Evaluating and prioritizing appropriate renewable energy sources is inevitably a complex decision process. Various information and conflicting attributes should be taken into account. For this purpose, multi-attribute decision making (MADM) methods can assist managers or decision makers in formulating renewable energy sources priorities by considering important objective and attributes. In this paper, a new extension of compromise ranking method with interval numbers is presented for the prioritization of renewable energy sources that is based on the performance similarity of alternatives to ideal solutions. To demonstrate the applicability of the proposed decision method, an application example is provided and the computational results are analyzed. Results illustrate that the presented method is viable in solving the evaluation and prioritization problem of renewable energy sources.

Flow-shop problems, as a typical manufacturing challenge, have become an interesting area of research. The primary concern is that the solution space is huge and, therefore, the set of feasible solutions cannot be enumerated one by one. In this paper, we present an efficient solution strategy based on a genetic algorithm (GA) to minimize the makespan, total waiting time and total tardiness in a flow shop consisting of n jobs and m machines. The primary objective is to minimize the job waiting time before performing the related operations. This is a major concern for some industries such as food and chemical for planning and production scheduling. In these industries, there is a probability of the decay and deterioration of the products prior to accomplishment of operations in workstation, due to the increase in the waiting time. We develop a model for a flowshop scheduling problem, which uses the planner-specified weights for handling a multi-objective optimization problem. These weights represent the priority of planning objectives given by managers. The results of the proposed GA and classic GA are analyzed by the analysis of variance (ANOVA) method and the results are discussed.

This paper considers an economic lot and delivery scheduling problem (ELDSP) in a fuzzy environment with the fuzzy shelf life for each product. This problem is formulated in a flexible job shop with unrelated parallel machines, when the planning horizon is finite and it determines lot sizing, scheduling and sequencing, simultaneously. The proposed model of this paper is based on the basic period (BP) approach. In this paper, a mixed-integer nonlinear programming (MINLP) model is presented and then it is changed into two models in the fuzzy shelf life. The main model is dependent to the multiple basic periods and it is difficult to solve the resulted proposed model for large-scale problems in reasonable amount of time; thus, an efficient heuristic method is proposed to solve the problem. The performance of the proposed model is demonstrated using some numerical examples.

This paper considers a multi-objective integrated production-distribution problem (IPDP) for multi-product and multi-production facility with limited capacity vehicles over a multi-period horizon in a two-level supply chain. In order to consider uncertainty of a real supply chain, some fuzzy parameters are considered for costumer demands, machine and labor levels of each manufacturer. The proposed model minimizes total production, inventory and distribution costs and total delivery time, simultaneously and the performance of the proposed model is evaluated on several randomly generated instances. The results show that integrating production and distribution decisions is more efficient than making these two decisions, separately.

The hub location problem appears in a variety of applications, including airline systems, cargo delivery systems and telecommunication network design. Hub location problems deal with finding the location of hub facilities and the allocation of demand nodes to these located hub facilities. In this paper, a new model for the capacitated single allocation hub covering location problem is presented. Instead of using capacity constraints to limit the amount of flow received by the hubs, the second objective function is introduced to minimize service times in the hubs. The service time in the hubs includes the waiting time of received flows in a queue and the time to get services. Due to the NP-hardness of the problem, a new weight-based multi-objective imperialist competitive algorithm (MOICA) is designed to find near-optimal solutions. To validate the performance of the proposed algorithm, the solutions obtained by the MOICA are compared by the exact solutions of the mathematical programming model.