Stochastic Dominance Efficiency Tests under Diversification

This paper focuses on Stochastic Dominance (SD) efficiency in a finite empirical panel data. We analytically characterize the sets of unsorted time series that dominate a given evaluated distribution by the First, Second, and Third order SD. Using these insights, we develop simple Linear Programming and 0-1 Mixed Integer Linear Programming tests of SD efficiency. The advantage to the earlier efficiency tests is that the proposed approach explicitly accounts for diversification. Allowing for diversification can both improve the power of the empirical SD tests, and enable SD based portfolio optimization. A simple numerical example illustrates the SD efficiency tests. Discussion on the application potential and the future research directions concludes.Stochastic Dominance, Protfolio Choice, Efficiency, Diversification, Mathematical Programming

Slack Matching Quasi Delay-Insensitive Circuits

Slack matching is an optimization that determines the amount of buffering that must be added to each channel of a slack elastic asynchronous system in order to reduce its cycle time to a specified target. We present two methods of expressing the slack matching problem as a mixed integer linear programming problem. The first method is applicable to systems composed of either full-buffers or half-buffers but not both. The second method is applicable to systems composed of any combination of full-buffers and half-buffers

A three-stage model for closed-loop supply chain configuration under uncertainty

In this paper, a general closed-loop supply chain (CLSC) network is configured which consists of multiple customers, parts, products, suppliers, remanufacturing subcontractors, and refurbishing sites. We propose a three-stage model including evaluation, network configuration, and selection and order allocation. In the first stage, suppliers, remanufacturing subcontractors, and refurbishing sites are evaluated based on a new quality function deployment (QFD) model. The proposed QFD model determines the relationship between customer requirements, part requirements, and process requirements. In addition, the fuzzy sets theory is utilised to overcome the uncertainty in the decision-making process. In the second stage, the closed-loop supply chain network is configured by a stochastic mixed-integer nonlinear programming model. It is supposed that demand is an uncertain parameter. Finally in the third stage, suppliers, remanufacturing subcontractors, and refurbishing sites are selected and order allocation is determined. To this end, a multi-objective mixed-integer linear programming model is presented. An illustrative example is conducted to show the process. The main novel innovation of the proposed model is to consider the CLSC network configuration and selection process simultaneously, under uncertain demand and in an uncertain decision-making environment

Minimizing water and energy consumptions in water and heat exchange networks.

This study presents a mathematical programming formulation for the design of water and heat exchangers networks based on a two-step methodology. First, an MILP (mixed integer linear programming) procedure is used to solve the water and energy allocation problem regarding several objectives. The first step of the design method involves four criteria to be taken into account., ie, fresh water consumption (F1), energy consumption (F2), interconnection number (F3) and number of heat exchangers (F4). The multiobjective optimization Min [F1, F2] is solved by the so-called ɛ-constraint method and leads to several Pareto fronts for fixed numbers of connections and heat exchangers. The second step consists in improving the best results of the first phase with energy integration into the water network. This stage is solved by an MINLP procedure in order to minimize an objective cost function. Two examples reported in the dedicated literature serve as test bench cases to apply the proposed two-step approach. The results show that the simultaneous consideration of the abovementioned objectives is more realistic than the only minimization of fresh water consumption. Indeed, the optimal network does not necessarily correspond to the structure that reaches the fresh water target. For a real paper mill plant, energy consumption decreases of almost 20% as compared with previous studies

Optimization of orbital assignment and specification of service areas in satellite communications

The mathematical nature of the orbital and frequency assignment problem for communications satellites is explored, and it is shown that choosing the correct permutations of the orbit locations and frequency assignments is an important step in arriving at values which satisfy the signal-quality requirements. Two methods are proposed to achieve better spectrum/orbit utilization. The first, called the delta S concept, leads to orbital assignment solutions via either mixed-integer or restricted basis entry linear programming techniques; the method guarantees good single-entry carrier-to-interference ratio results. In the second, a basis for specifying service areas is proposed for the Fixed Satellite Service. It is suggested that service areas should be specified according to the communications-demand density in conjunction with the delta S concept in order to enable the system planner to specify more satellites and provide more communications supply

Integrating fuzzy TOPSIS and goal programming for multiple objective integrated procurement-production planning

In this paper, a four-phase approach for Integrated Procurement-Production (IPP) tactical planning in a multiechelon, multi-product and multi-period Supply Chain (SC) network is proposed. To account for ambiguity and vagueness in some real-world data and preferences, in the first phase of the approach, the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy TOPSIS) method is used to obtain the overall performance and risk ratings of the suppliers with regard to a set of qualitative and quantitative criteria. In the second phase, we introduce a novel multi-objective possibilistic mixed integer linear programming model (MOPMILP) for solving an IPP planning considering conflicting goals simultaneously: maximization of the overall performance and minimization of the overall risk. Then, after converting this MOPMILP model into an equivalent crisp multi-objective mixed integer linear programming (MOMILP) model, we use the Goal Programming (GP) approach to solve this MOMILP model in order to find an efficient compromise solution (i.e. an efficient procurement production plan) for the whole SC. The proposed approach and solution methodology are validated through a numerical example