Journal of Industrial Engineering and Management Studies
Volume:6 Issue: 2, Summer-Autumn 2019

Iranian electronic products supplier industries are developing day by day and modern techniques and facilities are assigning as well as many promotions about green products supply chain as input materials introduced into the generation cycle of industries. Current cluster study of Iranian Electronic Products Manufacturing Industries (IEPMI) comprised a technical and hierarchical evaluation carried out as the objective of current research. It was used SPSS and Excel Software to classify and analysis about 33 IEPMI via an unsupervised model, Additive Ratio Assessment (ARAS), Simple Additive Weighting (SAW) and Data Envelopment Analysis (DEA) models. Finally, a hierarchical cluster classification has developed for the 33 industries pertaining to 5 main criteria as well as the total inventory of input, output materials and facilities employed. It was found that the ranking systems based on ARAS and SAW presented the same results for IEPMI. DEA model was also classified IEPMI in terms of efficiency score.

Retailers commonly offer discounts to encourage consumers to purchase more products thereby increasing retailers’ revenues. This article focuses on modeling the seller pricing decisions by using agent-based approach when the price, as a tool of revenue management, decreases. Considering the seller as an agent who uses price changes to maximize its total revenues, the objective of this research is to find the proper seller’s decision about the rate of discount on products in 3 different scenarios. In the first scenario, all products’ price elasticity of demand are the same and the products have relatively elastic demand. In the second scenario, all goods have the same price elasticity of demand and have relatively inelastic demands. The third scenario presents a combination of the first and the second scenarios in which the price elasticity of demand of products are different and goods with elastic and inelastic demand are placed next to each other. Also, all goods in each scenario are substitutes. In the first scenario, reducing the price causes the downward trend in rate of profit even though the discount could increase the revenue. In the second scenario, the agent behaves differently which offering the discount does not increase the revenue. In the third scenario, the products’ discount increases the revenue with a slope less than the first scenario. Also, the discount for all products doesn’t cause income growth. Therefore, some goods without any discount remain in shelf. Consequently, the proposed model in this research shows the proper rate of discount on each product in different product layouts.

The two-stage assembly flowshop scheduling problem has been studied in this research. Suppose that a number of products of different kinds are needed to be produced. Each product is consists of several parts. There are m uniform machines in the first stage to manufacture the components (parts) of products and there is one assembly station in the second stage to assembled parts into products. Setup operation should be done when a machine starts processing a new part and setup times are treated as separate from processing times. Two objective functions are considered: (1) minimizing the completion time of all products (makespan) as a classic objective, and (2) minimizing the cost of energy consumption as a new objective. Processing speed of each machine is adjustable and the rate of energy consumption of each machine is dependent of its processing speed. At first, this problem is described with an example, and then needed parameters and decision variables are defined. After that, this problem is modeled as a mixed integer linear programming (MILP) and GAMS software is applied to solve small problems. To solve this bi-objective model, Epsilon Constraint algorithm is used on some test problems obtained standard references. Data of test problems were obtained from previous references and new parameters have been adjusted for considered problem. Conflicting of two considered objective functions has been valid through the result. In additional, result of solving test problems and sensitivity analysis show that how we can reduce energy consumption by adjusting completion times.

This paper identified and ranked the lean product factors effecting economic function of production companies in Mazandaran province. 24 sub-factors and five main factors were categorized including supplier management, purchase and provision system, human resource organization, organizing, leadership, and IT. The findings of the paper show that the factor of supplier management is the most influential factor in economic function of production companies and IT has the least effect on the economic function of production companies.

This paper describes an application of fuzzy multi-objective quadratic model with flexible constraints for optimal allocation of limited available water resources among different water-user sectors. Due to the fact that, water resource allocation problem is one of the practical and essential subjects in real world and many of the parameters may be faced by uncertainty. In this paper, we present α - cut approach for transforming fuzzy multi-objective quadratic programming model with flexible constraints into a crisp form. By using this approach a multi-parametric multi-objective programming model corresponding to α and parameters of flexible constraints is obtained. One of the advantages of this model is that the α - cut level is not determined by the decision makers. Actually, this model itself can calculate the α - cut level. In order to achieve a desired Pareto optimal value of multi-parametric multi-objective model, we use goal programming method for illustration of water resource allocation with sensitivity analysis of lower bound of parameters in flexible constraints. To illustrate the efficiency of the proposed approach, we apply it for a real case problem of water resource allocation.

In this research, the integrated sourcing and inventory policy problem in a pharmaceutical distribution company is investigated. In order to select the superior solution, a new tool is introduced. Sourcing is one of the most critical issues in pharmaceutical industry. In addition, drug inventory shortages can cause irreparable humanitarian crises. However, only a limited number of studies has been focused on integrated sourcing and inventory policy of drugs so far. In real-world problems, it is difficult to calculate the exact cost of inventory shortage such as company reputation and humanitarian crises. To overcome this obstacle, in this study, the number of shortage is considered as a separate objective. Likewise, demands of the distributors and breakdowns of suppliers are stochastic, and due to the complicated nature of the problem is difficult to calculate the objective function by using classic methods. So, simulation is used for estimating the objectives of the problem. It’s been proved that the problem of this study is NP-Hard. Therefore, a metaheuristic multi-objective particle swarm optimization (MOPSO) method is used to find the optimal solution. To test the reliability of the model and the proposed algorithm, a real drug distributing problem is used and after estimating a Pareto front, the best answer is chosen by The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.

In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the second one maximizes the machines reliability through minimizing machines failure. In addition, some routes are considered to produce each part based on operational requirements. An appropriate route is selected respect to the costs and operational time. Some parameters are considered under uncertainty in two categories. The first category such as demand is dependent on market condition and the uncontrolled competitive environment. The second one includes some parameters for production system and machines that are directly related to plans organized by production management. A robust optimization approach is used to deal with parameters uncertainty to produce feasible and optimal solutions. Furthermore, for validation and implementation of results in real world, a case study is investigated. Computational results show that the robust model reports better values for objective functions compared to the scenario-based model. In fact, Pareto-front which are resulted by robust model are dominated by scenario-based models’ Pareto front.  Sensitivity analyses on main parameters of the problem are performed to drive some managerial insights that help corresponding decision makers to provide suitable and homogenous decisions in a production system.

Cycle time is one of the viable parameters which needs to be optimised as much as possible whenever the manufacturing industry is trying to improve efficiency, cost base and customer responsiveness. This systematic study presents on the reduction of cycle time for productivity improvement in the manufacturing industry. In industries, cycle time should be focused due to the high need of balancing man, machine, materials, methods and management. It must be renowned that the reduction of cycle time is not an easy task. Productivity improvement process involves many factors in achieving the maximum reduction of unnecessary time for higher improvement. The appropriate approaches to be implemented includes lean manufacturing tools, value stream method, method-time measurements, just in time for inventory control, motion study, process study, VAT plant classification, total productive maintenance, improved MRP (material requirements planning)-based production planning, theory of constraint, linear programming and other simulation related techniques. The V, A or T types of plant classification can also be classified using optimisation production technology (OPT).

This paper addresses an integrated multi-echelon location-allocation-inventory problem in a ‎stochastic supply chain. In a bid to be more realistic, the demand and lead time are considered ‎to be hemmed in by uncertainty. To tackle the proposed supply chain network design problem, ‎a two-phase approach based on queuing and optimization models is devised. The queuing ‎approach is first deployed, which is able to cope with inherent uncertainty of parameters. ‎Afterwards, the proposed supply chain network design problem is formulated using a mixed‏-‏integer nonlinear model. Likewise, the convexity of the model is proved and the optimal ‎inventory policy as closed-form is acquired. Inasmuch as the concerned problem belongs to ‎NP-hard problems, two meta-heuristic algorithms are employed, which are capable of ‎circumventing the complexity burden of the model. The numerical examples evince the efficient ‎and effective performance of the solving algorithms. Lastly, sensitivity analyses are conducted ‎through which interesting insights are gained.

In every country, the efficiency and probability of small and medium firms will cause to economic growth. In this regard, present study aimed to investigate the effective factors on the performance of active industrial clusters in large and industrial provinces by Panel-VAR model during 2006-2015. Results indicated that the access to loan, production rate, cluster size, marketing sector in cluster, closeness of cluster to the market, and the increase of manager’s experience have a positive effect while bank facility interest can negatively influence on the performance of industrial clusters.

Multi-echelon distribution mechanism is common in supply chain design and logistics systems in which freight is delivered to the customers through intermediate depots (IDs), instead of using direct shipments. This primarily decreases the cost of the chain and consequences of environmental (energy consumption) and social (traffic, air pollution, etc.) logistic operations. This paper develops a novel multi-objective mixed-integer linear programming model (MOMILP) for a two-echelon green capacitated vehicle routing problem (2E-CVRP) in which environmental issues and time windows constraints are considered for perishable products delivery phase. To validate the proposed mathematical model, several numerical examples are generated randomly and solved using CPLEX solver of GAMS software. The ε-constraint method is applied to the model to deal with the multi-objectiveness of the proposed model. Finally, the best Pareto solution for each problem is determined based on the reference point approach (RPA) as one of the most effective techniques to help the decision-makers.

Data Envelopment Analysis (DEA) is one of the methods most widely used for measuring the relative efficiency of DMUs in the world today.  The efficiency evaluation of the network structure opens the “black box” and considers the internal structure of systems. In this paper, a three-stage network model is considered with additional inputs and undesirable outputs and obtains the efficiency of the network, as interval efficiency in presence of the imprecise datum. The proposed model of this paper simulates a factory in the factual world with a production area, three warehouses and two delivery points. This factory is taken into consideration as a dynamic network and a multiplicative DEA approach is utilized to measure efficiency. Given the non-linearity of the models, a heuristic method is used to linearize the models. Ultimately, this paper concentrates on the interval efficiency to rank the units. The results of this ranking demonstrated that the time periods namely, (24) and (1) were the best and the poorest periods, respectively, in context to the interval efficiency within 24 phases of time.