting the distribution centers results in lower costs and pollution emission. The results indicate that supply chain costs and environmental impacts increase by increasing the demand. The number of established distribution hubs also increases by increasing the demand.

Equipment breakdown adds to the cost of production and considerably affect the overall equipment efficiency in automated lines due to unplanned downtime. Preventive maintenance with appropriate actions has been considered to enhance products quality, equipment reliability and minimize the probability of system brake down or failure. To this end, this study conducted a reliability status of nine packaging facilities, from the perspective of existing failure data of production system in the Nigerian multinational bottling plant. Failure data of the production system were stratified and analyzed to achieve the failure interval of each of the facilities and the sub-systems. Stratification of failure data resulted to an established input format that fitted the Pareto chart analysis, Weibull Distributions and Reliability/Failure Time analysis.  The results showed that the facility with minimum value of reliability was filler machine. A standby filler system was therefore recommended in order to prevent unnecessary idleness of the other facilities especially when the production target is high.  The study concluded that, analysis of downtime in a production/manufacturing system assisted in predicting the likely failure interval and hence a preventive maintenance scheduled was proposed.

Scheduling is one of the key parameters to maintain competitive advantage of organizations, and can directly affect productivity, reduce production time and increase the profitability of an organization. Job shop scheduling problem (JSSP) seeks to find the optimal sequence of performing various jobs related to group of machines. The purpose of this paper is to provide a multi objective to optimize makespan, energy consumption and machine erosion in flexible JSSP. The problem of this paper is to assign each operation to a machine and to order the operations on the machines, such that the maximal completion time (makespan) of all operations is minimized. The obtained model belongs to NP-Hard class of optimization problems. In terms of overcoming NP-hardness of the proposed model and solve the complicated problem, a non-dominated sorting genetic algorithm (NSGAII) is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm (NRGA) is developed to validate the results obtained and test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms. In this study, to evaluate the performance of these algorithms, they were statistically analyzed using T-test. Ultimately, results of the selected model were ranked by applying the technique for order of preference by similarity to ideal solution (TOPSIS).

In recent years, the relationship between the concepts of operations management and finance management has been an attractive area of research among researchers. One of the emerging areas at the beginning of the 21st century in the literature of operations and supply chain management is the topic of supply chain finance (SCF). SCF is a new concept that provides efficient financing of the supply chain, where all parties can balance the working capital and improve cash flow at a reduced cost by utilizing the buyer's or other parties' credit rating. Hence, in this study, an approach to optimize financing based on the Stackelberg model in a three-level supply chain, considering the circumstances in which the supplier is financially constrained for fulfillment the buyer's order and funded by the bank as another member of the supply chain based on the purchase order financing (POF) is discussed. For this purpose, a nonlinear mathematical programming model has been developed to maximize the payoff function of the partners.

In this paper, a mathematical model is presented for the integrated planning of maintenance, quality control and production control in deteriorating production systems. The simultaneous consideration of these three factors improves the efficiency of the production process and leads to high-quality products. In this study, a single machine produces a product with a known and constant production rate per time unit and the production process has two operational states, i.e. in-control state and out-of-control state, and the probability of the state transition follows a general distribution. To monitor the process, sampling inspection is conducted during a production cycle and a proper control chart is applied. In the developed model, there is no restriction on the type of the control chart. Therefore, different control charts can be applied in practice for quality control. The lot size produced in each production cycle is determined with respect to the production rate of the machine and the proportion of conforming and non-conforming items produced in each cycle. In this study, preventive maintenance and corrective maintenance as perfect maintenance actions and minimal maintenance as imperfect maintenance action are applied to maintain the process in a proper condition. The objective of the integrated model is to plan the maintenance actions, determine the optimal values of the control chart parameters and optimize the production level to minimize the expected total cost of the process per time unit. To evaluate the performance of this model, a numerical study is solved and a sensitivity analysis is conducted on the critical parameters and the obtained results are analyzed.

This paper presents an approach for the fault diagnosis in the state of fault in a machine by using a combination of the Dempster–Shafer (D-S) theory. At the first, feature extractions in each state have been combined based on evidential reasoning (ER) using kind of sensor information such as vibration, acoustic, pressure, and temperature, to detect and diagnose machine failure. Then, the main fusion will be obtained. In this process, the mass function assignment of any sensors to feature extraction, respectively, in every state of the machine is fused to indicate state quality. Within this framework, we propose a new way for main fusion to derive a consensus decision for fault diagnosis. In this paper, an approach developed to apply the evidential reasoning by defining adaptively weights into the improvement of the D–S evidence theory instead of the probability theory and the D–S evidence theory alone. Instead of using the evidential reasoning approach, this new approach applies entropy weighting in the D-S theory, in which all available data are used for making a decision. Entropy weighting can measure the uncertainty level of the fault decision and assist in obtaining a less uncertain fault decision. It is defined adaptively weights based on ambiguity measures associated with information obtained from each sensor. The ambiguity measure is defined by Shannon’s entropy. Many industries use old machines due to cost savings or lack of purchasing power. Maintenance policies in these factories are based on determining their fault experimentally and traditionally. Therefore, the main goal of this paper uses the improved evidence reasoning algorithm using a kind of sensor information to carry out fault diagnosis in these industrials. Then, a numerical example and a case study involving the ball mill machine in fault diagnosis are presented to show the rationality and efficiency of the proposed method.

Avoiding the passengers extra waiting time is a vital task for rail planners. The current research focused on minimizing the passenger waiting time on the presence of real frequently random occurred disturbances. Details of the proposed model are on the 1st line of Tehran underground rail rapid transit. All fitness functions are validated using the analysis of variance (ANOVA) by applying the hypothesis testing method. Also, a validated discrete-event computer simulation model is applied to examine the average waiting time per passenger as the key performance measure under different scenarios generated using full factorial design of experiments. The validity of the obtained optimal solution, i.e., train headway times is confirmed at a 95% level of reliability. Also, simulation outcomes indicated that the proposed response surface meta-model could efficiently provide a more reliable train operation plan to ensure a desirable level of system resiliency on the presence of random disturbances. The numerical results indicated that wait time could be reduced by 14.8% for passengers as compared with the baseline train headway plan.

The importance of IT services in the life of businesses has led organizations to seek continuous evaluation of the quality of their IT services. In this regard, IT service management best practices such as Information Technology Infrastructure Library (ITIL), have introduced several processes for management of IT services and defined different KPIs for evaluation of each process, so that organizations can evaluate and analyze the quality of their IT services through these KPIs. Despite this fact that evaluation of each ITIL process using mentioned KPIs requires considerable time and money, organizations are looking for solutions to invest on the most effective KPIs to improve their ITSM processes in pursuit of their business requirements. Although there are some researches over process evaluation methods in different areas, there is no scientific research in ITSM process evaluation. This study proposes the unique method for ITSM evaluation using ITIL KPIs based on defined critical success factors (CSF). In addition to that, Simultaneous Evaluation of Criteria and Alternatives (SECA) model as one of the newest MCDM methods has been used for KPI prioritization. Based on the results, we recommended the order of KPIs in ITSM process performance evaluation. This research helps organizations to improve their ITSM processes by investment on the most effective KPIs.

This paper deals with optimizing the multi-door cross-docking scheduling problem for incoming and outgoing trucks. Contrary to previous studies, it first considers the simultaneous effects of learning and deteriorating on loading and unloading the jobs. A mixed-integer linear programming (MILP) model is developed for this problem, in which the basic truck scheduling problem in a cross-docking system is strongly considered as NP-hardness. Thus, in this paper, meta-heuristic algorithms namely genetic algorithm, imperialist competitive algorithm, and a new hybrid meta-heuristic algorithm, resulted from the principal component analysis (PCA) and an imperialist competitive algorithm (ICA) called PCICA are proposed and used. Finally, the numerical results obtained from meta-heuristic algorithms are examined using the relative percentage deviation and time criteria. Results show that the hybrid PCICA algorithm performs better than the other algorithms in terms of the solution quality. Computational results indicate when the learning rate increases, its decreasing effect on processing time will growth and the objective function value is improved. Finally, the sensitivity analysis also indicates when the deterioration rate is reduced, its incremental effect is decreased over time.

Nowadays, environmental deterioration is one of the most noticeable issues in logistics, so that the organizations are required to control the triggers of environmental contaminations generation. One of the most effective steps in addressing this term is to design transportation network considering CO2 emission limitation. In this paper, a vehicle routing problem with simultaneous pickup and delivery with heterogeneous fleet and environmental measurement consideration is proposed. Introduced two objectives mathematical modeling, with the help of the weighted LP metric method has become to a combined dimensionless objective. The formulated optimization problem is solved in small dimensions using General Algebraic Modelling System (GAMS) approach and specifically BARON solver respect to the nature of the mathematical equations. The results obtained from simulations are discussed to confirm the effectiveness of the proposed method in dealing with the desired example. Because of NP-hardness, Discrete Invasive Weed Optimization (DIWO) meta-heuristic algorithm is applied.

Due to the increasing progress in various industries, paying attention to the internal processes of the organizations is more visible to stay on the competitive scene. Therefore, many organizations attempt to simplify and evaluate their internal processes using re-engineering. By reviewing the conducted studies, it can be stated that one of the existing problems in the implementation of re-engineering projects is the selection of the optimal portfolio of processes. Hence, this study aims to provide a bi-objective mathematical model for selecting processes in the re-engineering project by considering two key assumptions include improvement in achieving organizational goals and staff resistance. To this end, first, the impact of processes on organizational goals is specified by experts and then the goals’ weights are obtained using a fuzzy Best Worst Method. Finally, the proposed model is solved by an augmented ε-constraint method and the optimal portfolio of processes is selected. Also, a public Hospital of Sari as a real-world case study is employed to set the values of model parameters. Finally, the obtained results are reported and using a sensitivity analysis, several directions are provided. The results show that changes in the staff resistance directly affects the second objective function, while changes in the improvement created by each process affect the first objective function. Also, changes in costs have little effect on either objective functions.

This paper addresses a novel two-stage model for a Sustainable Closed-Loop Supply Chain (SCLSC). This model, as a contribution, provides a balance among economic aims, environmental concerns, and social responsibilities based on price, green quality, and advertising level. Therefore, in the first stage, the optimal values of price are derived by considering the optimal level of advertising and greening. After that, in the second stage, multi-objective Mixed-Integer Linear Programming (MOMILP) is extended to calculate Pareto solutions. The objectives are include maximizing the profit of the whole chain, minimizing the environmental impacts due to CO2 emissions, and maximizing employee safety. Besides, a Lagrangian relaxation algorithm is developed based on the weighted-sum method to solve the MOMILP model. The findings demonstrate that the proposed two-stage model can simultaneously cope with coordination decisions and sustainable objectives. The results show that the optimal price of the recovered product equals 75% of the new product price which considerably encourages customers to buy it. Moreover, to solve the MOMILP model, the proposed algorithm can reach to exact bound with an efficiency gap of 0.17% compared to the optimal solution. Due to the use of this algorithm, the solution time of large-scale instances is reduced and simplified by an average of 49% in comparison with the GUROBI solver.