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.

Split delivery vehicle routing problem is one of the traditional types of routing problems in which the demand of different points can be divided among vehicles and the objective is to minimize the path length, which vehicles travel. In this paper, fuel cost of vehicles which is assumed to be dependent on their traveled path and load is considered as the objective functions. Namely, the cost of the consumed fuel is proportionate to the unit of load carried per unit of distance. In order to solve the proposed model a new memetic algorithm is developed which has two rows. The performance of the proposed algorithm for 21 standard problems is compared with the optimum solutions obtained from mathematical programming standard solver and the solutions of the same algorithm with single row solution representation. The results express the efficiency of developed algorithm.

We study a location-inventory problem in a three level supply chain network under uncertainty, which leads to risk. The (r,Q) inventory control policy is applied for this problem. Besides, uncertainty exists in different parameters such as procurement, transportation costs, supply, demand and the capacity of different facilities (due to disaster, man-made events and etc). We present a robust optimization model, which concurrently specifies: locations of distribution centers to be opened, inventory control parameters (r,Q), and allocation of supply chain components. The model is formulated as a multi-objective mixed-integer nonlinear programming in order to minimize the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. Moreover, we develop an effective solution approach on the basis of multi-objective particle swarm optimization for solving the proposed model. Eventually, computational results of different examples of the problem and sensitivity analysis are exhibited to show the model and algorithm & apos; s feasibility and efficiency.

This paper presents a multi-objective location problem in a three level supply chain network under uncertain environment considering inventory decisions. The proposed model of this paper considers uncertainty for different parameters including procurement, transportation costs, supply, demand and the capacity of various facilities. The proposed model presents a robust optimization model, which specifies locations of distribution centers to be opened, inventory control parameters (r, Q), and allocation of supply chain components, concurrently. The resulted mixed-integer nonlinear programming minimizes the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. The model also minimizes the variability of the total cost of relief chain and minimizes the financial risk or the probability of not meeting a certain budget. We use the ?-constraint method, which is a multi-objective technique with implicit trade-off information given, to solve the problem and using a couple of numerical instances, we examine the performance of the proposed approach.

Nowadays, one of the challenges in the organizations according to budget limitations in the com-panies, is how to prioritize their project portfolios in the events of their strategies. In other words, organizations are seeking to allocate resources in order to gain the maximum profit ac-cording to budget limitations. In this article, a hybrid decision making method is used to priori-tize construction project portfolio by considering sustainability criteria. First, Lexicographic method is applied to weight the sustainability criteria. Then, by considering the weights derived from the Lexicographic and sustainability criteria, projects are prioritized based on VIKOR method. The proposed method of this study is applied for a case study of projects in the refinery scope.

Personnel selection is one of the most important problems that organizations have to deal with. Competent personnel is one of the key factors for the success of organizations. Project manager selection due to special requirements is significantly important. A project manager must have the ability of managing costs, time and resources through the optimistic way. Furthermore he/she has to own general management skills and benefit from adequate information about the project context. Project managers in petroleum industry carry very important duties than other project managers. In this research, we try to develop a model in order to select a project manager for pe-troleum industry. The proposed model is based on multi criteria decision making and a statistical method named principle component analysis (PCA). The methodology considers all of the im-portant criteria and benefit from an experienced expert panel in order to extract the weights of the criteria. Also a numerical example demonstrates the function of the model and is verified by VI-KOR method.