Journal Of Industrial Engineering International
Volume:15 Issue: 3, Summer 2019

In this paper, we focus on outsourcing activities optimization problem in single period setting. In some situations, capacity planning or outsourcing is a one-time event and can be modeled as a single period problem. The aim of this research is to balance the trade-off between two echelons of a supply chain consisting of a single outsourcer and a single subcontractor. Each part is composed of a failure-prone single machine that produces one product type to satisfy market requirements. The outsourcer’s manufacturing system is not able to satisfy the demand; in this case, outsourcing is allowed to recover the lack of capacity. We consider that the subcontractor can satisfy the demands of strategic clients and rent his machine for the outsourcer under a win–win partnership contract. We assume that the hazard failure rate depends on time and the adopted manufacture rate. When unforeseen failures occur, minimal repairs are implemented. Overhaul can be performed to reduce the degradation effects. Hence, we develop a mathematical model to define a profitability interval so that both parties of supply chain can be considered as winners. We seek to determine the contract parameters that suit both parties (duration, start and end dates, the production and outsourcing rates). Then, we develop an exact algorithm to solve the problem of single period optimization, which offers a better execution time through a series of test problems. Finally, we consider a sensitivity analysis based on outsourcing parameters (cost, periodicities, etc) to analyze their effects on partial costs and individual profit of each part, as well as the total profit generated by the system.

The buffer allocation problem is an NP-hard combinatorial optimization problem, and it is an important design problem in manufacturing systems. The research proposed in this paper concerns a product line consisting of n unreliable machines with n − 1 buffers and a preventive maintenance policy. The focus of the research is to obtain a better trade-off between the buffer level and the preventive maintenance actions. This paper proposes a dynamic control of the buffers’ level and the interval between two consecutive preventive actions. The set of the parameter of the proposed policy allows choosing the reduction in the costs or the increment of the throughput rate. A simulation model is developed to test the proposed model to the solution proposed in the literature. The proposed policy leads to better results in terms of total costs reduction keeping high production rate, while the design of a fixed level of buffer works better for lower production rates required.

The issue discussed in this paper is a bi-level problem in which two rivals compete in attracting customers and maximizing their profits which means that competitors competing for market share must compete in the centers that are going to be located in the near future. In this paper, a nonlinear model presented in the literature considering customer preferences is linearized. Customer behavior means that the customer patronizes the most attractive (most comfort) location that he/she wants to be served among the locations of the first-level decision maker (Leader) and the second-level decision maker (Follower). Four types of exact algorithms have been introduced in this paper which include three types of full enumeration procedures and a developed branch-and-bound procedure. Moreover, a clustering-based algorithm has been presented that can provide a good approximation (a good lower bound) to the mentioned binary problem. For this purpose, the numerical results obtained are compared with the results of the full enumeration, heuristic and the branch-and-bound procedure.

In real-world applications, costs for products are not deterministic: neither static nor dynamic. They actually tend to be non-stationary and cross-correlated. To overcome this drawback, there have been some efforts by researchers to extend the Wagner–Whitin algorithm to consider stochastic costs. However, they assume that the information of probability density function of random costs exists. This paper applied a robust approach in reformulating the uncertain lot-sizing problem and used the Wagner–Whitin algorithm to find an optimal solution of its robust counterpart. The solution of the proposed algorithm in an example from the literature is compared with the classical one.

In this paper, Burr-type XII ̄X synthetic schemes are proposed as an alternative to the classical ̄X synthetic schemes when the assumption of normality fails to hold. First, the basic design of the Burr-type XII ̄X synthetic scheme is developed and its performance investigated using exact formulae. Secondly, the non-side-sensitive and side-sensitive Burr-type XII ̄X synthetic schemes are introduced and their zero-state and steady-state performances, in terms of the average run-length and expected extra quadratic loss values, are investigated using a Markov chain approach. Thirdly, the proposed schemes are compared to the existing classical runs-rules and synthetic ̄X schemes. It is observed that the proposed schemes have very interesting properties and outperform the competing schemes in many cases under symmetric and skewed underlying process distributions. Finally, an illustrative real-life example is given to demonstrate the design and implementation of the proposed Burr-type XII ̄X synthetic schemes.

One of the main tasks facing all European countries for the next few years is the creation of the most dynamically organized transport sector. The constant passenger and freight traffic lead to congestions and pollutions at the transport highways, having negative impact on a person. Thus, introduction of new technologies, addressing the interrelated problems of optimizing transport flows and improving the environmental footprint of transport, is an overriding priority. In this respect, approaches that allow analyzing the reliability of a vehicle as an object characteristic, reflecting the ability of a product to operate without sudden changes in its quality in real time, are of considerable interest. This is reflected in the development of preventive diagnostic systems (warning the driver of a possible failure of the systems and the car as a whole). The concept for formation of normative and methodological support for research in the field of reliability of complex systems (including vehicles), which can be adopted as a basis for the development of a database of a preventive diagnostic system, is proposed.

We consider a rich tanker trailer routing problem with stochastic transit times for chemicals and liquid bulk orders. A typical route of the tanker trailer comprises of sourcing a cleaned and prepped trailer from a pre-wash location, pickup and delivery of chemical orders, cleaning the tanker trailer at a post-wash location after order delivery and prepping for the next order. Unlike traditional vehicle routing problems, the chemical interaction properties of these orders must be accounted for to prevent risk of contamination which could impose complex product-sequencing constraints. For each chemical order, we maintain a list of restricted and approved prior orders, and a route is considered to be feasible if it complies with the prior order compatibility relationships. We present a parallel computation framework that envelops column generation technique for large-scale route evaluations to determine the optimal trailer-order-wash combinations while meeting the chemical compatibility constraints. We perform several experiments to demonstrate the efficacy of our proposed method. Experimental results show that the proposed parallel computation yields a significant improvement in the run time performance. Additionally, we perform sensitivity analysis to show the impact of wash capacity on order coverage.

In this paper, instead of the classical approach to the multi-criteria location selection problem, a new approach was presented based on selecting a portfolio of locations. First, the indices affecting the selection of maintenance stations were collected. The K-means model was used for clustering the maintenance stations. The optimal number of clusters was calculated through the Silhouette index. The efficiency of each cluster of stations was determined using the Charnes, Cooper and Rhodes input-oriented data envelopment analysis model. A bi-objective zero one programming model was used to select a Pareto optimal combination of rank and distance of stations. The Pareto solutions for the presented bi-objective model were determined using the invasive weed optimization method. Although the proposed methodology is meant for the selection of repair and maintenance stations in an oil refinery Company, it can be used in multi-criteria decision-making problems.

Right now employment of polices and tools to decrease the carbon emission through electricity generation from renewable resources is one of the most important problem in energy policy. Tradable Green Certificate (TGC) is an economics mechanism to support green power generation. Any country has the challenge to choose an appropriate policy and mechanism for design and implementation of TGC. The purpose of this study is to help policy makers to design and choose the best scenario of TGC by evaluating six scenarios, based on game theory approach. This study will be useful for increasing the effectiveness of TGC system in interaction with electricity market. Particularly, the competition between thermal and renewable power plants is modeled by mathematical modeling tools such as cooperative games like Nash and Stackelberg. Each game is modeled by taking into account of the two following policies. The results of the six scenarios and the sensitivity analysis of some key parameters have been evaluated by numerical studies. Finally, in order to evaluate the scenarios we calculated the level of social welfare in the all scenarios. The results of all models demonstrate that when the green electricity share (minimum requirement) increases the TGC price decreases. Moreover, in all scenarios when the minimum requirement is 100% then the maximum level of social welfare is not met. Also when the minimum requirement is less than 50%, the scenarios with the market TGC price policy have more social welfare in comparison with the scenarios with fixed TGC price policy.

The present article formulates the scenarios that the organization will be probably facing with, using the uncertain factors in business environment, and it also selects the most robust strategies of organization for dealing with the formulated scenarios using the fuzzy information expressed by the experts in fuzzy inference system. The present article aims to provide a method enabling the scenario programmers to employ robustness philosophy using the scenario planning potentials and fuzzy inference system at the decision-making stage of the general process of strategy formulation. The process helps the strategic managers of the organizations to determine their business future clearly and enables them to select their robust scenario in the current market that is uncertain. After the introduction of the robust strategic planning methodology and illustrating its different stages, the selected strategies of them will be compared at the end of the article by implementing the strategic planning method in a practical case. The results of the research have been examined as a case study for carpet industry.