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"Many decision problems in Operations Research are defined on temporal networks, that is, workflows of time-consuming tasks whose processing order is constrained by precedence relations. For example, temporal networks are used to model projects, computer applications, digital circuits and production processes.Optimization problems arise in temporal networks when a decision maker wishes to determine a temporal arrangement of the tasks and/or a resource assignment that optimizes some network characteristic (e.g. the time required to complete all tasks). The parameters of these optimization problems (e.g. the task durations) are typically unknown at the time the decision problem arises.This monograph investigates solution techniques for optimization problems in temporal networks that explicitly account for this parameter uncertainty. We study several formulations, each of which requires different information about the uncertain problem parameters."

"This paper addresses a new robust multi objective multi period model for supply chain planning under uncertainty considering quantity discounts. The proposed model maximizes the current proht of the distributor by making a balance between the total costs of the supply chain and the distributor company s revenues of selling products and also maximizes the company s expected profit by introducing brands and taking the risk of loss on it. Considering uncertainty in the purchasing cost, selling fees, and demand fluctuations, the new robust multi objective mixed integer programming model is solved as a single objective mixed integer programming model by utilizing the LP metrics method. By settling regulatory penalty parameters and considering different economic scenarios, the robustness and effectiveness of the developed model are verified with the data from BEH PAKHSH Company, a commodities distributor in Iran. The outcomes show that the proposed model is a promising approach to run an efficient supply chain."

"ABSTRACTThis paper focuses on the robust resource constrained project scheduling problem (RCPSP) with discrete time/resource trade offs, in which activity duration and resource are uncertain variables. Combining flexible RCPSP (FRCPSP) with robustness, a discrete mathematical model is developed and resource leveling problem objective is considered to describe the flexible resource allocation comprehensively. In addition, surrogate measures are also introduced, providing an accurate estimate of the schedule robustness. Priority based heuristic methods and resource assignment heuristic are employed to generate and modify the priorities of selected activities, meanwhile obtain the different executing modes. Furthermore, each surrogate measure is compared according to the scheduling performance through the computational experiments. The practicability of proposed multi objective mathematical model and the efficiency of algorithm are verified by a numerical example. Finally, the performance analysis is also presented by the robustness assessment, and the results prove that the proposed approach is more effective than the traditional one."

"The research presented here, opening up space for synergies and innovation. It is directly applicable for relevant practical problems and has been carried out in cooperation with utility and dumping companies, infrastructure providers and planning offices. A close and direct connection to the practice of water management has been established by involving application-oriented know-how from the field of civil engineering. On the mathematical side all necessary disciplines were involved, including mixed-integer optimization, multi-objective and facility location optimization, numerics for cross-linked dynamic transportation systems and optimization as well as control of hybrid systems. Most of the presented research has been supported by the joint project „Discret-continuous optimization of dynamic water systems“ of the federal ministry of education and research (BMBF)."

"This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology."

"Penelitian ini membahas tentang pembuatan model optimasi perencanaan produksi pada kondisi ketersediaan bahan baku yang terbatas dan tidak pasti di industri turunan minyak nilam. Tujuan optimasi ini adalah meminimalkan biaya produksi, biaya penyimpanan, dan biaya backlog. Dalam penelitian ini metode peramalan yang digunakan dengan bantuan software Oracle Crystall Ball dimana model peramalan yang didapat autoregressive integrated moving average, dan double moving average. Untuk model optimasi linear programming, penyelesaiannya dengan menggunakan bantuan software Lingo 9.0. Model yang dihasilkan dapat mengefisienkan 19,09%. Selain itu dianalisis mengenai kemungkinan menaikan harga pembelian bahan baku dari kondisi standar. Berdasar perhitungan diperoleh nilai trade off 1,67% terhadap kondisi aktual.

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This study discusses the optimization model at industrial production planning in patchouli oil derivatives industry. Objective of this optimization is to minimize the production costs, carrying costs, and backlogging cost. In this study the forecasting methods used moving averages method, the solution uses software support Oracle Crystall Ball. The optimization model of linear programming is optimized by Lingo. Resulting model can streamline 19.09% of the actual conditions in the absence of planning. In addition, Further analysis of efficiency can also be done by raising the purchase price of raw materials less than 1,67% if supplying to be under certainty."

"The monograph addresses a problem of stochastic analysis based on the uncertainty assessment by simulation and application of this method in ecology and steel industry under uncertainty. "