Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

A decision model for natural oil buying policy under uncertainty

A manufacturer, in a fast moving consumer goods industry, buys Natural oils from a number of oil suppliers world-wide. The prices of these oils are the major raw material cost in producing the consumer goods, which are also sold world-wide. The volatility in the international prices of the Natural oils has signi¯cant impact on the planning and budgets decisions. Since the oils are bought and the ¯nished products are sold in markets throughout the world, the manufacturer is exposed to a variety of market uncertainties and the resulting risks. These uncertainties are the raw material prices, the demand and the therefore the selling prices for the finished goods- all of which influence the profitability of the manufacturing firm. The risks can be minimised by entering into futures contract of appropriate duration, that is, by following a schedule of "forward"' purchase of oil (with specific series of future delivery dates) with the oil suppliers. We formulate this problem as a two-stage Stochastic Program (SP) using the futures and the spot prices for the Natural oil. This SP model gives robust decisions that hedge against the uncertainties in the Natural oil prices and the demand for the finished products. The uncertainty in the oil prices and the demand are modelled through a scenario generator. We have constructed a decision support system (DSS) that integrates the SP model, the scenario generator and the solution algorithm. This DSS also provides the decision maker a profile of the risk and return exposures for different policies

Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed