Coordination improves the profit of all the members in a supply chain. In this paper, a novel coordination mechanism is introduced in a retailer-manufacturer supply chain in which the retailer can adopt either an American option mechanism or a wholesale mechanism to satisfy the market demand throughout a multi-period horizon. The manufacturer follows a make-to-order production policy. This mechanism gives the retailer the right to select the more beneficial mechanism in each period. The retailer as the leader of the chain decides how many options should be purchased at the beginning of the horizon through a mixed-integer mathematical model. This model addresses the uncertainties in the market demand and the market price, simultaneously. A scenario planning approach is used to treat the random variables within the model. Also, an optimal scenario reduction model is adapted to reduce the computational complexity of the problem. Finally, a numerical experiment is designed to validate the performance of the model. The results demonstrate a remarkable improvement in the profit of both members. Moreover, a number of experiments are performed to show how the option price, the exercise price and the interest rate affect the performance of the contract.

We introduce a new robust simulation optimization method in which the probability of occurrence of uncertain parameters is considered. It is assumed that the probability distributions are unknown but historical data are on hand and using φ-divergence functionality the uncertainty region for the uncertain probability vector is defined. We propose two approaches to formulate the robust counterpart problem for the objective function estimated by Kriging. The first method is a minimax problem and the second method is based on the chance constraint definition. To illustrate the methods and assess their performance, numerical experiments are conducted. Results show that the second method obtains better robust solutions with less simulation runs.

Given the high costs of construction and maintenance, an optimum design methodology is one of the most important steps towards the development of transportation infrastructure, especially freeways. However, the effects of different variables on the decision-making process to find an optimal variant have caused the choice to become a very difficult and professional task for decision makers. So, the current paper aims to determine the optimal variant route for Isfahan-Shiraz freeway through MADM approaches. First, evaluation indices for an optimal route variant are derived through literature review and expert panel assessment. Then, a dynamic fuzzy GRA method is used for weightings and optimal route selection. Bases on the results, the road longevity, views of NGOs and route integration are identified as the highest-weighted criteria in route variant prioritization. Further, Route 3 is defined as the priority for the optimal variant for Isfahan–Shiraz freeway, which is the main basis in practice.

Abrasive Water Jet Machining is one of the novel nontraditional cutting processes found diverse applications in machining different kinds of difficult-to-machine materials. Process parameters play an important role in finding the economics of machining process at good quality. This research focused on the predictive models for explaining the functional relationship between input and output parameters of AWJ machining process. No single set of parametric combination of machining variables can suggest the better responses concurrently, due to its conflicting nature. Hence, an approach of Multi-objective has been attempted for the best combination of process parameters by modelling AWJM process using of ANN. It served a set of optimal process parameters to AWJ machining process, which shows a development with an enhanced productivity. Wide set of trail experiments have been considered with a broader range of machining parameters for modelling and, then, for validating. The model is capable of predicting optimized responses.

In this paper, a multi-period model for blood supply chain in emergency situation is presented to optimize decisions related to locate blood facilities and distribute blood products after natural disasters. In disastrous situations, uncertainty is an inseparable part of humanitarian logistics and blood supply chain as well. This paper proposes a robust network to capture the uncertain nature of blood supply chain during and after disasters. This study considers donor points, blood facilities, processing and testing labs, and hospitals as the components of blood supply chain. In addition, this paper makes location and allocation decisions for multiple post disaster periods through real data. The study compares the performances of “p-robust optimization” approach and “robust optimization” approach and the results are discussed.

Today, with the advancement of technology in the production process of various products, the achievement of sustainable production and development has become one of the main concerns of factories and manufacturing organizations. In the same vein, many manufacturers try to select suppliers in their upstream supply chains that have the best performance in terms of sustainable development criteria. In this research, a new multi-criteria decision-making model for selecting suppliers and assigning orders in the green supply chain is presented with a fuzzy optimization approach. Due to uncertainty in supplier capacity as well as customer demand, the problem is formulated as a fuzzy multi-objective linear programming (FMOLP). The proposed model for the selection of suppliers of SAPCO Corporation is evaluated. Firstly, in order to select and rank suppliers in a green supply chain, a network structure of criteria has defined with five main criteria of cost, quality, delivery, technology and environmental benefits. Subsequently, using incomplete fuzzy linguistic relationships, pair-wise comparisons between the criteria and sub-criteria as well as the operation of the options will be assessed. The results of these comparisons rank the existing suppliers in terms of performance and determine the utility of them. The output of these calculations (utility index) is used in the optimization model. Subsequently, in the order allocation process, the two functions of the target cost of purchase and purchase value are optimized simultaneously. Finally, the order quantity is determined for each supplier in each period.

A sequential production line is defined as a set of sequential operations within a factory or distribution center whereby entities undergo one or more processes to produce a final product. Sequential production lines may gain efficiencies such as increased throughput or reduced work in progress by utilizing specific configurations while maintaining the chronological order of operations. One problem identified by the authors via a case study is that, some of the configurations, such as work cell or U-shaped production lines that have groups of buffers, often increase the space utilization. Therefore, many facilities do not take advantage of the configuration efficiencies that a work cell or U-shaped production line provide. To solve this problem, the authors introduce the concept of a buffer cluster. The production line implemented with one or more buffer clusters maintains the throughput of the line, identical to that with dedicated buffers, but with the clusters reduces the buffer storage space. The paper derives a time based parametric model that determines the sizing of the buffer cluster, provides a reduced time space for which to search for the buffer cluster sizing, and determines an optimal buffer clustering policy that can be applied to any N-server, N+1 buffer sequential line configuration with deterministic processing time. This solution reduces the buffer storage space utilized while ensuring no overflows or underflows occur in the buffer. Furthermore, the paper demonstrates how the buffer clustering policy serves as an input into a facility layout tool that provides the optimal production line layout.

This study, both descriptive and exploratory, aims to examine the scientific literature on the supply chain performance measurement (SCPM) in the automotive sector over the last 12 years. The analysis featured 30 articles corresponding to the research topic of the Scopus database. The most pertinent articles, authors, keywords, countries, research laboratories and journals are presented using the analysis of bibliographies on the subject from 2007 to 2018 in the Industrial Engineering domain. The conclusion is performed by the systematic analysis that the automotive sector is well controlled on the subject of performance. The selected studies show that lack of understanding of the performance measurement system (PMS) and lack of management dedication are the main obstacles. Finally, various guidelines are provided to optimize the supply chain in the automotive sector.

The proposed work presents the design and application of many-objective Jaya (MaOJaya) algorithm to optimize many-objective benchmark optimization problems. The basic Jaya algorithm is modified by introducing non-dominated sorting and tournament selection scheme of NSGA-II. The reference point mechanism is introduced to traverse algorithm towards the best solutions. The basic Jaya algorithm is modified while preserving its essential properties. The Tchebycheff – a decomposition based approach is used to simplify the complex MaOPs. The proposed MaOJaya algorithm is tested on DTLZ benchmark functions with objectives ranging from three to ten to measure its applicability and effectiveness to solve many-objective optimization problems. The IGD and Hypervolume performance metrics are used to evaluate the performance of proposed MaOJaya algorithm. The obtained IGD and Hypervolume values compared with the best known results and it is observed that, the proposed MaOJaya algorithm gives competitive or better results than known best results.

Scheduling and layout planning are two important areas of operations research, which are used in the areas of production planning, logistics and supply chain management. In many industries locations of machines are not specified, previously, therefore, it is necessary to consider both location and scheduling, simultaneously. This paper presents a mathematical model to consider both scheduling and layout planning for parallel machines in discrete and continuous spaces, concurrently. The preliminary results have indicated that the integrated model is capable of handling problems more efficiently.