Plant capacity notions in a non-parametric framework: a brief review and new graph or non-oriented plant capacities Abstract Output-oriented plant capacity in a non-parametric framework is a concept that has been rather widely applied since about twenty-five years. Conversely, input-oriented plant capacity in this framework is a notion of more recent date. In this contribution, we unify the building blocks needed for determining both plant capacity measures and define new graph or non-oriented plant capacity concepts. We empirically illustrate the differences between these various plant capacity notions using a secondary data set. This shows the viability of these new definitions for the applied researcher.

Money’s importance from the religious perspective Abstract Operational research and finance have natural connections. However, operational research represents a device to be used for catching financial phenomena, and such a device is usually mediated by social norms and corresponding relevant parameters. This paper contributes to this debate by focusing on a particular social norm—namely, religiosity- and its importance to the role of money. Such relationship is here treated under a quantitative perspective. In particular, we provide an econometric-statistic comparison between religion and money importance. The methodological toolkit is tested on high quality empirical data coming from a recent survey of Romanian population involving 842 persons, from the many faiths in the considered country. Specifically, statistical techniques include best fit curves analysis and data cross tabulations are checked using Chi squared test. The distinctions between different religious people beliefs relating to money are discussed. Insights regarding perceptions of different religious denominations are provided. Subsequent effects on entrepreneurship behavior are tested using Logit regression models. Results state that each religion-based segment of population has its own way to understand the importance of money, to promote and to evaluate the power of money, and finally to manage important inter-connections around the money.

Solving the shift and break design problem using integer linear programming Abstract In this paper we propose a two-phase approach to solve the shift and break design problem using integer linear programming. In the first phase we create the shifts, while heuristically taking the breaks into account. In the second phase we assign breaks to each occurrence of any shift, one by one, repeating this until no improvement is found. On a set of benchmark instances, composed by both randomly-generated and real-life ones, this approach obtains better results than the current best known method for shift and break design problem.

KNN and adaptive comfort applied in decision making for HVAC systems Abstract The decision making of a suitable heating, ventilating and air conditioning system’s set-point temperature is an energy and environmental challenge in our society. In the present paper, a general framework to define such temperature based on a dynamic adaptive comfort algorithm is proposed. Due to the fact that the thermal comfort of the occupants of a building has different ranges of acceptability, this method is applied to learn such comfort temperature with respect to the running mean temperature and therefore to decide the suitable range of indoor temperature. It is demonstrated that this solution allows to dynamically build an adaptive comfort algorithm, an algorithm based on the human being’s thermal adaptability, without applying the traditional theory. The proposed methodology based on the K-Nearest-Neighbour algorithm was tested and compared with data from an experimental thermal comfort field study carried out in a mixed mode building in the south-western area of Spain and with the Support Vector Machine method. The results show that K-Nearest-Neighbour algorithm represents the pattern of thermal comfort data better than the traditional solution and that it is a suitable method to learn the thermal comfort area of a building and to define the set-point temperature for a heating, ventilating and air-conditioning system.

Cross-efficiency aggregation method based on prospect consensus process Abstract The arithmetic average method is usually adopted to aggregate cross-efficiency in traditional cross-efficiency methods. However, this method not only underestimates the importance of self-evaluation, but also ignores the subjective preference of decision-makers. This paper thus introduces prospect theory to describe the subjective preference of decision-makers in the aggregation process when they face gains and losses, then a new method is constructed to aggregate cross-efficiency. Based on the differences between the psychological expectations and aggregation results, the expectations are constantly adjusted until a consensus on aggregation results is reached. An aggregation result that is more acceptable to all decision-making units can then be obtained. Finally, the proposed method is applied to aggregate the cross-efficiency of 27 industrial robots to illustrate its effectiveness and convergence.

Modeling and optimization of biomass quality variability for decision support systems in biomass supply chains Abstract A feasible alternative to the production of fossil fuels is the production of biofuels. In order to minimize the costs of producing biofuels, we developed a stochastic programming formulation that optimizes the inbound delivery of biomass. The proposed model captures the variability in the moisture and ash content in the biomass, which define its quality and affect the cost of biofuel. We propose a novel hub-and-spoke network to take advantage of the economies of scale in transportation and to minimize the effect of poor quality. The first-stage variables are the potential locations of depots and biorefineries, and the necessary unit trains to transport the biomass. The second-stage variables are the flow of biomass between the network nodes and the third-party bioethanol supply. A case study from Texas is presented. The numerical results show that the biomass quality changes the selected depot/biorefinery locations and conversion technology in the optimal network design. The cost due to poor biomass quality accounts for approximately 8.31\(\%\) of the investment and operational cost. Our proposed L-shaped with connectivity constraints approach outperforms the benchmark L-shaped method in terms of solution quality and computational effort by 0.6\(\%\) and 91.63\(\%\) on average, respectively.

Managing foreign exchange risk with buyer–supplier contracts Abstract We model the optimal choice of the contract terms of a foreign exchange risk sharing supply contract between a buyer and supplier who are located in two different countries, when the supplier quotes a wholesale price in its currency, and both parties are mean variance expected utility maximizers. We extend the model to examine alternatives to the risk sharing contract, which are the wholesale price contract without risk hedging and a wholesale price contract with transfer of risk by the buyer to an options dealer. We empirically apply the model, to two different currencies of the supplier, by assuming that the buyer is based in the U.S., while the supplier is based in one of two countries, which are Switzerland and the U.K. Our results show that the performance of the risk sharing contract provides a substantial improvement in the total expected utility of both partners to the contract, over both the wholesale price contract without risk hedging and the risk transfer contract.

An ADMM algorithm for two-stage stochastic programming problems Abstract The alternate direction method of multipliers (ADMM) has received significant attention recently as a powerful algorithm to solve convex problems with a block structure. The vast majority of applications focus on deterministic problems. In this paper we show that ADMM can be applied to solve two-stage stochastic programming problems, and we propose an implementation in three blocks with or without proximal terms. We present numerical results for large scale instances, and extend our findings for risk averse formulations using utility functions.

Joint optimization of software time-to-market and testing duration using multi-attribute utility theory Abstract An optimal software release strategy is a well-investigated issue in software reliability literature. Comprehensive testing is expected before releasing the software into the market to enhance the reliability and security of the software device. In recent years, few analysts have recommended the scheme for software projects that support releasing the software early in the market and continue the testing process for an added period in the field environment even after the software is distributed. These studies are based on one common assumption that the efficiency of the software engineers in detecting the faults occurs at a consistent rate throughout the testing phase. However, bug-identification rate may experience discontinuity at the software release time. In software engineering, the time-point at which fault detection rate changes is termed as change-point. Consequently, an alternative software release policy is proposed in the present paper, which offers a generalized framework for fault detection phenomenon using the unified approach. An extensive analysis of software time-to-market and testing duration based on cost-efficiency and reliability measures is discussed by considering the change in tester’s fault detection rate. A multi-criteria decision making technique known as multi-attribute utility theory is applied to optimize the software release policy under field-testing (FT) and no field-testing (NFT) frameworks. The relevance of the optimization problem is illustrated using a numerical example, comprising both the exponential and S-shaped bug-detection process.

Approximate dynamic programming for the military inventory routing problem Abstract The United States Army can benefit from effectively utilizing cargo unmanned aerial vehicles (CUAVs) to perform resupply operations in combat environments to reduce the use of manned (ground and aerial) resupply that incurs risk to personnel. We formulate a Markov decision process (MDP) model of an inventory routing problem (IRP) with vehicle loss and direct delivery, which we label the military IRP (MILIRP). The objective of the MILIRP is to determine CUAV dispatching and routing policies for the resupply of geographically dispersed units operating in an austere, combat environment. The large size of the problem instance motivating this research renders dynamic programming algorithms inappropriate, so we utilize approximate dynamic programming (ADP) methods to attain improved policies (relative to a benchmark policy) via an approximate policy iteration algorithmic strategy utilizing least squares temporal differencing for policy evaluation. We examine a representative problem instance motivated by resupply operations experienced by the United States Army in Afghanistan both to demonstrate the applicability of our MDP model and to examine the efficacy of our proposed ADP solution methodology. A designed computational experiment enables the examination of selected problem features and algorithmic features vis-à-vis the quality of solutions attained by our ADP policies. Results indicate that a 4-crew, 8-CUAV unit is able to resupply 57% of the demand from an 800-person organization over a 3-month time horizon when using the ADP policy, a notable improvement over the 18% attained using a benchmark policy. Such results inform the development of procedures governing the design, development, and utilization of CUAV assets for the resupply of dispersed ground combat forces.